What Made Humans Really Human? Cooperation and “Collective” Action at the Dawn of Humanity

Certainly actual computational simulations of human behavior in the most remote past lack cognitive complexity. But this is not a source of troubles and problems, but a consequence of the emphasis on the generality and simplicity of human behavior and how the apparently unconscious repetition of simple actions produces the self-organized emergence of complex organization properties.

In any case, we do not think that the real problem of “intelligence” and “ra­tionality” lies in the cognitive explicit content in the “mind” of each agent within the simulation. Obviously, we do not have in the present data about what men and women believed in ancient times. It is important to remember that we do not need to “recreate” the past as believed by people that lived then. It is the distance between our present problems and what happened in the past that motivates our emphasis on long term process and collective action instead of individual motivations. Probably this is not the case when studying a past situation that is relatively near to our present experience: the past desires of our grandparents and grandmothers may still constraint our decisions here and now. But there is no way that the desires of one person having lived more than 200 years ago can constrain what we want to do here and now. Therefore, when investigating the most remote past we are interested not in the individual but in collective action: what a population of a particular size made in the past can affect still what a new population that is a reproduction of the former one is able to do today (Oliver et al.

The real issue is precisely that intentional actions of the individual agents give rise to functional, unaware collective phenomena. It is not that actual computer simulations ignore the basis of individual agency and that prehistoric people nor their computational surrogates were deprived of believes, desires and intentions beyond their direct survival (acquiring food), but social scientists are making emphasis on the analytical importance of understanding the roots of social self-organization before taking into account the role of the creative individual in a world constrained by unaware collective action. The fact that some characteristi­cally human attributes are “mechanically” simple has been argued during the last 40 years. We do not need to affirm that prehistoric people were underdeveloped stupid people that acted like animals. Supposedly “modern” behavior like coop­eration, alliance, technological innovations, etc. seems to be in fact the consequence of relative simple and plain mechanisms.

In this way, Janssen and Hill (Chap. 3) show how cooperation has a mechanical nature and, in some cases, it can be seen as adaptive. Fort et al. (Chap. 5) incor­porate cultural transmission, that is, learning, to understand the speed of change in human populations during the Neolithic. They also argue that cultural transmission and economic change can be analyzed in adaptive and mechanical terms (although explicitly non-linear and non-monotonic) and not necessary the result of rational decisions nor cognitive states in the mind of agents.

Distributed computer simulation is one of the hallmarks for investigating this subjects given their ability to generate macro level behaviors caused by micro-level decisions of individual agents characterized by bounded rationality, decision-making autonomy, sociality, and dynamic interactions among them. According to that view the output of the social mechanism is a pattern of emergent behavior (self-organizing, collective behavior) which is difficult to anticipate from knowledge of the individual agents' behavior (Shoham and Tennenholtz 1997; Bedau 2003; Sawyer 2005; Neumann 2009; Helbing 2012; Helbing and Balietti 2013; Jennings et al. 2014; Schieve and Allen 2014; McHugh et al. 2016). For instance, any emergence of sociopolitical complexity should be considered as the resulting long and slow process produced by the model is generative, not deter­ministically hard-wired or causally pre-determined in any way (see Cioffi-Revilla and Bogle's contribution to this volume, Chap. 13). This reflection points to the necessary distinction between individual activity and their aggregated consequences, that is, between micro-motives and macro-behavior (Schelling 1978; Mella 2008). It is the same as distinguishing between agency and structure. In macro explanations, the set of individuals is viewed as a structure that can be characterized by a number of variables, whereas in micro motive explanation the structure is viewed as emer­gent from the interactions between the individuals. These models as well as real phenomena, for example, the societies, are dynamic because they change in time; therefore, a model will consist not only of structure but also of agency.

A good example of the way of taking into consideration intelligence as a col­lective phenomenon rather than as the simple summation of individual abilities was Jim Doran's EOS system, probably the first computer simulation of historical events in the true sense of word (Doran and Palmer 1995a, b, Doran et al. 1994).

