Abstract
Choosing a mate is perhaps the most important decision a sexually reproducing organism makes in its lifetime. And yet, psychologists lack a precise description of human mate choice, despite sustained attention from several theoretical perspectives. Here, I argue this limited progress owes to the complexity of mate choice and describe a new modeling approach, called “couple simulation,” designed to compare models of mate choice by challenging them to reproduce real couples within simulated mating markets. I present proof-of-concept simulations that demonstrate couple simulation can identify a population’s true model of mate choice. Furthermore, I apply couple simulation to two samples of real couples and find that the method (a) successfully reconstructs real-world couples, (b) discriminates between models of mate choice, and (c) predicts a wide range of dimensions of relationship quality. Collectively, these results provide evidence that couple simulation offers a framework useful for evaluating theories of human mate choice.
The choice of a romantic partner is among the most consequential decisions a person ever makes. On the scale of deep time, mate choice directly affects patterns of reproduction, making mate selection a source and target of powerful selection pressures. On the scale of a lifetime, mate choice affects every corner of life: where people live and work, with whom they have children and families, how they spend their time and money, and whom they rely on for support. The quality of our mating relationships is accordingly robustly linked to physical health, mental health, and financial success (Antonovics & Town, 2004; Holt-Lunstad et al., 2010; Robles et al., 2014).
These facts have motivated long-standing interest in mate choice from psychologists, biologists, sociologists, anthropologists, economists, geneticists, and others. These diverse scholars have generated large and productive literatures on human mating, shedding much light on the antecedents and consequences of mate choice. However, despite this sustained interest in human mating broadly construed, the specific decision processes by which people select their romantic partners have remained substantially in the dark—to such a degree that some have asked whether human mate choice is inherently unpredictable (Joel et al., 2017; Li et al., 2013; Lykken & Tellegen, 1993).
Here, I argue that the limited progress in understanding mate choice owes in part to the complexity of human mate selection and the empirical difficulties this creates for evaluating theories of mate choice. To illustrate, I first review several of the traditional approaches to theorizing about human mate choice, spanning social psychological, evolutionary, and cognitive science approaches, as well as some of the barriers to progress these approaches have encountered. Next, I discuss some of the advantages of computational modeling for theorizing about mate choice as well as some of the unique difficulties computational models pose. Finally, I propose a new computational modeling approach, called “couple simulation,” designed to alleviate these difficulties by enabling empirical tests of complex models of human mate choice. I will present evidence that this couple simulation method can identify accurate models of mate choice in simulations, that this method can discriminate between models of mate choice in real-world data, and that it possesses power to predict overall relationship quality. This evidence suggests that couple simulation provides a novel approach that can facilitate both comparison and revision of models of human mating and accelerate progress in understanding the nature of human mate selection.
Traditional Theoretical Approaches to Human Mate Selection
To appreciate the challenges posed by theorizing about human mate choice, and therefore the potential benefits of computational modeling, it is valuable to first have a sense of the landscape of mate choice theory. The far-reaching consequences of human mate choice have attracted interest in mate selection from a wide array of perspectives. Theorists from these diverse perspectives have shared a common goal of understanding the nature of romantic partner selection. However, their work has followed from varied assumptions and emphases and has thus yielded only partially overlapping—and, at times, conflicting—sets of mate choice theory. Three theoretical approaches are of particular relevance to psychological perspectives on human mate choice: the traditional social psychological approach, the evolutionary approach, and the cognitive science approach. These approaches are not strict alternatives: Distinctions between these approaches are blurry as they can have partially overlapping sets of premises, methodologies, and predictions and they are often intentionally integrated with one another (e.g., Fletcher et al., 1999; Miller & Todd, 1998). Nonetheless, they do have sufficiently different histories and emphases that they are worth briefly considering separately.
Traditional Social Psychological Approaches to Mate Choice
Although most relationships research within social psychology focuses on ongoing romantic relationships, there is a long history of social psychological theorizing on the determinants and processes of romantic partner choice. Three variables loom large in traditional social psychological theories of mate choice. The first is proximity: People are likely to enter into romantic relationships with those who live near them and with whom they frequently interact (e.g., Bossard, 1932). This occurs presumably both in part due to access and because familiarity increases attraction (Zajonc, 1968). The second variable is similarity: People tend to pair with self-similar others, at least along dimensions such as religious or political attitudes (Newcomb & Svehla, 1937). Attraction has long been observed to grow with the proportion of self-similar characteristics (Byrne & Nelson, 1965). Dissimilarity produces romantic repulsion as well (Rosenbaum, 1986). The third important variable is reciprocity in liking: In interpersonal relationships in general, and in romantic relationships in particular, we tend to like those who like us (e.g., Kenny & La Voie, 1982)—especially potential partners who like us more than they like other people (Walster et al., 1973). Furthermore, these three variables are not necessarily independent. For instance, preference for similarity might emerge in part because self-similar others are anticipated to reciprocally like us more (Condon & Crano, 1988).
Interdependence theory provides a broader theoretical framework from which much social psychological theorizing on mate choice derives (Thibault & Kelley, 1959). Interdependence theory conceptualizes relationship seeking and maintenance behavior in terms of people managing the rewards they receive and costs they endure as part of their romantic relationships. According to interdependence theory, a person compares the net rewards that they receive or that they expect to receive with two values: their comparison level (CL), or the net rewards a person believes they deserve from their relationship, and their CL for alternatives (CLalt), roughly the net rewards expected from a person’s next best outside option. These comparisons are thought to regulate people’s satisfaction with their relationships and their motivation to abandon their relationships, respectively.
Interdependence theory’s emphasis on maximizing rewards and minimizing costs has particular relevance for the processes of partner selection. If all people attempt to maximize their net benefits by only staying in the most beneficial of their available options, then people who offer the greatest number of rewards at lowest cost to others will tend to be the most sought-after relationship partners. These people will have the greatest number of options to choose among, and so can choose the partners that offer them the greatest net rewards in return. To the degree that there is agreement about who provides the greatest rewards, the result will be assortative mating: Partners will tend to have correlated desirabilities such that the most consensually favorable people tend to pair with one another (Ellis & Kelley, 1999; Rusbult, 2003). This assortative mating importantly offers an alternative explanation for similarity between romantic partners to preference for similarity per se: People may be similar to their partners on desired dimensions such as physical attractiveness because desirable people tend to choose desirable partners (Kalick & Hamilton, 1986).
Interdependence theory also places special emphasis on mutuality and interactions at the dyad level, rather than at the person level. Central to interdependence theory is the fact that the rewards and costs a person receives from their relationship depends not only on their own behavior but on the decisions and behavior of their partner as well. Understanding relationships, partner choice included, therefore requires attention to the whole relationship, person and partner. The mutuality at the core of interdependence theory also creates the need for risk regulation (Murray et al., 2006). Reaping the rewards of a relationship requires seeking and building a connection with a romantic partner; however, doing so also poses the risk of costly rejection by that partner. Risk regulation theory proposes that people calibrate their relationship promotion behaviors to their perceived risk of rejection, seeking closeness when acceptance appears likely, but seeking self-protection when rejection seems likely. Risk regulation can, among other things, offer an explanation for the importance of reciprocity in partner choice: Two partners’ risk regulation systems can become calibrated to one another, with one partner’s relationship promoting behavior providing the other partner assurance that rejection is unlikely, promoting prorelationship behavior in return. Reciprocal exchange of these assurances will deepen as interdependence grows and the costs of rejection grow with it. Through such attention to rewards and benefits at the dyad level, interdependence theory offers explanations for important phenomena at the relationship initiation stage and beyond.
Evolutionary Approaches to Mate Choice
Evolutionary approaches, spearheaded by evolutionary psychologists and anthropologists, draw on natural and sexual selection as guides for theory building on the nature of human mate choice. Central to these approaches is an evolutionary psychological model of mind, according to which the brain is a natural computer composed of many specialized information processing systems (Cosmides & Tooby, 1995). These information processing systems are themselves expected to be adaptations designed by selection to solve “adaptive problems”: historically recurrent problems faced by human ancestors that, if solved, would have increased reproduction. Each of the mind’s psychological adaptations is expected to capture information from its environment, process that information, and produce behavioral, affective, or cognitive outputs that would have historically been tributary to solving some recurrent problem faced by human ancestors (Cosmides & Tooby, 1995). This form-function link lends evolutionary theory its heuristic value to psychologists: If one has an understanding of the adaptive problems faced throughout human evolutionary history (function), one also has clues to the likely information processing adaptations contained within the mind (form).
A key theory for evolutionary perspectives on mate choice is Trivers’s parental investment theory (Trivers, 1972). At the core of parental investment theory is an acknowledgment that producing and rearing offspring requires the expenditure of finite resources, chiefly time and energy, rendering reproduction inherently costly. However, the costs of reproduction are not equal for all individuals. The variance in reproductive costs is meaningful because, all else equal, greater reproductive costs means scarcer lifetime reproductive opportunities. Those individuals for whom reproduction is costlier face a problem of reaping maximum benefits from few opportunities and therefore benefit from more judicious mating than those for whom reproductive opportunities are plentiful. In most species, females systematically experience greater reproductive costs, explaining the general pattern of sexual dimorphism across species in which males are typically larger in size, exhibit more extravagant ornaments and display behaviors, and mate more indiscriminately, whereas females are typically smaller in size, cryptic, and more selective in mate choice. The notable exceptions to this general trend are “sex-role reversed” species, such as phalaropes, in which males bear greater reproductive costs, and consequently—and in accordance with parental investment theory—tend to be smaller and drabber than the flashier females (Trivers, 1972).
Of relevance to parental investment theory, human reproduction is particularly costly (Aiello & Key, 2002; Valeggia & Ellison, 2009), owing in part to the heavy energetic demands of human infant brains (Ellison, 2009; Kuzawa et al., 2014) and the difficult nature of human foraging (Kaplan et al., 2000). This is thought to be one reason why our species is one of the very few mammal species to engage in long-term, pair-bonded mating in which males and females cooperate to rear offspring, jointly shouldering our high costs of reproduction (Kaplan et al., 2001; Key & Aiello, 2000). On the basis of parental investment theory, this unique long-term mating strategy is predicted to have favored the evolution of selectivity in romantic mate choice in both males and females (Buss & Schmitt, 1993; Trivers, 1972). Inspired by this prediction, the predominant emphasis within the evolutionary literature on long-term mate selection has been on documenting the nature of this selectivity, especially as guided by the meta-hypothesis that the content of modern human desires will reflect features that would have been beneficial to human ancestors (Buss, 1989; Symons, 1979).
Furthermore, parental investment theory itself can be conceptualized as a special case of a broader theoretical framework within evolutionary theory known as life history theory (Kaplan et al., 2001; Stearns, 1992). Although primarily emphasized by evolutionary anthropologists, life history theory has been taken up by psychologists as well—albeit not without some controversy (Del Giudice, 2020; Nettle & Frankenhuis, 2019; Zietsch & Sidari, 2019). Life history theory begins with the observation that all organisms fundamentally face a problem of capturing energy from their environments and applying that energy to reproduce successfully. But solving this problem requires first accomplishing a variety of subtasks: growing a body to maturity, defending that body from pathogenic infection, repairing that body from environmental damage, foraging successfully for resources, gestating offspring, parenting offspring, and so on. The energy available to accomplish all these vital tasks is finite, and resources allocated to one task cannot be allocated to another. This forces trade-offs, for instance, between future and current reproduction: Energy spent on growing one’s body may facilitate greater reproduction in the future, but comes at a cost of energy that could have been spent reproducing now. Organisms must evolve adaptations that prudently manage allocation of finite resources to these competing tasks based on the expected fitness returns of alternative investment portfolios (Kaplan et al., 2001).
One classic life history trade-off is between the quantity of offspring produced and the quality of investment offered to each. Human offspring in particular require substantial parenting to be able to successfully reproduce themselves (Kaplan et al., 2000); however, all energy invested in parenting one offspring is energy that could have been spent producing new offspring. Mating and parenting consequently trade off (Hurtado & Hill, 1992; Marlowe, 1999; Valeggia & Ellison, 2009), where time and energy spent searching for new mates appears to come at a cost to parenting and vice versa. Life history theory therefore connects mate selection intrinsically to all the problems of life; to a life history theorist, understanding mate selection requires attention not to just mating itself, but to the whole-organism balance of life history trade-offs.
Cognitive Science Approaches to Mate Selection
A final theoretical approach worthy of brief mention is what I will call the “cognitive science” approach. These are approaches that draw on theories and methods from disciplines such as judgment and decision-making, economics, statistics, and computer science to attempt to uncover the decision processes involved in human mate selection. Generally, the most formal of the theoretical approaches to mate choice, cognitive science theorists less frequently study real-life mating behavior, but rather engineer simplified scenarios that capture essential elements of real mate choice while abstracting away details. These scenarios can then serve as conceptual laboratories for developing and testing decision-making strategies.
The cognitive science approach does not offer so much as an alternative theoretical perspective to the prior approaches; rather, it is complementary. In a sense, whereas social psychological and evolutionary approaches more heavily emphasize the proximate and ultimate tasks human mating psychology attempts to solve, cognitive science approaches focus on offering precise models of the information processing systems the mind could use to accomplish those tasks. For this reason, cognitive science approaches are often integrated with social psychological and/or evolutionary approaches.
One classic and well-studied example of such a scenario is the “secretary problem” (also known as the “dowry problem”; see Todd & Miller, 1999). This scenario imagines a hiring manager (or a bachelor/ette) attempting to hire the best secretary (or choose the best mate) out of a pool of eligible candidates. Candidates can be evaluated only one at a time, simulating, for instance, a sequence of dates with different potential partners. Each candidate has an overall quality, visible only after evaluation, and which may be a function of a variety of characteristics (e.g., computer skills and time management or kindness and physical attractiveness). Once a candidate is selected from the pool, the decision is final—reflecting the relative permanence of romantic bonds; furthermore, once a candidate is declined, they cannot be returned to later—approximating the scorn of a spurned suitor.
Decision strategies can be tested on this basic problem structure and evaluated on the basis of the quality of their chosen candidates. For example, satisficing strategies perform well in such sequential search problems. These strategies search through an initial subset of candidates to observe the distribution of candidate quality. Afterwards, they select the first candidate who is as good or better as the best candidate observed in the initial search (Miller & Todd, 1998). Although developed on artificial scenarios, decision strategies developed from cognitive science approaches can be compared with decisions made by real humans in analogous scenarios (e.g., see Beckage et al., 2009; Brandner et al., 2020).
A critical distinction among cognitive science approaches is between optimizing and satisficing strategies (Gigerenzer & Todd, 1999). Optimizing strategies follow more directly from the traditional economic, utility maximizing perspective. These strategies are designed to make decisions so as to optimize some important criterion: for instance, maximizing rewards of a given partner choice, minimizing search costs, maximizing the stability of mated pairs, or combinations thereof. Satisficing approaches instead follow more from a perspective of bounded rationality. Satisficing strategies do not attempt to produce choices that are necessarily optimal on any dimension; rather, they are designed to produce choices that are “good enough.” The motivation for such strategies is the recognition that human computational power is finite: We have only so much attention, so much memory, so much time, and many demands on these limited computational resources. Satisficing strategies can require minimal use of finite resources while still producing performance comparable to more complex and demanding optimizing strategies, rendering them potentially more feasible and more biologically plausible alternatives to optimizing strategies (Miller & Todd, 1998).
The savings in complexity afforded by satisficing strategies are argued to more than make up for any deviations from optimality. For example, within the secretary problem, the optimal strategy for maximizing one’s probability of selecting the very best mate available is to search through the first 37% of one’s expected mate pool without making a choice and then afterward picking the first mate who is as good or better than the best mate encountered in the initial search. However, a satisficing strategy of searching for just a good mate—but not necessarily an optimal mate—can find higher quality mates on average using shorter search times (Miller & Todd, 1998). For this reason, many theorists in the cognitive science tradition, especially those in the satisficing tradition, place a heavy emphasis on constraints on choice and on simplicity of decision-making strategies.
