Risk tolerance during conflict: Evidence from Aleppo,Syria

Abstract

When war breaks out, how important are risk preferences to explaining why some individuals stay in conflict zones while others take flight? We examine risk tolerance among rebel combatants and civilians in Aleppo, Syria using a variation of the Eckel-Grossman Choice Game. Field work in Syria was conducted in 2013–14 with a total of 232 participants to include both Syrian civilians and active rebel fighters in Aleppo and Idlib Province, as well as among Syrian refugees in neighboring Turkey. Compared to Syrians in other locations, people in rebel-held territory of Aleppo, both combatants and non-combatants, are significantly more risk tolerant. We consider possible explanations for elevated risk preferences in Aleppo based on self-selection, adaptive learning, a sense of self-efficacy to affect future outcomes, conflict-related grievances, and in-group solidarity. Our analysis suggests that self-selection based on access to resources and a strong sense of self-efficacy may explain higher propensity for risk-taking. Overall, our results speak to a plausible sorting mechanism during conflict where risk averse individuals select out of conflict, while highly risk tolerant individuals are more prone to discount the inherent dangers of remaining in conflict zones. Our results provide new micro-level explanation for why some societies become mired in conflict traps involving highly risk tolerant fighting communities.

Keywords

civil war conflict insurgency rebel groups risk preferences Syria

Introduction

When facing violence, how important are risk preferences to explaining why some individuals stay in conflict zones and take up arms while others take flight? Are combatants naturally more risk tolerant than non-combatants? And which is the riskier gamble: staying in a combat zone or leaving one? Though risk preferences are often implied in various conflict literatures, more clarification is needed. At the micro-level, the literature on military mobilization commonly focuses on selective incentives, grievances, and social sanctioning for explaining who fights and who does not, often with mixed results (Humphreys & Weinstein, 2008). Research on civilians in conflict is usually divided between civilians held captive by combatants on one hand and the blurring of lines between civilians and combatants on the other (Kalyvas, 2006; Wood, 2003; Fabbe, Hazlett & Sinmazdemir, 2017), while the literature on refugee movement often emphasizes economic means and opportunity and the threat of violence to explain who stays in conflict zones and who flees (Adhikari, 2013). We attempt to bridge these literatures by focusing on risk preferences to understand why people stay and fight in conflict zones while others flee. Based on field research in the case of war-torn Syria, we examine risk preferences among civilians and combatants in the rebel held and contested areas of eastern Aleppo using a variation of the Eckel-Grossman (2002, 2008) choice game from behavioral economics. We find that risk tolerance is elevated among combatants and non-combatants in rebel-held Aleppo compared to people in other locations in Syria and to Syrian refugees abroad in neighboring Turkey. Self-reported rationales for leaving, staying, and fighting help validate the belief that exiting the conflict is a far safer strategy than staying and/or fighting. Our results suggest a plausible sorting mechanism where risk averse individuals flee conflict, while risk tolerant individuals are more likely to stay and fight.

Research on risk preferences: From the lab to the field

Individual decisionmaking under conditions of risk and uncertainty has been an ongoing puzzle for the social sciences (von Neuman & Morgenstern, 1947; Arrow, 1965). This is in part because conventional explanations of risk-seeking behavior based on expected utility models are often empirically violated (Starmer, 2000). Instead, risk-taking behavior appears to have important psychological, environmental, and possibly genetically transmitted components. For example, Kahneman & Tversky (1979) illustrate how risk propensities can be altered by framing effects, and people become more risk tolerant when facing prospective losses. However, Binswanger (1980) and Holt & Laury (2002) find that people generally tend to have stronger preferences for risk aversion than risk-loving behavior. Eckel & Grossman (2008) show that women tend to be more risk averse than men in risk-taking experiments. Apicella et al. (2008) find that risk-taking is positively associated with increased levels of testosterone in males, which has also been linked to overconfidence in one’s self-efficacy and aggressive behavior (Johnson et al., 2006). Furthermore, Dohmen et al. (2012) find evidence of intergenerational transmission of risk preferences from parents and local environments where people live via positive assortative mating. Becker et al. (2014) suggest that heterogeneity in risk preferences around the world can be traced to genetic diversity linked to ancient migration patterns.¹

Risk propensities have also been examined cross-culturally using a range of behavioral and survey instruments. Andersen et al. (2010) find good congruence between lab and field experiments on risk. Dohmen et al. (2011) observe consistency between general attitudinal and behavioral measures of risk-taking. Vieider et al. (2015) also find strong evidence of generalizable risk preferences using survey and experimental risk instruments in a comparison of 30 countries, making cross-cultural comparison of risk possible.

Researchers are increasingly studying risk propensities under real world conditions of threat and uncertainty linked to natural disasters and conflict. Cameron & Shah (2015) find evidence of elevated risk-taking in the aftermath of severe flooding and earthquakes. Eckel, El-Gamal & Wilson (2009) observe heightened risk tolerance among evacuees following Hurricane Katrina. Kim & Lee (2014) find that children who were exposed to violence during the Korean War have long-term risk aversion five decades after the war. Callen et al. (2014) offer evidence that exposure to violence and fearful recollections creates a ‘premium on certainty’ (risk aversion) in Afghanistan. Jakiela & Ozier (2015) show that individuals in the aftermath of Kenyan electoral violence are also more risk averse. In contrast, Voors et al. (2012) find that victims of violence in Burundi are more risk tolerant.

At present, the literature on violence is only beginning to consider the importance of risk preferences to understanding micro-level behavior. However, risk preferences of combatants and civilians during violence and the effects of violence on those preferences remains ambiguous and deserves further investigation. Our study considers risk preferences under conditions of ongoing conflict in Syria. Our research is one of the first to consider the role that risk tolerance might play in differentiating among combatants, non-combatant civilians, and refugees in civil war.

Risk-taking during conflict

To understand risk preferences, we begin with the assumptions of the expected utility model, which predicts that risk-taking is a function of the expected value of possible outcomes. Key to the model is the ability to calculate the relative costs and benefits of different choices and the probability that a choice will lead to a desired outcome. Of course, the costs of remaining in conflict zones could be quite high: psychological trauma, physical pain and suffering, and possibly death. It may also be difficult to assess relative risks and probabilities of adverse outcomes because it is unclear what tangible benefits one might receive or what costs might be incurred from taking up arms or otherwise remaining in conflict zones. Furthermore, the decision to flee conflict could also carry considerable risk: abandoning one’s home and community for a potentially perilous journey to an uncertain future. Which is the more risky gamble during conflict: to fight, to take shelter in place, or to flee?

First, we consider the hypothesis that refugees are more risk averse than those who remain in conflict zones. In general, research on refugee flight during conflict shows that the intensity of violence and the threat of future violence are strong predictors of when people leave conflict zones, while economic means and opportunity play a critical role in determining who leaves and who stays (Davenport, Moore & Poe, 2003; Adhikari, 2013). In assessing risk during conflict, individuals may follow the logic of the expected utility model. They stay until the potential costs of remaining in conflict zones become untenable, at which point they leave, provided they have the ability to do so (Hirschman, 1970). Assuming variation in individual risk tolerance, one might predict that more risk averse people leave first, while more risk-taking individuals will elect to stay in conflict zones for longer periods of time, though continually updating and re-evaluating relative risks of staying vs. leaving.