Doran's model shows the consequences of cooperation at work and cultural transmission among hunter-gatherer systems. “Cooperation” is among the most analyzed causal factors for the modern understanding of “intelligent” decision-making in prehistory (Salgado et al. 2014). Current studies on cooperation as a social mechanism with relevant consequences for decision making are based on the effects of kinship and/or territoriality to reinforce the existence of social ties within clusters and to maintain group identity and shared practices. People have a preference for interacting with others who share similar traits and practices, which “naturally” diversifies the population into emergent social clusters. In the real world, as well as in a simulated world, individuals may display “in-group favor­itism” (Hammond and Axelrod 2006), also called “parochialism” (Bowles and Gintis 2004; Koopmans and Rebers 2009; Fernandez-Marquez and Vazquez 2014; Gintis et al. 2015; Santos et al. 2015; Salas-Fumas et al.

Analytically defining the consequences of cooperation according to the principle that “connected attracts” we make an important advance towards the explanation of apparently complex social behavior among small scale societies. More precisely, current simulations show that ethnicity can be understood in terms of the tendency of people with connected (or similar) traits (including physical, cultural, and atti­tudinal characteristics) to interact with one another more than with people uncon­nected (or dissimilar features). In addition, we can introduce the principle of social influence (i.e., the more that people interact with one another, the more similar they become) which runs at the level of communication and the formation of a socio-cognitive level. This influence process produces induced ethnicity, in which the disproportionate interaction of likes with likes may not be the result of a psychological tendency but rather the result of continuous interaction.

Computer simulation has allowed the discovery that social mechanisms that normally lead to cultural convergence—cooperation, influence and transmission— can also explain how population have diversified culturally through the ages (Read 2003, 2010). This conclusion is based on pioneer research by Axelrod (1997a, b). He proposed an abstract model based on the fundamental principle that the transfer of ideas occurs most frequently between individuals who are similar in certain attributes such as beliefs, education, social status, and the like. This study draws some interesting conclusions experimenting with different parameter configura­tions, including the non-intuitive result that the average number of stable regions formed decreases as the size of the territory increases.

In any case, Axelrod's model is too simple to be used as an effective model of ethnogenesis. For instance, it has been proved (Klemm et al. 2003, 2005; San Miguel et al. 2005; Gracia-Lazaro et al. 2009) that if random noise is introduced at a low rate (allowing cultural traits to change randomly with a small probability), the basic dynamics of the ethnicity and influence model will drive the population away from cultural diversity and toward cultural homogeneity. This may happen because the introduction of random shocks perturbs the stability of cultural regions, eroding the borders between the groups. This allows the system to find a dynamical path away from the metastable configuration of coexisting cultural domains, toward the stable configuration of ethnogenesis.

Cultural drift raises the question of whether the above explanation of cultural diversity will hold if agents were permitted to make errors or develop innovations. Parisi et al. (2003), working also on the lineage of Axelrod's assumptions, have simulated a process of expansion of a single human group in an empty territory and looking at what happens to this group's previous culture when during the expansion process both cultural assimilation between neighboring sub-groups and random internal changes in the culture of each subgroup took place. By allowing multiplex influence, it is no longer possible for a deviant to lure its neighbors by influencing them one at a time. This strengthens the effects of ethnical homogeneity by insuring that agents can never be influenced in a direction that leaves them with less in common with their neighbors overall. If within-group interaction preference is the mechanism by which global convergence generates local diversity, then strength­ening the tendency toward convergence might have the counterintuitive effect of allowing stable diversity to emerge. Parisi et al. show that Axelrod's result of no complete cultural homogenization is obtained even if we abandon the assumption that neighboring groups with completely different cultures cannot influence each other. In this expanded model not only assimilation is with the dominating culture of a site's entire neighborhood but, most importantly, there is no role of pre-existing cultural similarity as a determinant of cultural assimilation.

