Abstract
Few disciplines are as vast and diverse as that of foresight. But diversity can lead to fragmentation; to strengthen it as an asset, it is necessary that the fundamentals of its uniqueness are firmly established. Roots for this appear within several factors of oneness embedded in foresight methodological apparatus: first of them, the global toolbox that shows the transition from a research of certainty to anticipation of uncertainty and from very simple tools sketching the reality to a more subtle approach of complexity; second, are the reading grids, namely, here “foresight lens,” that generate these tools and include the worldview, the capacity for discernment, and the creation of such foresight lenses; and finally, and still upstream of paradigms, the different types of knowledge required for the practice of foresight: dynamic, systemic, epistemic, and diagnostic. Built upon these three factors, a meta-method of foresight emerges, transverse to all other methods, inclusive and enriched—not dissolved—by diversity.
Few areas of study are as vast as that of foresight. Its object of study could be an organization (large or small, public or private, individual or collective, informal or institutional), a territory (local, national, global, or at any other scale in between), a specific issue (social, economic, or technological), or even an individual. Its sphere of knowledge runs the spectrum from exploration to creation of futures, from anticipation to imagination, from risk detection to design of desired futures. There are also multiple means by which knowledge is created: they range from the expertise of an individual to the collective reasoning of laypeople, not to mention the work of groups of decision makers. Finally, foresight is practiced not only by its own specialists, but also those of other disciplines, not only by faculty, but also consultants, not only by Europeans, but also Americans, Asians, Africans, with each group bringing its own unique cultural perspective.
This discipline is, thus, by definition and practice, highly diverse. However, for this diversity to be a strength and not a source of fragmentation, the fundamentals of its unity and oneness as a discipline must be solidly established. In the case of French prospective, this oneness is relatively apparent since it has been defined by its “inventor” Gaston Berger: 1 a “normative” foresight that does not look to predict the future but rather anticipate new problems that may arise and find new and desirable solutions, in accordance with humanistic values. 2 This is clearly more about innovation and change than about futurology. However, if one uses a wider scope to consider “foresight” as the entire field of Futures Studies, 3 it becomes more difficult to find a common core. Therein lies the interest in developing an overarching method that would bridge the gap between differences in concept and practice to produce this desired fundamental oneness, while preserving their rich variety.
The goal of this research is, thus, to discover the overarching method that lies beyond various methods and elaborate from it a more structured approach. The results are displayed in two articles. This one addresses the problem that it seeks to answer; the second one will describe the three successive phases of this metamethod.
Where can one search for this central oneness in the discipline? We will turn toward methodology to discover or elaborate the commonalities between the various forms of foresight. Generally speaking, methodology is the analysis of how one proceeds in a specific sphere of knowledge. We must, thus, think about and understand how we practice foresight, not in terms of content, horizons, or objectives, but rather in terms of method (see “A Global Toolkit” section).
A method is usually defined by a sequence of tasks. Because many works4,5 have already described Futures Studies (FS) methods from this standpoint, we will look here at two other components of a method: the implicit filters that shape one’s way of looking at the future—the “foresight framework” (see “A Foresight Framework” section)—and the different types of knowledge needed for futuring (see “Different Types of Knowledge” section).
Before tackling the heart of the issue, it must be noted that the different classifications we will discuss in the first part of this research breach to some degree the conceptual barriers * between method (as we have previously defined, of a more deductive nature), process (manner by which one progresses through a line of reasoning, of a rather experimental, inductive nature), tool (means by which one obtains a result), and technique (set of processes specific to an activity, relying on scientific knowledge).
It is not in the scope of this research to develop a systematic methodological critique of each of these classifications, nor to assert their exhaustiveness. Indeed, these classifications are mere examples of the diversity of approaches in the subject matter, be they sound or unfounded. †
We will thus use the broader definition of method as a “set of means by which one obtains any result.”
