The evolution of altruism through war is highly sensitive to population structure and to civilian and fighter mortality
Mark Dyblea,1
aDepartment of Anthropology, University College London, WC1H 0BW London, United Kingdom
Edited by David C. Queller, Washington University in St. Louis, St. Louis, MO, and accepted by Editorial Board Member James F. O’Connell February 1, 2021 (received for review May 31, 2020) The importance of warfare in the evolution of human social Bowles’ model suggests that warfare between groups could, in behavior remains highly debated. One hypothesis is that intense theory, select for both parochialism (out-group hostility) and warfare between groups favored altruism within groups, a hy- within-group altruism when individuals form small and geneti- pothesis given some support by computational modeling and, in cally differentiated groups that occasionally go to war with one particular, the work of Choi and Bowles [J.-K. Choi, S. Bowles, another and where success in these wars is determined by the Science 318, 636–640 (2007)]. The results of computational models proportion of parochial altruists in each group. are, however, sensitive to chosen parameter values and a deeper As set out in other work by Bowles (2, 13), differences in the assessment of the plausibility of the parochial altruism hypothesis frequency of altruists between groups is critical to the coevolu- requires exploring this model in more detail. Here, I use a recently tion of altruism and war—if individuals frequently migrate be- developed method to reexamine Choi and Bowles’ model under a tween groups or if groups are large, altruistic individuals are much broader range of conditions to those used in the original unlikely to become sufficiently concentrated. This raises the paper. Although the evolution of altruism is robust to perturba- question of how much population structuring is necessary for tions in most of the default parameters, it is highly sensitive to altruism to evolve in Choi and Bowles’ model and how this group size and migration and to the lethality of war. The results compares to empirical estimates of population structuring in
show that the degree of genetic differentiation between groups contemporary small-scale societies. Although previous work on ANTHROPOLOGY F ’ ( ST) produced by Choi and Bowles original model is much greater parochial altruism estimated that FST (a measure of genetic than empirical estimates of FST between hunter-gatherer groups. variation explained by differences between groups) was ∼0.08 When FST in the model is close to empirically observed values, between contemporary hunter-gatherer populations (2), these altruism does not evolve. These results cast doubt on the impor- estimates were based on a wide variety of genetic markers in- tance of war in the evolution of human sociality. cluding some which are poor indicators of whole-genome genetic differentiation (26). Subsequent estimates based on differences altruism | war | population structure | parochial altruism | agent-based in autosomal data suggest that differences between groups who modeling could plausibly compete suggest that it is much lower than this (27–29) and similar to that seen in chimpanzees (26). This raises hile humans are capable of cooperation, tolerance, and two questions for the Choi and Bowles model. First, is the degree Wgenerosity toward others, we are also capable of prejudice, of population structure produced by the model similar to em- violence, and war. Although superficially at odds, these two sides pirical estimates? Second, does altruism in the model evolve of human behavior are sometimes closely related, with warfare when population structure is similar to the empirical estimates? promoting within-group solidarity and acts of individual sacrifice. The association between intergroup conflict and intragroup al- Significance truism has led evolutionary theorists including Darwin (1) to – “ hypothesize that the two may have coevolved (2 5). The pa- Many evolutionary theorists have suggested that the human ” rochial altruism hypothesis as typically conceived (6) holds that capacity for altruism was forged in war, with cohesive and if groups containing more altruistic individuals were able to out- altruistic groups outcompeting their selfish neighbors. Assess- compete groups containing fewer altruistic individuals, this could ing this “parochial altruism” hypothesis relies largely on com- have provided positive selection for both within-group altruism putational modeling. Here, I reexamine a well-known model “ ” – and out-group hostility ( parochialism ) (3, 6 8). that explores the coevolution of altruism and war. As well as The plausibility of the parochial altruism hypothesis depends clarifying the importance of factors such as the lethality of war partly on the likelihood that warfare was commonplace during to fighters and civilians, the results show that the evolution of human evolutionary history, a claim lent some support by ar- altruism in this model relies on a degree of genetic differenti- chaeological evidence of mass killings (9, 10) and ethnographic ation between groups that exceeds that seen among hunter- – data from contemporary or historic small-scale societies (11 16) gatherers. Furthermore, when the model produces a more re- – but which remains highly debated (17 20). However, even if alistic population structure, altruism does not evolve, casting warfare were commonplace in human evolutionary history, this doubt on the plausibility of the parochial altruism hypothesis. would not necessarily mean that it was an important force in selecting for within-group altruism. Since direct evidence of past Author contributions: M.D. designed research, performed research, analyzed data, and selection pressures on altruism and war are unavailable to us, we wrote the paper. rely on exploring the coevolutionary dynamics of parochial and This article is a PNAS Direct Submission. D.C.Q. is a guest editor invited by the altruistic behaviors using mathematical or computational mod- Editorial Board. eling. Several models exploring parochial altruism have been Published under the PNAS license. advanced (2–4, 13, 21) as part of a wider literature on the pos- 1Email: [email protected]. sible impact of warfare on the evolution of human sociality This article contains supporting information online at https://www.pnas.org/lookup/suppl/ (22–25). Of these models of parochial altruism, arguably the doi:10.1073/pnas.2011142118/-/DCSupplemental. most influential is a model by Choi and Bowles (3). Choi and Published March 8, 2021.
