Why Humans Have Made Life Even More Complex and Difficult. The Making of the State and the Origins of Class Struggle

The economic change implied in the transition from predator and forager based survival to full productive economies based on agriculture, herding and stock­breeding subsistence settled the basis for a new social organization and new forms of political decision making.

Different authors have generated computer simulations to understand how hierarchical decision-making could have affected inter-group conflicts sometime through the historical evolution of human society (Mark 1998; Suleiman and Fis­cher 2000), the dynamics of status symbols in hierarchically ordered societies (Pedone and Conte 2001), the consequences of wealth distribution (Impullitti and Rebmann 2002), the coevolution of farming and private property (Bowles and Choi 2013; Bowles et al. 2010; Cockburn et al. 2013; Angourakis et al. 2015; Biscione et al. 2015; Gallagher et al. 2015), the deification of historical figures and the emergence of priesthoods (David-Barrett and Carney 2015), the origins of war (Duering and Wahl 2014) and the Neolithic transition from egalitarianism to leadership and despotism (Levine and Modica 2013; Powers and Lehman 2015). Those models and simulations explain how, despite being an unlikely event, farming and a new system of property rights jointly emerged when they did, as an emergent property of the new possibilities of unambiguously demarcate and defend the new wealth produced and stored by farmers—crops, dwellings, and animals—.

Computer simulations results thus challenge unicausal models of historical dynamics supposedly driven by advances in technology, population pressure, adaptation to climatic change or other exogenous influences (Pujol et al. 2005). Especially important to understand the development of means of production and the consequent emergence of new relations of production is the possibility to simulate computationally the emergence of specialization, in which different individual agents spontaneously assuming different roles in the execution of the task (Parisi and Nolfi 2005). The most effective strategy includes primitive forms of “situated” specialization in which identical individuals play different roles according to the circumstances such as leading or following the group. These forms of functional specialization seem to be due to the need to reduce interference between potentially conflicting sub-goals such as moving toward the rest of the group to maintain aggregation and moving toward the target. Imagine a group of agents that has to reach a target in the environment but to be rewarded they must approach the target by maintaining reciprocal proximity. If the agents are initially dispersed in the environment, they may be unable to perceive each other and therefore they may be unable to aggregate and then move together toward the target. The solution is to evolve some signaling behavior that allows the group to aggregate. On this ques­tion, Cokburn et al. (2013) add the effect of social influence to increase the level of specialization. Building on these assumption, these authors have created a model that incorporates both economic state and social influence. Agents are influenced by competition from other agents in their topographically based social network.

To sum up, it is the mechanism of change itself which produces the emergence of new social configurations. This idea is basic to understand the evolution from prehistoric small-scale societies to historical complex polities. This has been a traditional topic for archaeologists, anthropologists, historians and social and political theorists (Lull and Mico 2011), and we can read more different theories than theoreticians have thought thereof. Fortunately for us, Henri Francfort has shown how 2000 years of historical narratives can be easily resumed in a few hundred lines of computer code (Francfort et al. 1989; Francfort 1997). In any case, politogenesis should be never reduced to the only one evolutionary pathway leading to the statehood (Grinin 2009). The early state formation was only one of many versions of development of complex late archaic social systems. The state is nothing more than one of many forms of the post-primitive socio-political orga­nization; these forms are alternative to each other and are able in certain conditions to transform to one another without any loss in the general level of complexity.

Foundational work on the idea to simulate the historical processes towards the origin of state societies and complex polities was Epstein and Axtell's Sugarscape model. It simulates the behavior of artificial people (agents) located on a landscape of a generalized resource (sugar). Agents are born onto the Sugarscape with a vision, a metabolism, a speed, and other genetic attributes. Their movement is governed by a simple local rule: “look around as far as you can; find the spot with the most sugar; go there and eat the sugar.” Every time an agent moves, it burns sugar at an amount equal to its metabolic rate.

This computing example shows how complexity unconsciously emerges as a side effect of individual decisions (Mark 1998). Here complexity refers to diver­sified patterns of social organization and political institutions controlling, con­straining and determining social behavior. The original Sugarscape model has been updated and modified many times (Costopoulos 2015). The Virtual Anasazi project, as reviewed in the preceding section, was a direct consequence of Epstein work, addressed to the empirical testing of the social principles behind the model (Swedlund et al.