A Global Toolkit
The main problem of the identification of a global toolkit is not looking for information but processing its profusion. Indeed, methods used by foresight are highly varied, from simple two-dimensional analytical schematics to more sophisticated methodologies, such as the scenario building method. 6 In the past few decades, several classifications have been developed by different authors in different countries, but they face the prospect of becoming obsolete or evolving as this half-century-old discipline continues to develop. A new classification system, transversal to those that preceded it while offering stability over time, thus, seems necessary to account for any state of the art of the discipline.
We established this new system of classification ‡ by first researching existing classifications (see “Existing Classifications” section), then by creating one that best accounts for the evolution of the methods (see “The Chosen Categorization” section), based on consultation with respected futurists, such as Dr. James Dator from the University of Hawai’i and Dr. Eleonora Barbieri Masini from the Gregorian University in Roma. We then sorted most of the existing methods in this new classification system. An avenue of further work could be to bring together a group of seasoned futurists to consider how this categorization system may further be improved.
Existing Classifications
How do current classifications differ from one another? It is possible to distinguish two broad types of typologies: by nature or by organization.
A typology by “nature” or objective
These classifications separate different methods by their nature—for example, predictive or exploratory, subjective or objective—or by their goal—for example, normative or extrapolative. Dr. Eleonora Barbieri Masini, 3 by means of example, thus identifies three categories of methods.
Subjective or intuitive methods: instead of solely relying on facts or information, subjective or intuitive methods are grounded in knowledge, experience, talent, and the intuition of experts. Well-known methods of this type include review of concepts (a contradictory discussion within a panel of experts), brainstorming (simple rules that aim to spur an open discussion), the Delphi method (a key subjective method that can render consensus among experts, who answer anonymously and never meet), and the cross-impact matrix (a more developed version of the Delphi method that seeks to remedy its shortcomings).
Objective methods: whether they be extrapolative or normative, objective methods are always based on information and facts. They use variables and indicators to describe reality. We can define variables as elements that describe a situation, an event, or a domain. These variables can change depending on the context they describe; however, some general variables do exist. The choice of variables in objective methods is extremely important, since they tend to change in time and space. Indicators, descriptors of variables, include whatever is observable, visible and indicate a deeper dimension. Indicators, either quantitative or qualitative, thus shed light upon that which is often beneath the surface but that can be useful for decision makers. Objective methods can be extrapolative (when they analyze the impact of a future event on the system itself and, when possible, on the entire social system of which this system is a part) or normative (when they identify goals that the analyzed system hopes to reach in the future).
Systemic methods: the systemic approach was born out of the need to face the increasing complexity of reality and to detect the relationships between the elements of which it is composed. In systemic methods, the links between system components are more important than the individual elements. The system can be considered as open or closed, depending on whether it interacts with its environment or not. There are various systemic methods among which the most well-known are the following. “Global models” can be descriptive and aim to represent the running of the “world” system through chosen variables; or serve as an early warning, answering questions such as “what will happen if . . .?” (the what-if method); or be normative when they aim to prescribe indications for action in relation to specific objectives. Strategic management concerns the development of good business planning. Environmental scanning—taking into account the environment or context of the analyzed subject—more closely resembles a conceptual approach than a rigid technique. These systemic methods also include multiperspective risk management and issues management, according to Masini.
In conclusion, although this categorization was particularly useful in the early stages and development of foresight, it does not appear to fulfill its function as effectively today. Indeed, as changes become more and more rapid and interdependent, objective and subjective methods become closer to systemic methods, as it is in the case of scenario building.
