Simulating Social Life After Prehistory

As soon as we enter those historical periods in which written sources can be found, read and analyzed, the effort for a computational formalization of historical explanation is less evident in the current scientific literature.

The exception to this apparent lack of interest in the computer simulation of ancient societies can be the study of the rise-and-demise of ancient empires, a historical subject that has been an important topic for computer simulation. Since the early days of authors like Hosler et al. (1977) and Dickson (1980), computer algorithms have been used to reproduce and to understand the collapse of ancient worlds (Lowe 1985; Renfrew 1987; Parisi 1998; Brunk 2002; Janssen and Scheffer 2004; Dalfes 1997; Hunt and Elliott 2005). Most of those methods used non-linear equations to model the way an economic system ceased to be efficient sometime in history. This is still an important area of research (Davidson 2010; Scarborough and Burnside 2010; Flores et al.

There is a single contribution in this book related to the computer simulation non-prehistoric worlds for which we have appropriated written sources. Trescak et al. (Chap. 14) present a novel approach that can significantly decrease the cost and effort required for simulating everyday life of ancient inhabitants of virtual cities, while still capturing enough detail to be useful in historical simulations. The authors show how it is possible to design a small number of individual avatars and then automatically simulate a substantially large crowd of virtual agents, which will live their lives in the simulated city, perform choirs and rituals as well as other routine activities that are consistent with their social status. The key novelty of this approach that enables simulating such sophisticated crowds is the combination of physiological needs—for generating agent goals, emotions and personality—for choosing how to fulfil each goal and genetically informed propagation of appear­ance and personality traits—to propagate aspects of appearance and behavior from a small sample of manually designed individuals to large agent groups of a desired size. The usefulness of the approach is demonstrated by applying it to simulating everyday life in a reconstruction of the ancient city of Uruk, 3000 B.C. In the model, the authors have enriched computational agents with personalities and emotions, which affect their decisions when creating a plan for a current goal. This approach may even lead to emergent agent behavior that appears to be closer to human-like reasoning.

In the previous example, fisherman represents a specific social group of the simulated population. Social groups combine certain classes of individuals that fulfill their goals in a similar way. Combining individuals into social groups allows the authors to define and program actions on a group level, rather than having to do this on individual level, reducing effort in defining crowd behavior. They consider again the case of an ancient Mesopotamian fisherman who has to trade fish with a spear maker in order to replace his broken fishing spear. The solution implies the explicitly formalization of social norms that, captures rules and protocols that drive agent interactions. As a result, agents can use these norms in reasoning to create plans for their current goal. This provides agents the ability to automatically per­form their actions depending on their assigned social group. In order for agents to be able to select an action that is most relevant for their personality, such action has to be annotated by following personality facets: temptation, gregariousness, assertiveness, excitement, familiarity, straightforwardness, altruism, compliance, modesty and correctness. Using values of personality facets, the agent selects an action that provides the highest utility for its personality type.

Researchers at the University of Chicago and Argonne National Laboratory (Altaweel 2008; Altaweel et al. 2006; Altaweel and Christiansen 2004; Christiansen and Altaweel 2004, 2006; Wilkinson et al. 2007a, b, 2013) take a different approach for the historical understanding of ancient Mesopotamian societies. They have modeled the trajectories of development and demise of Bronze Age settlement systems for both the rain-fed and irrigated zones of Syria and Iraq. The recon­structed landscape near the ancient city of Assur is used as the example setting to test the effectiveness of simulated cultivation strategies. These methods include sole dependence on biennial fallow and rainfall, gravity flow irrigation, application of manure, and the integration of all these approaches. Results obtained within this computer model attempt to delineate agricultural constraints and potential benefits of the specific anthropogenic processes and strategies addressed. The investigation intends to prove that systems of ancient Near Eastern cities co-evolved in an intimate relationship with their environment, primarily by means of the aggregation through time of smaller fundamental units (e.g., households). The model allows for the scaling up of a settlement from a single household to a village, and ultimately to an urban center with its appropriate array of subsidiary and neighboring settlements.

The first empires in the Old World seem to be an ideal domain for computer simulation using historical information to calibrate the key model parameters. Palmissano and Altaweel (2015) have simulated explanatory models of settlement hierarchy in Central Anatolia during the Old Assyrian Colony period. Symons and Raine (2008) have investigated the spread of information and population aggre­gation in a somewhat ideal agrarian society based on an abstracted Egyptian landscape containing villages, flood plain, and river. The agents represent farming households which exchange information and migrate around the landscape moti­vated by the availability of surplus food (used as a proxy for quality of life). The model follows the aggregation of hamlets into larger villages that occurred during the pre-dynastic period. The results presented are intended to correspond to a one hundred to five hundred year time-span in the period roughly 3800-3300 BC. One of the drivers of Symons and Raine model is the variability of the Nile flood, since this determines the changing fertility of the land. The fertility is modelled as a function of perpendicular distance from the Nile. There are no records of land tenure in the pre-dynastic period.