PNAS 2021 Vol. 118 No. 11 e2011142118 https://doi.org/10.1073/pnas.2011142118 | 1of6 Downloaded by guest on September 29, 2021 As set out by Rusch (6), answering these questions is critical to δc, and all dead agents are replaced by the offspring of surviving our assessment of the plausibility of the parochial altruism hy- members of the winning group. When reproduction occurs, new pothesis for the evolution of human altruism. agents mutate to a random phenotype with probability μ. Each Another reason to explore the Choi and Bowles model in generation, a proportion of agents from each group determined more detail is that while computational modeling can be highly by parameter m migrate to a random group (although note that informative, the results of all models will be sensitive to the since agents may replace dead members of other groups during choice of initial conditions and default parameters. While some war, this migration is not the only way for genes to move between parameters can be grounded in ethnographic data, others will be groups). In addition to the original model, I added a third “gene” too abstract to ground empirically, and in all cases it is important with six alleles that is inherited and mutates with the same to explore the impact that each parameter has on model out- probability as the “altruism” and “parochialism” genes but which comes (in this case the evolution of altruism). Most computa- has no effect on fitness. This “neutral” gene allows the mea- tional models do this by using a fix-all-but-one approach in which surement of population structure from locus that is not under one parameter is varied while all others are kept at their default selection (33). values. Choi and Bowles use this fix-all-but-one sensitivity anal- ysis for five of their model parameters. However, the fix-all-but- Results one method reduces the exploration of the model outcomes to a I replicated Choi and Bowles’ original model and carried out small part of parameter space and limits our understanding of 60,000 simulations. In each simulation, parameters were set the relationship between each parameter and the model out- randomly within the ranges listed in Table 1 and model outcomes come and of interactions between parameters (30). Although the were recorded. Across these simulations, the mean proportion of fix-all-but-one approach employed by Choi and Bowles was the altruists in the population (f A) was strongly correlated with standard approach used at the time, methods have subsequently group size and migration rate, moderately associated with the been developed to explore model parameter space more fully lethality of war to both fighters and civilians and with the costs of (30–32). Here, I use a Fitting to Idealized Outcomes (FIO) altruism in the public goods game, and weakly associated with method developed by Gallagher, Shennan, and Thomas (30) to the payoffs of tolerance toward neighboring groups (Table 2). Of reexamine the results of Choi and Bowles’ model of parochial the 60,000 simulations, 29,158 (48.6%) resulted in a mean pro- altruism in order to 1) explore the results of the model in more portion of altruists in the population across all generations (f A) detail and under a broader range of conditions and 2) calculate of >0.5. Histograms of the parameters that produced these the degree of population structure produced by the model and 29,158 simulations are shown in Fig. 1. compare this to empirical estimates. I find that while warfare in the model can lead to the evolution of altruism, it only does so Altruism and the Lethality of War. Two parameters determine the when groups are far more genetically differentiated than groups lethality of warfare in the model: δf and δc. For the proportion of of contemporary or recent hunter-gatherers are estimated to be. fighters dying in war (δf), there is a moderate negative rela- tionship with the proportion of altruists (ρ = −0.17) such that The Choi and Bowles Model altruism is more likely to evolve (f A > 0.5) when fewer fighters In their model (3), Choi and Bowles consider a population living die in war (Fig. 1A). Conversely, for the parameter that deter- in 20 groups of n agents. Agents have a behavioral phenotype mines the probability of civilians dying (δc), there is a strong determined by two “genes.” The first determines whether they positive relationship with the proportion of altruists (ρ = 0.32) are “altruistic” (A) or “nonaltruistic” (N), and the second de- such that altruism is unlikely to evolve unless civilians (who are termines whether they are “tolerant” (T) or “parochial” (P). all non-PAs) die in war. These parameter-expanded results Thus, an agent can be a parochial altruist (PA), parochial non- demonstrate two intuitive but important points—that parochial altruist (PN), tolerant altruist (TA), or tolerant nonaltruist (TN). altruism will not evolve unless a large proportion of fighters (and In each generation of the model, there is a within-group inter- their PA phenotypes) survive, and a moderate or large propor- action and a between-group interaction. The within-group in- tion of civilians (and their non-PA phenotypes) die when the teraction consists of a “public goods” game in which altruists pay fighters from their group lose in war. These results lend support a cost (c) to contribute a benefit (b) to a communal pot that is to a central feature of the Choi and Bowles model—that altruism then divided equally between all group members. All else being in the model is selected as a result of the dynamics of warfare equal, the dominant strategy in this game is to be a nonaltruist between groups. Fix-all-but-two simulations varying δ and δ “ ” f c free-rider who receives benefits from altruistic group mates show that increases in δc and decreases in δf from the default without paying a cost themselves. However, the model also values make it unlikely that f A > 0.5 (Fig. 2A). contains a between-group phase in which groups are randomly paired with another group and have an interaction that can be Intragroup Altruism and Intergroup Tolerance. During the within- either hostile or tolerant. The probability of the interaction being group phase of the model, altruists pay a fitness cost (c), which tolerant is determined by the proportion of tolerant agents in the was negatively correlated with fA across simulations (ρ = −0.33) two groups. If a tolerant interaction occurs, tolerant agents re- such that altruism was less likely to evolve when being an altruist ceive a positive fitness payoff equal to the number of tolerant had a higher fitness cost (Fig. 1C). Fix-all-but-two simulations agents in the rival group multiplied by the parameter g.Ifa show that the benefit that altruists provide to group mates has hostile interaction occurs, the groups will go to war with a virtually no effect on the evolution of altruism (Fig. 2B). Simi- probability determined by the difference in the proportion of larly, the payoffs of tolerant interactions with other groups (pa- parochial agents in each group. Thus, while all parochial agents rameter g) are only weakly associated with fA across the (PAs and PNs) can be thought of as agitating for hostility, only parameter range explored here (ρ = −0.05, Fig. 1D). parochial altruists (PAs) actually “go to war” as fighters. War can result in either a draw or with the group with more parochial Population Structure. The two parameters that were most strongly altruists winning. When a draw occurs, fighters die with a correlated with fA were group size (n) and migration between probability determined by the parameter δf and are replaced by groups (m). There were strong negative correlations between the offspring of surviving members of their own group. When the these parameters and fA such that altruism was less likely to group with more parochial altruists wins, fighters on both sides evolve when groups were larger and migration between groups die with the probability δf, civilians (i.e., non-PAs) of the losing was more frequent (Table 2 and Fig. 1 E and F). The sensitivity group die with a probability determined in part by the parameter of the model results to n and m can be clearly seen in the
2of6 | PNAS Dyble https://doi.org/10.1073/pnas.2011142118 The evolution of altruism through war is highly sensitive to population structure and to civilian and fighter mortality Downloaded by guest on September 29, 2021 Table 1. Parameters, constants, and outcomes of the model Type Symbol Description Default value Range or value used in FIO simulations
Parameter n Group size 26 [6, 46] Parameter g Tolerance benefit 0.001 [0,0.002]
Parameter δc Lethality of war to civilians 2.5 [0,5] Parameter δf Lethality of war to fighters 0.14 [0,0.28] Parameter m Migration 0.25 [0,0.5] Parameter c Public goods game cost 0.01 [0,0.02] Constant μ Mutation rate 0.005 0.005 Constant — Number of groups 20 20 Constant b Public goods game benefit 0.02 0.02 Outcome f A Mean proportion of altruists over all generations —— Outcome f P Mean proportion of parochialists over all generations —— Outcome f PA Mean proportion of parochial altruists over all generations ——
Outcome FST Genetic differentiation between groups ——