Late Daniel Bell (American sociologist, professor emeritus at Harvard) distinguished three other “by nature” categories of foresight methods: 7
Extrapolative methods, which project societies from past and present into the future through extrapolation of trends
Macrohistorical methods (close to the foresight anthropology of Gaston Berger), which examine the fundamental and historic elements of social change
Normative methods, which project trends into the future according to choices of specific frameworks (objectives, visions of the future)
Yet again, the lines between these categories tend to blur. On one hand, the macrohistorical approach tends to increasingly become integrated with the other two, a trend that will surely continue in the future (see below); on the other hand, due to the strengthened link between foresight and decision/action, most foresight exercises nowadays integrate extrapolative and normative methods within the same approach. 8
One of the most world-renowned futurists, Dr. James Allen Dator from the University of Manoa (Hawai’i), uses four categories of methods: 9
Insight, or methods founded on intuition, which use experience and/or in-depth knowledge of history to anticipate the future
Flashlight, or predictive methods, based on analysis of current and past trends
Lighthouse, or exploratory methods, focused on the search of what could be fundamentally new in the future
Normative, or methods focused on creating the future, based on values, preferred visions, and alternatives
This categorization fine-tunes those preceding it and maintains its pertinence as an analytical typology by nature or objective. However, it does not take into account the integrative methods that are currently being developed, especially in territorial foresight. 10
A typology following the organization of the method (study or exercise § )
The foresight guide of the European project FOR-LEARN 11 offers a categorization of methods along three criteria: the level of participation (citizens vs. experts), of dependence on expertise, and of interactivity.
With this framework, the guide proposes the below classification style, which appears to be awkward to manipulate (Figure 1: the shape inside the circle indicates the frequency of use of each method within the panel analyzed by the European Foresight Monitoring Network, or EFMN; see Box 1).
A possible grouping of foresight methods.
The STEEP (Society, Technology, Economy, Environment, Politics) Analysis.
The STEEP analytical grid
Most futurists use existing analytical grids, such as STEEP. The STEEP analytical grid is one of the simple means of exploring multidimensional problems in a way that gives depth and coherence to the issue at hand and is likely to facilitate the creation of solutions. It answers systematically the following questions: how is the human community of this system (territory, group, society) characterized? How does it create knowledge? How are the goods and services produced by the system exchanged? What is the space and the environment that it inhabits? What are the rules that govern the whole of the system? It includes the five broad components of any human system:
Society: that which relates to the human being as an individual and as a social being. This covers demography, beliefs, culture, lifestyles, health, education, citizenship, and quality of life.
Technology (science, techniques, and knowledge more broadly): production, capitalization, and the spread of knowledge.
Space: physical space, the biosphere, and extraterrestrial space. The environment as biosphere (ecology) and nonterrestrial space (solar system, etc.) must be distinguished from space as a physical territory (resources, development), which can be either natural or artificial, built or not. Urban issues fall under this category.
Economy: the processes of acquisition, production, and exchange, and the recycling of goods and services. This covers the interior economy (situation and evolution, socioeconomical issues, critical activities, etc.), the exterior economy (economic models and policies, globalization, independence), and the processes of globalization.
Politics: the organization of states and the relationships between one another. Politics are the structure and operation (theoretical or practical) of a society. They cover regulations, stability, the internal or external stability of this society, its internal relations, and its relations to other similar groups. This category brings together interior politics, foreign policy, and geopolitics, under the lens of public politics, political regimes, administrative systems, rights and duties, and international relations.
Several variations stem from this general model, such as SITEEP (Society, Individual, Technology, Economy, Environment, Politics), which focuses specifically on the individual, or Politics, Economics, Technology and Culture (PETC) used by Grimes. 24
Indeed, several methods present the criteria couples (expertise vs. interactivity; creativity vs. evidence) simultaneously or alternatively. This is the case with morphological analysis, which can combine substantiated forecasts with invented disruptive factors, or can be used both in the realm of a panel of experts and in an interactive manner (alternation) within the same exercise.
In reality, the organizational modalities of the process of foresight, although intrinsically correlated to the methods used, do not appear to be a pertinent differentiating criterion for methodology, as the same method is often used with different organizational modalities.
Other possible categorizations
Beyond these two broad typologies, one can envision three other types of classification.
By destination: one could, for example, classify foresight methods based on the specific application domains of foresight: global foresight, technological foresight, organizational foresight, and territorial foresight. However, many methods, such as the scenario building method or the Delphi, are used in several different domains. This classification, thus, falls prey to redundancy and cannot be selected.
By country: here again, this classification is susceptible to bias, since the country of origin of the method is not always known and the country of application can be even harder to gauge. Some methodologies are more widespread, while others are not, often due to linguistic reasons. Nonetheless, this type of classification has been attempted within the European project FOR-LEARN, 11 but it is only of indicative value considering the low representativeness of the sample of users.