Sugarscape derived models are not the only ones to understand the formation of heavily institutionalized groups of people. Some alternative models emphasizes the “benefits” of leadership and the long term consequences of social division of labor in the process towards increasing hierarchy in the political organization. Especially relevant for this purpose are mathematical models showing how wealth accumu­lation depends on the ‘social relation' between two classes: owners or workers. As a result, a society may evolve towards an unequal outcome with few rich and many poor individuals (Roemer 1985; Walby 2007; Chadefaux and Helbing 2010; Russo 2014).

For instance, Powers and Lehman (2014) have modeled the historical coevo­lution of individual preferences for hierarchy alongside the degree of despotism of leaders, and the dispersal preferences of followers.

The most popular computational theories of the origin of state are those con­sidering the nonlinear effects of violence and warfare on the emergence of complex polities. Most of these models are reexaminations of the classical hypothesis by Carneiro (1970). However, the particular characteristics of computational models have allowed to integrate both extremes of the same continuum: altruism—bene­fiting fellow group members at a cost to oneself—and conflict hostility toward individuals not of one's own ethnic, racial, or other group—. The idea is that neither

violence nor altruism would have been viable singly, but by promoting group conflict, they could have evolved jointly (Bowles 2008, Choi and Bowles 2007).

Spencer (1998) proposed a mathematical model of political growth in chiefdoms (societies with centralized but not internally specialized authority) and states (so­cieties with centralized and also internally specialized authority), based on differ­ential equations. A major conclusion of the exercise is that the emergence of a primary state is likely to be accompanied by a considerable expansion in the political-economic (sustaining) territory of the polity. A related issue is how peo­ples who successfully resist incorporation can help shape the developmental tra­jectory of an expanding state. Spencer (2014) proposes a model of the dynamic between an expanding polity and its neighbors suggesting that the effectiveness of incorporation is positively related not simply to the size of the expanding polity, but rather to a positive rate of change in the expanding polity's growth relative to that of resisting polities. Variable relationships of incorporation and resistance will cause the shape of the expanding state's growth trajectory to be not regular and sym­metric, but instead asymmetric and non-uniform.

Reynolds and Lazar (2002) added the effects of aggregation to a computer model of territorial expansion. With increased aggregation it was no longer possible for a single individual to monitor the entire aggregation. In order to control thousands of farmers, laborers, and warriors, it was required that many tasks be delegated to administrative, scribal, architectural, craft, and military specialists. This resulted in the formation of the state. In order to produce larger degrees of aggregation the span of leadership needed to be extended. This level of aggregation was achieved by changing the meaning of existing relations, implying that only the immediate off­spring of current leaders had the right and duty to lead. This allowed leaders to aggregate wealth and resources over generations and extend alliances over larger numbers of surrounding villages. These changing relationships produced a system in which the actors were relentlessly competing, resulting in periodic outbursts of violence. In this system, the culturally defined goals of a leader were to have as many farmers, craftspeople, and warriors under his control as possible. The two main strategies for reaching those goals were: (1) alliance building-through feast­ing, gift-giving, and bride exchange; and (2) warfare, mostly at the level of raiding and burning rival villages. The escalating warfare lead to a major shift in emphasis on site location from access to high quality agricultural land to the need for defensible locations. This change supported the shift from a ranked society to a stratified one by restricting the ability of lower ranks to marry with those from upper ones. This over time resulted in two basic strata, the elites and the com­moners. The pragmatics behind this shift in the meaning of social relationships was engendered by the need to incorporate other conquered, highly ranked elites into the fold via intermarriage valley wide (Jayyousi and Reynolds 2013).

Griffin and Stanish (2007, 2008), Griffin (2011) have modeled how complex early polities expand in size over time to accommodate population growth. Polities also expand due to fusion with other polities, when adjacent polities came into conflict when no empty land separating them remained for expansion. The net result is consolidation, which can be explained in terms of overt conquest or intimidation, forming alliances, religious legitimization, rewarding loyalty, marriage, etc. Inter­nally there was competition between factions within each polity. It seems reason­able to expect that the larger a polity the greater the number of internal factions and hence the more likely resistance would occur. This relationship can be modeled by assuming that the probability of resistance for any one settlement was constant, so the likelihood of resistance somewhere in a polity increased as the number of its settlements grew. The same effect was achieved in the current model by spatially uniform random occurrences of resistance. Polities came into conflict when a set­tlement is added and bridges the gap between two or more polities. This corre­sponds to one or more of these neighboring polities attempting to expand into the buffer zone separating them. The center of the prevailing polity retained its current location and became the center of the newly constituted fused polity. The other competing centers became satellite settlements in the new larger polity. The com­petition's winner is determined by comparing the effective strengths of two, three or four competing centers with the strongest being the winner. The assumption was that the strength of agrarian polities would have been determined by a combination of center's population size and resources discounted by distance. The simulation of these mechanisms concludes that:

• Population rank-size distribution for an area surrounding a single dominant center will be primate immediately before fission and transition to convex thereafter.