Theories and Models: Challenges and Prospects
These traditional theoretical approaches reveal a healthy record of theorizing on human mate choice. Over the last century, human mate choice has attracted the attention of a wide array of researchers, hailing from psychology, anthropology, biology, sociology, computer science, and economics, each drawing on a wealth of sophisticated theoretical approaches. And yet, a precise and accurate description of human mate choice remains elusive. What explains this limited progress? Two theoretical challenges appear particularly troublesome for advancing theories of human mate choice.
First, the diversity of theoretical approaches to mate choice can be both a blessing and a curse. Each theoretical tradition has its own respective emphases and corresponding strengths, for instance, among others, social psychology’s attention at the dyadic, rather than merely individual level; evolutionary approaches’ harnessing of the heuristic value of evolutionary theory; and cognitive science approaches offering the precision of formal decision models. However, the strengths of individual approaches can be challenging to export across theoretical borders. Some of this difficulty comes from the somewhat independent development of the different approaches, resulting in only partially overlapping emphases such that topics that are of great importance to one approach are virtually unheard of in others. For example, there are no constructs within evolutionary psychology that precisely correspond to the interdependence theorist’s “transition list” (Kelley, 1984)—a formal tool for describing the space of situations a dyad can encounter and the pathways they take through this space based on their decisions—and no constructs within social psychology that correspond to evolutionary psychology’s “adaptive problem.” Communication of theory in one tradition inevitably neglects concepts that are first principles in another, rendering cross-communication difficult. Some attempts have found noteworthy success: For instance, the ideal standards model integrates an evolutionary perspective on mate preferences with a social psychological perspective, including interdependence theory (Fletcher et al., 1999). In addition, Miller and Todd (1998) integrated principles of cognitive science approaches with evolutionary approaches. Nonetheless, integrated theories of choice that fully draw strength from all approaches have yet to emerge.
A second roadblock to theoretical progress concerns the nature of theorizing within the human mate choice literature. The dominant mode of theorizing within most of psychology is verbal: the psychologist articulates their theoretical assumptions verbally, imagines a counterfactual world in which those assumptions are true, and then derives hypotheses and predictions by deducing logically what the imagined counterfactual world would be like. This is a powerful approach for a wide range of theoretical questions and is the modal mode of theorizing for at least the social psychological and evolutionary psychological approaches to mate choice.
However, verbal theorizing can pose problems for understanding systems that are highly complex: that is, those that contain many parts that interact in multiple ways, especially when these interactions unfold over time (Wilensky & Rand, 2015). Such systems can strain the limits of human reasoning powers, making correct predictions nonobvious even when theoretical assumptions are clear. This can also stymie fruitful communication as logical reasoning processes are private and opaque: A given researcher is not necessarily consciously aware of all the theoretical assumptions they themselves are making or the logical deduction processes they are using en route to a prediction, nor do they necessarily communicate all these to other theorists. Verbal theoretical communication is also not always clear as different words can be interpreted differently by different people: As an example, the evolutionary psychological concept of the “environment of evolutionary adaptedness” is frequently misunderstood as referring to a literal and specific environment, despite actually referring to a statistical composite of selection pressures (Tooby & Cosmides, 1990).
These theoretical challenges, including strained communication and difficulty deriving precise predictions, have clearly plagued the human mate choice literature. For instance, prominent mating and relationship theorists cannot even agree with one another about what predictions actually follow from existing theories of mate choice (e.g., see Eastwick et al., 2014; Fletcher et al., 2020; Li & Meltzer, 2015; Schmitt, 2014). The result of these difficulties has been a literature rich in empirical observations yet still lacking a clear, precise answer to the simple question: How exactly do people pick their romantic partners?
Computational Models Can Help Solve the Theoretical Challenges of Mate Choice
Computational models can facilitate progress toward answering this question by solving many of the problems faced in theorizing about mate choice. Computational models do not constitute distinct theories from verbal models nor do they constitute empirical data in and of themselves. Rather, computational models represent a mode of theorizing. In a computational modeling framework, rather than imagining a counterfactual world and deriving predictions through reasoning, the researcher instead expresses their theoretical assumptions in computer code—specifically, code that literally creates a simulated world in which the researcher’s theoretical assumptions are true. The researcher can then derive predictions by directly observing and measuring features of this simulated world. Theoretical assumptions can still spring from any of their traditional sources: for instance, intuition, observation, or from meta-theories such as evolutionary theory. And just as with verbal models, model predictions can be compared against the real world to determine their veracity and thereby assess the plausibility of the model’s underlying assumptions.
How do such models help solve the problems faced by theories of mate choice? There are at least three key benefits to expressing theory in terms of computational models: computational models help researchers generate predictions, they facilitate communication between theorists, and they more closely represent the operation of the mind.
Computational models help generate predictions
Computational models can aid in the development and testing of theories because they render predictions unambiguous, even for highly complex systems. Formal models, such as computational models, are “logical machines” that convert assumptions into predictions (Gunawardena, 2014; Smaldino, 2017). No matter how complex a model may be, computer code executes literally, unconstrained by the limits of human reasoning and with no room for (mis)interpretation or logical error. For this reason, assuming the researcher has made no technical errors in programming their model, whatever output a model produces is the correct set of predictions, given its input assumptions. If a theory is expressed correctly in computational terms, there can be no ambiguity about what that theory predicts: A model either produces a given output or it does not.
This property of computational models provides three further benefits. First, for highly complex systems, computational models often produce outputs that are not obvious from their assumptions (Wilensky & Rand, 2015). This is because computational models are not restrained by the limits of human reasoning about complex systems; as such, these models can “see” consequences that follow from a theory’s assumptions that humans otherwise could not see (Kenrick et al., 2003). These surprising outputs constitute novel predictions that can be used to more thoroughly test and evaluate theories.
Second, the “logical machine” nature of computational models makes them invaluable aids for theory revision. Any inadequacy of a model’s predictions—again, barring technical errors—must stem from inadequacies in its assumptions (Gunawardena, 2014). Deviations of a model’s predictions from reality therefore tell you that, in some way, its assumptions deviate from truth. Revising a model to make more accurate predictions consequently results in revised theories that make more plausible assumptions. Simply put, model building is theory building.
Finally, the predictive power of computational models can also lend them practical utility. As the popularity of dating apps and similar services attests, there is great felt need for automated systems that can predict, among a sample of single individuals, who is likely to be a good romantic match for whom. Especially to the degree that mate choice processes relate to the quality of subsequent relationships (as suggested by some theories, e.g., Fletcher & Simpson, 2000), computational models of mate choice could potentially be applied in an automated fashion and at scale to help identify relationships that are likely to be happy, fulfilling, and supportive of wellbeing.
Computational models help scientific communication
In addition to their predictive benefits, computational models also aid in communicating theory for at least two reasons. First, in contrast to verbal theorizing, where even an unarticulated assumption can still influence a theorist’s logical reasoning, in computational modeling, any assumption not expressed in a model’s code will exert no influence on the model’s behavior. Adequately modeling a given theory therefore requires articulating all essential assumptions in plain text. This forces theorists to be specific about how they believe abstract concepts translate into on-the-ground behavior (Wilensky & Rand, 2015). The transparency enforced by computational models also lays bare all of a theorist’s assumptions for scrutiny both by themselves and by others.
Second, computational models provide a lingua franca for communication across subdisciplinary boundaries. Instead of communicating theory in the subdiscipline-specific and history-laden jargon of verbal models, computational models force researchers to express theory in a common format: functional computer code. Unlike abstract phrases such as the “environment of evolutionary adaptedness,” there is less room for ambiguity about what a piece of computer code means: It means the operations that it executes. Even an evolutionary psychologist ignorant of the terminology of interdependence theory can appreciate the workings of a computational model derived from interdependence theory. And even better, they can take the computer code underlying this interdependence model and modify it to incorporate the assumptions they find important. By providing a common format for theorizing, computational models can foster integration across theoretical approaches and facilitate the development of better theories overall.
Computational models better represent the mind
The final benefit of computational modeling concerns the relationship between computational models and mind. Modern psychologists generally endorse some version of a computational model of mind in which the human mind is an information processing device that captures information from its environment and uses it to regulate body and behavior. Any complete description of the mind must include descriptions of this information processing—that is, it must provide descriptions of the mind at Marr’s algorithmic level of explanation (Marr, 1982). Expressing theories as computational models fosters algorithmic explanations by forcing theories to more closely mimic the information processing that the mind actually does.
This mimicry can be invaluable. Many processes that appear simple in the abstract turn out to be diabolically complex once one attempts to build a system that reproduces them. This fact was demonstrated most famously by the “summer vision project,” a failed attempt to build, over just a single summer, a computer system that could see and detect objects (Papert, 1966). This project failed in producing computer vision, but succeeded in revealing that the computational challenges of producing human-like vision were orders of magnitude more complex than previously appreciated. Attempting to build model systems that can do what humans do makes clear the engineering problems that the mind must be solving and gives clues as to how the mind might be doing it. Or as Richard Feynman stated succinctly, “What I cannot create, I do not understand.”
The Challenges of Computational Models of Mate Choice
By facilitating prediction generation, enabling cross-disciplinary communication, and more closely approximating the mind, computational modeling provides invaluable tools for advancing theory on human mate choice. These and other advantages have motivated multiple recent calls for greater incorporation of formal modeling into psychological theorizing at large (e.g., Frankenhuis & Walasek, 2020; Robinaugh et al., 2020; Smaldino, 2020a, 2020b) Nonetheless, computational models are not a panacea and do present their own unique challenges. Some of these challenges of course pertain to the technical challenges of computational modeling: It requires technical skills of writing and understanding computer code in which few psychologists receive formal training. Evaluating particularly complex computational models can also demand both programming skill and access to considerable computational resources. Addressing these challenges will likely require structural changes, at the discipline level, to our training programs, infrastructure, and research philosophies (Smaldino, 2020b).
Other challenges are more theoretical in nature. In particular, good models must approximate reality to some degree. After all, “the ultimate model of a cat is of course another cat, whether it be born of still another cat or synthesized in a laboratory” (Rosenblueth et al., 1943, p. 23). However, just as a perfect map of the world would be as large as the world itself, and therefore impractical to use (Rosenblueth et al., 1943), accurate models of complex systems are necessarily highly complex themselves. Model building therefore poses a trade-off: Simple models can be easy to develop and evaluate, but omit many elements of the real world; complex models can be more accurate in that they can capture more of these real-world elements, but they are more challenging to develop and evaluate. The optimal balance of the trade-off between simplicity and accuracy depends on at least two things: (a) the complexity of the underlying system and (b) the availability of tractable tools for evaluating complex models. Simpler systems can be modeled accurately by even simple models; better model evaluation tools can facilitate more accurate modeling of more complex systems.
Human mate choice has thus far laid at an unfortunate juncture between these two dimensions. Mate choice is itself highly complex, necessitating complex models for accurate description. However, tools for easily evaluating complex models of mate choice have yet to emerge. Accurate computational models of human mate choice have consequently proven challenging to develop. Capitalizing on the strengths afforded by computational modeling, and thereby advancing theory on human mate choice, will thus require model evaluation tools that can better grapple with the inherent complexity of human mate selection. A task analysis helps put in sharp relief the complexity of human mate choice and the resulting complexity of human mate choice models. This makes clear the difficulties faced in evaluating models with presently available tools and the potential value of a new, more powerful approach.
The Complexity of Mate Choice
Consider building a robot that can select mates from a human mating market. On its path to love, this robot will encounter several problems (Figure 1). First, the robot will encounter an array of potential romantic partners who vary across an infinite number of dimensions. Some potential partners will have more symmetrical faces, others will have more symmetrical arm hair; some will be wealthier in cash, others will possess larger stamp collections; and some will be healthier, others will have healthier cousins. There is no end to the number of dimensions along which the robot could compare its potential mates, but the robot must make a choice in finite time.

A diagram of the mate choice process, highlighting the three key tasks of forming preferences, integrating preferences, and navigating dynamic mating markets. Mate preference integration and mate choice decision algorithms are represented as gray and black boxes, respectively, to reflect the current state of knowledge on these processes.
The choice of which dimensions deserve attention could, to the robot, be arbitrary. As far as the robot is concerned, why should it care more or less about a potential mate’s financial wealth than their philatelic wealth? However, to the engineer of real-world mate choice psychologies—natural selection—the choice of comparisons is far from trivial. In the eyes of selection, the true determinant of a potential mate’s “value” as a romantic partner is the reproductive benefits that mate would offer if they were successfully attracted (Sugiyama, 2015). Individuals whose value judgments correlated with these reproductive benefits would select mates who allow them to produce more offspring on average, increasing the representation of whatever genetic endowments contributed to the development of their judgment mechanisms. As a result, selection’s sieve would swiftly replace psychologies ignorant of this mate value dimension with psychologies that are not. If our robot’s mate choice psychology is to emulate real human mate choice psychology, we must design it so that its value judgments are attuned to mate value. And yet, only an infinitesimally small subset of all perceivable differences would be useful to the robot as indicators of mate value.
To successfully select a mate out of this sea of variation, the robot must first establish preferences. That is, the robot needs some ability to form abstract trait-relevant representations that enable it to (a) identify, out of the infinite possibilities, which trait dimensions are relevant to evaluate and (b) assign valence to each of these dimensions, such that some regions of the trait space are evaluated favorably relative to other regions. The “preferences” themselves—the psychological parameters that dictate evaluations as a function of perceptions—could be implemented in a variety of ways, including ideal point values, preferred ranges, or as preference functions that describe how trait values should be transformed into valenced evaluations.
The robot next needs a means to apply its preferences to estimate its potential mates’ mate values. Here, the robot faces a second challenge: Potential mates vary at least somewhat independently across preference dimensions. This means that a mate evaluated positively on one preference dimension is not guaranteed to evaluate as positively on another. A corollary of this is that any given potential mate will be preferable on some dimensions but not on others. To select among its set of imperfect potential options, the mate choice robot needs to somehow integrate its individual evaluations across dimensions to form an overall assessment of each potential mate under consideration. The robot could use all its individual evaluations at this stage or relatively few. This evaluation process could also take several forms, including checklists, weighted sums, similarity functions, or other decision heuristics. And the output evaluations could exist in a variety of formats, from a continuous value estimate, to an ordinal ranking, to a binary “go/no go” signal.
Finally, once the mate choice robot has evaluated one or more potential mates, it needs to apply these evaluations to determine which mates to pursue and ultimately select. Here, in addition to no option being perfectly appealing, the mate choice robot faces two new problems. First, no one is ever the only person searching on the mating market; the mate choice robot will now experience competition from mating rivals. To the degree that the robot’s preferences overlap with its competitors, it may find its most preferred partners removed from the market by rivals that are either more appealing, more decisive, or both. Relatedly, the robot will face a second problem in that human mate choice is mutual. It is not enough for the robot to determine the partner it prefers the most; that partner must also pick the robot in return. And, of course, there is no guarantee that the robot’s most preferred partners will return the robot’s affection.
This third task of navigating the mating market poses the most complex of all the problems the mate choice robot will encounter as the optimal solutions depend on the distribution of potential mates, the distribution of rivals, and the robot’s own traits and preferences. The robot must also take care to search the mating market efficiently. The robot has only finite time and energy to dedicate to mate evaluation and pursuit; in addition, the robot must manage opportunity costs as the resources dedicated to pursuing one mate require forgoing potentially superior options. The robot must balance exploiting known options and exploring less-known options to find suitable partners while minimizing search costs. Further complicating these issues, on the mating market, the robot is embedded within a dynamic social system, where any individual’s decisions affect the decisions available to everyone else. Navigating this stage of mate choice requires decision rules that govern evaluations, mate pursuit, and/or mate rejection contingent on the robot’s shifting circumstances.