The regions bordering the Aegean Sea were also witnesses of important his­torical events some 4000 years ago. Knappett et al. (2008) have created a spatial network model to understand the scale of cultural (and economic) interaction between the Cyclades, Crete, the Greek mainland, the Dodecanese and coastal Asia Minor during the later periods of the Middle Bronze Age (c. 2000-1600 BC). In this period, there appear to be substantial changes in transport technology between the Early Bronze Age and the later Middle Bronze Age, with the advent of the sail. The main hypothesis is that the centrality and size of the historic site of Knossos (Crete) and the growth of Minoanisation, may be related. The authors propose a mathematical model of ‘imperfect optimisation' to describe such historical maritime networks, encoding, metaphorically, the notion of gravitational attraction between objects in space. The ‘gravitation' in this case is a balance of social forces, expressed by networks with settlements of particular sizes and links of particular strengths. The model can be tweaked by giving different relative importance to the cultivation of local resources or to trade, and to show what happens when a member of the network suddenly disappears. The model incorporates some sense of func­tion: regional interaction networks must accrue some benefit, balanced against their costs. Hence the model works on the assumption of some basic optimisation. Secondly, the model takes account of geographical distances while not being strictly determined by them. The model is neither fully bottom up like agent-based modelling, which tends to aggregate scales very coarsely, nor entirely top down; it is set up in such a way that the interactions between the level of the site and that of the network as a whole can be explored.

Investigating further in the same historical domain, Chliaoutakis and Chalki- adakis (2015, 2016) have developed a functional ABM system prototype for sim­ulating an artificial ancient society of autonomous agents residing at the Malia area of the island of Crete during the Early Bronze Age. At its current implementation, the ABM allows exploring the sustainability of specific agricultural technologies in use at the time, so we can examine their impact on population size and dispersion; and it allows for the incorporation of any other technology that needs to be mod­eled. In addition, it allows us to assess the influence of different social organization paradigms on land use patterns and population growth. Importantly, the model incorporates the social paradigm of agents self-organizing into a “stratified” social structure, and continuously re-adapting the emergent structure, if required. The investigation is based on a self-organization algorithm incorporating a set of agent relations influencing the various social interactions, and a decentralized structural adaptation mechanism, suitable for open and dynamic organizations. Simulation results demonstrate that self-organizing agent populations are the most successful, growing larger than populations employing different social organization paradigms. Specifically, self-organization is compared to egalitarian-like and static hierarchical organization models. The success of this social organization paradigm that gives rise to “stratification” that is, non-egalitarian societies, and provides support for so-called “managerial” archaeological theories which assume the existence of dif­ferent social strata in very early period; and consider this early stratification a pre-requisite for the emergence of the Minoan Palaces, and the hierarchical social structure evident in later periods.

Insights into historical region-wide political consolidation have been suggested by simulation results from an agent-based model based on historical data of human societies circa 2500 BC to AD 1000 in the Lake Titicaca basin of Peru and Bolivia (Griffin and Stanish 2007). The agents' behavior was modeled as micro-level condition-action rules based on the hypothesized causal factors of: agriculture, migration, competition, and trade. The approach to modeling political dynamics was inspired by Lars-Erik Cederman's agent-based Emergent Polarity model of early nation-state geopolitics (1997, 2002), which the authors adapted for pre-Inka historical scenario. This model simulated the consolidation of small polities into large ones which may then fission back into small independent entities and sub­sequently consolidate again, reminiscent of the recycling pattern observed in pre-state chiefdoms. The spatial end state of each simulation run has been classified as one of several alternative political configurations, based on the number of sovereign states remaining: unipolar, bipolar, multipolar, or nonpolar. In the same way, each simulation run of the current model was classified as one of seven alternative Titicaca political prehistories, one of which corresponded to what the record indicates to us actually happened. The authors have insisted in the temporal dimension to the classification scheme to distinguish not only the end state but the trajectory through time to reach that configuration.