However, a plausible counter-argument is that under some circumstances, the potential risks of refugee flight might outweigh the risks of remaining in a conflict zone. For example, the path to safety could be perilous. Civilians might have to pass through front lines or territory controlled by opposition forces where they could be highly vulnerable to attack. Leaving a conflict zone also may entail the potential loss of personal property as well as separation from family and friends, and disruption of social networks that people in conflict rely on for survival. International efforts to provide refugee shelter and assistance may be lacking, in which case the decision to leave a conflict zone is both potentially costly and a risky endeavor. This counter-argument would predict the null hypothesis that risk tolerance is uncorrelated with decisions to remain or flee conflict-affected areas. Both carry risks.

Another counter-argument can be found in the ‘certainty premium’ hypothesis advanced by Callen et al. (2014). They find in Afghanistan that exposure to violence may have long-term traumatic effects on individuals that make them highly risk averse. The certainty premium hypothesis would predict that individuals with greater conflict exposure become more risk averse. If so, individuals who remain in highly volatile conflict zones could be potentially more risk averse than those who flee (the reverse hypothesis). In an uncertain, hostile environment, sheltering in place may seem like the safest strategy.

Yet another counter-argument might emphasize the heterogeneity of different actors in conflict zones. If not everyone is there voluntarily, it may be necessary to draw distinctions between active combatants and civilians who do not support their causes (Weinstein, 2006; Humphreys & Weinstein, 2008). To understand the risk preferences of those who willfully participate in conflict, the expected utility model is more problematic. Active participation in combat carries potentially high risks of trauma, injury, and death, and individuals may have incentives to free ride rather than join combatant groups (though see Kalyvas & Kocher, 2007). This argument would predict heterogeneous risk preferences in conflict zones depending on one’s role and level of commitment to the cause of violence. Combatants and their supporters could have elevated, possibly irrational levels of risk tolerance compared to those who might remain in conflict zones but not necessarily support combatant causes or choose to free ride.

In summary, in evaluating different perspectives on risk preferences during conflict, we test the following hypothesis against alternative hypotheses of null, reverse, and mixed effects regarding risk tolerance:

Risk tolerance during conflict: People who remain in conflict zones have elevated risk tolerance.

Mechanisms and scope conditions

If individuals in conflict zones are in fact high risk-takers, what might account for their greater tolerance for risk? We consider and explore plausible explanations for risk tolerance based on self-selection, adaptive learning, a sense of self-efficacy to affect future outcomes, grievances, and social solidarity. We also propose scope conditions on the generalizability of our hypothesis to various conflict conditions.

First, we consider whether higher risk propensity is due to self-selection on high risk-taking individuals. Evidence of heterogeneous risk tolerance within populations is common in the literature. Dohmen et al. (2012) and Becker et al. (2014) underscore important cultural and genetic contributions to risk preferences based on socialization and positive assortative mating. Eckel & Grossman (2008) and Apicella et al. (2008) also point to important gender effects of risk-taking linked to male testosterone levels, which are also associated with overconfidence and aggressive behavior associated with conflict (Johnson et al., 2006).² Research from psychology also shows a strong link between fear and risk aversion, often triggered by conflict (Lerner & Keltner, 2001). Self-selection offers one explanation for how people in conflict might sort based on risk tolerance. Individuals with low risk tolerance (possibly linked to genetics, gender, and psychology) flee conflict whenever possible.

Next, we consider whether risk-taking is an adaptive learning behavior, conditioned by the environment (March, 1996; Denrell, 2007). Research in psychology on protection motivation theory would suggest that risk-taking could be an adaptive (or maladaptive) coping mechanism for dealing with perceived threats and vulnerabilities (Rogers, 1975; Floyd, Prentice-Dunn & Rogers, 2000). People in conflict zones adapt to the conditions at hand, especially when they have no other choice. As individuals become more exposed to dangers and uncertainty, they grow accustomed to living with extreme risk. Danger becomes normalized and risk tolerance is a necessity for survival. Rather than selecting for risk tolerant individuals, conflict simply compels individuals to adjust upward their tolerance for risk.

Another possibility is that people in conflict zones assume risks because they are committed to the cause of violence. We consider this a grievance or activist-based explanation for risk tolerance. A number of studies suggest that grievances and activist causes may function to mobilize individuals to engage in higher risk forms of collective action (McAdam, 1986; Krueger & Maleckova, 2003; Weinstein, 2006; Arjona & Kalyvas, 2012; Souleimanov & Aliyev, 2015). Psychologists have also found strong linkages between anger and risk tolerance (Lerner & Keltner, 2001). People in conflict zones may take calculated risks to advance the goal of defeating a perceived adversary in pursuit of a cause.

Risk-taking could also be mediated by the prospects of future gains or loses. A number of scholars have applied prospect theory to understanding individual decisionmaking behavior during conflict (Levy, 1997; McDermott, 2004; Mercer, 2005; Tezcür, 2016). Prospect theory would predict that risk tolerance is reference dependent – people treat gains and losses differently and are more willing to take risks to preserve the status quo or prevent future losses (Kahneman & Tversky, 1979). However, whether or not people take risks to avoid losses may be mediated by one’s sense of self-efficacy to affect future outcomes (Krueger & Dickson, 1994; Tezcür, 2016). Research on optimism bias suggest that individuals are often susceptible to wishful thinking and will take risks when they overestimate their chances of winning and discount the possibility of losing (Weinstein, 1980; Wrangham, 1999; Bracha & Brown, 2012). We examine how people’s beliefs about their present and future prospects might impact risk tolerance in the context of an ongoing conflict.

We also consider whether increased risk tolerance could be motivated by a desire to protect kinship and social networks. Individuals take risks out of a sense of social solidarity with those who share their experiences in the conflict zone. A number of scholars have emphasized the blurring of lines between civilians and combatants, who represent different parts of a broader holistic fighting community with different roles and functions to play (Wood, 2003; Kalyvas, 2006; Parkinson, 2013). There is also enduring evidence in the military literature that individual bonds between combatants are more important to understanding fighting motives than the actual cause of war (Shils & Janowitz, 1948; Whitehouse et al., 2014). Risk tolerance may also be part of an evolutionary mechanism for why some individuals altruistically self-sacrifice for the survival of the larger group (Choi & Bowles, 2007). We explore whether risk-taking is borne out of a sense of in-group solidarity within the context of a fighting community.