Solving the Problems of Mate Choice
These are some of the key problems people face in the course of selecting a romantic partner. But how, precisely, does human mating psychology solve these problems? The answer to this question has been of interest to human mating researchers for decades. However, research effort has not been distributed equally across the separate tasks of choice. Consequently, human mating research has made more progress in understanding some aspects of the design of human mating psychology than it has for others.
Mate preferences
The component of human mating psychology that has received by far the most attention is the nature of mate preferences. Charting the content of human mate preferences has been a central line of research in both evolutionary and social psychology for decades. This research has discovered, in accordance with parental investment theory, that humans are picky in mate choice and ideally desire partners who possess many features (Buss, 1989; Fletcher et al., 1999; Marlowe, 2004). Their laundry lists, however, are not arbitrary, and appear to home in on features that likely would have been valuable in our ancestral past.
Perhaps the most cited paper in this literature is Buss’s (1989) exploration of preferences across cultures. Buss surveyed more than 10,000 participants from 37 cultures around the world, asking participants to rank as well as rate the importance of a variety of characteristics in potential mates. Participant responses showed theoretically consistent and cross-culturally universal patterns: For instance, around the world, people most strongly valued characteristics such as kindness and intelligence in a potential romantic partner. However, preferences were also universally sex differentiated: Men around the world expressed stronger preferences for physical attractiveness and relative youth compared with women, whereas women expressed stronger preferences for older age and good financial prospects compared with men. These results are robust across time: Buss’s mate preference questionnaire was itself adapted from a questionnaire used by Hill (1945) on American college students approximately 40 years earlier with similar results. And these mate preference findings have continued to replicate across cultures since (Chang et al., 2011; Kamble et al., 2014; Souza et al., 2016; Walter et al., 2020; Zhang et al., 2019).
Mate preference research has been a vibrant research area since Buss (1989). Research laboratories, working across cultures, across time, and using a variety of methods, have produced evidence for a wide array of mate preferences, including for age (Kenrick & Keefe, 1992), body shape (e.g., waist-to-hip ratio, shoulder-to-hip ratio, and lumbar curvature; Hughes & Gallup, 2003; Lewis et al., 2015; Perilloux et al., 2010; Singh, 1993; Sugiyama, 2004), facial masculinity/femininity (Little & Hancock, 2002; Perrett et al., 1998), health (DeBruine et al., 2010; Jones et al., 2001), intelligence (Li et al., 2002; F. W. Marlowe, 2004), kindness (Buss, 1989; Hatfield & Sprecher, 1995), relatedness (Lieberman et al., 2007), resources (Li et al., 2002; Wiederman, 1993), status/dominance (Bryan et al., 2011; Lukaszewski & Roney, 2010), and symmetry (Grammer & Thornhill, 1994; Jones et al., 2001).
Mate preference integration
The next task in human mate choice, the integration of mate preferences into evaluations of potential mates, has received comparatively little attention. The default assumption appears to be that human mate preferences are integrated according to a linear combination, where preferences apply a slope to trait values such that potential mates with more desirable characteristics are evaluated more positively, especially when those characteristics are strongly preferred. This has motivated some work attempting to infer the slopes participants apply to characteristics when assessing the overall appeal of different potential mates (e.g., Eastwick et al., 2014; Wood & Brumbaugh, 2009).
This is a plausible model, but is not the only possibility. In fact, studies on attraction and mate choice find that slopes relating preferences to choices infrequently line up with people’s self-reported preferences. This has been taken as evidence that self-reported preferences do not map onto “true” preferences (e.g., Eastwick et al., 2014) or perhaps even that attraction is fundamentally unpredictable (Joel et al., 2017). However, an alternative possibility is that preference integration functions are more complex, rendering these slopes uninformative. For instance, Miller and Todd (1998) proposed that preference integration is governed by categorical thresholds, rather than continuous slopes. In their model, preferences act as critical thresholds, where a potential mate must meet one aspiration before being compared with another. Such a series of preference thresholds provide a computationally simple way to narrow a pool of potential mates, who vary on multiple dimensions, down to a subset of mates with acceptable characteristics.
A third and related possibility is that mate preferences serve as a template of an ideal romantic partner to which actual potential mates are compared. There are a variety of similarity metrics the mind could employ to make this comparison. The most natural of these is the Euclidean distance, the straight-line distance between ideals and actual partners through a multidimensional space of mate preferences. Such a distance provides a continuous value that reflects the degree to which a potential mate embodies a set of ideals overall. Other similar metrics include the Manhattan distance, which tracks absolute deviations from preferences, or the cosine similarity, which tracks similarity in orientation between a preference vector and a trait vector.
Research comparing these hypothesized models of mate preference integration is limited and so is our understanding of how human mating psychology accomplishes this important task. Nonetheless, some evidence suggests that the Euclidean distance model is a good initial model of human preference integration psychology. People are more attracted to potential mates who fall shorter Euclidean distances from their ideal mate preferences (Conroy-Beam & Buss, 2017; although also see Brandner et al., 2020). Accordingly, people in real romantic relationships tend to be mated to partners whose traits are near to their preferences in Euclidean terms (Conroy-Beam, 2018; Conroy-Beam & Buss, 2016). Furthermore, people whose traits are short Euclidean distances from the preferences of the opposite sex in general—that is, who are higher in Euclidean mate value overall—show evidence of experiencing greater power of choice on the mating market (Conroy-Beam, 2018). They tend to set higher ideal standards, pair with partners who are closer to their own preferences, and tend to pair with high mate value partners. This pattern is cross-culturally robust, replicating in 45 countries from around the world (Conroy-Beam et al., 2019). Finally, within ongoing romantic relationships, discrepancies in Euclidean mate value predict feelings of relationship satisfaction (Conroy-Beam et al., 2016).
Actual mate choice
Whereas the psychology of mate preference integration is becoming faintly illuminated, the area of human mating that remains most in the dark is perhaps the most important: actual choice. The designs of human mate preference psychology and preference integration psychology reveal how people evaluate their potential mates. However, they do not tell us how people use these evaluations to select actual romantic partners out of their pool of potentials. This is the most challenging task of mate choice, as here individuals must navigate dynamic mating markets, constrained by the availability of partners, the behaviors and choices of romantic rivals, and the challenge of securing mutual attraction from preferred potential mates.
The complexity of this final task is likely responsible for the slow development of computational models of the decision processes involved in actual mate choice. Any model that can accurately describe how people navigate their mating markets will necessarily propose several interacting processes that are difficult to measure, manipulate, or observe. To illustrate, here I will consider four exemplar models of mate choice: (a) the Kalick-Hamilton model, (b) the aspiration threshold model, (c) the Gale-Shapley algorithm, and (d) the resource allocation model. This is by no means an exhaustive list. However, the first three of these models are the most established models in the literature; most other models are variations on their major themes. The RAM is a new model proposed here that fills important gaps left open by the first three.
The Kalick-Hamilton Model
The first model I will consider, the Kalick-Hamilton model (KHM), derives from social psychological theory on mate choice. This model was first proposed in Kalick and Hamilton’s (1986) classic paper on assortative mating. A robust finding from the interpersonal attraction literature is that people tend to mate somewhat assortatively for physical attractiveness: Attractive people tend to have attractive partners. Kalick and Hamilton (1986) sought to provide an explanation for this observation. At the time, the dominant hypothesis for assortative mating was a preference for similarity: that people desire partners who are close in attractiveness to themselves. Kalick and Hamilton tested an alternative explanation: Mating markets are competitive, and the people who are most in demand (i.e., physically attractive people) are most likely to successfully pair with partners they most desire (i.e., physically attractive partners). To evaluate the plausibility of this hypothesis, they set up a model of a mating market incorporating competitive, mutual, and probabilistic mate choice. This model represents a computational expression of the long-standing prediction of assortative mating from interdependence theory (Ellis & Kelley, 1999; Rusbult, 2003).
Overall, their model is simple. At each time step, agents pair into dates at random. On these dates, each agent first assesses the attractiveness of their paired partner. Next, agents decide whether to make an offer of commitment to their date partners based on the result of a weighted coin flip. The probability of each agent making an offer of commitment is proportional to two things: the attractiveness of the agent’s partner and the number of dates the agent has been on already. As the agent’s partner becomes more attractive, a decision to commit becomes more likely; furthermore, as the agent goes on an increasing number of unsuccessful dates, it becomes less discriminating and increasingly likely to offer commitment to less attractive partners. If two agents make mutual offers of commitment, they pair and exit the mating market. If either declines, the agents end their date and reenter the mating market. This pairing and coin flipping process iterates until all possible agent couples form. Although this model was designed to explain assortative mating for attractiveness, it can be extended to mate choice based on multiple preferences: If one has a model of mate preference integration, one can replace “physical attractiveness” in the decision process with overall mate value evaluations.
The KHM has several desirable features as a computational model of human mate choice. First, it is algorithmic: It provides a set of unambiguous steps that can move a population of single individuals into a population of romantic pairs based on their mate preferences. A population of mate choice robots that followed the Kalick-Hamilton choice algorithm could successfully solve all the problems real people encounter in selecting a romantic partner. Second, the KHM accomplishes this pairing while respecting the mutual nature of human mate choice: All individuals get to choose whom they pair with and each person’s choice matters in their outcome.
Nonetheless, this model is relatively simplistic. It is also highly stochastic, in terms of both initial mate approach and final mate choice. Agents are paired with random partners at each time step and thus are no more likely to encounter highly appealing potential mates than they are to encounter highly unappealing partners—a seemingly unrealistic assumption. Furthermore, the final mate choice comes down to a simple, albeit weighted, binomial. This means that a proverbial “10” always has some probability of committing to a “1” and a “1” always has some probability of declining an interested “10.” Stochasticity per se is not undesirable—some degree of stochasticity in mate choice might make functional sense for solving specific problems encountered in mate choice, and so some stochastic elements may be realistic and desirable features of mate choice models. Nonetheless, given the central importance of mate choice to reproduction, it seems highly unlikely that selection would craft such a mate choice psychology as random as the KHM implies.
The Aspiration Threshold Model
Moving up one step in complexity, we can consider what I will call the “aspiration threshold model” (ATM). This is an abstraction of a model proposed in several forms (e.g., Miller & Todd, 1998; Simão & Todd, 2003; Todd et al., 2005) but is closest in form to (but still somewhat different from) the version proposed in Todd and Miller (1999). The ATM follows from a merger of the evolutionary and cognitive science approaches to mate choice theory, and in particular falls within a satisficing approach to decision-making.
In this model, individuals are assumed to have personal aspiration levels: thresholds of mate value which potential mates must meet and which are learned through experience on the mating market. Agents begin their mating careers with uninformative thresholds—for instance, seeking at least a “5” on the proverbial 10-point mate value scale. Then, just as in the KHM, each agent goes on a date with a random partner at each time step. If an agent is single, it will make an “offer” to any potential mate whose mate value exceeds their aspiration threshold. Agents temporarily pair with their date partners if they make mutual offers of commitment. After agents enter a temporary pairing, they continue to go on dates with new random partners, but adjust their decision rules slightly: Now they will make an offer to a new partner only if that new partner both exceeds their aspiration threshold and is higher in mate value than their current partner. If this new offer is accepted, the agent breaks up with their current temporary partner and enters a temporary relationship with the new mate. Temporary pairs become permanent once agents have stayed paired to one another for a preset duration.
At the same time that individuals are making pairing decisions, they are also calibrating their aspiration thresholds. Each agent in this model pays attention to the offers they receive from others and adjusts their aspiration threshold accordingly. When an agent does not receive a commitment offer from a potential mate who is below their threshold, suggesting they have been too ambitious, they adjust their threshold downward slightly; when they receive commitment offers from mates above their thresholds, suggesting they have been too conservative, they adjust their thresholds upward. Iterating this learning process across random dates rapidly yields aspiration thresholds that are finely tuned to the agents’ mate values (see Supplemental Figure 1). These thresholds then help agents pair into good matches, given their mate values.
Like the KHM, the ATM is appealing as a model of mate choice in that it is algorithmic and incorporates mutual mate choice. Furthermore, the proposed learning process is elegantly efficient. This model is still relatively stochastic; however, it has two features that render it less stochastic than the KHM. First, whereas the ATM is still stochastic in terms of mate approach—individuals pair into dates randomly, and no structures in the environment or the agents’ psychology bias these random encounters toward high or similar mate value partners—it is not stochastic in terms of actual mate choice: In the ATM, choice is governed by a deterministic aspiration threshold. Second, the ATM allows for mate switching: Agents can “hold on” to an appealing partner provisionally but then “trade up” if a better, mutually interested partner appears. This mate switching process might help to smooth out some of the randomness introduced by random mate approach, yielding an algorithm less likely to choose relatively low mate value partners or deny relatively high mate value partners.
The Gale-Shapley Algorithm
In contrast to the KHM and ATM, the Gale-Shapley Algorithm (GSA) offers a fully deterministic model of human mate choice. Fitting most with the cognitive science approach to mate choice, and in particular a traditional, rational utility maximizing approach, this algorithm is an economic model designed to solve the “stable marriage problem” (Gale & Shapley, 1962). Despite the name, this is not a problem of mate choice per se but rather is a general problem of sorting lists into pairs according to ordered preferences: for instance, pairing medical school students who vary in qualifications with residency positions that vary in prestige. The algorithm proceeds in a series of steps. First, each unpaired male makes an offer to his most preferred female. If an unpaired female receives any offers, she temporarily pairs with whichever suitor she prefers most and rejects any others. If a paired female receives an offer, she compares her current mate with her suitors. If she prefers her current partner to all her suitors, she rejects each suitor and stays with her current mate; if she prefers a suitor to her current partner, she jettisons her mate and pairs with the new male, rejecting her former partner and all other suitors at the same time. Any unpaired males next make new offers to their most preferred females among those who have not yet rejected them. This process repeats until all possible couples form.
Like the prior two models, the GSA is algorithmic and unambiguous. Unlike the prior models, it is completely deterministic: It is nonrandom in both mate approach and mate choice and, given the same start conditions, will yield the same pairs each time it runs its course. The GSA also has a potential advantage in that it is guaranteed to find pairs that are “stable”: Among all pairs, no individual will prefer to be with another partner who would also prefer them in return. This stability may or may not be a feature of real human mate choices, but could be useful for applications of mate choice models in, for instance, making partner recommendations.
Nonetheless, this algorithm suffers a critical limitation in that it is inherently asymmetric: Only one sex actively pursues their preferred partners. Females only act on their preferences when they have competing offers from males. As a result, pairings are more optimal with respect to male preferences than they are for female preferences. Such asymmetry in choice could be a reasonable assumption for a variety of nonhuman species, but is not likely consistent with human mate choice (Stewart-Williams & Thomas, 2013).
The Resource Allocation Model
Each of these models differs on a number of dimensions, for instance, stochasticity, symmetry, and overall complexity. However, they each share one noteworthy feature: They are all “binary” in the sense that they all assume that, at a given time point, each individual is either fully pursuing a given potential mate or is not pursuing them at all. This is a somewhat peculiar assumption, given that human attraction at least appears to be continuous: people are strongly attracted to some potential mates, moderately attracted to others, and weakly attracted to others still.