Some models emphasize the key role of the interactions between households, institutions or spatial entities to generate a processual explanation of the emergence of a hierarchical urban system (Batty, 2001; Schmitt and Pumain 2013). In terms of transition, the model should simulate the transition from a loose position (random or uniform seeding of the entities) to the emergence of a spatial hierarchical and organized structurement without such an objective contained in the rules of model. Among the relevant models, we can mention the SIMPOP model, in which the settlement entities are the agents, the assumption being that there are interaction processes at the meso-geographic level that cause the trajectory of the settlement system in one direction rather than another. Starting from an initial situation in which we only count for agricultural villages poorly differentiated in function and size of cities emerging crescent acquire new functions and the possibility to exchange with broader geographic ranges. The growth or decline of a city will depend on the success of its trade with other villages and cities with which it interacts (Sanders et al. 1997; Pumain et al., 2009). The interference zones of influence of other cities creates a context of long distance competition in which the city must develop its position.

An interesting domain for applying these models to the historical formation of ancient cities is Greece. Rivers and Evans (2014) have re-examined the onset of centralisation in mainland Greek city states of the 9th and 8th centuries BCE. The aim is to model the onset of ‘urbanisation', by which is meant the emergence of dominant settlements within community territories as a result of a transference of ‘sovereignty’ from villages to create larger associations centred upon these domi­nant settlements. The authors have compared two cost-benefit model in which the benefits arise from exchange between sites, assuming that larger sites getting most benefit from exchanging with larger sites. These non-linear benefits are offset against the cost of sustaining the network, assumed linear in the total network activity.

Around the 10th century BC, the rural villages in South Etruria (now Tuscany and Latium, Italy) began to disappear and a number of cities started to arise. The accepted grounds of these events deal with defense and safety reasons. Bianchi and Marcialis (2013) attribute the birth of the proto-cities to a sustainability crisis in the mining villages and asserts that mining technicians imposed such transition on farmers in order to carry out a sustainable reorganization of the whole system of settlements and, as a corollary, to strengthen their ruling role. The authors illustrate the proposed hypothesis by means of a simulation model roughly reproducing the described event. The model is based on the idea that the city birth can be interpreted as a discontinuity in the social system behavior. An unsustainable growth may have caused a crisis in the Etruscan village system. The formation of a new form of social aggregation, the city, would have achieved an organizational change and restored sustainability.

Ceconni et al. (2015), address the same historical case. In this simulation, the Etrurian territory is divided into a grid of square cells, with each cell characterized by three properties—(a) soil quality and presence of natural resources, (b) existence of water courses, (c) morphology of the ground from the point of view of defen- sibility—and the model assumes that each settlement decides what to do on the basis of these properties. The simulation reproduces the process that led to the appearance towards the end of the second millennium of a few large centers in well-defended sites and numerous small settlements. During each cycle of the simulation, each settlement (a) takes into consideration its N (number of inhabi­tants), (b) defines its zone of control with respect to the surrounding area, and (c) calculates how many resources are available for its inhabitants, resulting in the relative value of resources per capita. The simulation can develop according to two different types of dynamics; a positive and a negative dynamics. A positive dynamics means that the number of inhabitants of a settlement increases together with an increase in the size of its zone of control. Therefore, the available resources also increase and the value of the resources per capita remains high. The settlement is a prosperous one. On the contrary, a negative dynamics implies an increase in the number of inhabitants but not of the settlement’s zone of control because of the presence of the zones of control of other settlements or because the zone of control is made of soil with low productivity. In this case, the resources per capita become insufficient, and the settlement is in trouble. When the resources do not meet the needs of a settlement’s inhabitants, the number of inhabitant decreases and, if is reduced to zero, the settlement disappears. The virtual Ancient Southern Etruria appears to be divided into five main zones of control and four of these five zones of control correspond to the historical proto-urban centers of Orvieto-Volsinii, Tarquinia, Cerveteri and Veio. In the simulation, already during the virtual Early Bronze Age the system seems to undergo a collapse, going from 250 villages to about 60, while in the following centuries it remains roughly stable, with limited fluctuations. This diverges from what we know from the archaeological evidence which tells us that, after an increase between the beginning of the Early Bronze Age and Middle Bronze Age, the total number settlement remains pretty stable until, in the First Iron Age, the number of settlements is drastically reduced. This phe­nomenon is interpreted as due to a gradual but steady population growth but it is not captured by our simulation.

Crabtree (2016) has explored trade relationships between Etruscans and the native Gauls. She examines the first five centuries of wine consumption (from ~600 B.C. to ~100 B.C.), analyzing how preference of one type of luxury good over another created distinctive artifact patterns in the archaeological record. She has created a simple agent-based model to examine how the trade of comestibles for wine led to a growing economy and a distinctive patterning of artifacts in the archaeological record of southern France. This model helps shed light on the processes that led to centuries of peaceable relationships with colonial merchants, and interacts with scholarly debate on why Etruscan amphorae are replaced by Greek amphorae so swiftly and completely.