Finally, we offer several scope conditions on the generalizability of our research hypothesis. In particular, we argue that our hypothesis most likely applies to conditions where civilians are relatively free to exit conflict as opposed to models where civilians are held hostage by insurgents and/or governing forces (Kalyvas, 2006; Fabbe, Hazlett & Sinmazdemir, 2017). The freedom to exit also implies that those who remain in conflict zones face greater threats of present/future violence than those who flee. Second, we argue that our hypothesis most likely holds where violence against civilians is indiscriminate as opposed to selective, that is, combatants and civilians face similar risks within the conflict zone (Kalyvas & Kocher, 2007). Hence, our research speaks most directly to cases of conflict where civilians are targeted for mass expulsion, ethnic cleansing, or government efforts to ‘drain the sea’ of insurgents and their supporters (Downes, 2007; Lyall, 2009). Finally, our hypothesis would be best supported in conflict environments where civilians and combatants constitute a holistic fighting community, each taking risks for the sake of a cause, rather than heterogeneous groups of actors with divergent preferences (Wood, 2003). Such conditions appear applicable to the Syrian case, which we now discuss in further detail.

Rationale for case selection

We test our hypothesis in the context of the conflict in Aleppo, Syria. In the wake of the Arab Spring, protests calling for the ouster of Syrian President Bashar al-Assad began in early 2011 and were countered with violent repressive measures by the Assad regime.³ By the end of 2011, Syrian opposition groups had militarized under the umbrella organization of the Free Syrian Army and the armed insurgency was underway. Since 2011, the Syrian opposition has splintered into a range of factions to include both moderate secular resistance groups and religious sectarian armies affiliated with Al Qaeda and the Islamic State (ISIS). The city of Aleppo played a strategic role for opposition forces linked to the Free Syrian Army, which occupied the eastern part of the city, while the Assad government controlled the western part and subjected the opposition to relentless air and artillery bombardments. It was under these conditions that our study began in August 2013.

One of our main rationales for conducting this study in Aleppo was to understand risk preferences during high intensity, ongoing conflict. Citizens of eastern Aleppo faced a number of agonizing choices: whether to stay in the city or flee abroad leaving family, friends, and property behind; whether to remain sheltered or venture out for provisions in the face of air strikes, shelling, and snipers; how to ration savings, food, and medical supplies when the duration of the conflict is uncertain; and whether to support the opposition insurgents or oppose them? A key question for our study is whether such conditions lead to selection on individuals who are more risk tolerant. The few studies which have been conducted on risk and conflict exposure appear to find mixed results (Voors et al., 2012; Callen et al., 2014). One possibility is that risk preferences are highly sensitive to timing of conflict exposure and other scope conditions such as exit options (Hirschman, 1970), which in the case of Aleppo were generally viable (Fabbe, Hazlett & Sinmazdemir, 2017). Our study attempts to assess risk preferences when conflict is intense and ongoing, and outcomes are uncertain. To test our hypothesis, we compare risk preferences in Aleppo to the neighboring province of Idlib, which at the time of our research was behind the front lines, largely under rebel control, objectively safer than Aleppo, and a primary relocation point for those fleeing Aleppo. We argue that conditions in Aleppo were increasingly approaching that of a holistic fighting community, where civilians were remaining in/selecting out of Aleppo based in part on their support for rebel insurgents. Beyond Syria, we also compare Aleppo to major destination points for Syrian refugees in Turkey, namely a large UNHCR refugee camp in Kilis and urban pockets of Gaziantep, which were both teeming with civilians fleeing the Aleppo region, as well as ex-combatants. Our hypothesis predicts that people who remain in Aleppo will be highly risk tolerant compared to these safer locations inside Syria and abroad.

Experimental protocols

To measure risk tolerance, we use a variation on the Eckel-Grossman Choice Game (hereafter EG choice game) as well as standard survey instruments measuring risk attitudes. In general, researchers have found good consistency among different survey and behavioral measures of risk (Dohmen et al., 2011). We chose the EG choice game for its simplicity, which we believe will help reduce confusion in the field and noise in the data (Dave et al., 2010). We use two versions of the EG choice game. In each game, the subject is presented with five options. Option 5 has the highest potential payoff of 500 Syrian Pounds (about $5). Option 1 is a sure pay-off of 100 Syrian Pounds (see Table I). However, in one game, the expected value of risk-taking is linearly increasing while in the second game, expected values are a non-linear, inverted U-shape (see Figure 1). We use the non-linear game to measure whether subject risk preferences correspond to expected utility models of risk. In the second game, the expected value for the highest payoff (500 Syrian Pounds) is no greater than the sure pay-off (100), so extreme risk-taking is not a logical decision given the expected value calculation. The non-linear game assesses whether risk-takers are rational utility maximizers.

The two risk games are conducted at the beginning of the study along with a series of dictator games with various in-group and out-group treatments. Subjects are told that they will be paid for one of the tasks they complete, but they are not told for which task they are paid.⁴ After the risk games, subjects completed a survey

Table I.

EG choice game (linear and non-linear expected values)

(a) EG choice game (linear expected values)
Which of the following options would you prefer?
Option
5	50% chance of receiving 500	50% chance of receiving 60
4	50% chance of receiving 400	50% chance of receiving 70
3	50% chance of receiving 300	50% chance of receiving 80
2	50% change of receiving 200	50% chance of receiving 90
1	100% chance of receiving 100
(b) EG choice game (non-linear expected values)
Which of the following options would you prefer?
Option
5	20% chance of receiving 500	80% chance of receiving 0
4	40% chance of receiving 400	60% chance of receiving 0
3	60% chance of receiving 300	40% chance of receiving 0
2	80% change of receiving 200	20% chance of receiving 0
1	100% chance of receiving 100

Figure 1.

Expected value calculations in two variants of the EG choice game

on wide-ranging themes. The survey also included several attitudinal measures of risk as a robustness check on experimental risk preferences.

Data collection

Data were collected in month-long waves between August 2013 and May 2014. Subjects in Aleppo were recruited in August–October 2013, subjects in a neighboring province of Idlib were recruited in December 2013–January 2014, and Syrian refugees were recruited inside and around a UNHCR refugee camp in Kilis Turkey in January–April 2014. Idlib region was selected as a point of comparison to Aleppo inside Syria and within rebel-controlled territory. Idlib was far less dangerous than Aleppo at the time of our study.⁵ Refugees were sampled in Kilis and Gaziantep, Turkey because the Kilis refugee camp and Gaziantep are the closest points in Turkey from Aleppo and Idlib and a primary initial destination for refugee seekers from those regions.

A total of 232 participants completed the risk games. Subjects were recruited using cluster sampling techniques by a local enumerator. We oversample males in the civilian and refugee groups to be comparable to the combatant sample, though we have female participants to control for gender effects. The local enumerator conducted the risk games face-to-face in a public location, but without interference from others.⁶ Due to the challenges of conducting field research during an active conflict, we are not able to make population inferences from our data. Our research is highly

Table II.