To address this, here I propose a model new to the literature, called the resource allocation model (RAM), designed to capture this continuous nature of attraction. It draws loose inspiration from life history theory. Life history trade-offs are most often conceptualized as trade-offs between investment in different fitness-relevant processes—for instance, between an organism investing its limited resources in growing its body larger, maintaining that body, pursuing mates, or parenting offspring. This perspective is illuminating. But we can also “zoom in” on the resources invested into each process and see that investment trade-offs have a hierarchical nature. With the time and energy invested in mate pursuit, we have a finite set of resources that can now be invested across a set of potential mates. These investments face a similar problem of trade-offs: every hour spent courting one potential mate is an hour that cannot be spent courting another. Therefore, just as life history trade-offs are conceptualized as a process of deciding how to allocate finite resources across a set of vital processes, mate choice can be thought of as a process of deciding how to allocate finite resources across a set of potential mates.
The RAM assumes that two things are relevant to this allocation decision: the mate value of each potential mate and their likelihood of reciprocally investing their finite resources in return. Allocation in this model occurs in two phases. First, each agent allocates their limited mating resources to their potential mates in direct proportion to their relative mate values. Mates evaluated as high in mate value receive a greater initial proportion of an agent’s resources (e.g., the suitor commits more of their time to higher mate value partners), whereas mates low in mate value receive fewer resources. At this stage, resources are spread wide and thin, and differential allocations could be taken to represent differences so small as taking the time to make a flirtatious comment to a particularly appealing partner.
After this initial allocation, however, agents reallocate their resources in proportion to their degree of mutual investment shared with each mate. Agents disinvest from mates who did not return their initial investment and shift these freed-up resources to mates who showed stronger mutual interest because no social partner is more valuable than a partner who values you in return (Tooby & Cosmides, 1996). Iterating this reallocation process causes agents to gradually drift from broadly investing resources in many potential mates to heavily investing in few, reciprocally interested partners (Figure 2). Compared with the small allocations given to potential mates at the start of the resource allocation process, the relatively large allocations given to specific partners by the end of mate choice could be taken to represent the substantial time and energy dedication demanded by a committed romantic relationship. This RAM therefore provides a model of human mate choice that is unique among the prior models in that it is simultaneously symmetrical, deterministic, and continuous.

A social network representation of mate choice according to the resource allocation model. Nodes represent individual agents and edges represent the strength of mutual investment between agents, with edge width proportional to the product of investment given and received. Agents initially broadly invest in many potential mates (left), but as time progresses gradually concentrate their resources onto few, mutually interested partners (middle, right).
As a novel model, the RAM integrates concepts from across the traditional theoretical approaches to human mate choice. This demonstrates the value of computational models for building novel theories that integrate concepts from multiple perspectives. The RAM borrows from both evolutionary and cognitive science approaches an emphasis on navigating constraints on finite resources. It additionally unites these previously disparate senses of constraint by pointing out the nested nature of resource allocation: Life history trade-offs between competing processes are what constrain the resources available for mate choice decision-making. The RAM furthermore points out a symmetry between these two senses of trade-off: Life history trade-offs and on-the-ground mate choice both involve an analogous problem of deciding on an investment schedule that allocates finite resources across sets of differentially beneficial options so as to maximize returns.
The RAM additionally borrows from traditional social psychological theory by being the only model among the four considered here to explicitly emphasize reciprocity in the decision-making context. Directly analogous to risk regulation, agents in a RAM invest in a potential partner to the degree that they are confident that their partner will reciprocate their interest. This emphasis on reciprocity also allows the RAM to capture the emergence of romantic relationships over time in a way that binary choice models do not. Furthermore, compared with the three prior models, the RAM is the only model in which an agent’s own pursuit decisions are a joint function of both their own and their partner’s prior behavior, creating a dyadic emphasis not unlike interdependence theory’s. The incorporation of mate value into initial allocation decisions finally also helps to produce the assortative mating anticipated by interdependence theorists.
Evaluating and Comparing Models of Human Mate Choice
The four models above reflect computational expressions of theory from across the traditional theoretical approaches to understanding human mate choice. Table 1 summarizes these models in terms of their noteworthy features, theoretical origins, and theoretical emphases. Each of these models above could, in principle, approximate the set of decision rules used by human mating psychology to guide actual mate choice. But how, as mating researchers, can we decide which model is most plausible overall? This question has proven challenging to answer. Each model proposes several decision processes, some of which are challenging to manipulate or measure in the laboratory. For example, how would one reliably measure subtle changes in resource allocation across a person’s pool of potential mates, or the change in one’s aspiration threshold across a lifetime of mating experiences? Even worse for making clear comparisons, some models, such as the KHM, propose that final mate choice is at least partially stochastic, whereas others assume deterministic mate choices. Devising critical tests that can distinguish between any two of these models using real mate choice data is difficult enough. Finding the best model in the complete set is exponentially more difficult. Without tools that can facilitate this model evaluation, developing new, more accurate models of mate choice becomes challenging, and progress in the development of mate choice theory is slowed.
Origins, Features, and Key Theoretical Emphases of Mate Choice Models.
The most common method for evaluating mate choice models within the literature is by assessing their ability to reproduce group-level statistics observed in human data. For instance, Kalick and Hamilton (1986) evaluated the plausibility of their model on the basis of its ability to reproduce the correlation in physical attractiveness commonly observed in human samples. Other studies have similarly evaluated different models of mate choice on the basis of their ability to reproduce correlations indicative of assortative mating (Conroy-Beam, 2018; Hitsch et al., 2010; Smaldino & Schank, 2012; Xie et al., 2015). Another common approach to model validation is to assess whether models reproduce broad demographic trends, such as rates of marriage as a function of age (French & Kus, 2008; Knittel et al., 2011; Smaldino & Schank, 2012; Todd et al., 2005).
These can be useful criteria. An accurate model of human mate choice must be able to yield, for instance, realistic marriage rates. Nonetheless, they are also relatively abstracted: Although mate choice models are models of individual choices, these comparison approaches evaluate models on their ability to reproduce observations at the population level. There is no guarantee that models that perform well in predicting aggregate trends also perform well in predicting the specific individual choices that make up the aggregate. Different models, with radically different underlying mechanics, can produce degrees of assortative mating or marriage rates that are broadly similar to one another and to human data even if they make quite different choices at the individual level. Equifinality is a challenge for any method of model comparison, but criteria as simple as assortative mating correlations render fine discrimination among different models of mate choice especially difficult. Despite this, model comparisons that evaluate the ability of alternative models to predict individual choices are relatively rare (although see Beckage et al., 2009; Brandner et al., 2020).
The absence of precise tools for evaluating models of mate choice, in conjunction with the complexity of mate choice itself, likely accounts for the relatively slow progress in this research area. By comparison, mate preference researchers have ample means to evaluate novel hypotheses concerning mate preferences, including survey methods (e.g., Fletcher et al., 1999; Li et al., 2002), archival data analysis (e.g., de Sousa Campos et al., 2002; Kenrick & Keefe, 1992), or revealed preference designs (e.g., Mogilski et al., 2014; Wood & Brumbaugh, 2009). Preference research has accordingly been fertile, with regular documentation of previously undiscovered mate preferences. However, a researcher who goes through the considerable effort of generating a novel mate choice model has no clear means to demonstrate that their new model is more or less plausible than any of the prior possibilities. Absent such a useful model comparison metric, human mating researchers cannot iteratively refine theoretical models of mate choice and build toward a more accurate understanding of human mating over time.
Couple Simulation: A More Powerful Approach for Comparing Models of Mate Choice
To help address these issues and to accelerate progress in understanding human mate choice psychology, here I propose a new modeling approach, called “couple simulation,” designed to facilitate comparisons of alternative models of mate choice. Figure 3 diagrams the approach. At its core, couple simulation is an application of agent-based modeling: a type of computer simulation in which groups of autonomous agents interact with one another and their environments according to preprogrammed decision rules (Jackson et al., 2017; Smith & Conrey, 2007). In essence, couple simulation takes a sample of scrambled romantic couples and attempts to reorder them using simulated mating markets. Different models of mate choice are compared on their ability to accurately reorder the scrambled couples.

A diagram of the couple simulation method. This method simulates a sample of couples as a mating market. The outcome of interest is the proportion of sampled couples accurately reproduced by a given simulated mating market.
Couple simulation proceeds in three steps. The first step is collecting samples of people who have already made mate choices: for instance, both members of ongoing romantic relationships. From these participants, one must measure factors hypothesized to be relevant to the participants’ mate choices. This could be, for example, measures of their ideal mate preferences and their corresponding characteristics. Second, these measurements are used to create avatar agents for each participant: a simulated representation of each person that inherits their preferences, traits, or any other mate choice factors. These avatar agents next select each other as romantic partners according to a hypothesized model of mate choice: For instance, the avatar agents could enact the KHM or the GSA. Third and finally, one must compare the couples predicted by each model of mate choice to the observed, real-world couples.
This couple simulation method makes two key assumptions. First, it assumes researchers are able to measure, with reasonable accuracy, the factors that actually drove participant mate choice. However, it is relatively agnostic as to what these factors actually are. They could be stated mate preferences, but they could also be revealed preferences, demographic information, or contextual factors, depending on the researcher’s particular hypotheses. They key assumption is that the measured factors are reasonably close to the factors that were truly involved in the participants’ original mate choices. Second, for each participant, the method assumes that the other participants in the sample are sufficiently representative of that participant’s actual outside options. This assumption is obviously most strongly satisfied in samples from closed mating markets, in which each participant actually knows the other participants in the sample. However, couple simulation can still be fruitfully applied in random samples of strangers as long as the sample is representative of the broader population, and therefore, the sampled participants are similar to the kinds of people each participant selected among, even if not identical to each participant’s home mating market.
Beyond these assumptions, couple simulation provides researchers broad latitude to manipulate features of the agent avatars and their mating market. This includes the preferences or other mate choice factors the avatar agents inherit, the algorithms by which agents integrate these factors into evaluations of particular mates, and the decision rules by which agents use these evaluations to guide their mate pursuit and choice.
The central outcome of interest in couple simulation is the proportion of real-world couples successfully reproduced by a given mating market. Whatever set of factors, integration functions, and decision rules best approximates the true nature of participant mating psychology should tend to perform best in pairing avatar agents with their real-world partners. Deviations from the true set of decision rules for a population should cause agents to make different decisions than did their real-world counterparts and should therefore tend to pair avatar agents with incorrect partners. Couple simulation accuracy therefore provides a criterion measure for comparing alternative models of human mating: A higher simulation accuracy for a given simulated mating market should imply that market lies in greater proximity to the true model of mate choice. If this is true, researchers can use couple simulation to empirically assess the relative plausibility of different models of mate choice, including those that propose different mate preferences, different preference integration algorithms, or different choice algorithms.
Here, I introduce and validate this couple simulation method in two ways. First, I present a series of proof-of-concept agent-based models that demonstrate that couple simulation can identify a population’s true mate choice algorithm in theory. In these models, I generate identical populations of simulated agents who select one another as mates using different mate choice algorithms. I then draw random samples from these populations, apply couple simulation to each sample, and demonstrate that each population’s true mate choice algorithm does indeed tend to best reproduce couples sampled from that population. Second, I present the results of applying the couple simulation method to two samples of real-world romantic dyads. These samples show that couple simulation on real human data produces moderately high simulation accuracy, discriminates between alternative models of mate choice, identifying some as being more plausible than others, yields longitudinally stable simulation accuracy, and although not explicitly predicted, also has power to predict a wide array of dimensions of romantic relationship quality. All model code, analysis code, model data, and participant data for these models and analyses are available on the Open Science Framework: https://osf.io/dj254/?view_only=c135d7c752fc427ca5078f67c3c21ff6.
Couple Simulation: A Proof of Concept
The first step in validating couple simulation as an approach for evaluating models of mate choice is establishing that it can work in principle: That is, given a theoretical sample of mated pairs, can couple simulation accurately recover the population’s true mate choice algorithm? To answer this question, I set up a series of proof-of-concept agent-based models (Figure 4).

A diagram of the couple simulation proof-of-concept models. A random population of agents pairs into couples according to alternative models of mate choice. Random samples of couples are then run through the couple simulation method to compare each model’s ability to reproduce the original couples.
In these models, five identical populations of agents select one another as mates according to different mate choice algorithms: each of the four models discussed above as well as a population in which mate choice is random. I then draw random samples of varying size from these couple populations. On each sample, I apply the couple simulation method, attempting to reproduce the sampled couples using each mate choice algorithm. The key outcome of interest is whether and when each couple population’s mate choice algorithm outperforms the alternative algorithms in reproducing the sampled couples.
Model Setup
Generating agent populations
To conduct these simulations, I first used a sample of human data to generate a population of realistic agents. This sample was composed of n = 382 participants, themselves members of k = 191 committed, heterosexual romantic dyads. Participants in this sample reported their ideal mate preferences using a mate preference questionnaire composed of 20 7-point bipolar adjective scales. This questionnaire asked participants to report, for example, how intelligent they preferred their ideal mate to be on a scale from 1 meaning “Very Unintelligent” to 7 meaning “Very Intelligent.” In addition to reporting their ideal preferences, participants also rated themselves and their actual romantic partner on the same dimensions. These self and partner ratings were averaged together to form a composite trait rating for each participant on each of the 20 dimensions.
I generated agents based on this sample in a three-step process. First, to generate traits and preferences, the agent generation procedure sampled randomly with replacement from each trait and preference dimension up to the desired total population size: N = 50,000. This sampling drew separately from male and female participants to preserve realistic sex differences. This resulted in a population of agents, each with 20 traits and preferences, whose distributions closely approximated the distributions of the corresponding traits and preferences in the human data.
However, because each trait and preference dimension was generated separately, the resulting dimensions were uncorrelated—a notable difference from the real-world data. To correct this, the agent generation procedure next standardized each dimension and then multiplied the standardized trait and preference matrix by the Cholesky decomposition of the corresponding human correlation matrix. The Cholesky decomposition converts a given complete matrix into a triangular matrix which, if multiplied by its transpose, returns the original complete matrix. As such, the Cholesky decomposition can be conceived of as a matrix equivalent of taking the square root of a matrix. This decomposition is useful for a variety of operations; in particular, for this case, multiplying a matrix of random variables by the Cholesky decomposition of a correlation matrix yields a new matrix with variables that follow the distribution of the random matrix but a correlation matrix that follows the decomposed correlation matrix. Completing this operation for the proof-of-concept agent population yielded a matrix of standardized agent traits and preferences with approximately human-like distributions and a human-like correlational structure. Finally, the agent trait and preferences were rescaled to the real-world data’s mean and standard deviation and truncated to a range of 1.0 to 7.0. Supplemental Table 1 presents the means and standard deviations of agent preference and trait values compared with the same values for the human data. Supplemental Table 2 presents the same for correlations between preferences and trait values.
The end result of this agent generation process was a population of N = 50,000 agents, half male and half female, each with 20 traits and mate preferences which closely mimicked the distributions and correlation matrices of real-world data. This population was then divided into 100 random subgroups of 500 agents, each with 250 female agents. This division was intended to simulate the fact that real-world participants will often be sampled from different social networks and will not mutually know one another.
Generating agent couples
The next step in these proof-of-concept simulations was to create populations of agent couples that differed only in the mate choice model used to generate them. To accomplish this, I created five copies of the initial agent population, maintaining all the agents, their traits and preferences, and their subgroup memberships. Agents within each population copy then proceeded to select one another as mates within their subpopulations. Because of the Euclidean distance’s prior success as a model of human mate preference integration (Conroy-Beam et al., 2019), all agents computed the mate value of their potential partners by integrating their preferences according to a Euclidean algorithm. Agents calculated the Euclidean distance between their own preference vector and each potential mate’s trait vector. These distances were then rescaled such that a mate who perfectly matched an agent’s preferences received a mate value of 10.0, whereas a mate who was as far as possible from an agent’s preference vector received a mate value of 0.0. How agents used these mate values depended on which model of mate choice they used to select their partners. Each population used one of the five models of mate choice: random, Kalick-Hamilton, aspiration threshold, Gale-Shapley, or resource allocation.