Instead of the exploration of a new territory by an ethnically homogenous population, Matthews (2008) has simulated the sudden arrival of a different ethnic group, and how it behaves with local populations. One might expect that in a culture with a very high rate of drift, new cultural regions may be absorbed very rapidly as common features may appear regularly by chance, facilitating interaction across boundaries. The results of this experiment suggest that despite such high levels of drift, distinct regions may persist for significant periods of time. In general though, it is possible to conclude that in relatively homogeneous cultures with low rates of cultural drift (as may be expected to be found in isolated, monoculture regions), any distinct cultures which do form are likely to persist for significant periods of time before being assimilated into the surrounding culture. These distinct cultures may appear through a number of possible mechanisms (including perhaps Axelrod's suggested local-interaction model), but an obvious example might be an invading or migrating group of people from a distant region with a very different culture. Finding aspects of culture in common with the invaders may be difficult, reducing the chances of further interaction and absorption. The second result sug­gests that even in a culture with a high rate of drift (such as a modern, fast-changing multicultural society) it may take a considerable amount of time for a new cultural group to integrate into its surroundings (Matthews 2008).

As ethnogenesis increases and activity restricts within the ethnically homoge­nous group, agents converge on their cultural characteristics; yet if there is enough heterogeneity in the population, this similarity among group members can also make them even more dissimilar from the members of other groups (Barcelo et al. 2013a, b and 2015). Ultimately, this can produce cultural groups that are so dis­similar from one another that their members cannot interact across group bound­aries. If cultural influence processes create differentiation between two neighbors such that they have no cultural traits in common, we should allow these individuals to alter the structure of the social network by dropping their tie and forming new ties to other individuals. Centola et al. (2007) have proposed a model where the network of social interactions is not fixed but rather evolves in tandem with the actions of the individuals as a function of changing cultural similarities and dif­ferences. The use of the level of heterogeneity in the population as a control parameter, allows to map the space of possible co-evolutionary outcomes and thereby show how network structure and cultural group formation depend on one another. These results address the question of how stable cultural groups can be maintained in the presence of cultural drift.

Contrary to Axelrod's claim, the effect of one cultural feature does not inherently depend on the presence or absence of others, but only so in dyadic relations where similarity matters. Boyd and Richerson (1987), McElreath et al. (2003), Heinrich and Heinrich (2007) have proved that if people preferentially interact in with people who have the same culture as they do, and if they acquire their markers and coordination behaviors by imitating successful individuals, groups distinguished by both norm and marker differences may emerge and remain stable despite significant mixing between them. Under such rules, within a group the behavior which is initially most common will reach fixation, as individuals with the less common behavior are less likely to receive the payoff. The successful behavior will also develop a marker associated with it as individuals sharing this marker will also be more likely to interact with each other and receive the higher payoff. These eth­nically marked positions are examples of attractors within the model.

Some other important enhancements of Axelrod's model of the dissemination of culture includes: Barbosa and Fontanari (2009), Kim (2010), Bednar et al. (2010), Dutton et al. (2010), Lanchier (2012), Valori et al. (2012), Hawick (2013), Pfau et al. (2013), Kang et al. (2014), Gowdy and Krall (2015), Roos et al. (2015), Kovacevic et al. (2015), Upal (2015). Those research teams have constructed a diversity of computer models that allows the dynamic understanding of four hall­marks of culture: coordinated behavior, coherent cultural signatures, substantial within culture diversity, and cross cultural differences. In general, these approaches combine a social drive to coordinate with an individual desire for internal consis­tency. As a result, the formation of in-group favoritism in terms of the developing of a meaningful cultural signature implies that individuals within a community conform their behavior to match one another's, and also that there is some rela­tionship that ties their behaviors and beliefs together from one activity or domain to the next, creating consistency across behaviors. In addition to conforming, people also choose to be around those who act as they do, what curbs group mergers because people avoid interacting with others who are not like themselves. Adding social influence to ethnogenesis exacerbates these effects: when individuals interact with others like themselves, and also actively become more similar to them, polarization between groups is even more pronounced. If individuals can use social markers to increase the likelihood of acquiring the behaviors adaptive in their context, markers and behaviors can become associated, and markers can in fact become exaggerated beyond initial differences between the populations.