Descriptive statistics

Variable	Description	Mean	SD	N
Risk game linear EV	Ranges from 1 (low risk) to 5 (high risk)	2.86	1.27	228
Risk game non-linear EV	Ranges from 1 (low risk) to 5 (high risk)	3.35	1.42	232
Not afraid to take risk	‘I am not afraid to take risks.’ Responses range from 1 = strongly disagree to 4 = strongly agree.	2.57	1.07	254
Avoid risk	‘I avoid risks whenever possible.’ Responses range from 1 = strongly disagree to 4 = strongly agree.	2.84	1.17	256
Aleppo, Syria	1 = subject in Aleppo, 0 = not in Aleppo	0.37	0.48	256
Idlib, Syria	1 = subject in Idlib, 0 = not in Idlib	0.20	0.40	256
Turkey	1 = subject in Turkey, 0 = not in Turkey	0.43	0.50	256
FSA fighters	1 = FSA fighter, 0 = not FSA fighter	0.24	0.43	256
Civilians inside Syria	1 = civilian inside Syria, 0 = not civilian inside Syria	0.33	0.47	256
Refugees in camp in Turkey	1 = refugee in camp in Turkey, 0 = not refugee in camp	0.23	0.42	256
Refugees outside camp in Turkey	1 = refugee outside camp in Turkey, 0 = not refugee outside camp	0.20	0.40	256
Female	1 = female subject, 0 = male subject	0.14	0.35	256
Age	Subject age in years from 18 to 60	29.7	9.58	249
Education	Subject education from 1 = no formal education to 4 = post-secondary education	2.48	0.74	249
Employed	1 = working before the war, 0 = unemployed, not working	0.85	0.36	249
Time in current location	‘How long have you been in the location where you are currently living?’ 1 = less than a week, 7 = more than 2 years	4.69	1.50	256
Internally displaced	‘Were you forced to flee your home to another location in Syria?’ 0 = no, 1 = yes	0.44	0.50	256
Victimization	Index of victimization based on self-reported exposure to violence, personal injury, injury, death or disappearance of close friends and family members (ranges from 0 to 1 with increasing victimization)	0.59	0.23	256
Property damage	Index of property damage based on self-reported damage or destruction of home or business property (ranges from 0 to 1 with increasing damage)	0.29	0.27	256

exploratory, but we attempt to go beyond convenience samples which are commonly used in lab and lab-in-the-field experiments. This research design received IRB approval, and we took very seriously the safety and well-being of our enumerator and subjects in designing and conducting this study.⁷ Table II provides summary statistics of key demographics for our sample.

Results

We begin by considering reasons why combatants, civilians, and refugees rationalize their decisions to stay, fight, or flee Syria. Which choice (fighting, staying, or leaving) is perceived as the least risky gamble? From their responses, a clear pattern appears to emerge. Refugees indicate a strong sense of risk aversion. When we ask refugees to indicate reasons why they left Syria, ‘it is too dangerous to stay’ was the most common reason and the main reason offered (Online supplementary information [henceforth SI] Table IA). In contrast, a majority of civilians inside Syria say they are there ‘to help those who are fighting’, ‘to protect my home, property, family’, and lack resources to flee. Only a small minority think that leaving Syria would be more dangerous than staying in place (SI Table IB). Finally, active combatants in Syria indicate that they are fighting ‘to defeat the Assad regime’, ‘to take revenge on Assad’s forces’, and ‘to defend their communities’ from attack. Few indicate that they were forced to join rebel groups but are instead fighting voluntarily (SI Table IC). Collectively, these responses suggest that refugees clearly understand the elevated risks posed by staying in the conflict zone and opted to flee, while those who stay in Syria appreciate and accept the risks of staying in order to support the rebel insurgency, to project their homes and property, and for lack of resources to flee to

Figure 2.

Risk preferences in the choice games (kernel density plots)

safer areas. Hence, Syrians seem quite aware about which choices (staying and fighting) are the riskier gamble. We now seek to validate these initial responses with more refined measures of risk tolerance and aversion.

Our hypothesis predicts that risk tolerance should be greater in the high-intensity conflict region of Aleppo compared to other areas. At first glance, our hypothesis appears to have merit. Figure 2 presents kernel density plots of risk-taking in the linear (upper panels) and non-linear (lower panels) EG choice games for Syrians outside Aleppo compared to Syrians in Aleppo.⁸ Ordering decisions in the risk game from 1 = most risk averse to 5 = most risk tolerant, the mean risk preference for Aleppo is 3.14 (SD = 1.13, N = 81) compared to 2.71 outside Aleppo (SD = 1.33, N = 147) in the linear version of the game. The difference in means is significant using parametric t-tests (t = 2.578, p < 0.0054) and non-parametric Mann-Whitney tests (z = 2.637, p < 0.0084). Results are even stronger in the non-linear version of the game, where the mean for Aleppo is 4.07 compared to 2.94 outside Aleppo (t = 6.42, p < 0.0000; z = 6.05, p < 0.0000), indicating that risk-taking is not clearly rational (i.e. conditioned on expected values). Overall, people in Aleppo appear much more risk tolerant than other Syrians in the more peaceful, stable province of Idlib and in refugee locations in Turkey.

To further assess heterogeneity in risk tolerance in our sample, we run OLS regression models where the dependent variable ranges from 1 (most risk averse) to 5 (most risk tolerant) using the linear EG risk game. Table III shows that people in Aleppo are more risk tolerant than Syrians outside Aleppo (Models 1–2), and that both rebel fighters and civilians in Aleppo are more risk tolerant than other comparison groups (Model 3). Our findings are robust to extended controls for gender, age, education, pre-conflict employment, internal displacement and migration, and conflict-related victimization (Models 2–3).⁹ Next, we use the second EG risk game with

Table III.

Risk tolerance in experimental behavior (OLS regression)

	(1)	(2)	(3)	(4)	(5)	(6)
DV = Risk game	Linear EV	Linear EV	Linear EV	Non-linear EV	Non-linear EV	Non-linear EV
Aleppo, Syria	0.428**	1.249**		1.132**	1.604**
	(0.166)	(0.367)		(0.176)	(0.326)
Idlib, Syria		0.688*			0.266
		(0.396)			(0.355)
Rebel fighter			1.328**			1.699**
in Aleppo			(0.377)			(0.340)
Civilian			1.149**			1.478**
in Aleppo			(0.411)			(0.381)
Rebel fighter			0.289			0.473
Idlib			(0.359)			(0.318)
Civilian Idlib			0.494(0.450)			–0.041(0.426)
Refugee		0.937**	0.914**		0.174	0.140
camp, Turkey		(0.330)	(0.332)		(0.351)	(0.352)
Female		–0.464	–0.476		–0.469	–0.491^†
		(0.326)	(0.329)		(0.295)	(0.296)
Age		–0.005	–0.005		–0.001	–0.002
		(0.009)	(0.009)		(0.010)	(0.010)
Education		–0.0343	–0.0384		–0.119	–0.124
		(0.139)	(0.140)		(0.128)	(0.128)
Employed		0.150	0.173		0.159	0.190
before war		(0.241)	(0.243)		(0.287)	(0.289)
Time in current		–0.077	–0.064		–0.113^†	–0.092
location		(0.063)	(0.067)		(0.067)	(0.069)
Internally		0.186	0.178		0.105	0.0981
displaced		(0.254)	(0.260)		(0.223)	(0.224)
Victimization		0.011	–0.024		–0.230	–0.275
		(0.418)	(0.423)		(0.401)	(0.402)
Property		0.0542	0.0638		0.692*	0.717^†
damage		(0.418)	(0.422)		(0.412)	(0.414)
Constant	2.707**	2.463**	2.432**	2.939**	3.371**	3.314**
	(0.109)	(0.648)	(0.659)	(0.112)	(0.618)	(0.623)
Comparison	Not in	Turkey,	Turkey,	Not in	Turkey,	Turkey,
group	Aleppo	not in refugee camp	not in refugee camp	Aleppo	not in refugee camp	not in refugee camp
Observations	228	214	214	232	218	218
R2	0.0218	0.0382	0.0329	0.143	0.157	0.157