By stage in the foresight process: this classification by stages of the used methodology reflects the search for a more transversal categorization. It is mentioned in the FOR-LEARN project’s online Guide of foresight 11 as a functional classification, with the three functions (diagnostic, prognostic, and prescriptive ¶ ) corresponding to specific stages in the exercise of foresight. These functions are the same in the French methodology used in territorial foresight: understand, anticipate, propose. 8
Although this categorization is helpful in choosing a method for each stage, it does not allow for a classification of all of the tools of foresight, especially the integrative methodologies that deal with these three stages within a single approach.
The Chosen Categorization
Given this critical review, a new, transverse typology was created on the basis of five broad types of foresight methodologies: extrapolative and predictive methods (e.g., trends analysis, Delphi), exploratory methods (e.g., what if?, disruption analysis), normative methods (e.g., alternatives, visioning), integrative methods (e.g., scenario building, Horizon Scanning), and transformative methods (e.g., Causal Layered Analysis, Spiral Dynamics).
Its goal is to best unite the existing categories into a single classification grid of foresight methods. Although this process is relatively easy with the classic categorizations such as Dator, Inayatullah, and Voros, it becomes more delicate in the case of Masini’s, as these two classifications come together in a matrix way instead of a superposition.
For the sake of clarity, they are clustered below into three groups. The first two (normative and anticipatory methods) group together the most classic methods, whereas the last one (second generation methods) deals with methods novel by their processes or objectives.
Anticipatory methods
Methods of anticipating the future, often referred to as forecasting, are the oldest chronologically. Most of them, such as Delphi and multicriteria methods, date to just after World War II and were created in the United States.
They are usually divided into two categories: predictive methods and exploratory methods.
Predictive methods seek to “predict” the future that has the highest probability of happening. In the French line of thinking, they are on the hinge between prevision and foresight. According to the classification of Voros, the methods in this category fulfill these two criteria:
Answer the question: “what will happen?” or “is it likely to happen?”
Be used to attempt to predict/foresee where the identified trends could lead (extrapolation)
Exploratory methods look to answer the questions: what could happen? What if? Their objective is to find alternatives to trend-based projections supplied by predictive methods. They also seek to discover events that could require major adaptations, if they ever happened and could not be changed (wild cards, X-events). Impact studies of these events are part of this category. 12
Normative methods
Normative methods seek to answer the question “what should happen?” to decide which future to build. They deal with reasonably desirable futures, a small part of the possible futures.
There are three groups of methods in this category:
Methods allowing the elaboration of the vision of a desirable future (visioning);
Methods relating to participation/concertation, used as much in the public sector as part of anticipatory democracy as in the private sector; and
Methods relating to the determination of objectives and their corresponding strategies, mostly used in organizations, particularly in companies: strategic foresight.
Second generation methods
Given increasing complexity, simple methods of anticipation have become insufficient to confront ongoing change, whether that be by anticipating or preparing for it.
Two broad sets of methods have emerged:
Integrative methods that are not necessarily new methods in and of themselves but rather cognitive processes that integrate different existing methods into a unique one; the scenario building method of M. Godet 6 is a good example.
Transformative methods that seek to understand the evolution of the human species and the patterns of change that emerge from it. They aim less to predict or create specific changes than to provide a better understanding of the processes of transformation generating the futures.
The first of the integrative methods, both chronologically and by importance, is undeniably the method of scenario building. Next come the systemic methods 3 and a recent group of methods titled “integral futures” 13 that originate from the work of the philosopher Ken Wilber. Finally, a last group brings together the computerized processes (mostly business tools) that allow us to capitalize on the different methods by integrating them into a single platform.
Transformative methods are not, strictly speaking, methods but rather ways of thinking the future in terms of transformation. They help to identify the invariant elements of human evolution and the patterns that regularly emerge from it (macrohistory) and draw the Big Picture, a synthetic vision of history and future. 14 This category of methods is the most upstream of the processes on thinking the future.