Random mate choice
Agents within the random population paired into random heterosexual dyads irrespective of their own mate values or the mate values of their potential partners.
KHM mate choice
Agents within the Kalick-Hamilton population selected mates by going on a series of dates. On each date, each agent paired with a random opposite-sex partner. Agents then decided whether to make an offer of commitment to their date partner based on a single draw from a random binomial distribution. The operationalization of the model used here follows that of Kalick and Hamilton (1986). The probability of a commitment offer was calculated as such:
ATM mate choice
Agents in the ATM used mate value thresholds to determine whom they selected as partners. Agents began their mate search with thresholds set to an arbitrary value of 5.0; however, the choice of the starting value has little effect on the model’s conclusion. As in the KHM, in each time step of mate choice, aspiration threshold agents paired into dates with random opposite-sex partners. Single agents made an offer of commitment to any date partner whose mate value was greater than or equal to their current mate value threshold. If two agents made mutual offers of commitment, they temporarily paired with one another but remained on the mating market. These temporarily paired agents continued to go on dates with other random opposite-sex agents and would switch partners if they received an offer from a date partner whose mate value was both greater than or equal to the agent’s current threshold value and greater than the mate value of the agent’s current temporary partner. Temporary partnerships became permanent if neither agent found a superior partner after 50 dates, after which both agents exited the mating market.
Agents also updated their aspiration thresholds based on the results of each date. If an agent received a commitment offer from a potential mate whose mate value was above their threshold, the agent increased their threshold by 5% of the difference between their threshold value and the date partner’s mate value. If an agent was rejected by a mate to whom they did not make a commitment offer, the agent decreased their threshold value by 5% of the difference between their threshold and the rejected partner’s mate value. Adjusting by 5% yields large enough changes so that thresholds become calibrated to agent mate value over time, but not so large that thresholds are determined only by the agents’ prior few dates. The entire mate selection process repeated for a number of iterations equal to the total group size (i.e., 500 agents in this case); this provided ample time for agents to calibrate their thresholds and find romantic partners.
GSA mate choice
Agents in the Gale-Shapley population followed the GSA as described above. At each time step in this model, a random unpaired male made an offer of commitment to the female who was highest in mate value to him and had not yet already rejected him. If she was single, she temporarily paired with the male. If she was paired, she compared his mate value with the mate value of her current partner; she rejected whichever male was lower in mate value and temporarily paired with the higher mate value male. This process iterated until all possible couples formed.
RAM mate choice
Agents within the resource allocation population possessed 10 abstract resource points that they invested in their potential mates. These resource points are intended to represent the proportion of an individual’s total resource budget which they have allocated to mate search effort (as opposed to, for instance, parenting, maintenance, or growth). These mating effort resources—including time, energy, and money—are then allocated differentially across potential mates to regulate mate pursuit. An individual can be conceived of as having a finite set of resources per unit time—for instance, there are only so many hours within a week which one can dedicate to courting partners. Resource point allocations can be conceived of as the proportions of an individual’s total mating effort budget dedicated to each potential mate per unit time. As time passes, allocated resources are spent, but new resources become available (e.g., freshly foraged calories, a new week’s worth of time, or a new month’s paycheck), and new allocation priorities can be set based on the individual’s prior experience.
More specifically, as operationalized, agents in the RAM initially invested their resource points in direct proportion to each potential mate’s relative mate value according to the function
Agents then proceeded through 100 iterations of reallocation. These iterations can be conceived of as representing the passage of time (e.g., each iteration could represent a day or a week of time) and the accompanying replenishment of resources; the precise number of iterations used is unimportant as long as it is sufficiently long for agents to converge on their chosen partner. Within each of these iterations, each agent computed, for each partner, their mutual investment with that partner as
In all populations, agents selected partners only from alternatives available within their subpopulation. The result was five populations of agent couples that were based on identical starting populations but that differ in both overall size and exact pair composition by virtue solely of the distinct mate choice algorithms that produced the couples.
Applying couple simulation
With the couple populations generated, the next step in the proof-of-concept models was to apply the couple simulation method to determine whether the method could correctly recover each population’s true mate choice algorithm. To test this, the proof-of-concept models drew samples of couples from each population. These samples were drawn randomly with respect to agent subpopulation to simulate sampling real-world participants from different social networks. One hundred samples were drawn at each of the seven different sample sizes: k = 50, 75, 100, 200, 300, 400, and 500 dyads. Within each sample, the model applied the couple simulation method, simultaneously simulating the sampled agent couples in five different mating markets, each underpinned by one of the five mate choice algorithms. These choice algorithms were operationalized identically as when used to generate the initial couple populations. The accuracy of each choice algorithm was calculated as the proportion of the originally sampled couples that the algorithm correctly reproduced. If the couple simulation method can accurately identify a population’s true model of mate choice, each population’s ground truth mate choice algorithm, relative to the alternative algorithms, should tend to more accurately reproduce sampled couples on average and in the majority of samples.
Model Results
Evaluating deterministic models with couple simulation
The ability of the couple simulation method to identify a population’s true mate choice algorithm is most straightforward for those populations in which mate choice was deterministic. These choice algorithms contain no stochastic elements; as such, given the same initial sample of individuals, these algorithms produce the same couples each time they run. Deterministic algorithms therefore make a singular prediction for each sample of couples and have a singular accuracy value that can be compared with the accuracy values of other models.
Figure 5 presents the results of the proof-of-concept models, focusing on the two deterministic mate choice algorithms: the GSA and the RAM. Three key findings are observable from this figure. First and most importantly, the couple simulation method does in fact correctly recover the true model of mate choice within each of these populations. When the GSA was the ground truth mate choice algorithm, this algorithm had significantly higher simulation accuracy across 100 random samples relative to the RAM at all sample sizes above k = 100, as indicated by the 95% confidence intervals (CIs). When the RAM was the ground truth mate choice algorithm, this model significantly outperformed the GSA in couple simulation at all sample sizes. It is also notable that the RAM appears more reproducible overall than the GSA: When the RAM is the true model, simulation accuracies are generally higher for both models and the RAM outcompetes the GSA at all sample sizes. This may occur because the RAM more heavily weighs all individuals’ preferences in making mate choices, unlike the asymmetric influence of preferences found in the GSA.

Couple simulation accuracy for deterministic models of mate choice. (A) GSA population and (B) RAM population. Error bars represent 95% confidence intervals.
Second, although couple simulation performs well at reproducing sampled couples in general, simulation accuracy declines overall as sample size increases. In both populations, simulation accuracy begins relatively high at the smallest sample sizes, with both models reproducing more than 50% of the sampled couples when the sample size is k = 50 dyads. However, this performance declines in both populations, with each model reproducing less than 30% of couples when the sample size is k = 500. This decline in simulation accuracy was not predicted in advance and the reasons for its existence are not obvious. However, one probable contributor is that larger mating markets give agents more and potentially more appealing alternative options: With a larger sample size, the probability increases that the mating market contains a genuinely better match for each agent than their chosen partner. These superior matches represent partners the agent would have chosen had they been available when the agents made their original mate choices. Although these matches perhaps better reflect the agents’ desires, they also reduce model simulation accuracy by causing agents to deviate from their original mate choices.
Nonetheless, the third key observation available in Figure 5 is that although overall simulation accuracy tends to decline with sample size, discrimination between the true and the alternative model tends to increase. This is clearer for the GSA: In the two smallest sample sizes, the GSA fails to significantly outcompete the RAM even when it is the population’s true mate choice algorithm. However, the GSA has consistently greater simulation accuracy than the RAM once sample sizes increase beyond k = 100. This gain in discrimination is particularly clear when looking at the proportion of samples in which the true model was the best performing model (Table 2). When the sample size is just k = 50, the GSA outperforms the RAM in couple simulation in just 51% of samples, no better than chance. By the time the GSA is significantly outperforming the RAM on average, it is still the best performing model in just 68.5% of samples. However, once the sample size is at least k = 300, the GSA outcompetes the RAM in at least 80% of samples. Stated another way, a decision heuristic that assumes that the best performing algorithm is the true algorithm will yield the correct inference in at least 80% of randomly drawn samples of k = 300 dyads in which the true model is tested. These results indicate that if decision processes guiding mate choice are in fact deterministic, the couple simulation method can be used to identify relatively plausible models of mate choice and can go so far as to correctly identify a population’s ground truth mate choice algorithm, given just random samples of couples from the population.
Proportion of Samples in Which True Mate Choice Algorithm Had Highest Simulation Accuracy.
Evaluating stochastic models with couple simulation
Incorporating stochastic models of mate choice makes evaluating the validity of couple simulation more complex. Unlike deterministic algorithms, stochastic mate choice algorithms produce slightly different couple configurations each time they run even on the same initial sample of couples. For this reason, these stochastic models do not have a single couple simulation accuracy value; rather, they have a distribution of simulation accuracies. To compare the performance of stochastic models with other models, these accuracy distributions must be summarized by some value that allows for unambiguous but fair comparison of their overall performance. Initial intuitions for such a summary value might include the mean performance of a stochastic model or a model’s best performance across some number of model runs; however, both of these summary methods cause stochastic models to systematically underperform in couple simulation (see Supplemental Materials Section S2). A better summary method equates a stochastic model’s simulation accuracy with what I will call the model’s “best likely” performance.
The intuition behind the best likely performance summary is this: The observed simulation accuracy for a given run of a stochastic model of mate choice can be thought of as a random draw from a binomial distribution, where n is the number of couples in the sample and p is the unknown “true” accuracy of the model in the population. The best likely accuracy approach estimates the best likely value of p by using the model’s observed average performance to parameterize a beta distribution from which the p estimate is selected. To apply this approach in the proof-of-concept models, I ran each stochastic model 100 times on each sample of agent couples and calculated the average percentage of couples accurately reproduced by each stochastic model across 100 model runs. I then parameterized a beta distribution with α equal to this average percentage and β equal to the average percentage of incorrectly reproduced couples.
The resulting beta distribution represents a continuous distribution of probable true accuracy values, given the observed performance of each stochastic model. The accuracy value used to represent each model’s performance was then taken as the value at the upper bound of the 95% CI within the model’s estimated beta distribution—that is, the value at the 97.5th percentile within the distribution. For each stochastic model, this value represents an estimate of the best likely true accuracy of that model within the population in that this value represents the largest accuracy value that would not be conventionally considered significantly different from the model’s accuracy distribution.
Figure 6 presents the results of using this best likely summary to estimate the performance of stochastic models in couple simulation. With this summary method, the couple simulation method does produce correct inferences in the aspiration threshold population, given sufficient sample size. Starting at k = 200, the ATM has significantly higher simulation accuracies on average than all alternative choice algorithms. Furthermore, this does not occur merely because the best likely estimation procedure is overly generous: The ATM does not outcompete the GSA or RAM in the populations where these algorithms are the ground truth.

Results of couple simulation using best likely accuracy estimation for stochastic models. (A) Random population, (B) KHM population, (C) ATM population, (D) GSA population, and (E) RAM population. Error bars represent 95% confidence intervals.
However, couple simulation does not perform well in the random and Kalick-Hamilton populations. All models performed poorly in reproducing observed couples in both of these populations, with all models averaging just M = 2.04% simulation accuracy across sample sizes in the random population and just M = 5.57% in the Kalick-Hamilton population. This is in contrast to an average simulation accuracy of M = 17.04% across models and sample sizes in the aspiration threshold population. Overall, reproducing couples that were originally produced according to either the random or Kalick-Hamilton mate choice algorithms appears to be difficult for all mate choice algorithms.
This poor performance is especially clear when k = 300 or greater, where the couple simulation method tends to have the best discrimination between models. At these sample sizes, no model achieves greater than 10% simulation accuracy in either the random or Kalick-Hamilton populations; the couple simulation method does not perform this poorly in the other three populations even when k = 500. The couple simulation method furthermore fails to yield correct inferences on average in either of these populations, with the random model and KHM producing similar performances in the random population and the ATM performing best in the Kalick-Hamilton population (although achieving just M = 5.68% accuracy, 95% CI = [5.61%, 5.74%], when k = 300). This suggests that if the true model of mate choice is as stochastic as these models are, all models will perform poorly in reproducing observed couples in couple simulation and differential accuracy between models will cease to be informative.
Similar results are obtained when analyzing the proof-of-concept models in terms of the frequency with which couple simulation produces correct inferences. Supplemental Figure 2 presents confusion matrices showing the proportion of samples in which each model produced the highest simulation accuracy across all populations and across all sample sizes. These matrices echo the average performance plots in showing that couple simulation will not frequently yield accurate inferences in the random or Kalick-Hamilton populations, regardless of the sample size. However, for the models that are sufficiently deterministic so as to produce higher overall simulation accuracies—the ATM, GSM, and RAM—discrimination is substantially better. Again, once the sample size is above k = 300, a decision heuristic that assumes the most accurate model is the true model will yield the correct inference in at least 80% of samples in each of these three populations.
One concern raised by these results is that couple simulation and the best likely estimation procedure are “overfit” to the ATM. That is, this method only yielded correct inferences for one of the three stochastic models and so may not yield correct inferences for other stochastic models either. To determine the risk of this, I created slightly stochastic versions of the GSA and RAM and tested them against one another and their deterministic counterparts in a separate set of proof-of-concept models (see Supplemental Materials Section S3). The best likely estimation procedure yielded correct inferences from couple simulation in this set of populations, with each model—stochastic or deterministic—producing the highest simulation accuracy, both on average and in the majority of samples, only in those populations where it was in fact the ground truth model of mate choice. This suggests that couple simulation’s poor performance in the random and Kalick-Hamilton populations reflects a limitation imposed by highly stochastic mate choice, but not by stochastic mate choice in general.
Lessons from the Proof-of-Concept Models
Overall, these proof-of-concept models reveal several important things about couple simulation. First, if mate choice is reasonably deterministic, as in the aspiration threshold, Gale-Shapley, and resource allocation populations, the couple simulation method can successfully reveal a population’s true model of mate choice. Applying the couple simulation method to random samples of mated couples will result in the true model of mate choice outperforming alternative models in reproducing sampled couples both on average across samples and in the majority of samples drawn. Attending to the relative simulation accuracy of alternative models can therefore provide a means of determining which models of mate choice are relatively more plausible.
Second, couple simulation cannot accurately discriminate between models of mate choice when the true model of choice is highly stochastic, as in the random and Kalick-Hamilton populations. This is a limitation of the method. However, the practical consequences of this limitation are likely to be minimal for at least two reasons. The first is theoretical: Given the incredible importance of mate choice to fitness, it is unlikely that human mate choice truly is as stochastic as the random or KHM models imply. Throughout human evolutionary history, random mating would have left the critical fitness bottlenecks of reproduction, inheritance, and parenting up to chance. Individuals who selected their mates randomly would have been severely outcompeted by individuals who were more discerning about their choice of romantic partner. For this reason, modern humans are likely endowed with psychologies that attempt to inject as much order into mate choice as possible.
This is not to say that there is no randomness in mate choice or mate choice psychology. At a minimum, for instance, the pool of potential mates available is a random function of time and place—a level of randomness that couple simulation emulates by exposing each agent to a random sample of alternatives. Mate choice psychology may also contain random elements itself; for instance, random mate pursuit as in the ATM could function to motivate exploration of a wide set of potential mates. Nonetheless, over human evolutionary history, decision psychologies that were as random as the random model and KHM would be unlikely to lead to reliably fitness-beneficial mate choice and as such would likely be replaced by mate choice psychologies capable of less random mate choices, even if not fully deterministic choices. For this reason, across populations, true models of mate choice are unlikely to be more stochastic than the couple simulation method can tolerate.