A parallel approach has been Skyrms's study that patterns of coordinated behavior can best be explained from an adaptive dynamic perspective where the agents are only boundedly rational (Skyrms 2001). Skyrms concentrates on three mechanisms that can divert an abstract population of hunters from hunting hare on an individual disorganized basis to hunting stag and achieve a kind of collective equilibrium. The solutions are built on various correlation and anti-correlation devices. First, spatial and network embeddedness offer a correlation device that allows for the clustering of stag hunters (local interaction) and for efficient imita­tion, learning, and reproduction that all take place in the spatial or network locality. Second, these interactions change endogenously over time, such that network dynamics (due to partner selection) favors cooperation at the end. Adaptive dynamics operate on both strategy and interaction structure, which quickly shifts the balance in favor of cliques of stag hunters, without any kind of rationality assumed. Third, signaling and in particular signaling systems in which signals are not cheated and correctly interpreted can evolve and provide the solution towards equilibrium as collect ive action. Reinforcement learning helps to move coordi­nation games to a signaling system in which signals are unambiguous (meaningful) with probability equal to one.

In this model rationality and common knowledge still play a role, although much less of it is required to explain game theoretical equilibria concepts than in standard motivations. The main problem is to give a convincing story of why agents will coordinate their behavior so as to establish a collective good. At first sight, social cooperation seems to be a prisoner’s dilemma, or in the n-player case, a public goods game. In this game, by cooperating an individual helps all the other members of the group, but at a cost to himself. Therefore, a self-regarding player will never cooperate. It follows that social cooperation requires altruistic players—people must cooperate even though this is personally costly and the others alone benefit from one’s prosocial behavior. It is easy to see why the public goods game is an allegory for social cooperation among humans. For instance, if we all hunt, if hunting is dangerous and exhausting, and we must share the kill equally, then a self-regarding hunter will prefer to shirk rather than hunt. Cooperation in this case requires altruistic hunters. Skyrms’ point, however, is that if the game is repeated indefinitely, then cooperation among self-regarding agents is possible using what are known as “trigger strategies.” A trigger strategy for a player is to cooperate as long as all other players cooperate as well. However, the first time one player defects, the trigger strategy dictates that all players defect on every succeeding round. It is easy to see that in this case, even selfish players will cooperate on all rounds, because the gains they have from defecting on one rounds may be swamped by the losses incurred by not benefiting from others’ efforts on the succeeding rounds. The implication of Skyrms’ position for social theory is quite dramatic. If he were correct, it would follow that humans could cooperate very effectively even if they were perfectly self-regarding, with absolutely no need for altruistic prefer­ences, empathy, no predisposition for cooperating and sharing, nor any other prosocial behavior that goes beyond simple mutualism: An individual would help the group only as a byproduct of helping himself.

This is not the proper place to discuss the empirical validity of such ideas, but to insist in what can be done to understand the mechanical basis of collective action in terms of agent interaction. The model has been enhanced by Skirms and colleagues (Skyrms 2004, 2010, 2013; Huttegger and Skyrms 2013; Santos et al. 2008; Pacheco et al. 2011), and discussed critically by other authors (Bulbulia 2011; Starnini et al. 2011; Moreira et al. 2012; Tomasello et al. 2012; Wagner 2012; deBoer 2013; Song and Feldman 2013; Shaw 2015; Riebling and Schmitz 2016; Plikynas and Raudys 2016, among many others).

The idea of cooperation as a basis of cultural differentiation is resounding heavily in archaeology and anthropology. Madsen and Lipo (2015) have introduced an extension of the Axelrod’s model of cultural differentiation in which traits have prerequisite relationships, and where social learning is dependent upon the ordering of those prerequisites. Their results point to ways in which archaeologists can build more comprehensive explanations of the archaeological record of the Paleolithic as well as other cases of technological change.