Heckbert (2013) has presented preliminary results from his MayaSim model, an integrated agent-based, cellular automata, and network model representing the ancient classical Maya social-ecological system (ca. 250-900 AD). The model represents the relationship between population growth, agricultural production, soil degradation, climate variability, primary productivity, hydrology, ecosystem ser­vices, forest succession, and the stability of trade networks. Agents representing settlements develop and expand within a spatial landscape that changes under climate variation and responds to anthropogenic impacts. The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya. This investigation aims to identify candidate features of a resilient versus vulnerable social-ecological system, and employs computer simulation to explore this topic, using the ancient Maya as an example. Complex systems modelling identifies how interconnected variables behave, considering fast-moving variables such as land cover change and trade connections, meso-speed variables such as demographics and climate variability, as well as slow-moving variables such as soil degradation.

Watts (2013) has modeled some aspects of Hohokam economics. The Hohokam were an ancient Native American culture centered on the present-day US state of Arizona during the period AD 200-1450. The objective of this research has been to first identify a variety of economic models that may explain patterns of artifact distribution in the archaeological record. Those models were abstract representa­tions of the real-world system reconstructed on the basis of microeconomic theory, and economic anthropology hypotheses. Those hypotheses have been implemented into an agent-based model, and run to assess whether any of the models were consistent with Hohokam ceramic datasets. The results su workshop procurement and shopkeeper merchandise, provided the means of distributing pottery from specialist producers to widely distributed consumers. Perhaps unsurprisingly, the results of this project are broadly consistent with earlier researchers’ interpretations that the structure of the Hohokam economy evolved through time. Growing more complex throughout the Preclassic, and undergoing a major reorganization resulting in a less complicated system at the transition to the Classic Period.

Those investigations show the relevant paper of the advanced production economies in ancient times and how computer simulation allows reconstructing its functioning from incomplete and sometimes partial written sources from the past. Among the aspects we need to consider there is the practice of irrigation in ancient kingdoms to increase productivity. Irrigation systems, with their many entities, social and physical, their many interactions within a changing environment and emergent properties, are typical examples of systems for which agent-based mod­elling could yield fruitful analysis because of the highly detailed and complex relations between human actions and the social and material context. Ertsen (2011), Murphy (2012) and Altaweel (2013) show how interactions between humans, hydrology and hydraulics within irrigation systems have historically created pat­terns of water use. Both studies are based on a modelling-based approach gener­ating flows in ancient irrigated environments, as it yields new insights in the way irrigation has succeeded in sustaining human civilization—or failed to do so, pointing out to the fact that we should not explain how irrigation-based societies collapse after centuries or even millennia, but why these societies did not collapse each and every day. It is the combination of modelling daily interactions by agents and water fluxes that will build better understanding of irrigation systems as anthropogenic landscapes resulting from activities of individuals, households, and groups, within hydraulic and hydrological boundaries setting the material context.

Kuznar and Sedlmeayer (2005) have developed a flexible agent-based computer simulation of pastoral nomad/sedentary peasant interaction that can be adapted to particular historical and social settings. The authors focus on how environmental and material factors may have conditioned individual agent response has allowed the modeling of how collective behaviors (mass raiding, genocide) can emerge from individual motives and needs. Many factors influence tribal conflict in the modern world (ethnicity, global politics). However, these simulations reinforce the analyses of some social scientists that argue such conflicts are the inevitable result of the breakdown of land use in the face of growing populations, marginal habitats, and an unprecedented ecological crisis. An alternative model has been offered by Cohen and Ackland (2012a b). Angourakis et al. (2014) have created an abstract agent-based model describing a mechanism of competition for land use between farming and herding addressed to understanding “oases” economic systems in historical central Asia. The aim is the exploration of how mobility, intensity, and interdependence of activities can influence land use pattern. After performing a set of experiments the authors compare the implications of each condition for the corroboration of specific land use patterns. In this way, the overall extension of farming in oases can be explained by the competition for land use between farming and herding, assuming that it develops with little or no interference of climatic, geographical, and historical contingencies.

A particular application of this way of studying the historical sources of inter-cultural conflict is Altaweel and Paulette (2013), who have investigated the long-term effects of economic interaction between nomadic and sedentary groups in the Bronze Age Near East. To keep things as simple as possible, they have modeled only a single, small sedentary community and a single nomadic group. The nomadic group visits the village for a portion of each year as a part of its annual migration pattern, and it is during these visits that economic exchanges take place. In a series of simulation runs, the authors varied the timing of the nomadic visit and the resources available to each group, and they tracked the impact of these changes on the economic life of the settlement and its inhabitants.