The second reason is methodological: Because all models of mate choice perform poorly when mate choice is highly stochastic, researchers can detect stochastic mate choice using couple simulation by paying careful attention to simulation accuracy across models. If a researcher were to apply couple simulation to a sample and observe very low simulation accuracies from all models, this researcher can and should infer that mate choice within their study population is substantially random—at least with respect to the features they measured. In this context, they should not attend to which of the poorly performing models performs best as these differences in accuracy are not likely to be meaningful. A hard cutoff for overall simulation accuracy is difficult to recommend, as several factors appear to affect overall accuracy, especially sample size. Keeping sample size in mind, researchers should instead be increasingly cautious in interpreting differential simulation accuracy across models as overall accuracy decreases; lower simulation accuracy across models should suggest greater randomness in the true model of mate choice, and should warrant greater caution in interpreting relative differences between alternative models.
Finally, these proof-of-concept models provide some recommendation about optimal sample sizes for applying the couple simulation method. In all populations, simulation accuracy declines with increasing sample size for all models of mate choice. Very large sample sizes would be both practically prohibitive to collect in human samples and could drive simulation accuracy low enough to prevent distinguishing between models. However, smaller sample sizes yield smaller gaps in simulation accuracy between the true model of mate choice and alternatives. A sample size close to k = 300 couples appears to provide a good trade-off between overall simulation accuracy and discrimination between models of mate choice.
Couple Simulation in Human Samples
The proof-of-concept models indicate that the couple simulation method can be used to successfully compare alternative models of mate choice in simulated samples of couples. However, despite these promising simulation results, it is important to establish that couple simulation can also perform well in reality. To assess the applicability of the couple simulation method to real human data, I applied the method in two samples of real-world romantic couples. The purpose of these applications was not to lend support to any particular model of mate choice per se, but rather to answer two central questions: (a) can couple simulation achieve at least moderate simulation accuracy when applied to real human data and (b) if so, do some models actually outperform others? If the answer to these questions is “yes,” this suggests that couple simulation can lend evidentiary support to some theoretical models of mate choice over others using real human data.
Study 1
Participants in the first study were the n = 382 people (k = 191 heterosexual romantic dyads) used to generate the agent populations for the proof-of-concept models. Participants were recruited using Qualtrics’s survey panel service; the recruitment criterion was that participants were in a romantic relationship and were cohabitating at the time of participation. The target sample size was k = 200 dyads. A total of 230 dyads were initially recruited. Of these, 39 dyads were excluded for at least one participant reporting being nonheterosexual, yielding the final sample size of k = 191 heterosexual dyads. Participants ranged in age from 21 to 82 years and were M = 49.86 years old on average (SD = 14.48) and were in their relationships for Mdn = 12.08 years. The majority of participants (347, 90.83%) were married; 4 were engaged, 14 were dating seriously, and 2 were dating casually. A total of 13 participants reported their relationship status as “other”; in text descriptions, these participants mostly described their relationships as being a “domestic partner” or “living with partner but not married.” No participants indicated that they were not in a committed romantic relationship with their partners. Participants completed the survey one at a time, with the first member of the couple completing the survey in its entirety and in privacy before their partner beginning.
All participants in this sample reported their ideal mate preferences in a long-term, committed, romantic partner using a mate preference questionnaire with 20 7-point bipolar adjective scales. These asked participants to report, for example, how intelligent they desired ideal long-term mate to be on a 7-point scale ranging from “Very unintelligent” to “Very intelligent.” The questionnaire asked about preferences known from the prior literature to be universally important (e.g., kindness, intelligence, health) or universally sex differentiated (e.g., physical attractiveness, financial prospects, masculinity/femininity, age); some preferences were included as they were expected to evoke more variable responding (e.g., religiosity, sociability). In addition to reporting their ideal preferences, participants also rated themselves and their partner on the same 20 dimensions. Self and partner ratings were averaged together for each participant to form composite ratings of each participant’s own trait standing on each dimension. Self-other agreement was reasonable for these ratings, averaging r = .60 across dimensions and ranging from a low of r = .44 to a high of r = .72. Supplemental Tables 1 and 2 summarize the means and standard deviations of these preference and trait ratings and their correlations, respectively.
Trait and preference ratings were used to parameterize a population of agents based on the human participants for use in couple simulation. I generated 382 agents, each of whom was assigned the 20 preference and 20 composite trait values of one real participant. The identity of this agent’s real-world partner was also saved. This population of avatar agents then ran independently through each of the five mate choice algorithms from the proof-of-concept models; these algorithms were operationalized identically as in the proof-of-concept models. The GSA produced a matrix of paired agents; the simulation accuracy of this model was calculated as the proportion of these pairs that matched the real-world couples on which the avatar agents were based. The RAM ran for 100 model steps. Two agents were saved as a couple in this model if, by the end of the 100 model steps, both agents mutually allocated the majority of their resources to the other. Because not all agents achieved a mutual match, this resulted in 169 couples of a total 191 possible (88.48%); the simulation accuracy of the RAM was computed as the proportion of the real-world couples accurately reproduced in these model-predicted couples. Unpaired agents were included in this proportion and were considered inaccurate predictions by default.
The stochastic models—the random model, KHM, and ATM—were each run 100 times in total. Each run produced a matrix of predicted couples, and the accuracy of a model run was calculated as the proportion of real-world couples accurately reproduced in that run’s predicted couple matrix. I then calculated the average percent accuracy for each model across the 100 runs and used this to estimate each model’s best likely accuracy using the same procedure as in the proof-of-concept models.
CIs on simulation accuracy were estimated through bootstrapping. For all models, this was accomplished by sampling, with replacement, from the models’ simulation accuracies to generate 10,000 random bootstrap samples. For the deterministic models, the bootstrap procedure sampled with replacement from the model’s couplewise simulation accuracy vector—a binary vector of length n, with each element being either 1 (if that participant’s relationship was accurately reproduced) or 0 (if that participant’s relationship was inaccurately reproduced), producing 10,000 vectors of length 382. The accuracy of each of these bootstrap samples was calculated as the sum of the vector. For stochastic models, the bootstrap resampled from the couplewise accuracy vectors across the 100 runs of each model. This produced 10,000 382 by 100 resampled accuracy matrices for each stochastic model; simulation accuracy was then calculated for each bootstrap sample exactly as in the original sample. For all models, the limits of the 95% CIs were estimated as the values in the 2.5th and 97.5th percentile in the resulting vectors of 10,000 bootstrap accuracies. A separate set of proof-of-concept analyses indicate that this is a viable method of estimating CIs on simulation accuracy (see Supplemental Materials Section S4).
Couple simulation accuracy
Figure 7 presents the results of applying the couple simulation method to this sample, including the simulation accuracy for each model as well as 95% CIs. Two findings are key here. First, although the random model and KHM perform poorly, as they do in the proof-of-concept models, couple simulation in this sample is substantially above floor across models. The average simulation accuracy across all models is 24.88%, indicating that the couple simulation method can successfully reproduce real human couples and that the true model of mate choice, within at least this population, is not likely to be so stochastic as to render differential accuracy uninformative.

Couple simulation results for Study 1’s sample of romantic dyads. Error bars represent bootstrapped 95% confidence intervals.
Second, and more importantly, the couple simulation method also showed discriminative simulation accuracy. Not all models performed equally well, and performance ranged from the random model reproducing just 2.38% of real-world couples up to the best performing model, the RAM, reproducing a surprising 48.69% of the real-world couples. By comparison, a prediction model that, rather than simulating mate choice, merely guessed that each participant was paired with the potential mate who best fulfills their mate preferences would accurately identify no more than 26.96% of couples. It is also worth noting that the rank order of model accuracies and the relative difference between models are qualitatively similar to the same results in the proof-of-concept population in which the RAM was the ground truth mate choice algorithm.
A paired permutation test can estimate a traditional p value comparing any two models. When applied to this sample, such a test indicates that the RAM is in fact significantly more accurate than the next best model, the GSA, p < .001. Nonetheless, this result should be interpreted cautiously, as the sample size (k = 196) is smaller than the sample size where couple simulation more accurately discriminates between alternative models (k = 300).
Relationship quality
Furthermore, a surprising finding emerged from this sample relating simulation accuracy to relationship quality. The RAM performed best at reproducing the couples in this sample; nonetheless, this model still inaccurately reproduced more than 51% of the real-world couples. Much of this inaccuracy surely stems from the fact that the RAM is not a perfect model of human mate choice. However, some of these inaccurate predictions may also reflect that some of the couples are in some way unstable. Although no relationship between simulation accuracy and relationship quality was predicted in advance, several prior hypotheses predict that variables and processes related to mate choice (e.g., preferences, CLs, or mate value discrepancies; e.g., Conroy-Beam et al., 2016; Fletcher & Simpson, 2000; Shackelford & Buss, 1997) will be related to subsequent relationship quality. These hypotheses generally propose reciprocal relationships between mate choice and relationship quality, where unfavorable mate choices motivate negative appraisals of relationships, and these negative appraisals then motivate search for more favorable partners. Couple simulation was designed to evaluate models of choice, and not to model these relationship quality appraisals. However, to the degree that couple simulation accurately simulates participant mate choices, and to the degree that choice processes do relate to subsequent relationship quality, couple simulation may nonetheless attain power to predict features of relationship quality.
To explore this possibility, I took advantage of a separate set of measures available in this sample. In addition to the preference and trait ratings described above, participants also completed a battery of relationship quality, relationship behavior, and personality measures. These measures were largely exploratory in nature and included a number of pilot measures as well as measures intended for separate projects. Here I focused analysis on those measures that most clearly tapped into overall relationship quality. This included two measures of relationship satisfaction, the Quality of Marriage Index (QMI, Norton, 1983) and the satisfaction subcomponent of the Perceived Relationship Quality Components Questionnaire (PRQC; Fletcher et al., 2000); a measure of investment and two measures of commitment, from the Investment Model Scale (Rusbult et al., 1998) and the PRQC; a measure of jealousy, the Multidimensional Jealousy Scale (Pfeiffer & Wong, 1989); a measure of attachment, the Experiences in Close Relationships Short Form (Wei et al., 2007); and a measure of romantic love, the Triangular Love Scale (Sternberg, 1997). Participants completed all these measures in random order.
To analyze these, I conducted a series of multilevel models predicting relationship quality scores from simulation accuracy. Each of these models had participants nested within dyads, with random intercept terms, to account for the nonindependence of dyadic data. For each, I also standardized the relationship quality measures prior to analysis. Simulation accuracy was a binary predictor: Each couple was either accurately reproduced by the RAM or inaccurately reproduced. Standardizing the outcome variables in these models means that the resulting b values can be interpreted much like a Cohen’s d: The b reflects the standard deviation difference between accurately and inaccurately reproduced couples.
Consistent with the possibility that the couple simulation method was capturing aspects of relationship quality, couples that were accurately reproduced by the RAM had significantly more positive scores on each measure of relationship quality. Figure 8 plots the differences across dimensions. Participants in accurately reproduced couples reported significantly higher relationship satisfaction according to the QMI (b = .46, SE = .12, p < .001, 95% CI = [.22, .71]) and the PRQC (b = .49, SE = .13, p < .001, 95% CI = [.24, .74]). These participants also reported greater investment in their relationship according to the Investment Model Scale, b = .33, SE = .12, p < .001, 95% CI = [.09, .57], as well as more commitment according to the Investment Model Scale (b = .43, SE = .13, p < .001, 95% CI = [.18, .67]) and the PRQC (b = .29, SE = .13, p = .021, 95% CI = [.05, .54]). Members of accurately reproduced couples additionally reported stronger feelings of romantic love, b = .52, SE = .13, p < .001, 95% CI = [.27, .76]. Finally, these participants reported lower levels of jealousy, b = −.44, SE = .12, p < .001, 95% CI = [−.68, −.20]; attachment avoidance, b = −.64, SE = .12, p < .001, 95% CI = [−.89, −.40]; and attachment anxiety, b = −.36, SE = .12, p = .003, 95% CI = [−.59, −.12].

Differences in relationship quality in Study 1 as a function of couple simulation accuracy for the resource allocation model. For ease of comparison, the y-axis represents the percentage of maximum possible score for each relationship quality variable. Boxes reflect standard boxplots. Box edges represent the first and third quartiles (Q1 and Q3, respectively); the center line represents the median. The length of each whisker represents no more than 1.5 times the interquartile range (IQR); points are individual observations less than Q1 or greater than Q3 by more than 1.5 times the IQR.
In addition, the predictive power of simulation accuracy does not appear to be explained by any of a variety of potential predictors of relationship quality. To compare potential sources of simulation accuracy’s predictive power, I ran a series of control analyses that fit multilevel models predicting QMI satisfaction from both RAM accuracy and a control variable: one of either mate preference fulfillment, self mate value, partner mate value, partner-self mate value discrepancy, partner-potential mate value discrepancy, or relationship length. I limited these analyses to just QMI satisfaction to limit the number of models fit. Simulation accuracy remained a significant predictor of relationship satisfaction in each of these control analyses (largest p value = .026). This suggests that couple simulation’s power to predict relationship quality does not derive from couple simulation merely picking up on low-level predictors of relationship quality but rather relates to higher level features of mating markets themselves.
To see one such potential feature, we can compare true relationships and predicted relationships for those participants who were mispaired by the RAM. These participants were no more similar in overall mate value to their predicted partner than to their true partner, b = .17, SE = .13, p = .204, 95% CI = [−.09, .43]. However, predicted partners were better matches to participants’ preferences than were their real partners, b = .43, SE = .02, p < .001, 95% CI = [.26, .62]. This observation also provides some insight into why simulation accuracy declines with sample size: In larger samples, the likelihood is greater that, for each participant, the simulated mating market will contain an alternative that better fulfills their mate preferences. To the extent that couple simulation mispairs participants with more preference fulfilling alternatives, overall simulation accuracy will tend to decline in larger samples.
Finally, one concern with these results is that they may simply reflect biases shared between participant preferences and partner perceptions; for instance, perhaps participants who misperceive their partners as embodying their preferences are more likely to be reproduced accurately and are more likely to report higher relationship quality. However, all couple simulation results, for both simulation accuracy and relationship quality, persist even when participants’ own perceptions of their partner are removed from all analyses—albeit with smaller effect sizes (see Supplemental Materials Section S6).
Furthermore, not only did the RAM significantly predict each of these relationship quality dimensions, but according to the Akaike information criterion (AIC), models predicting relationship quality from resource allocation simulation accuracy were better fits to the data than models predicting the same dimensions with the GSA or the ATM. To predict relationship quality using the ATM, I estimated the best likely simulation accuracy for each couple and recoded this as a one if the best likely accuracy was above .5 and as a zero otherwise. The RAM was the superior model for predicting relationship quality for nearly all dimensions; the only exceptions to this were for PRQC commitment (ΔAIC = 2.237 relative to the ATM), relationship investment (ΔAIC = 4.50 relative to the GSA), and attachment anxiety (ΔAIC = 1.31 relative to the GSA).
Overall, these results suggest that couple simulation not only has the power to discriminate between alternative models of mate choice but also can identify happy, committed romantic relationships and models that perform better at reproducing couples overall tend to also have greater power to predict relationship quality. This set of findings is particularly surprising in that none of the models of mate choice utilize information about relationship quality: All are based exclusively on mate preferences and traits. Nonetheless, these models attain power to predict individual relationship satisfaction, commitment, investment, love, jealousy, and attachment.