• Strengthening each of four integrative processes by adjusting its associated parameter will decrease the time averaged rank-size convexity for the entire grid.

• Subordinate population centers articulated to a primate center or another sub­ordinate center will be observed within polities.

Gavrilets et al. (2014) have developed a spatially explicit agent-based theoretical model of the emergence of early complex polities via warfare. In this model polities are represented as hierarchically structured networks of villages whose size, power, and complexity change as a result of conquest, secession, internal reorganization (via promotion and linearization), and resource dynamics. A general prediction of our model is continuous stochastic cycling in which the growth of individual polities in size, wealth/power, and complexity is interrupted by their quick collapse. The model dynamics are mostly controlled by two parameters, one of which scales the relative advantage of wealthier polities in between and within-polity conflicts, and the other is the chief's expected time in power. Our results demonstrate that the stability of large and complex polities is strongly promoted if the outcomes of the conflicts are mostly determined by the polities' wealth/power, if there exist well-defined and accepted means of succession, and if control mechanisms are internally specialized. The authors present a dynamic quantitative model exploring the origin and operation of early human complex society, focusing on both the size and complexity of emerging polities as well as their longevity and settlement patterns. They systematically examine the effect of parameters such as system size, the effect of polity power on the probability of winning a conflict, tribute level, variation in productivity between individual villages, span of control, and chief's average time in power. The polities in the model exhibit a strikingly fluid nature resembling so-called “chiefly cycles.” Unexpectedly, the largest effect on results is due to just two parameters: the scaling of the polity power to the probability of winning a conflict, and the chief's average time in power.

Rowthorn et al. (2014) have developed the effects of behavioral and populational differences in an artificial society divided into 2 hereditary classes: a warrior elite and a productive class. The model entails that the extra cost warriors must incur to train and equip their children for war determines the relative sizes of both classes and the degree of economic inequality. Higher costs of warrior children imply a greater economic advantage for warriors and a smaller ratio of warriors to producers.

Nevertheless, what characterizes complex polities is not only conflict, authority and coercion, but ultrasociety, the ability of humans to cooperate in large groups of genetically unrelated individuals (Centola et al. 2005; Turchin 2015). Such coop­eration can take many forms: volunteering for the army when the country is attacked, willingly paying taxes, voting, helping strangers, refusing to take bribes, etc. In each case, the result of cooperation is production of a public good, while the costs of cooperation are born privately. Sustained cooperation requires a solution to the collective action problem stemming from the tension between the public nature of benefits yielded by cooperation and private costs borne by cooperating agents. Social norms and institutions are among the most important ways of solving this problem. Ultrasocial institutions are institutions that enable cooperation at the level of larger-scale human groups. They are characterized by the tension between benefits they yield at the higher level of social organization and costs borne by lower-level units. Of particular interest are ultrasocial institutions, which play a role in the integration of largest-scale human groups; institutions that enabled the transition from middle-range societies (simple and complex chiefdoms) to archaic urban states and subsequently to large-scale empires and modern nation-states (Turchin 2015).

Strong macrohistorical regularities suggest that the rise of any particular mega-empire was not a random result of a concatenation of unique events; general social mechanisms must have been at work. Building on the ideas of the fourteenth century thinker Khaldun, Turchin (2003, 2009; Turchin and Gavrilets 2009; Turchin et al. 2013) has proposed a “mirror-empire” model as one common route to mega-empire. This model postulates that antagonistic interactions between nomadic pastoralists and settled agriculturalists result in an autocatalytic process, which pressures both nomadic and farming polities to scale up polity size, and thus military power. In many cases, as happened repeatedly in China and Ancient Egypt, the result of this process is the simultaneous rise of an agrarian empire and a nomadic imperial confederation on their respective sides of the steppe frontier. However, if the agrarian state does not have a deep hinterland to expand into, it may lose the scaling-up race to the nomadic polity, and is conquered by it. What is the balance of forces favoring cooperation of lower-level units and, therefore, their ability to combine into higher-level collectives? Here “units” and “collectives” are social groups at different levels of hierarchical complexity. For a society to grow in size, it has to make repeated transitions from the ith to (i + 1)th level. The success of each transition depends on the balance of forces favoring integration versus those favoring fission. Thus, evolution of traits promoting integration at the i + 1 level is favored by (1) increasing cultural variation among collectives and decreasing variation among lower-level units, and (2) increasing the effect of the trait on the fitness of collectives and reducing the effect at the lower level. Consequently, it is expected that large states should arise in regions where very different people are culturally in contact, and where interpolity competition (i.e., warfare) is particularly intense.