Robust standard errors in parentheses. ** p < 0.01, * p < 0.05, ^† p < 0.1.

non-linear expected values to assess whether risk-taking preferences in Aleppo function according to predictions of expected utility. In Models 4–6, we find that risk-taking in Aleppo violates the expected utility model. People in Aleppo are even more risk-loving than in the linear EG risk game. They do not appear to be taking expected value of risk into consideration when making their decisions.

Extended controls in Models 3 and 6 also address whether risk-seeking behavior is the result of adaptation/conditioning to violence or a function of self-selection. To account for conditioning to violence, we develop indices of conflict exposure based on time spent in Aleppo, displacement into Aleppo from other regions, and self-reported exposure to violence (saw violent acts, personally injured or nearly injured, family members injured, killed or missing, close friends injured killed or missing) and property destruction (home or business damaged or destroyed). These controls are either uncorrelated with risk tolerance or do not clearly confound the effects of being in Aleppo on risk tolerance in Table II. This would suggest people in conflict zones are more likely

Table IV.

Risk tolerance in survey attitudes (OLS regression)

	(1)	(2)	(3)	(4)
Variables	I am not afraid to take risks	I am not afraid to take risks	I avoid risks whenever possible	I avoid risks whenever possible
Aleppo, Syria	0.372*	0.270**	–0.275**	–0.506*
	(0.084)	(0.0157)	(0.012)	(0.0817)
Idlib		0.125		–0.247^†
		(0.188)		(0.0822)
Refugee		–0.105		–0.386^†
camp, Turkey		(0.0672)		(0.0954)
Female		0.000828		–0.166
		(0.518)		(0.117)
Age		–0.00350		0.00675
		(0.00941)		(0.0105)
Education		0.252**		–0.0475
		(0.0236)		(0.0415)
Employed		0.216**		–0.261
before war		(0.0165)		(0.125)
Time in current		–0.0342		0.0537
location		(0.0597)		(0.0522)
Internally		0.0721		–0.193
displaced		(0.108)		(0.132)
Victimization		0.0400		0.316
		(0.142)		(0.409)
Property		–0.107		0.355
damage		(0.230)		(0.139)
Constant	2.435**	1.881**	2.938**	2.857*
	(0.084)	(0.0117)	(0.012)	(0.500)
Comparison group	Not in Aleppo	Turkey, not in refugee camp	Not in Aleppo	Turkey, not in refugee camp
Observations	254	236	256	238
adj. r2	0.028	0.024	0.009	0.003

Robust standard errors clustered by location in parentheses. ** p < 0.01, * p < 0.05, ^† p < 0.1.

a self-selected group of extreme risk-takers rather than simply individuals who have learned to adapt their risk preferences to living in a conflict environment.

Finally, perhaps our experimental games are a poor measure of risk preferences, so we consider alternative specifications of the dependent variable. As a robustness check, we turn to attitudinal measures of risk in our survey. In the survey, subjects are asked whether they agree with the following: ‘I am not afraid to take risks’ and ‘I avoid risks whenever possible’. Responses are order from 1 = strongly disagree to 4 = strongly agree. Table IV reports OLS regression results for risk tolerance from survey attitudes.¹⁰ As with our risk games, we find that subjects from Aleppo are more risk tolerant than those outside Aleppo in their attitudes, especially rebel fighters. Overall, attitudinal measures of risk correspond to behavioral measures in predicting risk tolerance.¹¹

What might make people in Aleppo more risk tolerant than other Syrians? Here we explore four possible explanations based on (1) self-selection vs. adaptive conditioning to the environment, (2) a sense of self-efficacy to affect future outcomes, (3) conflict related grievances, and (4) in-group solidarity using survey measures from our data. In Table V we report a number of mediator

Table V.

Probing for explanations for greater risk tolerance in Aleppo (OLS regression)

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Variables	Feel unsafe in current location	Lack help needed to leave	Lack basic resources	Optimistic about the present	Optimistic aboutthe future	Oppose negotiating with Assad gov.	Oppose making concessions for peace with Assad gov.	For US military intervention	Feel close to others here
Aleppo	0.385*	0.526**	0.352**	1.336**	0.764**	0.665**	0.896**	0.648**	0.525**
	(0.160)	(0.184)	(0.0849)	(0.187)	(0.233)	(0.195)	(0.179)	(0.216)	(0.131)
Refugees	–0.551**	N/A	–1.147**	0.590**	0.837**	0.478*	0.116	0.173	0.0573
	(0.165)		(0.0754)	(0.186)	(0.219)	(0.202)	(0.179)	(0.212)	(0.131)
Female	–0.111	0.402^†	0.319**	–0.850**	–0.245	0.152	0.469^†	–0.608*	–0.415*
	(0.140)	(0.207)	(0.0974)	(0.221)	(0.267)	(0.214)	(0.255)	(0.270)	(0.182)
Age	–0.00514	0.000972	–0.00215	–0.00503	0.00991	–0.0207*	–0.00792	–0.0106	0.00356
	(0.00612)	(0.00947)	(0.00307)	(0.00757)	(0.00930)	(0.00832)	(0.00682)	(0.00868)	(0.00442)
Education	0.238**	–0.121	0.00353	–0.0220	–0.0134	0.115	0.197^†	0.0256	–0.152
	(0.0840)	(0.142)	(0.0446)	(0.102)	(0.128)	(0.103)	(0.106)	(0.116)	(0.0981)
Working	0.154	0.358	0.0865	0.231	0.198	–0.575**	–0.261	–0.244	0.279
	(0.165)	(0.259)	(0.0839)	(0.205)	(0.252)	(0.219)	(0.206)	(0.246)	(0.179)
Constant	1.750**	1.927**	2.449**	2.015**	1.856**	3.156**	2.003**	3.015**	3.394**
	(0.309)	(0.432)	(0.153)	(0.353)	(0.424)	(0.403)	(0.340)	(0.428)	(0.313)
Observations	220	127	188	236	237	218	216	233	195
Adj. r2	0.177	0.0974	0.730	0.187	0.0596	0.106	0.189	0.0294	0.112