Phillips et al. (2014) test the hypothesis that the development of extra-somatic weapons could have influenced the evolution of human cooperative behavior. In their simulations, the authors found that cooperative strategies performed significantly better, and non-cooperative strategies significantly worse, under sim­ulated weapons use. They conclude that the development of extra-somatic weapons throws new light on the evolution of human altruistic and cooperative behavior, and particularly ‘strong reciprocity'. The notion that distinctively human altruism and cooperation could have been an adaptive trait in a past environment that is no longer evident in the modern world provides a novel addition to theory that seeks to account for this major evolutionary puzzle. With a stronger substantive goal, Shennan et al. (2015) have analyzed two distinct material cultures (pottery and personal ornaments) from Neolithic Europe, in order to determine whether archaeologically defined “cultures” exhibit marked discontinuities in space and time, supporting the existence of a population structure, or merely isolation-by-distance. They have investigated the extent to which cultures can be conceived as structuring “cores” or as multiple and historically independent “packages”. More theoretical studies are those by Burtsev (2005), who has vali­dated a model of cooperation based on the assumptions of heritable markers, constrained resource, and local interactions with the real data on aggression in archaic egalitarian societies, and Briz et al. (2014a, b), Santos et al. (2015) who suggest a model providing insight on how the spatial concentration of resources and agents' movements in the space can influence cooperation. Through carefully calibrating the model parameters with ethnoarchaeological data from ancient Patagonia fisher-foragers, the authors conclude that the emergence of informal and dynamic communities that operate as a vigilance network preserves cooperation and makes defection very costly. Also using ethnographical data on hunter-gatherers, Janssen and Hill (2014) have explored the implications of social living, cooperative hunting, variation in group size and mobility. Their simulations show that social living decreases daily risk of no food, but cooperative hunting has only a modest effect on mean harvest rates. This research is related with their results in Chap. 3 in this book.

Savarimuthu et al. (2011b) use an agent based model to simulate a hunter-gatherer society where the norms of the society are affected by changing environmental conditions. In particular, the authors are interested in exploring how norms might change in a society based on the changes to the type of resources available in the society. Also based on ethnoarchaeological data to calibrate empirically the model parameters, Barcelo et al. (2013b, 2015) have simulated how small sized groups (less than 10 households) died by starving because the impos­sibility to build a high enough number of social ties. Given that the probabilities of interaction and labor exchange are conditioned on the existence of some shared belief elements, agents should be able to adapt their identity in response to the identity of agents with them they have arrived to cooperate successfully. Cultural consensus is built adaptively from the communalities among individual identities of agents connected at a precise time-step. The higher the interaction, the higher identity likelihood. It is also assumed that the higher the perceived similarity in reference cognitive models (social memory), the higher the probabilities of coop­erating and creating social aggregates conditioning social reproduction and how individual identities are transmitted to new generations.

Consequently, the study of intentionality in the remote past is not a question of individual “intelligence” or “rationality”. The examples here quoted contribute to sustain the view that prehistoric people did not die as often as imagined as a direct consequence of scarcity. Instead of the traditional image of prehistoric hunters fighting for survival in hard environments, modern research based on computer simulation suggests that social exchange networks were easy to build and negotiate, allowing hunting success even in the case of low availability of resources and the poor efficacy of working instruments (Younger 2003, 2004; Ladefoged et al. 2008; Helbing et al. 2011; Gurven et al. 2012; Neumann and Secchi 2016).

This explanation is based on the assumption that “cooperation” when survival is not individually attained implies an investment in labor that produces a common benefit. Such investment comes from agents whose survival has been effectively attained individually, and it is produced at no cost, because there is no surplus accumulation beyond the survival level. The benefit is not only individual (some agents survive thanks to the help of others), but also common: a social aggregation emerges allowing technological diffusion and increasing a cultural consensus which can be necessary in the future. In that sense, positive interaction is not only the result of bounded rational decision making, but it is filtered by the specific social (cultural) identity of agents, a parameter that changes constantly because it is probabilistically conditioned by a number of social factors. Among hunter-gatherers, positive interaction depended on the predictable benefits of working together and sharing the results of collective work. The higher the inter­action, the higher the probability of hunting success. It is not the same as arguing that the higher the number of hunters, the higher the amount of meat. In some scenarios, the total amount of energy per person can be lesser, but hunting success is more frequent in the long run, that is, it is more probable. In the usual circum­stances of small bands with hardly efficient instruments for hunting and transport, the absence of cooperation made uncertain the probability of survival, given the increasing risk for hunting failure, even in the case of high animal availability in the area.