Cioffi-Revilla et al. (2007, 2010; see also Rogers 2013; Rogers et al. 2015) have simulated the rise and fall of polities in Inner Asia over a long time span, on the basis of nomadism effects on the economic, social and political structure. The time is defined as sufficiently long to include significant climate change. When climate changed, the biomass distribution on the landscape also changed, which in turn generated changes in the biological and social dynamics of animals and people, respectively. HouseholdsWorld is a spatial agent-based model of pastoral nomads living in a simple socio-natural system. The target system is a generic locality smaller than a region of Inner Asia shortly after ca. 500 BCE, the time period just prior to the rise of the Xiung-nu polity (ca. 200 BCE). The primary sources used for developing the HouseholdsWorld model were epigraphic, archaeological, ethno­graphic, and environmental, as detailed in the subsections below. Several of the patterns produced by simulation bear significant qualitative and quantitative resemblance to comparable patterns in the target system. For example, the distri­bution of wealth has the approximate form of a log-normal distribution, as a real-world distribution of household wealth usually should. Similarly, household movements show marked periodic fluctuations, as in the real world when nomads undergo seasonal travel following their herds. While the model does not attempt to produce a specific historical or empirically replicated replication, the overall qualitative and quantitative behavior of households, herds, and seasons are sup­ported by known features of the target system.

Concerning the study of later periods in ancient times, there is a growing interest in modeling Roman economy in terms of micro-behaviors, feedback, and local interaction. Before anyone can ask questions about growth, or market integration, or the degree to which Rome was ‘primitive’ versus ‘modern’, some scholars are focusing on individual decision making and networks of individuals at all geo­graphical scales and then using those networks as the substrate for computationally simulating individuals’ economic activities (Brughmans 2012). The idea that net­work relationships (and the institutions that emerge to promote these) are the mechanism through which ancient economies deal with incomplete knowledge is a powerful one because we can find and outline the traces of these networks through archaeology. The simplest of these essays are those by Graham (2005, 2006) who has tried to understand the geography of the Empire from the point of view of a person traveling through ancient roads. The author takes the lists in the historical written Itineraries, and recast them as networks of interconnected cities. The purpose is to know whether there are any significant differences between provinces’ connective network topography in terms of the transmission of information. One agent is given a piece of ‘knowledge’, which it may or may not share with those he encounters. The rate at which knowledge is transmitted therefore depends on the chance of transmission in any given encounter, and on the topology of the itinerary network. By controlling for the different variables, significant differences in how the different provinces’ networks facilitate the transmission of information may be observed.

Graham and Weingart (2015) have developed an agent-based model of the Roman extractive economy which generates various kinds of networks under various assumptions about how that economy works. This simulation of an ancient economy is based on four key mechanisms: (a) the generation of small parcels of capital to combat risk; (b) little homogenization of products; (c) opportunism; and (d) social networks where there is high local clustering and a few long-distance links. These mechanisms correspond well with the archaeology of the Roman economy and the picture we know from legal and other historical textual sources. The authors have formalized in Netlogo code their ideas concerning how economic networks might be formed; they then sweep the parameter space, the entire land­scape of possible outcomes; they compare that generated landscape of the model against known archaeological networks; and in the degree of conformity or dis­juncture between the model and observed networks they reevaluate the stories that have been told about the past, creating new models in the process. They assume they will never be able to simulate perfectly the formation processes that give rise to a particular archaeological network. To do so would require making a map as large as the territory it is intended to describe. The computer translation of a hypothetical model of Roman economy, and the role of social networks within that model should be couched in all appropriate caveats and warnings. Networks can be discerned and drawn out from archaeology, prosopography, and historical sources. If we can align networks from the ancient evidence to those generated from the model’s simulation of the ancient economy, we have a powerful tool for exploring antiquity, for playing with different ideas about how the ancient world worked.

Brughmans and Poblome (2016) take a very similar approach. They have pre­sented an agent-based network model simulating the social networks which repre­sent the flow of information and goods between roman traders. The concept of social networks is here used as an abstraction of the commercial opportunities of traders, acting as a medium for the flow of information and products. In the model 2000 traders are located at 100 sites and are connected in a social network. Four products are produced at four different ‘production sites’, and are subsequently distributed through commercial transactions between pairs of traders that are connected in the social network according to shortest-path-length links to reproduce the idea of “small world”. Preliminary results suggest that the local-knowledge variable has a limited effect on the wideness of goods distribution, whilst the proportion of inter-site links variable has a strong effect. Limited commercial knowledge can still give rise to wide differences in distributions, but only in systems with highly inte­grated markets. This means that the local-knowledge variable is not instrumental in giving rise to the pattern of interest, whilst the proportion-inter-site-links variable is. Limited availability and high uncertainty of information, and a weak integration of different markets in an economy governed by supply and demand, is unlikely to give rise to large differences in the distribution patterns of commercial goods. Preliminary results of this model therefore reject the claim that limited market integration, availability and reliability of commercial information in ancient Rome gave rise to differences in the wideness of products' distributions.