Robust standard errors in parentheses. ** p < 0.01, * p < 0.05, ^† p < 0.1.

models using observations about people in Aleppo that might explain elevated risk. Figure 3 also indicates the predictive marginal effect (expected value) of living in Aleppo on dependent variables in Table V.¹² First, people in Aleppo are aware of the fact that they are living under extremely hazardous conditions. People in Aleppo feel much less safe in their current location, but they also feel they lack help to leave, and lack access to basic resources such as food, water, and medical supplies compared to Syrians in other areas (Models 1–3).¹³ Given those realities, people in Aleppo are also surprisingly more optimistic about the present and future than people in other locations, suggestive of a strong sense of self-efficacy in the face of danger (Models 4–5).¹⁴ We also find some support for a grievance/activist based explanation for risk tolerance. People in Aleppo are more committed to the fight against the Assad regime, refuse to negotiate, and more wanting of US military intervention in the conflict than people in other areas (Models 6–8). In addition, there is also evidence of social solidarity; people in Aleppo feel much closer to one another than people in other areas (Model 9). Finally, these observations are generally consistent across samples of civilians and rebel fighters in Aleppo except where noted above (see SI Table VI, Online). Civilian and combatant responses in Aleppo are otherwise remarkably similar, which could indicate that those who remain in Aleppo increasingly constitute a holistic fighting community.

To explore causal mechanisms for why residents of Aleppo are more risk tolerant, we turn to mediation analysis (Baron & Kenny, 1986; Imai et al., 2010).¹⁵ In Table VI, we report the average causal mediation effects of several potential mediators of risk tolerance. We generally find the mediating effects of social

Figure 3.

Expected value of living in Aleppo on possible mediators of risk tolerance (with 95% confidence intervals)

Table VI.

Mediation analysis (95% confidence intervals in brackets)

	(1)	(2)	(3)	(4)
Variables	Lack basic resources	Lack basic resources	Optimistic for the present	Optimistic for the future
Risk game	Linear EV	Non-linear EV	Non-linear EV	Non-linear EV
Average causal mediating effect	–0.14[–0.36, 0.05]	–0.14[–0.36, 0.05]	0.21[0.04, 0.40]	0.09[0.01, 0.22]
Direct effect	0.65[–0.09, 1.12]	1.60[0.86, 2.07]	1.19[0.49, 1.65]	1.32[0.70, 1.71]
Total effect	0.51[–0.03, 0.94]	1.46[0.92, 1.88]	1.40[0.87, 1.82]	1.41[0.85, 1.81]
Percentage of the total effect that is mediated	0.26	0.10	0.15	0.07
Rho at which AMCE = 0	–0.08	–0.07	0.15	0.13
R²M*R²Y at which AMCE = 0	0.006	0.005	0.02	0.02

solidarity, grievances, and adaptive learning proxies to be weak or inconsistent with a few noteworthy exceptions. First, the effects of living in Aleppo on risk are partially mediated by a lack of basic resources. As individuals run out of basic resources (food, water, shelter, medicine), risk tolerance appears to decline, which runs contrary to the adaptive learning hypothesis. We found the mediating effects to be similar in both the linear and non-linear versions of the risk game. Sensitivity analysis, however, suggests that the mediating effect is somewhat weak and could be potentially explained away by unobservable confounders. In contrast, optimism about the present and future has a positive, enhancing effect on risk tolerance, but only in the non-linear version of the risk game. Sensitivity analysis also suggests that the mediating effect would be less easily confounded than that of lacking basic resources. It seems that self-efficacy in the form of optimism bias (i.e. wishful thinking) encourages irrational risk-taking.

Discussion

We are limited in our ability to make definitive causal inferences regarding elevated risk tolerance due to the observational nature of our data, so our explanations are highly exploratory. We acknowledge that risk tolerance could be a function of conditions in Aleppo prior to conflict, due to selection of more risk averse individuals out of Aleppo, an adaptation to the conflict environment, or all of the above. However, we find that time spent in Aleppo and conflict related victimization are generally uncorrelated with risk tolerance, casting doubt on pre-conflict and conflict-adaptive drivers of risk tolerance. Instead, evidence seems to point more clearly to selection effects. As individuals run out of basic resources (food, water, shelter, medicine), risk tolerance declines, that is, people are only as risk tolerant as they can afford to be and opt out of the conflict when they can no longer support themselves. Anecdotal evidence about scarcity and high costs of basic resources in Aleppo support our conclusions that individuals left once their savings were depleted, and they had no other options for food, water, and shelter. There is also a positive mediating effect of optimism about one’s present and future prospects on risk-taking, but only in the non-linear version of the risk game. It appears that access to resources and self-efficacy in the form of optimism bias (i.e. wishful thinking) encourages irrational risk-taking. Only those with an exceptionally high tolerance for risk could endure the relentless airstrikes by the Assad regime.¹⁶ Finally, we find that combatants and civilians in Aleppo share more similarities than differences with respect to risk tolerance. Our results suggest that Aleppo was transforming into a holistic fighting community of select civilians and combatants emboldened by their opposition to the Assad regime (Wood, 2003).

Conclusion

Based on behavior in risk games and attitudes toward risk from a survey, we find that people in a conflict most likely represent a self-selected group of high risk-takers. In the case of Aleppo, Syria, rebel combatants and civilians display stronger risk tolerance than people in other areas of Syria or Syrian refugees abroad, supporting our hypothesis that people who remain in conflict zones are highly risk tolerant. Such risk-taking is not clearly rational. Subjects in the risk game engage in risky behavior even when the expected value from risk-taking should preclude it. In contrast, refugees appear more risk averse and flee conflict when possible. Mediation analysis suggests that a sense of self-efficacy, as reflected by optimistic wishful thinking, is a potential mediator of irrational risk-taking. Hence, our results may support prospect theory-based accounts for participation in violence (Tezcür, 2016). When facing prospective losses, as was the case in the besieged city of Aleppo in 2013, individuals with a strong sense of self-efficacy are highly risk tolerant (Weinstein, 1980; Bracha & Brown, 2012).

To explain this heightened sense of self-efficacy, it is important to underscore the timing of our study in the context of the Syrian civil war. In 2013, many Syrians in Aleppo were still hopeful that the Free Syrian Army, which occupied most of Eastern Aleppo, could prevail in the conflict. Many also still believed that NATO and/or the United States might intervene militarily against the Assad regime as they had done against the Gaddafi regime in Libya. In retrospect, of course, this was clearly wishful thinking. However, it could explain why so many people were willing to discount the real risks of remaining under siege in eastern Aleppo. They overestimated the prospects of the rebellion’s success and paid a heavy price. In the end, the Free Syrian Army disintegrated, and eastern Aleppo was destroyed and recaptured by Assad’s military.