In this line of research, making emphasis on “collective” decision making, rather than on optimal rationality we can mention current work on technological change and innovation. Prehistoric people were not rude savages without any idea or necessity for innovate (Mithen 1990, 1991), rather they experimented constantly new materials and new abilities to expand their technology and increase the success of hunter and gathering activities needed for survival. Innovation was not the result of the individual geniality of a very clever man, “inventing” for his community. It was a collective phenomenon of experimentation and the higher probability of adopting some new tools or behaviors and rejecting others.

In a recent simulation of prehistoric hunter and gatherer populations from Patagonia (the southernmost part of South America), Barcelo and colleagues have created an artificial world in which subsistence was obtained by individual households by means of labor with the contribution of its own technology, whose efficiency was estimated according a parameter that range from 0.01 to 2. High efficiency indicates that all local resources can be managed independently of its difficulty of acquisition given the extreme performance of available technology. Low values are characteristic of human groups with hardly evolved instruments, in such a way that only a part of locally available resources are effectively managed. The efficiency of food preservation techniques is another technological factor, related with the overall level of development of means of production. Both factors —quantity of people to work and technological efficiency act upon the difficulty of acquiring and transforming resources into subsistence and hence on survival. But technological efficiency is not a fixed parameter; it changes because hunter-gatherers learn from others in the environment different ways of making tools. They compare the new tools when they cooperate and adapt some charac­teristics—but not all—from most successful hunters (Del Castillo et al. 2014; Barcelo et al. 2015). The result is an 5-shaped curve of technological innovation that exactly reproduce modern mechanisms of technological change. This charac­teristic model can be understood as the number of adopters of a new technique or tool rises slowly at first, when there are only few adopters in each time period. The curve then accelerates to a maximum until half of the individuals in the system have adopted. Then it increases at a gradually slower rate as fewer and fewer remaining individuals adopt the innovation. The 5-shape of a typical development curve can be viewed as the result of the process of exhausting a ‘solution space' of potential improvements: as the pool is explored and exploited there are fewer and fewer improvements remaining to be discovered, slowing the pace of improvement if the number of trials stays the same. Again, the 5-curve is produced in a setting where there is a finite potential for improvement. This result call for the resemblance between the process of technological change and innovation in the most remote past and the immediate present. Innovations to hunting equipment and storing tech­nology followed similar trajectories as hybrid corn among Iowa farmers, bottle-feeding practices among impoverished Third Worlders, new governance practices among Fortune 500 companies, chemical fertilizers among small-scale farmers, and the practice of not smoking among Americans. “Intelligence” of prehistoric people was like “intelligence” of our contemporaries (White 2008).

Creating a generative model of technological change is an interdisciplinary effort that should include researches in various fields, like demography, anthropology, paleo genetics, and human ecology. Important questions that should be addressed before we can quantify the parts of a population adopting an innovation or changing their cultural features include the establishment of methods for inferring past population structure, the timing of the adoption or change, the relative importance of demographic variations, and the possibilities of alternative hypotheses like demographic transitions, colonization events, and/or population extinctions. Among current computer simulations along this lines of research, we can mention Heinrich (2001), Ma and Nakamori (2005), Dawid (2006), O'Brien and Bentley (2011), Rush (2011), Zenobia and Weller (2011), Kiesling et al. (2012), Vespignani (2009, 2012), Boyd et al. (2013), Laciana et al. (2013), Papachristos et al. (2013), Nan et al. (2014), O'Brien et al. (2015), Porcic (2015), Spaiser and Sumpter (2016).

1.2.5