It can be of interest to compare this new research on Roman economy with medieval and post-medieval economies. Ewer et al. (2001) have created a multiagent-based model to understand the role of deliberative Agents in Analyzing Crisis Management in Pre-modern Towns. The model distinguishes among mer­chants, craftsmen, laborers and local authorities. Agents interact as consumers and suppliers via several markets. Within the course of simulation local authorities are capable of intervening in market processes and implementing measures for crisis management. Hodgson and Knudsen (2008) have developed a behavioural expla­nation for the emergence of high levels of property rights enforcement in Europe in Medieval times (11th to 13th centuries). The merchant guilds have a central role in our explanation. The authors have developed an agent-based model that allows a number of important but previously unexplored issues to be considered (such as the joint importance of price variation, guild stability and the effect of uncoordinated embargo pressures among multiple guilds). The main result is that almost perfect levels of property rights enforcement can emerge solely as a result of multiple guilds' uncoordinated embargo pressures and medium to high levels of price variation. In fact, both conditions were fulfilled in the Middle Ages. In this model, no reputation mechanisms are required; our results solely depend on behavioural adjustment. High levels of property rights enforcement can emerge instead as a result of guilds' embargo.

Frantz et al. (2013, 2015) have studied the functioning of the Maghribi Traders Coalition—a historically significant trader collective that operated along the North African coast between the 10th and 13th centuries, which acted as a closed group whose interactions were governed by informal institutions. Bekar and Read have studied of eleventh and fourteenth centuries in England, when innovations in property rights over land induced peasants to respond by trading small parcels of land as part of their risk coping strategy (Bekar and Read 2009; see also Ewert and Sunder 2001 for a related experiment with trading networks in Medieval North Europe). Those times witnessed a dramatic increase in inequality in the distribution of peasant estimates of the quantitative impact of land trades (motivated by behavior toward risk) on the distribution of landholdings. The authors employ an agent based modeling strategy in which decisions regarding pooling, saving, labor supply, and land transactions are rule based. Agents are initially endowed with an exogenous landholding. Each period agent draws a harvest realization from a random normal distribution transformed by the requisite mean and variance. Har­vests are independent across agents and through time. Agents pool and save out of current harvests. Smallholders work in the labor market; largeholders hire labor. Incomes are compared to a subsistence consumption bundle. An agent facing a subsistence crisis with a positive land position offers a parcel of land for sale. If, after depleting their land position, the agent is still below subsistence it experiences a subsistence crisis. An agent sufficiently above subsistence purchases parcels offered for sale. Agents sell land only when all other forms of insurance have been exhausted and they still face a serious subsistence crisis—treating land sales as an insurance mechanism of last resort. The authors test their explanation by simulating the dynamics of the land market, including differential reproductive success, part­ible inheritance, pooling and saving behavior, production parameters linking har­vest realizations through time, crisis levels of income, wage rates, and land prices. Our simulations reveal that transactions in the land market coupled with population growth produce levels of inequality and skew consistent with those observed in the data. Population growth alone, coupled with partible inheritance, can only explain a small portion of the observed inequality.

Suarez and Sancho (2011) have investigated using computer simulations a theoretical model of cultural dynamics in which the individuals’ behavior plays a strong role derives from the many cultural communities involved and the different scales used to study the spread of the baroque culture from Europe to America at the beginning of the Early Modern Period. The research explains the origins, evolution, transmission, and effectiveness of baroque artistic patterns, through the develop­ment of a model that rationalized the cultural and symbolic movements between Europe and Latin America, as well as the transformations and mutations that cul­tural objects undergo in their successive interactions with the variety of ecosystems and groups through which they pass on their journeys. The authors have created a Virtual Cultural Laboratory (VCL) using agent-based computational modeling that helps study how human culture has been historically transformed and transmitted through acts of learning, imitation, and the creation of cultural objects as they might be experienced by any human being from birth to death, independently of the specific community to which the cultural object or individual belongs. In general terms, the VCL addresses three different issues that are relevant for the historian and the cultural researcher. First, the VCL offers a platform to check on the effects of historical events and processes about which the researcher has comprehensive sets of data. Having the data lets the researcher to refine the model he is using to explain the given cultural and historic processes, as both data and model have to show a mutually coherent behavior. Second, when the researcher does not have good data about the phenomena he is studying, the VCL helps test the hypotheses and the assumptions used by the historian, and double-check the results of the simulation with the logic of those hypotheses. Third, the researcher can take advantage of the VCL by rehearsing different what-if scenarios that he knows did not happen, but whose results would be important to shed light into the context in which actual events took place.