fix-all-but-two simulations shown Fig. 2C—modest increases in estimated to be (26). Under parameter regimes that produce a these parameters from the default values of n = 26 and m = 0.25 population structure similar to those that have been empirically would mean that fA is unlikely to exceed 0.5. These parameters observed, parochial altruism does not evolve in the model. are so influential because they determine the degree of genetic The importance of population structure to the outcome of the differentiation between groups in the model, as shown by the model is consistent with work by Bowles on population structure strong negative correlations between FST and n (ρ = −0.95) and and social evolution (2, 13) and with the importance of pop- m (ρ = −0.16) and between FST and fA (ρ = 0.65, Table 2). ulation structure for explanations for social evolution more generally (34–37). Indeed, at a certain degree of abstraction, all Ethnographic Comparison. Given the importance of group size (n) explanations for the evolution of altruism rely on population and migration (m) to the evolution of altruism in the model, it is structuring of some kind (34, 37–39). For humans, the low de- important to select these values carefully; establishing parame- gree of genetic differentiation seen between hunter-gatherer ANTHROPOLOGY ters that reflect a plausible scenario in human evolutionary his- groups (26) is likely to be a consequence both of specific fea- tory is critical to our interpretation of the model and the tures of hunter-gatherer social organization such as bilocal res- plausibility of the parochial altruism hypothesis for humans. To idence (40) and high mobility (41) and also of more general do this, it is necessary to establish the degree of population features of human social organization such as tolerant relation- structure produced in the model and compare this with ethno- ships with neighbors facilitated by the recognition of affinal graphic estimates of FST. kinship (i.e., relationships with in-laws) (42, 43), and the for- ’ Mean FST under Choi and Bowles default parameters is 0.083 mation of multilevel societies (44, 45). Although these features (SD = 0.008, averaging over 100 simulations of 50,000 genera- of social organization were not necessarily present throughout tions). This is ∼7 times greater than the estimates of mean the entirety of human evolutionary history, there are also general = pairwise FST of 0.012 (SD 0.016) between hunter-gatherer features of ape life history that are likely to reduce genetic dif- groups (Fig. 3A and SI Appendix, Table S1) and those reported ferentiation between groups by reducing intragroup relatedness. for chimpanzees of 0.014 (SD = 0.009) (26). None of the simu- These include the production of single offspring rather than < lations explored above produced FST 0.02, so an additional set litters, multiple juvenile cohorts, and low female reproductive of simulations were run across even more expanded parameter skew (46, 47) and may explain why estimates of FST are similar in ranges for n and m (0 ≤ m ≤ 1 and 6 ≤ n ≤ 200) to identify chimpanzees and humans despite differences in social organi- parameter sets that would produce FST close to the empirical zation (26, 48, 49). Taken together, human life history and social estimates. Values close to the mean empirical FST estimate organization are unlikely to produce degrees of genetic differ- (0.012 ± 0.005) are produced when groups are much larger entiation between groups that are sufficient for intergroup con- (mean n = 96.6) and migration is much more frequent (mean flict to favor intragroup altruism in the Choi and Bowles model. m = 0.51) than the default values of n = 26 and m = 0.25. In It is important to emphasize that the results of this analysis simulations that produced FST close to this ethnographic esti- make no comment on the frequency of war in human evolu- mate, the proportion of altruists and parochial altruists evolving tionary history and do not dispute that the coevolution of in the model was much less than that observed in the default values from the original model (Fig. 3 B and C). In short, the degree of genetic differentiation between groups produced by Table 2. Spearman’s correlation coefficients between each Choi and Bowles’ original model is far greater than that seen A parameter or outcome and the proportion of altruists (f ), between hunter-gatherer populations (Fig. 3A) and when FST in P PA parochials (f ), parochial altruists (f ), and FST in the population the model is close to these ethnographically observed values, averaged across 60,000 simulations (FST values from 10,000 altruism does not evolve (Fig. 3 B and C). simulations only)