Beyond Syria, our results speak to research on risk tolerance in the face of danger, uncertainty, conflict, and unrest (Eckel et al., 2009; Voors et al., 2012; Cameron & Shah, 2015). The risk preferences of Syrians in Aleppo mirror Hurricane Katrina victims in the United States who withstood a deadly hurricane rather than heed calls to evacuate their homes (SI Figure 8). In contrast, we do not find evidence to support a certainty premium in the face of violence (Callen et al., 2014; Jakiela & Ozier, 2015). One possibility for the discrepancy in findings might be that elevated risk tolerance is a short-term response to violence and dangerous situations, while risk aversion is a more long-term legacy of severe conflict exposure. Our results may also be dependent on the presence of a holistic fighting community and civilian exit opportunities consistent with our initial scope conditions regarding selection on risk tolerance. As we have said, however, our research is exploratory and more extensive research should be conducted on the nature, timing, and degree of conflict exposure to arrive at more definitive conclusions about risk preferences during conflict.

Finally, if people in conflict zones represent a self-selected group of extreme risk-takers within a holistic fighting community, we pause to consider the long-term consequences of such a selection process. If there are cultural, environmental, and genetic components to risk preferences, then the sorting of individuals into and out of conflict on the basis of risk could have long-term effects on socialization and assortative mating, as has been identified in the broader literature (Dohmen et al., 2012). Because risk tolerance has been linked to overconfidence and aggressive behavior, especially in males (Johnson et al., 2006; Apicella et al., 2008), the purging of risk averse people from conflict zones creates an environment where individuals may feed off one another’s risk-loving tendencies, providing another micro-level explanation for why some societies become mired in conflict traps.

Supplemental Material

Supplemental Material, JPR824632_online_appendix - Risk tolerance during conflict: Evidence from Aleppo, Syria

Supplemental Material, JPR824632_online_appendix for Risk tolerance during conflict: Evidence from Aleppo, Syria by Vera Mironova, Loubna Mrie and Sam Whitt in Journal of Peace Research

Footnotes

Replication data

The datasets and replication instructions for the empirical analysis in this article, along with the Online appendix, can be found at .

Acknowledgments

We would like to thank Will H. Moore, Christian Davenport, and Jonah Schulhofer-Wohl for early feedback on this project. We also thank the anonymous reviewers for their valuable comments.

ORCID iD

Sam Whitt

Notes

References

Adhikari

Prakash

(2013) Conflict-induced displacement, understanding the causes of flight. American Journal of Political Science 57(1): 82–89.

Altonji

Joseph G

Elder

Todd E

Taber

Christopher R

(2005) Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. Journal of Political Economy 113(1): 151–184.

Andersen

Steffen

Harrison

Glenn W

Lau

Morten Igel

Elisabet Rutström

(2010) Preference heterogeneity in experiments: Comparing the field and laboratory. Journal of Economic Behavior & Organization 73(2): 209–224.

Apicella

Coren L

Dreber

Anna

Campbell

Benjamin

Gray

Peter B

Hoffman

Moshe

Little

Anthony C

(2008) Testosterone and financial risk preferences. Evolution and Human Behavior 29(6): 384–390.

Arjona

Ana M

Kalyvas

Stathis N

(2012) Recruitment into armed groups in Colombia: A survey of demobilized fighters. In: Guichaoua

Yvan

(ed.) Understanding Collective Political Violence. London: Palgrave Macmillan, 143–171.

Arrow

Kenneth J

(1965) Aspects of the Theory of Risk-Bearing. Helsinki: Yrjö Jahnssonin Säätiö.

Baczko

Adam

Dorronsoro

Gilles

Quesnay

Arthur

(2018) Civil War in Syria: Mobilization and Competing Social Orders. Cambridge: Cambridge University Press.

Baron

Reuben M

Kenny

David A

(1986) The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51(6): 1173.

Becker

Anke

Dohmen

Thomas

Enke

Benjamin

Falk

Armin

(2014) The ancient origins of the cross-country heterogeneity in risk preference (https://site.stanford.edu/sites/default/files/site_enke.pdf).

10.

Binswanger

Hans P

(1980) Attitudes toward risk: Experimental measurement in rural India. American Journal of Agricultural Economics 62(3): 395–407.

11.

Bracha

Anat

Brown

Donald J

(2012) Affective decision making: A theory of optimism bias. Games and Economic Behavior 75(1): 67–80.

12.

Callen

Michael

Isaqzadeh

Mohammad

Long

James D

Sprenger

Charles

(2014) Violence and risk preference: Experimental evidence from Afghanistan. American Economic Review 104(1): 123–148.

13.

Cameron

Lisa

Shah

Manisha

(2015) Risk-taking behavior in the wake of natural disasters. Journal of Human Resources 50(2): 484–515.

14.

Choi

Jung-Kyoo

Bowles

Samuel

(2007) The coevolution of parochial altruism and war. Science 318(5850): 636–640.

15.

Dave

Chetan

Eckel

Catherine

Johnson

Cathleen

Rojas

Christian

(2010) Eliciting risk preferences: Is simple better? Journal of Risk and Uncertainty 41(3): 219–243.

16.

Davenport

Christina

Moore

Will

Poe

Steven

(2003) Sometimes you just have to leave: Domestic threats and forced migration, 1964–1989. International Interactions 29(1): 27–55.

17.

Denrell

Jerker

(2007) Adaptive learning and risk taking. Psychological Review 114(1): 177–187.

18.

Dohmen

Thomas

Falk

Armin

Huffman

David

Sunde

Uwe

Schupp

Jürgen

Wagner

Gert G

(2011) Individual risk attitudes: Measurement, determinants, and behavioral consequences. Journal of the European Economic Association 9(3): 522–550.

19.

Dohmen

Thomas

Falk

Armin

Huffman

David

Sunde

Uwe

(2012) The intergenerational transmission of risk and trust attitudes. Review of Economic Studies 79(2): 645–677.

20.

Downes

Alexander B

(2007) Draining the sea by filling the graves: Investigating the effectiveness of indiscriminate violence as a counterinsurgency strategy. Civil Wars 9(4): 420–444.

21.

Eckel

Catherine C

Grossman

Philip J

(2002) Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and Human Behavior 23(4): 281–295.

22.

Eckel

Catherine C

Grossman

Philip J

(2008) Men, women and risk aversion: Experimental evidence. Handbook of Experimental Economics Results 1(1): 1061–1073.

23.

Eckel

Catherine C

El-Gamal

Mahmoud A

Wilson

Rick K

(2009) Risk loving after the storm: A Bayesian-network study of Hurricane Katrina evacuees. Journal of Economic Behavior & Organization 69(2): 110–124.

24.

Fabbe

Kristin

Hazlett

Chad

Sinmazdemir

Tolga

(2017) Displaced loyalties: The effects of indiscriminate violence on attitudes among Syrian refugees in Turkey. Empirical Studies of Conflict Project no. 7 (http://www.hbs.edu/faculty/Publication%20Files/18-024_c5dce162-c5de-4fb6-bda7-4798a24c3756.pdf).

25.

Floyd

Donna L

Steven

Prentice-Dunn

Rogers

Ronald W

(2000) A meta-analysis of research on protection motivation theory. Journal of Applied Social Psychology 30(2): 407–429.