This global foresight toolbox reveals a paradigmatic evolution of the discipline. From a search for certainties about the future (forecasting), futures studies have progressively become a search for uncertainty, through the generation of multiple possible futures 15 and the rejection of the idea of colonizing the future. 16 Where the simplicity of the model favored extrapolation (Delphi, cross-impact analysis), the growing awareness of complexity forces us to reconsider the pertinence of anticipations. Finally, from the use of forecasting tools, we have moved to a more integrated, complex, and diachronic way of thinking the future. The search for an overarching method of foresight is yet another step along this path.
A Foresight Framework
The human mind needs to sort information into a specific order to be able to interpret it. This manner of processing information works on three levels that foresight must take into account: the worldview, the analytical grid, and the foresight lens. Their combination forms the Foresight Framework.
Recognizing One’s Own Worldview
Futurists are required to understand the referential analytical grid of each thinker to understand its biases and orientations.17,18 Generally described as a paradigm, this worldview (echoing the concept of Weltanschauung), often implicit or even unconscious, is the underpinning of every attempt to interpret the world. It forms, as demonstrated by Jung 19 and Foucault, 20 not only a reference system to understand the world and the place we hold within it, but also an épistémè (representation system) that defines the entire production of knowledge at a given time period.
However, if this representation of the world is necessary to the logical order needed by human beings to give meaning to their environment and their actions, it also creates blind spots.21,22 This is why Morin
23
devotes the first chapter of his “Seven Complex Lessons in Education for the Future” to these blind spots of knowledge: Indeed, knowledge cannot be considered as a ready-made tool that we can use without first examining its nature. Furthermore, knowledge of knowledge is crucial in order to be able to confront the risks of error and illusion, ever present parasites of the human mind. It consists of arming the mind for the vital struggle for lucidity. We must integrate into our education the study of cerebral, mental and cultural characteristics of human knowledge, of its processes and modalities, and of the psychic and cultural dispositions that might put it in peril of error or illusion.
A better understanding of one’s own worldview is thus the first step on the path toward an overarching foresight methodology.
Understanding the Analytical Grid Being Used
The second step is focused on developing the ability to discern one analytical grid from another. Indeed, classic analytical grids such as the ideological analytical grid can, by their very nature, restrict thought to a single interpretative framework, to the exclusion of all others. For example, the Marxist analytical grid offers a reading of the world based on an economic infrastructure, a political superstructure, and a historic determinism, thus conditioning even the resulting data analysis.
This is why it is crucial to develop critical thinking, to identify the paradigms that guide analyses and published recommendations in the facts supplied by institutions (reports from statistical institutes, parliamentary reports, etc.), particularly when they seem eminently scientific, and thus objective. The multitude of controversies surrounding climate change is a good example of the clash between competing paradigms.
Using the Foresight Lens
Finally, the third step is the capacity to use or create, in full awareness, a foresight analytical grid—or foresight lens—considering the needs it fulfills and the blind spots it might create.
Any futures thinking relies, first of all, to a lesser or greater extent, on a situational analysis. In territorial foresight, it is referred to as a diagnostic. Semantically, the difference between the two concepts comes from the fact that situational analysis is purely based on fact, whereas diagnostic is interpretive, allowing the identification of the strengths and weaknesses of the object of study. However, both present three common characteristics: they are systemic, dynamic, and explanatory (see below).
Due to this particularly broad approach, many facts must be collected to proceed to this situational analysis/diagnostic. As foresight operates more from synthesis than analysis, the challenge becomes to give meaning to these facts to restore the sometimes paradoxical richness of real situations.
The foresight lens is a way to analyze a complex object, taking into account the many understandings and interpretations that can be drawn and, thus, forms a multidimensional, temporally dynamic, and global representation of this object. The foresight lens produces a synthesis that is explanatory (rather than descriptive) and systemic (relating to the mechanisms, articulations, and links between system variables). The foresight lens, thus, connects the components of the studied system and reinterprets the often agreed upon classical analyses, giving them a foresight dimension (trends, ruptures, structural rather than conjectural elements, etc.), including cultural adjustments (e.g., the Japanese concept of information is fundamentally different from the French one).