A P PA Discussion Symbol Parameter/outcome f f f FST ’ Here, I have reexamined Choi and Bowles model of parochial − − − − altruism, using a FIO method to explore parameter space more n Group size 0.54 0.64 0.63 0.95 m Migration −0.45 −0.42 −0.43 −0.16 fully and estimating the degree of population structure pro- c Public goods game cost −0.33 −0.05 −0.16 0.00 duced. The results of this reanalysis support the general claim δ Lethality of war to civilians 0.32 0.28 0.30 0.04 that intense competition between groups could, in theory, favor c δ Lethality of war to fighters −0.17 −0.15 −0.16 0.04 within-group altruism but suggest that this is only likely to occur f g Tolerance benefit −0.05 −0.28 −0.23 −0.01 when groups are far more genetically differentiated from each F Genetic differentiation of groups 0.65 0.76 0.74 — other than contemporary or recent hunter-gatherer groups are ST
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DEF
Fig. 1. Conditions favoring the evolution of altruism. Histograms showing the parameter values from the 29,158 simulations in which mean f A across
generations exceeded 0.5 for (A) lethality of war to fighters δf,(B) lethality of war to civilians δc,(C) cost of altruism in the public goods game c,(D) tolerance payoff g,(E) groups size n, and (F) migration m. Dotted lines indicate the default parameter values from the Choi and Bowles model.
altruism and intergroup conflict is a theoretical possibility more to evolve in the Choi and Bowles model. It is worth noting that broadly. In fact, this reanalysis clarifies the factors that may the Choi and Bowles model assumes that all parochial altruists promote the evolution of altruism through intergroup conflict. will go to war, whereas in actual human societies, active partic- Specifically, the evolution of altruism in the model is promoted ipation in war is usually restricted to young men (12, 22). Negative by low fighter mortality (low δf) and high civilian mortality fitness consequences of parochial altruism in noncombatants would during war (high δc), a small cost to altruism in within-group mean that altruism is less likely to evolve and may lead to intra- interactions (low c), small payoffs to tolerant interactions with familial and intergenerational conflicts of interest, especially if the neighbors (low g), and by small groups with low rates of migra- spoils of war are unequally distributed (22, 23). tion between them (small n and m). Even if these conditions The findings from this analysis provide further demonstration of were not met in humans, they may be met in other group-living the utility of the FIO method (30) for fully exploring the results of mammals living in small but genetically differentiated groups computational models. They highlight the conditions necessary for among which intergroup aggression is frequent such as meerkats the evolution of altruism through war and suggest that altruism will (36, 50), wolves (51), and banded mongooses (52). For banded only evolve in Choi and Bowles’ model of parochial altruism when mongooses, the observed degree of genetic differentiation be- competing groups are far more genetically differentiated than they tween groups (FST = 0.129) (52) would be sufficient for altruism are likely to have been in human evolutionary history.
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Fig. 2. Parameter interactions in “fix-all-but-two” simulations. Evolution of altruism when varying pairs of parameters relating to warfare (A), the payoffs of
within-group cooperation (B), and population structure (C). In each graph, dots represent simulations in which fA > 0.5. The red triangle represents the parameter values from the original model.
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Fig. 3. The effect of population structure on model outcomes. (A) Logistic regression predicting the probability of altruism evolving in a simulation (mean fA across generations > 0.5) and FST across 2,000 simulations in which n and m were varied (0 ≤ m ≤ 1 and 20 ≤ n ≤ 200) and all other parameters were kept at default values. (B) Mean proportion of altruists in the model (fA) under parameter sets that produce FST values close to (within ±0.005) those empirically observed and under the Choi and Bowles default values. (C) Mean proportion of parochial altruists (fPA) in the model under parameter sets that produce FST values close to (within ± 0.005) those empirically observed and under the Choi and Bowles default values.