26.

Harpending

Henry

Cochran

Gregory

(2002) In our genes. Proceedings of the National Academy of Sciences 99(1): 10–12.

27.

Hashemi

Nader

Postel

Danny

, eds (2013) The Syria Dilemma. Cambridge, MA: MIT Press.

28.

Heydemann

Steven

(1999) Authoritarianism in Syria: Institutions and Social Conflict, 1946–1970. Ithaca, NJ: Cornell University Press.

29.

Hirschman

Albert O

(1970) Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States. Cambridge, MA: Harvard University Press.

30.

Holt

Charles A

Laury

Susan K

(2002) Risk aversion and incentive effects. American Economic Review 92(5): 1644–1655.

31.

Humphreys

Macartan

Weinstein

Jeremy M

(2008) Who fights? The determinants of participation in civil war. American Journal of Political Science 52(2): 436–455.

32.

Iacus

Stefano M

King

Gary

Porro

Giuseppe

(2012) Causal inference without balance checking: Coarsened exact matching. Political Analysis 20(1): 1–24.

33.

Imai

Kosuke

Keele

Luke

Tingley

Dustin

(2010) A general approach to causal mediation analysis. Psychological Methods 15(4): 309–334.

34.

Jakiela

Pamela

Ozier

Owen W

(2015) The impact of violence on individual risk preferences: Evidence from a natural experiment. World Bank Policy Research Working Paper 7440.

35.

Johnson

Dominic DP

McDermott

Rose

Barrett

Emily S

Cowden

Jonathan

Wrangham

Richard

McIntyre

Matthew H

Rosen

Stephen Peter

(2006) Overconfidence in wargames: Experimental evidence on expectations, aggression, gender and testosterone. Proceedings of the Royal Society of London B: Biological Sciences 273(1600): 2513–2520.

36.

Kahneman

Daniel

Tversky

Amos

(1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2): 263–291.

37.

Kalyvas

Stathis

(2006) The Logic of Violence in Civil War. Cambridge: Cambridge University Press.

38.

Kalyvas

Stathis

Kocher

Matthew Adam

(2007) How ‘free’ is free riding in civil wars? Violence, insurgency, and the collective action problem. World Politics 59(2): 177–216.

39.

Kim

Young-Il

Lee

Jungmin

(2014) The long-run impact of a traumatic experience on risk aversion. Journal of Economic Behavior & Organization 108(2): 174–186.

40.

Krueger

Norris

Dickson

Peter R

(1994) How believing in ourselves increases risk taking: Perceived self-efficacy and opportunity recognition. Decision Sciences 25(3): 385–400.

41.

Krueger

Alan B

Maleckova

Jitka

(2003) Education, poverty and terrorism: Is there a causal connection? Journal of Economic Perspectives 17(4): 119–144.

42.

Lerner

Jennifer S

Keltner

Dacher

(2001) Fear, anger, and risk. Journal of Personality and Social Psychology 81(1): 146–159.

43.

Levy

Jack S

(1997) Prospect theory, rational choice, and international relations. International Studies Quarterly 41(1): 87–112.

44.

Lyall

Jason

(2009) Does indiscriminate violence incite insurgent attacks? Evidence from Chechnya. Journal of Conflict Resolution 53(3): 331–362.

45.

March

James G

(1996) Learning to be risk averse. Psychological Review 103(2): 309.

46.

McAdam

Doug

(1986) Recruitment to high-risk activism: The case of Freedom Summer. American Journal of Sociology 92(1): 64–90.

47.

McDermott

Rose

(2004) Prospect theory in political science: Gains and losses from the first decade. Political Psychology 25(2): 289–312.

48.

McDermott

Rose

Tingley

Dustin

Cowden

Jonathan

Frazzetto

Giovanni

Johnson

Dominic DP

(2009) Monoamine oxidase A gene (MAOA) predicts behavioral aggression following provocation. Proceedings of the National Academy of Sciences 106(7): 2118–2123.

49.

Mercer

Jonathan

(2005) Prospect theory and political science. Annual Review of Political Science 8(1): 1–21.

50.

Parkinson

Sarah Elizabeth

(2013) Organizing rebellion: Rethinking high-risk mobilization and social networks in war. American Political Science Review 107(3): 418–432.

51.

Pierret

Thomas

(2013) Religion and State in Syria: The Sunni Ulama from Coup to Revolution. Cambridge: Cambridge University Press.

52.

Rogers

Ronald W

(1975) A protection motivation theory of fear appeals and attitude change. Journal of Psychology 91(1): 93–114.

53.

Seale

Patrick

(1986) The Struggle for Syria: A Study of Post-War Arab Politics, 1945–1958. London: IB Tauris.

54.

Shils

Edward A

Janowitz

Morris

(1948) Cohesion and disintegration in the Wehrmacht in World War II. Public Opinion Quarterly 12(2): 280–315.

55.

Souleimanov

Emil Aslan

Aliyev

Huseyn

(2015) Blood revenge and violent mobilization: Evidence from the Chechen Wars. International Security 40(2): 158–180.

56.

Starmer

Chris

(2000) Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. Journal of Economic Literature 38(2): 332–382.

57.

Tezcür

Güneş Murat

(2016) Ordinary people, extraordinary risks: Participation in an ethnic rebellion. American Political Science Review 110(2): 247–264.

58.

Van Dam

Nikolaos

(2017) Destroying a Nation: The Civil War in Syria. London: IB Tauris.

59.

Vieider

Ferdinand M

Lefebvre

Mathieu

Bouchouicha

Ranoua

Chmura

Thorsten

Hakimov

Rustamdjan

Krawczyk

Michal

Martinsson

Peter

(2015) Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries. Journal of the European Economic Association 13(3): 421–452.

60.

von Neumann

John

Morgenstern

Oskar

(1947) Theories of Games and Economic Behavior, 2nd edition. Princeton, NJ: Princeton University Press.

61.

Voors

Maarten J

Nillesen

Eleonora EM

Verwimp

Philip

Bulte

Erwin H

Lensink

Robert

Soest

Daan P Van

(2012) Violent conflict and behavior: A field experiment in Burundi. American Economic Review 102(2): 941–964.

62.

Weinstein

Jeremy M

(2006) Inside Rebellion: The Politics of Insurgent Violence. Cambridge: Cambridge University Press.

63.

Weinstein

Neil D

(1980) Unrealistic optimism about future life events. Journal of Personality and Social Psychology 39(5): 806–820.

64.

Whitehouse

Harvey

McQuinn

Brian

Buhrmester

Michael

Swann

William B

(2014) Brothers in arms: Libyan revolutionaries bond like family. Proceedings of the National Academy of Sciences 111(50): 17783–17785.

65.

Wood

Elizabeth J

(2003) Insurgent Collective Action and Civil War in El Salvador. Cambridge: Cambridge University Press.

66.

Wrangham

Richard

(1999) Is military incompetence adaptive? Evolution and Human Behavior 20(1): 3–17.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.86 MB