In conclusion, the foresight lens links the inputted information to form a new vision of the studied system, even going as far as to give it a new meaning. It allows one to anticipate an evolution or to study new possible consequences, either through a better understanding of the given complex system or by the discovery of a new dimension.
Different Types of Knowledge
If we are to discuss the methodological apparatus of foresight, we must also understand what types of knowledge are being used to complete a foresight study or exercise, particularly in the UNDERSTAND phase (see above). Indeed, particularly in the case of territorial foresight, one often finds diagnostics based on an “absolute” (noncomparative), static (nondynamic), or descriptive (nonexplanatory) knowledge. This is why it is important to define the four overarching types of knowledge that foresight employs to better understand our own means of knowledge.
Dynamic Knowledge
Each given object is rooted in a past that precedes the very existence of the object: the time when the cause of the future object appeared. This search for an origin story allows us to answer the question “why does the object exist?” It is then necessary to study the history of the object to understand the evolutions that lead to its current state (transformations).
However, we often come across a specific hurdle to this dynamic knowledge: the culture of fixism. The immediate perception of their environment by primitive cultures led them to a fixist interpretation of nature and physical and biological processes, 25 understandable given their level of knowledge. Although those in antiquity surpassed this phase by introducing an evolutionist vision of the world, the paradigm in medieval Christianity reintroduced the reign of fixism again. This line of thought had not completely disappeared in the West even by the beginning of the twenty-first century, despite the intellectual revolution of the Renaissance. 26 We struggle to recognize change and, even more so, to admit it; as demonstrated by Berger, 27 we still search for fixity through analogy (everything is identical), the precedent (there is nothing new under the sun), and extrapolation (the pursuit of the past in the future). This is why we must make an effort to pursue this dynamic knowledge, an act that does not come naturally to us, to come to terms that both our paradigms are changing (end of dogma) and the world itself is not fixed or definitive, but rather in constant evolution, as should be our thinking to understand it.
Materially, this knowledge can be as easily translated into qualitative as well as quantitative facts. Although data-generated mathematical curves give a good illustration of the evolution of a given indicator, one tends to forget elements of dynamic knowledge, such as historical documents or maps and photographs. Dynamic knowledge is not solely mathematic, but encompasses all that allows one to make a temporal comparison.
Systemic Knowledge
The word “system” is used here in its original meaning of an “organization,” a “set,” that allows for “connections.”
28
We will employ the following definition: A system is a set of elements that interact with one another following specific rules. It is determined by the nature of its components, their interactions and the criteria that must be fulfilled to belong to this system (to determine whether a component belongs to the system as opposed to its environment). A sub-system, or module, is a system that is participating within a system of a higher rank. A system can be opened or closed, depending on whether it interacts with its environment or not. (https://en.wikipedia.org/wiki/System)
The aim of this second set of knowledge is to identify the components of the studied system and to understand how they function, in terms of both its internal interactions and its relationship with the external environment. Logic dictates that the previous step (dynamic knowledge) should have allowed the identification of most of the components of the object’s system.
Structural analysis 29 is one of the most classic methods to perform a systemic analysis. It identifies internal and external variables of the studied system, as well as the relationships that exist or could exist between them. One can further hone onto these relationships using set theory.
Systemic knowledge, thus, equates reality to a set whose elements maintain essential relationships between themselves and with the outside world. To detect all of these interactions, one must set a critical distance from the object.
Epistemic Knowledge
As we have seen, we will remain in fixism if we cannot evolve the fundamentals of our worldview. To accept the mobility of the world brought up by Berger, 30 we must reflect on our own means of knowledge (see “Recognizing One’s Own Worldview” section): how do we build our knowledge?