Methods approximate the mean 6.4 alleles for the microsatellite data included in I translated the Choi and Bowles model (3) into R using a combination of the Verdu et al. discussed below (27). As defined by Nei (53), FST (sometimes – published description of the model and their original MATLAB code and known as GST for polyallelic loci) is calculated as (HT HS)/HT, where HS is the – successfully replicated the main results of their paper and the original sen- average Hardy Weinberg heterozygosity across groups and HT is the total population heterozygosity. Although F estimates may potentially vary with sitivity analysis (SI Appendix, Figs. S1 and S2). To fully explore the results of ST ANTHROPOLOGY the model across parameter space, I used the FIO method set out by Gal- allele number, this is (as pointed out by Hedrick; ref. 54) not likely to occur lagher et al. (30) (also see refs. 31 and 46). I ran the model 60,000 times and, when the migration rate is greater than the mutation rate, as is the case in in each simulation, randomly set parameters within defined limits within this model. However, as additional checks, I show that FST for the neutral which the default parameter from Choi and Bowles was the mean (Table 1). locus in this model is robust across allele number (SI Appendix, Fig. S4) and I In each case, I recorded frequencies of the four phenotypes across 10,000 also compared empirical and simulated genetic differentiation according to ′ generations. This was sufficient to provide stable estimates of relationships the standardized measure G ST as defined by Hedrick (54). Doing so pro- between parameters and model outcomes (SI Appendix, Fig. S3). Two pa- duced very similar results (SI Appendix, Fig. S5). rameters from the initial model were treated as constants: mutation rate (μ) Empirical estimates of pairwise genetic differentiation between pop- was kept at the default value of 0.005 in all simulations as there was little ulations of contemporary or recent hunter-gatherers are listed in SI Ap- theoretical justification for varying it, and the number of groups in the pendix, Table S1 and were based on microsatellite data from Australian (29), population was kept at 20 as initial simulations suggested it had no effect on South American (28), and Central African (27) populations compiled by model outcomes. In the main simulations, the cost of contributing to the Langergraber et al. (26) with some exclusions. The data exclusions are of the within-group public good (c) was varied but the benefit (b) was not; initial pairwise differences between the Australian populations not listed as being simulations suggested that b had little effect on the model outcome (as from the more remote Arnhem, Gulf, or North regions listed in Walsh et al. demonstrated in Fig. 2B). In addition to the 50,000 simulations, I explored (29). The excluded populations are those in which it is likely that there have
three pairs of parameters (n and m, δc and δf, c and b) under even broader been higher rates of recent migration and admixture. Since the mean FST in parameter ranges, randomly setting the two parameters of interest the remote groups was higher than that among the Australian groups in 2,500 times but fixing all other parameters to the default values from the general, excluding the nonremote data increases the empirical estimates of original simulation (a “fix-all-but-two” approach). I also ran 2,000 additional genetic differentiation. The remaining dataset consists of 30 pairwise com- simulations with larger upper bounds for group size and migration (0 ≤ m ≤ parisons between contemporary or recent hunter-gatherer populations,
1 and 6 ≤ n ≤ 200) to find FST values close to empirical estimates (0.012 ± with a mean geographic distance between pairs of 270 km (SI Appendix, 0.005). In these simulations, all other parameters were set to default values. Table S1). To reduce computing time, FST was calculated in a subset of 10,000 of the 60,000 simulations. Comparing Empirical and Simulated Genetic Differentiation. In order to com- pare the degree of genetic differentiation produced in Choi and Bowles’ Data Availability. Secondary empirical data and model code are available in
model with empirical estimates, I calculated FST for a neutral six-allele the supporting information. “gene,” which is inherited and mutated in the same way as the altruism or parochialism genes but which is unlinked to them and which does not ACKNOWLEDGMENTS. I thank Jim Allen, Kevin Langergraber, Nichola influence fitness and is therefore considered a “neutral” locus. Six alleles Raihani, and Mark Thomas for helpful discussions about this project.
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