Instead of using computational theory to understand the evolution of complex political systems in history, Mezza-Garcia et al. (2014) have refined computer theory in terms of what they known on hierarchical political systems. According to these authors, the similarity between a Turing machine and hierarchical political systems can be explained by how the transformation of ‘inputs' into decisions in the latter is achieved, namely via sequential routes of rule-based activities that are assumed to take place in a closed manner amongst a selected group of individuals— the government. For those individuals who do not form part of the regime, and even for those who are members of a separate subsection of government, the computa­tion of the decision takes place in a ‘black box' until the moment of the ‘halt' and the output of a political decision is made available. Decisions in such political systems are made with a type of information processing that works in a linear framework of reference, but which is limited when finding optimal solutions in spaces of high complexity. In the suggested model, heterarchical political organi­zations operate with decision-making dynamics whose computation is performed by an open system, i.e., that is in interaction with the world in various levels simultaneously in a distributed, parallel, diffuse, real time and decentralized man­ner. Inputs and conditions can be modified during the computation, and external agents can therefore also interact with this process. Ideally, ‘outputs' or decisions are produced bottom-up from local interactions, rather than only implemented in a top-down manner at the expense of the complexity of human social systems and their environments.

As an example of computational models to understand the origins and formation of complex polities, Bogle and Cioffi-Revilla contribution to this volume (Chap. 13) implement a model about politogenesis in Sub-Saharan Africa. ZambeziLand demonstrates how a society of initially small and egalitarian groups could evolve into a complex society with a few large groups in response to changes in how individual members perceive their group and the state of extant leadership. The authors are interested in how ancient political centers originated and why they dissolved, analyzing sociopolitical phase transitions, whereby polities form and dissolve as people migrated to larger, more complex communities. The punctuated process of sociopolitical phase transitions, typical of polity cycling is explained by modeling the dynamic interplay among leaders and society members (individuals and groups) experiencing fluctuating conditions of leadership and loyalty during recurring times of stress affecting the local community. Larger and more complex polities were generated through a recursive, iterative process of collective action successes and failures by individuals and groups. The authors assume that the main structure of the fast process is universal and invariant, but the exact branching paths realized vary, depending on contingencies such as a situational change having endogenous or exogenous causes, a society perceiving or not the situational change, collective action occurring or not, success or failure in collective action being realized: hence, the term canonical. As situational changes recur in this particular society's model, a “fast process” punctuated by contingent events begins, including subsequent collective action choices made by society members (leaders and fol­lowers). Collective action may succeed or fail, depending on other contingent events. The outcome of each fast process results in the polity generating greater or lesser complexity when examined on a longer time scale or “slow process.” Recursive fast processes occur relatively quickly as the society succeeds or fails in solving collective action problems that arise in the normal course of its history, with sociopolitical results and effects accumulating over time in the slow process. The most significant result of the Cioffi-Revilla and Bogle's model is the demonstration via computational simulation that an initially egalitarian, homogeneous society can quickly coalesce into a small number of much larger differentiated groups.

A similar approach applied to Inner Asia (Central Eurasia) in the past 5,000 years has been published by Cioffi-Revilla et al. (2007, 2013, 2015), Rogers (2013), Rogers et al. (2015). In all cases, the simulations are based on Cioffi-Revilla's computational theory for the emergence of social complexity accounts for the earliest formation of systems of government (pristine polities) in prehistory and early antiquity. The theory is based on a fast process of stressful crises and opportunistic decision-making through collective action. This core iter­ative process is canonical in the sense of undergoing variations on a main recurring theme of problem solving, adaptation and occasional failure. When a group is successful in managing or overcoming serious situational changes (endogenous or exogenous to the group, social or physical) a probabilistic phase transition may occur, under a well-specified set of conditions, yielding a long-term (slow) process of emergent political complexity and development. A reverse process may account for decay (Cioffi-Revilla 2005, 2009).

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