Study 2
Study 1 produced several surprising findings. I therefore conducted a second dyad study with two aims: (a) to replicate the findings of Study 1, including the high on-average simulation accuracy, discrimination between models, and power to predict relationship quality and (b) given the apparent predictive power of simulation accuracy, to estimate the stability of this classification over time. Study 2 was conducted nearly identically to Study 1: Participants were cohabiting couples recruited using Qualtrics’s survey panel service. The key change was the addition of a longitudinal follow-up, completed at least 28 days after first participation. Participants in Wave 1 were n = 274 people making up k = 137 committed romantic dyads. Participants ranged in age from 24 to 89 years and were M = 48.88 years old on average (SD = 12.71). Again, nearly all participants were married at the time of participation (n = 246, 89.79%) and were in their relationships for Mdn = 17.42 years. Participants completed the same set of measures as in Study 1, with the addition of the complete PRQC, adding the intimacy, trust, passion, and love subscales. Descriptive statistics and correlations for preference and trait ratings are summarized in Supplemental Tables 1 and 2.
For Wave 2, participants were invited by email to complete the same set of measures 28 days after their initial participation. A total of n = 80 participants, making up k = 40 dyads, completed Wave 2, yielding a 70.80% attrition rate. Participants who completed both sessions of the study did not differ significantly from participants who only completed the first session in Wave 1 age, income, mate preference fulfillment, overall mate value, or relationship satisfaction according to the QMI or PRQC (ps range from .072 to .542). Participants who completed both sessions did report significantly higher feelings of romantic love at Wave 1 than people who did not complete both sessions according to both the Triangular Love Scale, t(183.95) = 2.13, p = .035, d = .26, 95% CI = [−.01, .52], and the PRQC, t(176.56) = 2.25, p = .026, d = .28, 95% CI = [.01, .54]. Participants were not limited in how long after their initial participation they could complete Wave 2; the average delay between Waves 1 and 2 was M = 31.40 days, with a range from 28 to 56 days.
Couple simulation accuracy
Participant trait and preference data were again used to parameterize agents for couple simulation. Figure 9 presents the simulation accuracy results for all participants in Waves 1 and 2. Just as in Study 1, simulation accuracy was high across the board with an average model accuracy of 25.40% in Wave 1 and 32.56% in Wave 2. Furthermore, the relative ordering of model accuracy replicated in both waves, with the RAM accurately reproducing the largest proportion of couples. Again, by comparison, a model that merely predicted each participant was paired with the potential mate who best fulfilled their preferences would accurately predict no more than 21.17% of couples in Wave 1 and 28.75% of couples in Wave 2. CIs were wider in Study 2 owing to the smaller sample size, especially for Wave 2; nonetheless, the RAM was still marginally significantly more accurate than the GSA, p = .063 in Wave 1. In Wave 2, the RAM did not significantly outperform the GSA, p = .223, and only marginally outperformed the ATM, p = .059. However, both sample sizes, with k = 137 at Wave 1 and k = 40, are well below the k = 300 sample size where discrimination between models tends to be higher. Overall, the simulation accuracy findings from Study 1 were replicated in Study 2.

Couple simulation for Study 2’s sample of romantic dyads. (A) Wave 1 and (B) Wave 2. Error bars represent 95% confidence intervals.
Relationship quality
Despite the smaller sample size, Study 2 also largely replicated the relationship quality findings from Study 1. Due to power concerns, I only analyzed data from Wave 1. Figure 10 shows that, again, participants whose relationships were accurately reproduced by the RAM reported higher relationship quality across nearly all dimensions. Analysis of relationship quality proceeded identically as for Study 1, and as such, all reported b values can be interpreted equivalently to Cohen’s d values. These participants reported significantly higher relationship satisfaction according to the QMI (b = .64, SE = .15, p < .001, 95% CI = [.35, .94]) and PRQC (b = .68, SE = .15, p < .001, 95% CI = [.39, .97]). Just as in Study 1, simulation accuracy’s power in predicting QMI satisfaction was not eliminated by controlling for mate preference fulfillment, self mate value, partner mate value, partner-self mate value discrepancy, partner-potential mate value discrepancy, or relationship length (all ps < .022). Accurately reproduced couples further reported more higher commitment according to the PRQC (b = .47, SE = .15, p = .002, 95% CI = [.18, .76]) and Investment Model Scale (b = .39, SE = .14, p = .007, 95% CI = [.11, .67]); higher investment (b = .70, SE = .15, p < .001, 95% CI = [.41, .98]); higher love according to the Triangular Love Scale (b = .66, SE = .15, p < .001, 95% CI = [.37, .96]) and the PRQC (b = .56, SE = .15, p < .001, 95% CI = [.27, .85]); and higher intimacy (b = .64, SE = .15, p < .001, 95% CI = [.34, .94]), trust (b = .48, SE = .14, p < .001, 95% CI = [.20, .76]), and passion (b = .67, SE = .15, p < .001, 95% CI = [.37, .96]) according to the PRQC.

Differences in relationship quality in Study 2 as a function of couple simulation accuracy for the resource allocation model. For ease of comparison, the y-axis represents percentage of maximum possible score for each relationship quality variable. Boxes reflect standard boxplots. Box edges represent the first and third quartiles (Q1 and Q3, respectively); the center line represents the median. The length of each whisker represents no more than 1.5 times the interquartile range (IQR); points are individual observations less than Q1 or greater than Q3 by more than 1.5 times the IQR.
These participants also reported significantly lower avoidant attachment than inaccurately predicted participants (b = −.53, SE = .14, p < .001, 95% CI = [−.81, −.24]). However, the difference in total jealousy was just marginally significant in Study 2 (b = −.28, SE = .15, p = .06, 95% CI = [−.56, .01]) and the difference in attachment anxiety was not significant (b = −.17, SE = .14, p = .20, 95% CI = [−.44, .09]).
As in Study 1, nearly all these relationship quality variables are generally better predicted by couple simulation accuracy according to the RAM, compared with the GSA or ATM. The only exception to this was for attachment anxiety, ΔAIC = 5.94 relative to the GSA. Attachment anxiety was significantly predicted by the GSA (b = −.38, SE = .14, p = .006, 95% CI = [−.65, −.11]) but not by the RAM. Unfortunately, unlike Study 1, the smaller sample size of Study 2 precluded analyses comparing predicted partners with true partners.
Longitudinal stability
Finally, given the apparent importance of couple simulation accuracy to predicting relationship quality, one feature worth examining is the stability of couple simulation accuracy: Do models of mate choice consistently predict the same subset of couples across time or is there variability in when a couple is accurately reproduced? To estimate this, I took advantage of the longitudinal design of Study 2. Here, I ran the couple simulation method twice: once using all participant data in Wave 2 and again using only those participants in Wave 1 who also eventually completed Wave 2. I focused analysis just on the RAM. To estimate the stability of simulation accuracy, I calculated the proportion of couples who maintained the same classification from Wave 1 to Wave 2 and noted the direction of change for those couples who did not. Supplemental Figure 3 presents these data. Between Waves 1 and 2, 80% of couples reproduced inaccurately in Wave 1 were also reproduced inaccurately by the RAM in Wave 2; only 20% of participants changed from inaccurate to accurate reproductions. A total of 72% of couples reproduced accurately at Wave 1 were also reproduced accurately at Wave 2; 28% of participants changed from accurate to inaccurate between time points. Overall, 75% of participants maintained their classification between Waves 1 and 2, indicating moderate stability of couple simulation over the course of 1 month.
A related question concerns the relationship between couple simulation accuracy and relationship quality. One possible explanation for the relationship between couple simulation accuracy and relationship quality is that the couple simulation method somehow captures enduring determinants of the happiness and stability of participants’ relationships. However, the reverse is also a possibility: Relationship quality could be driving simulation accuracy in a more fleeting manner. That is, people who, in the moment, feel positively about their relationship will evaluate their partner more positively with respect to their preferences, yielding more predictable couples, whereas people who feel more negatively about their relationship will evaluate their partner more negatively, yielding lower simulation accuracy.
These two possibilities make distinct predictions about the nature of Wave 2 simulation accuracy. If the former possibility is true, and couple simulation captures stable determinants of relationship quality, couple simulation accuracy in Wave 2 should be more strongly predicted by accuracy in Wave 1 than it is by relationship quality in Wave 2. However, if the second possibility is true, and couple simulation taps peoples’ in-the-moment feelings about their relationship, simulation accuracy in Wave 2 should be more strongly predicted by relationship quality in Wave 2 than by simulation accuracy in Wave 1. I tested these two predictions by fitting a set of multilevel logistic regressions, each predicting Wave 2 simulation accuracy simultaneously from Wave 1 resource allocation accuracy—based just on those participants who completed both waves—and each of the relationship quality measures from Wave 2. For all models, Wave 2 simulation accuracy was positively predicted by Wave 1 accuracy (all ps < .001) and was not predicted by any of the Wave 2 relationship quality measures (all ps > .715). This suggests that couple simulation accuracy is tapping into more stable determinants of relationship quality, rather than in-the-moment feelings toward relationships.
General Conclusions
Mate choice is a critical social decision with far-reaching consequences for human life and human evolution. Understanding the nature of human mate selection would provide insight into some of the most powerful selection pressures that historically shaped our species as well as into some of the most powerful everyday forces that shape our happiness and wellbeing. However, the importance of mate choice is unfortunately matched by its complexity. Mate seekers must navigate a raft of chaotic social processes, including identifying the best available partners among sets of imperfect options and reconciling their own desires with the shifting motivations of potential mates and rivals.
Verbal theoretical models of this complex process are challenging to reason about and to communicate. Computational modeling provides an alternative mode of theorizing that can alleviate these problems by facilitating prediction generation, forcing theoretical assumptions to be specific and transparent, reducing ambiguity in communication, and more closely emulating the workings of the mind. Yet computational models can be a double-edged sword in that accurate models of mate choice by necessity incorporate multiple interlocking components, rendering them challenging to test empirically. Evaluations of mate choice models have largely been limited to comparisons of aggregate trends between models and data, yielding relatively coarse contrasts compared with the sophistication of the underlying theories. This complexity of human mate choice, and the lack of precise model comparison tools, has slowed progress in this critical area of research.
Here, I have introduced a new computational modeling approach, the couple simulation method, for comparing complex models of mate choice. This method simulates real people in the context of experimental mating markets. Rather than comparing models of choice based on aggregate trends across couples, this method evaluates models of mate choice on the basis of their ability to accurately reproduce the real mate choices of individual participants. In proof-of-concept models, this method can successfully identify the true model of mate choice, given only samples of couples from simulated populations. In two human samples, this method achieves high simulation accuracy, discriminates between five models of mate choice, and has power to identify couples that are relatively happy and committed.
These preliminary results suggest that couple simulation has potential to stimulate theory development in human mate choice by providing an empirical metric for evaluating complex models of mate choice. Development of mate choice theory has been stymied by the difficulty of deriving predictions from mate choice theories (e.g., Eastwick et al., 2014; Schmitt, 2014) and little agreement about how to best test these predictions (e.g., Eastwick et al., 2019; Fletcher et al., 2020). By estimating the ability of computational models to accurately reproduce real-world mate choices, couple simulation provides a means to quantify the plausibility of any model of mate choice. If theories of mate choice can be expressed in computational terms, couple simulation provides a potent means to compare them—an essential step in building better models. By eliminating the roadblocks traditional approaches have faced, couple simulation opens a new avenue for advancing theory concerning the nature of human mate selection.
The RAM in particular demonstrates couple simulation’s utility in facilitating theory development and theoretical integration. The RAM borrows elements from each of the traditional theoretical approaches to mate choice: particularly, emphases on constraints and trade-offs from evolutionary (e.g., Kaplan et al., 2001) and cognitive science perspectives (e.g., Gigerenzer & Todd, 1999) and emphases on reciprocity (e.g., Walster et al., 1973) and the dyadic level of analysis (e.g., Thibault & Kelley, 1959) from social psychological perspectives. Prior to couple simulation, there would have been little means by which to determine whether this novel model was a more or less plausible description of human mate selection than any existing models. But couple simulation allows an empirical demonstration that the integrative approach bears fruit: The RAM performs best at reproducing real-world mate choices among the models tested here, suggesting it is a relatively plausible description of human mate choice.
This result also helps illustrate how couple simulation can help guide theory development in the face of an incomprehensibly large set of possible models. Table 3 presents a “parts list” for building computational models of mate choice. In this table are many of the key conceptual elements that vary across the models considered here as well as across models from the broader literature. Atomizing computational models in this way can facilitate theory development by providing a map of the space of possible models, clarifying the ways in which theoretical models differ from one another and highlighting the variations that contribute to model accuracy. Progress in theorizing about mate choice will come from expanding the parts list as well as by building and evaluating new models that combine the available parts in novel ways (see Gigerenzer & Todd, 1999 for a similar approach).
A “Parts List” for Constructing Computational Models of Mate Choice.
Note. Superscripts identify which example models include each model feature.
Kalick-Hamilton model; baspiration threshold model; cGale-Shapley algorithm; dresource allocation model.
However, true progress in advancing mate choice theory is unlikely to come from a brute force combinatorial approach. From even just the parts listed in Table 3, one could generate up to 7,020 unique models just by picking one unique part for each decision stage. This combinatorial explosion would only get worse if one allowed the combination of multiple parts at each stage or considered the new parts or decision stages that future research will assuredly discover. The most accurate description of human mate choice resides somewhere in this vast model space; however, testing all possible models is likely infeasible.
Couple simulation results can provide a much-needed compass for guiding explorations through model space. In the face of this paralyzingly large space of possibilities, it is crucial to prioritize some possible models for consideration over others. The best starting points are likely going to be those that borrow parts from models that have proven successful before—especially those parts that are unique to successful models. For example, from the parts list in Table 3, one can see that the RAM is unique in incorporating continuous choice and an explicit emphasis on reciprocity and trade-offs. The relatively high simulation accuracy achieved by virtue of these unique features suggests the best approach for future model development will be starting at the RAM’s point within model space and iteratively adding or removing model parts from there. By retaining those variations that tend to improve simulation accuracy, researchers can use couple simulation to hill climb through model space toward more accurate models of the mate choice process.
However, it should be stressed that, while the couple simulation method does appear useful for evaluating models of human mating, this does not mean that the couple simulation method should replace alternative sources of evidence. Rather, couple simulation should be used in tandem with other methods to guide explorations of human mating. Results from laboratory and field studies can provide evidence for what models of mate choice are plausible enough to be modeled in couple simulation; models proposing decision processes that conflict with evidence from these real-world studies should be considered only cautiously, regardless of their simulation accuracy. Laboratory and field studies can also provide qualitatively different kinds of evidence that test specific model features in a more focused fashion than couple simulation allows. For instance, longitudinal studies could be designed to observe changes in aspiration threshold over time or the reallocation of time and other resources across actual potential mates.
Reciprocally, couple simulation can provide suggestions as to which models are plausible enough to be worth testing in laboratory and field studies. Such studies are expensive and difficult to plan and implement, and the number of alternative hypotheses that can be tested in a given study is always limited; for these reasons, models that perform well in couple simulation should receive higher priority when deciding what models to test. Combining couple simulation with laboratory and field studies of mate choice decision-making will likely be the most efficient path for researchers to narrow the massive space of possible models of mate choice into fewer, plausible, empirically supported descriptions of human mating.
The success of the RAM also suggests that future models are likely to profit from even further theoretical synthesis. This could include incorporating parts of the other models: for instance, the KHM’s decline in standards over time, the ATM’s mate value learning process, or the GSA’s emphasis on pair stability. This could also include incorporating elements of theories virtually untouched here, for instance, attachment theory (Latty-Mann, & Davis, 1996) or sexual strategies theory (Buss & Schmitt, 1993). Couple simulation can quantify the value added by these theoretical revisions in simulation accuracy units, catalyzing the development of models that more deeply integrate concepts from across theoretical perspectives.