But not only economic mechanisms should be taken into account for under­standing social life in ancient times. The history of religion and the historical evolution of religiosity (Altran and Heinrich 2010; Whitehouse et al. 2012) can be an interesting domain for simulating historical non-economic dynamics. Czachesz (2007a, b) has advanced some algorithmic models of social behavior for understanding religiosity and look for ways of applying such models to the emergence of early Christian religion. The author puts forward the hypothesis that religious ideas emerge as a necessary consequence of the sophisticated “flocking” rules of human societies. Religion emerges from the interaction of a great number of participants with each other and their environment. Rituals are repetitive actions that emerge from these interactions. Texts (public representations) are environ­mental components that have been formed by the agents. Beliefs and experiences are generated by texts and rituals and describe the internal states of the agents. On a different level, however, also beliefs and experiences can be studied as distributed phenomena, inasmuch as they are emerging from the interaction of different parts within the human mind. His suggestion is that religious ideas emerge as a necessary side-effect of the sophisticated “flocking rules” of human societies. The large-scale dynamics of human societies emerge as agents make decisions based on interac­tions with our neighbors as well as on simulations of unknown, distant, and foreign human individuals. Some of the latter simulations are maintained in stabilized, stereotyped, and socially transmitted forms, such as national stereotypes. Ideas of religious agents are long-standing, stabilized, stereotyped, and socially transmitted simulations of distant or abstract persons. Religious agents, in fact, are often important family members, rulers, or distant, exotic people.

On a similar subject, Turchin (2003) has explored three alternative mechanisms of religious conversion and ethnic assimilation through history: the noninteractive, the autocatalytic, and the threshold models. Each model predicts a qualitatively different trajectory (the proportion converted/assimilated as a function of time). This means that using a model the historian can determine which theory better reflects the reality if he/she can find data on the temporal course of conversion. When fitting the model with historical data on conversion to Islam in Iran and Spain, results strongly supported the autocatalytic model and were nothing like trajectories pre­dicted by the two alternatives. Turchin concludes from this result that all models are by definition wrong, because they oversimplify the complex reality, but the auto­catalytic model is less wrong than the alternatives. It appears that the assumptions of the conversion process built into the autocatalytic model capture some important aspect of the historical reality of those territories at that time: once world religions got going, they generated a kind of momentum that allowed them to expand at approximately constant (per capita) rate. Dramatic events—world wars, imperial collapses, and nomadic invasions—did not derail these massive macrohistorical processes, at least in these particular cases (of course, certain kinds of events, such as the Christian Reconquista in Spain, are capable of reversing the tide of religious conversion).

Tomlinson (2009) has studied how ancestor veneration and other forms of commemoration may help to reduce social distance within groups, thereby encouraging reciprocity and providing a significant survival advantage. In his simulation, a prototypical form of ancestor commemoration arises spontaneously among computational agents programmed to have a small number of established human capabilities. Specifically, ancestor commemoration arises among agents that: (a) form relationships with each other, (b) communicate those relationships to each other, and (c) undergo cycles of life and death. By demonstrating that ancestor commemoration could have arisen from the interactions of a small number of simpler behavioural patterns, this simulation may provide insight into the workings of human cultural systems, and ideas about how to study ancestor commemoration among humans.

As examples of other non-economic models for understanding ancient worlds, we can mention a simulated Polynesian society that has been used to explain why, in Polynesia, growing stratification did not result in a devaluation of women's status, as most theorists had suggested (Small 1999). The computer model used to explore this problem—called TongaSim—attempts to emulate the basic social dynamics of Tonga, a Western Polynesian society. The program is capable of simulating the operation of a chiefdom with up to 100+ chiefly lines whose descendants marry and have children, create and maintain kinship relationships, exact and pay tribute, produce and redistribute agricultural wealth, expand in ter­ritory and go to war, and attempt to gain personal and group status. TongaSim was used to simulate the effect of warfare (a prime mover of stratification) on women’s status, specifically the custom of “fahu” that asserts the spiritual superiority of sisters and sister’s lines over brothers and their lines. Because of intermarriage patterns, this custom also serves to make higher status chiefly lines superior in kinship to lower status chiefly lines and, thus, supports traditional political power. The simulation showed that, despite the initial conflict between the interests of rising military chiefs and the fahu custom, the custom was appropriated by these rising chiefs, turning fahu’s political effects “on its head.” Ultimately in the sim­ulation, the fahu custom provided a vehicle for military chiefs to gain status and power. This, it is argued, is consistent with the lack of any historical evidence that the fahu was challenged and toppled during periods of growing warfare and stratification.