The work of Polak 18 and Medina Vasquez 31 teach us how important it is to better master the making of our images of the future. These are, indeed, subject to our personal filters (optimism, pessimism, blind spots), our values, our cultural perceptions of what is or is not possible, our very condition (gender, age). No hypothesis on the future can thus be truly objective. Short of being able to fight these biases, we must at the very least make them explicit, as they shape our way of seeing the world. 15
The choice of analytical grid is part of this epistemic knowledge. For example, whether we believe or not that a major civilizational transition is taking place can change our images of the future drastically, thus highlighting the importance of making our presuppositions explicit. This type of knowledge is particularly suited for analysis of the global context, necessary on one hand for critical distance (the system is not isolated but observed within the global context) and on the other hand for the analysis of external change factors that will influence the fate of the system.
The aim of epistemic knowledge is to make us reflect upon our own means of knowledge, both in terms of the critical distance the futurist must have vis-à-vis not only their own certitudes but also the observed system and the popular beliefs that surround it. 32 It is particularly useful in collective intelligence exercises where direct knowledge is preferred over intermediated knowledge. 2
Diagnostic Knowledge
Diagnosis differs from the three previous sets of knowledge in that it is less ideational and more concrete. Indeed, this type of knowledge allows us to make a clinical judgment of the studied system, highlighting, for example, strengths and weaknesses, engines or brakes of the system, given the current or proposed evolution. Comparative knowledge allows for an interpretation that is as objective as possible of the system, which will lead to its diagnosis.
Diagnostic knowledge of an object is similar to clinical knowledge of a patient: one must first identify the given system by describing the patient, whether that be a territory, an organization, or a sphere of activity or knowledge. One must then detect the symptoms (an exercise in listening and comparison) and synthetize this information to diagnose the patient’s illness. Of course, the doctor will go beyond mere diagnosis and will determine the appropriate remedy. Diagnostic knowledge encompasses this entire procedure.
One of the most key elements of diagnostic knowledge is the capacity to synthesize information, as diagnosis rests upon a consideration of the entire set of identified symptoms, not a meticulous analysis of a single given variable.
Diagnostic knowledge is the last rung on the cognitive ladder of the first phase of the overarching method (UNDERSTAND): feeding on the other three types of knowledge, it nonetheless stands out by its willingness to pass judgment on the system, based on the interpretation of the facts and the deeper understanding that arises from it. Although it might seem like the simplest to operate, due probably to its concreteness, it does set certain traps (false interpretations, errors in the facts, dead zones, etc.) that can only be navigated with a mastery of the entire knowledge set in these sections.
In summary, through dynamic knowledge, one can learn of the origins, causes, and evolutions of the object of study; through systemic knowledge, one can produce comprehensive analytical grids of the object, learning about the interrelations on which it depends and the manner in which it interacts dynamically with its environment; through epistemic knowledge, one can find the critical distance needed to tackle the facts in the most transparent and objective manner possible; finally, through diagnostic knowledge, one can form a synthesized judgment on the given object and potentially propose possible solutions.
Conclusion
The aim of this research has been to find a way to bridge the gap between the multiple understandings of what foresight/Futures Studies is.
After navigating different existing classifications, we established a new, aggregative, and transversal one, including extrapolative, exploratory, normative, integrative, and transformative methods.
Then we have designed a cognitive overarching approach to foresight, based on two pillars: a foresight framework identifying one’s worldview, analytical grids, and foresight lens as the major shapers of our way to look at the future, and a set of four specific types of knowledge (dynamic, systemic, epistemic, and diagnostic) required to peer into the future.
We do not claim to be exhaustive here. Our goal was only to find metaprinciples able to guide the elaboration of a metamethod for foresight, whose basics will be described in the second part of this article (next issue).
From this bottom line, so much research work is needed to provide a more robust, overarching approach. For the stake is no less than the plain recognition by the University of FS as a discipline in itself. This is the unique way to teach foresight at a broader scale, to develop proper research in labs with PhDs, and to grow the seeds of the next generation of futurists.
Footnotes
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) received no financial support for the research, authorship, and/or publication of this article.
Author Biography
). Former president of the World Futures Studies Federation (2005–2009), she now chairs the French Society for Foresight (2013–2016). Finally, she is a member of the Foresight section of the Economic, Social and Environmental Regional Council of Ile de France.