Future Directions
Theory Development
There are several open directions for future research that are likely to be tributary to the broader goal of stimulating theory development in human mate choice research. Some of these future directions pertain to the development of computational models that incorporate more elements of real mate choice and more realistic simulations of these elements. As argued above, computational models are formal expressions of theories; “logical machines” that convert theoretical assumptions about the nature of mate choice into predictions (Gunawardena, 2014; Smaldino, 2017). Developing computational models that make more realistic and more comprehensive assumptions thus provides a pathway to theories of mate choice that are more thorough and precise. Future research can advance theory development in the domain of human mate choice by applying couple simulation to further advance model development. Below, I detail seven specific future directions in which couple simulation could be applied to guide further theory development.
The clearest first step in this direction is to continue applying couple simulation to evaluate broader sets of computational models of human mate choice. For tractability, in developing and initially applying the couple simulation method, I have focused here on just five example models of mate choice. However, these are far from an exhaustive set. Although these models represent some of the most frequently cited models, there are alternatives within the literature (e.g., French & Kus, 2008; Knittel et al., 2011; Smaldino & Schank, 2012). Even the models considered here have alternative implementations. I considered a single, abstracted version of the ATM, most similar to that described in Todd and Miller (1999), but a variety of related versions of this model have been proposed before (e.g., Hills & Todd, 2008; Todd et al., 2005; Todd & Miller, 1999). The KHM has a variable parameter—the maximum number of dates agents go on before settling—which here I set to the value used in the original paper (Kalick & Hamilton, 1986); however, this parameter could instead be treated as a free parameter, fit to the data by finding a value that optimizes couple simulation accuracy. Similarly, within the ATM, I assumed a fixed and universal rate of threshold learning, but the degree to which each agent adjusts their threshold in response to experience could also be fit to the data as a free parameter. By testing broader sets of mate choice models in a couple simulation framework, future research will gain greater insight into what theoretical assumptions and model features yield greater simulation accuracy, expanding the parts list available for constructing novel models of choice.
Second, future models can also incorporate more realistic elements of constraint. Although all theorists agree in principle that, in mate choice, people are constrained by finite time, energy, and other resources, the exact sizes of people’s resource budgets are hard to know because these resources can be challenging to quantify. For this reason, while most of the models tested here incorporate time and resource constraint considerations, these constraints are relatively simplified and are fairly arbitrary over substantial ranges of manipulation. For instance, in Studies 1 and 2, the RAM’s simulation accuracy remains virtually unchanged even when the duration of mate choice is limited to just 10 iterations (accuracy = 46.60% and 45.46%, respectively).
More comprehensive theories of mate choice will require greater understanding of the precise temporal, energetic, financial, or computational constraints people are under when selecting romantic partners. Future research could move in this direction by attempting to measure real-world resource constraints more precisely to better inform the assumptions of future computational models of mate choice. Alternatively, and compatibly, constraint assumptions within computational models could be treated as free parameters and manipulated so as to find values that optimize simulation accuracy. In this way, through couple simulation, models and real-world data could reciprocally inform one another to yield more realistic estimates and models of constraints on mate choice.
Third, despite the name, couple simulation need not be applied to model the decisions of coupled individuals exclusively. Romantic relationships are not equally beneficial or desirable for all people (e.g., DePaulo & Morris, 2005), and many people remain unmated voluntarily. Although some of the models tested here, such as the RAM, can result in unpaired individuals, these unpaired agents at best represent involuntarily single people. If voluntarily single participants were recruited alongside mated couples, avatar agents could be simulated for these participants and their behavior modeled in simulated mating markets alongside avatars of mated people. Such mixed mating markets could be used to test models on their ability to reproduce voluntarily single people’s decisions to opt out of the mating market. Modeling the decisions of both single and mated people would yield theoretical models that more fully describe the complete mate choice decision space.
Fourth and relatedly, in the models tested here, all agents in the population pursued essentially the same mating strategy. This is not a necessary assumption, and future applications of couple simulation could test mixed models with strategic differences between agents. There is no reason to assume that all human mate choice, from all people, and under all contexts, proceeds according to the same algorithm, and so no one mate choice algorithm is guaranteed to be the best model in all contexts. Examining variability in mate choice algorithms will therefore be an essential step for future research. For instance, couple simulation could be used to test models in which different agents deploy different mate choice algorithms based on contextual factors or individual differences such as age, sex, personality, mating strategy, attachment style, relationship history, culture, or other factors.
Fifth, I have focused entirely on using couple simulation to compare mate choice algorithms. However, couple simulation could be used to evaluate models of all the tasks of mate selection. For instance, in all proof-of-concept models and in both human samples, I assumed that mate preference integration followed a Euclidean integration algorithm. This is not a necessary assumption; there are other algorithms that have been proposed as models for how preferences are integrated into evaluations of potential mates (Brandner et al., 2020; Conroy-Beam, 2018; Eastwick et al., 2014; Miller & Todd, 1998). Whereas I have held constant the model of mate preference integration and varied algorithms of choice, one could just as easily fix the algorithms of choice and vary the model of integration to observe what integration model most accurately reproduces real-world couples. Couple simulation could be pushed even further still to evaluate which preferences are important in mate choice.
Sixth, in all applications of the couple simulation method, I have additionally assumed that all individuals in the sample are equally considered as viable potential mates and, as such, all individuals have perfectly overlapping mating markets. This is not a necessary assumption and is possibly unrealistic as well. Some people are probably disqualified as potential mates prior even to the evaluation task of mate choice. This certainly includes people of the wrong gender relative to one’s sexual orientation, but could potentially also include people who are too old or too young relative to one’s age, or perhaps are just too extreme on some preference dimensions (e.g., too unkind, too unintelligent, etc.; Li et al., 2013). Indeed, people express a variety of “dealbreakers”: characteristics that, if possessed, are thought to evoke deeply negative evaluations of potential mates, regardless of other characteristics (Jonason et al., 2015). Relatedly, some have suggested that mate choice proceeds more by rejecting potential mates with negative characteristics than by pursuing potential mates with positive characteristics (Li et al., 2013; Long & Campbell, 2015). One could apply the couple simulation method to evaluate decision rules for determining the disqualification of potential mates with negative characteristics. For instance, one hypothesis testable by couple simulation is that people use a decision algorithm similar to the ATM (e.g., sequential aspiration; Miller & Todd, 1998) to cull their pool of potential mates initially and then select among the limited pool using a different mate choice algorithm such as the RAM.
Seventh and finally, all the models tested here omitted the actual mate search and perception process, simply assuming instead approximately veridical perception from all agents. Such uniformity in mate perception seems unlikely, given that participants disagreed to some extent with even their highly committed partners in their trait ratings. Future research could improve upon these models by incorporating models of the search process—and, crucially, allowing variability in perception of potential mates between agents. For instance, the mate search process could be modeled as similar to the classic multiarmed bandit problem, with the mate value of each potential mate being precisely knowable only to the degree that one has spent time actively pursuing that mate. Such a model could account for variability between people in their perceptions of potential mates as well as capture the exploration/exploitation trade-off inherent to real mate search.
The breadth of search that agents undertake is also worth some consideration. The GSA and RAM models both assume that agents are simultaneously evaluating and comparing all potential mates in their mating pool. In contrast, the KJHM and ATM models assume agents evaluate only one randomly encountered potential mate at a time. This difference in the breadth of mate search likely contributes somewhat to the difference in performance between these models. However, to test this possibility, future research must build models that vary more fully and systematically between complete and partial mate search. Intermediate models are possible as well: Agents could use random encounters to build up a pool of potential mates to which they apply their mate choice algorithms completely.
Relatedly, agents in the models tested here had all information about potential mates available to them immediately. However, in real mate choice, information about potential mates is revealed gradually, and different pieces of information are available at different points in time. Some information is available immediately—for instance, the physical attractiveness of potential mates is relatively easily observable. Other information, such as personality and intelligence, can be inferred indirectly relatively early on with moderate accuracy (Ambady & Rosenthal, 1993; Borkenau et al., 2004). But other information, especially the most critical information about how a potential mate will behave toward oneself (Lukaszewski & Roney, 2010), can only be observed by sampling observations from specific potential mates over time (Thibault & Kelley, 1959). Miller and Todd (1998) proposed a sequential model of mate choice in which different pieces of information are used to create a successive series of aspiration thresholds, with easily acquirable information used to select among potential mates at early stages of relationship development but slower emerging information setting thresholds for later relationship stages. This sequential aspiration hypothesis has not yet been modeled explicitly; however, the sequential use of information it proposes could be incorporated into models of mate choice and tested using couple simulation. Incorporating the sequential nature of real information search, and testing models that can accommodate the uncertainty it poses, will be an essential direction for future research.
Methodological Refinements
Other future directions pertain to more methodological concerns. This includes refining the couple simulation method itself and sampling data for couple simulation from a wider array of populations, under a broader set of relationship contexts, and employing more valid and informative measures. Five such methodological refinements offer clear next steps for future research.
First, these preliminary applications of couple simulation have exclusively utilized couples that are already engaged in very committed, long-term relationships. These couples have their advantages for use in couple simulation: These relationships represent the endpoints of the decision models couple simulation attempts to evaluate. Nonetheless, they have their disadvantages as well. In particular, there is no guarantee that the data collected from these participants, and used to parameterize their avatar agents, are representative of what these individuals were like when they made their initial mate choice. People of course change over time in mating relevant ways: they become older, become more educated, advance in their careers, and so on. And although mate preferences appear to be somewhat stable over time (Bredow & Hames, 2019), mate preferences do change across the lifespan (Cohen et al., 2019), and there is evidence that mate preferences also change after the initiation of relationships (Gerlach et al., 2019).
These changes are not uninteresting for understanding mate choice. To be in a long-term relationship, one must not only initiate a relationship with a partner but also maintain that relationship over time. As the existence and frequency of divorce and remarriage attest, people can and do abandon their mates in search of others—presumably at least in part due to disconnects between preferences and partners and/or the availability of more appealing outside options (Buss et al., 2017). For this reason, a mated person’s current preferences and traits are as much a part of explaining their mate selection as are their historical preferences and traits.
Nonetheless, successfully modeling initial mate choice is of clear value as well. And uncritical use of observed preference and trait values irrespective of their changes over time could lead to biased conclusions from the couple simulation method. If one has a model of how these changes occur, they can be adjusted for within the framework of couple simulation. Applying such an adjustment to the two studies reported here does result in lower simulation accuracies but does not change the central conclusions (see Supplemental Materials Section S5). Even better, the couple simulation method could be applied to simulating couples at earlier stages in the mate choice process. This could be accomplished by collecting data from newer couples or by, for instance, using couple simulation to attempt to prospectively predict the mate choices of single people.
Second and similarly, the couples used here for Studies 1 and 2 were both composed of relatively older adults in relatively long-lasting, established relationships. The median relationship lengths for these two studies were 12 and 17 years, respectively. Applying couple simulation to evaluate models of mate choice among younger people in more nascent relationships will be important as different models may perform well at explaining relationships at different stages. For instance, a model such as the ATM could describe how people decide to enter relatively casual, low-commitment, early-stage relationships, whereas the RAM could instead be describing how these early relationships develop into the highly committed relationships observed in Studies 1 and 2.
Relationship length had mixed associations with simulation accuracy in the studies tested here. A multilevel model predicting standardized relationship length from RAM simulation accuracy finds that couples do not differ in relationship length as a function of simulation accuracy in Study 1 (b = .14, p = .350, 95% CI = [−.15, .43]). However, in Study 2, couples accurately reproduced by the RAM tended to be in slightly shorter relationships than inaccurately reproduced couples (b = −.40, p = .023, 95% CI = [−.74, −.06]). That said, clearer understanding of the differences in mate choice as a function of relationship stage will come from studies able to use couple simulation to model samples composed exclusively of relatively nascent, established, or intermediate couples rather than the mixed samples used here. Applying couple simulation to test a variety of models in a variety of relationship stages and contexts will yield a clearer picture of the full trajectory of relationship development.
Third, participants sampled here were sampled from a national U.S. population, and this introduces several limitations that should be addressed in future research. First of all, these samples are of course overrepresented in psychological research in general, including mating research, and couple simulation should be used to explore both universals and variability in human mate choice across cultures (Barrett, 2020; Henrich et al., 2010). Second, the nature of mate choice in modern populations differs dramatically from the context in which our mating psychology evolved (Goetz et al., 2019). Whereas our mating psychology was designed for relatively small mating markets where any individual could expect to relatively exhaustively search and evaluate their pool of options, mate choice now takes place in anonymous mating markets on the scale of millions of people where no individual can expect to evaluate more than a fraction of the potential mates they could in principle pursue. This likely injects noise into the mate selection process and thereby noise into comparisons between models of mate choice. Third, and related to this limitation, participants in our samples do not know one another directly, and so the validity of couple simulation as applied to these samples rests on the assumption that the samples are at least representative of each participant’s actual mating market. Both of these latter limitations could be alleviated by drawing samples from smaller, more closed mating markets, either by leveraging smaller, more isolated social networks within broader social networks (e.g., college dormitories) or by applying couple simulation to data from non-WEIRD cultures with more isolated mating markets (Henrich et al., 2010). Furthermore, how mate choice differs in large, modern mating markets as opposed to smaller, more traditional mating markets is a research question couple simulation could help answer.
Fourth, another important future direction will be to determine a more effective way to evaluate stochastic models within the couple simulation method. Modeling stochastic model performance as a beta distribution allows for accurate discrimination among moderately stochastic models of mate choice. However, highly stochastic models—such as the KHM—still produce very low levels of couple simulation accuracy overall and poor discrimination between alternative models. Although it is unlikely that true human mate choice is as stochastic as the KHM, it would be ideal to have a means to demonstrate this empirically. Future researchers should continue to explore alternative means of modeling the performance of stochastic models of choice to allow discrimination between models even in populations where mate choice is highly random.
Fifth and finally, the preliminary results reported here also hint to couple simulation’s potential value as a practical tool for helping people form successful romantic relationships. Given the myriad benefits of positive romantic relationships (Antonovics & Town, 2004; Holt-Lunstad et al., 2010; Robles et al., 2014), any tool that can help guide people toward high quality romantic relationships could have important consequences for human wellbeing. Couples accurately reproduced by couple simulation—as opposed to those not accurately reproduced—reported higher relationship quality across a wide array of dimensions: satisfaction, love, commitment, investment, jealousy, attachment, and more. If this predictive power could be harnessed in longitudinal designs for prospective predictions of relationship quality, couple simulation could provide a mechanism for translating relationships theory into real-world benefits for everyday people.
Conclusion
Research on human mating has been successful in documenting the content of human ideals but has struggled to detail the subsequent tasks of mate selection. This progress has been slowed not by a lack of interest but in part by the inherent complexity of human mate choice. This complexity makes models of mate choice difficult to evaluate and therefore difficult to compare and improve. The couple simulation method introduced here provides a method of model comparison that can be used to evaluate complex models of mate choice using just samples of romantic couples. This method can recover the true model of mate choice in computer simulations; accurately reproduce nearly 50% of real couples in human samples; and identify couples with high-quality relationships across numerous dimensions. These results provide initial validation of couple simulation as a means to compare, revise, and improve models of human mate choice. By removing the barriers that have hindered human mate choice research, couple simulation has potential to galvanize theory development on the psychology of mate choice, providing a means to illuminate one of the most important decisions in human life and in human evolution.
Supplemental Material
sj-docx-1-psr-10.1177_1088868320971258 – Supplemental material for Couple Simulation: A Novel Approach for Evaluating Models of Human Mate Choice
Supplemental material, sj-docx-1-psr-10.1177_1088868320971258 for Couple Simulation: A Novel Approach for Evaluating Models of Human Mate Choice by Daniel Conroy-Beam in Personality and Social Psychology Review
Footnotes
Acknowledgements
I would like to thank David Buss, Ben Gelbart, Jaimie Krems, James Roney, and Katy Walter for helpful feedback on earlier versions of this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This material is based upon work supported by the National Science Foundation under Grant No. 1845586.
Supplemental Material
Supplemental material for this article is available online.
References
Supplementary Material
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