In a related way, Froese et al. (2014) have simulated the political life in ancient Teotihuacan, Mexico, from 100 AD to 500 AD. The authors have devised a mathematical model of the city’s hypothetical network of representatives as a formal proof of concept that widespread cooperation was realizable in a fully distributed manner. In the model, decisions become self-organized into globally optimal configurations even though local representatives behave and modify their relations in a rational and selfish manner. This self-optimization crucially depends on occasional communal interruptions of normal activity, and it is impeded when sections of the network are too independent. The authors relate these insights to theories about community-wide rituals at Teotihuacan and the city’s eventual disintegration.

Livni and Stone (2015) have simulated some aspects of pre-monarchic life in iron Age Israel, taking into account the potential cultural, civic, and social role of religious rituals and beliefs (i.e., the weekly Sabbath), in controlling deviation from social norms. The model begins with an analogy between spread of transgression (defined as lack of conformity with social norms) and of biological infection. Borrowing well-known mathematical methods, the authors have derived solution sets of social equilibrium and study their social stability. The work shows how a particular ritual in a complex polity could in theory enhance social resilience. The examination reveals that an institutionalized ritual had the potential to ensure a stable organization and suppress occasional appearances of transgression from cultural norms and boundaries. Subsequently, the model is used to explore an interesting question: how old is the Sabbath? The work is interdisciplinary, com­bining anthropological concepts with mathematical analysis and with archaeolog­ical parallels in regards to the findings.

War and violence have been regarded as relevant aspects for understanding historical evolution and social change (Younger 2012; Turchin et al. 2013). As a result of this interest in the formal study of conflict in ancient times, the computer replica of ancient battles has been one of the recurrent subjects of computer sim­ulation (Cederman 2003; Stover 2007; Graham 2009; Findley 2008; Findley et al. 2010; Stilman et al. 2011; Craenen et al. 2012; Loper and Turnitsa 2012; Wittek and Rubio-Campillo 2012; Sabin 2012). War in the origins of humanity (Philips et al. 2014), during the Neolithic (Duering and Wahl 2014), the Trojan War (Flores and Bologna 2013), in roman times (Rubio-Campillo et al. 2015), in the medieval period (Murgatroyd et al. 2012) or later (Girardin and Cederman 2007; Rubio-Campillo et al. 2013) has been simulated. Models of ancient and modern armies can be then used as a virtual laboratory, where different hypotheses are tested under varying scenarios what allows the study behavioral action at any scale, involving tens of thousands of agents within the context of modelling logistical arrangements relating to the battle, or taking into account how the resilience of formations to combat stress may increase exponentially when they contain just a small percentage of homogeneously distributed individual agents (warriors) with higher psychological resistance. In this way, the computer model of a battle can show different possible courses of action, the influence of random movements, the influence of landscape, the consequences of the differences in weaponry or soldiers training, logistics and the “geniality” of generals and commanders. Distributed simulation is the only viable approach to deal with a problem of such scale and complexity.

The other side of violence is mortality. Computational simulation can be the most obvious way to explore the consequences of famines in historical perspective, be there the result of violence, structural problems of the economic mechanisms or climatic transformations (Watkins and Menken 1985; Wassermann 2007; Curran et al. 2015). Ewert et al. (2003, 2007) have explored using agent-based technology the relationship between hunger and early market dynamics in order to understand the consequences of mortality crises in Pre-Modern European towns explains how to implement a model in which historical famines may be simulated. Zhang et al. (2011) considers the role of climate-change.

Mortality can also be the result of epidemics. Black Death in the middle ages is one of the best known historical examples (Bossack and Welford 2015). Voigt- lander and Voth (2013) have simulated how a major shock to population can trigger a transition to a new steady state with higher per-capita income. The Black Death was such a shock, raising wages substantially. The model shows that demand for urban products increased and urban centers grew in size. European cities were unhealthy, and rising urbanization pushed up aggregate death rates. This effect was reinforced by diseases spread through war, financed by higher tax revenues. In addition, rising trade also spread diseases. In this way higher wages reduced population pressure. The authors suggest in a calibration exercise that our model can account for the sustained rise in European urbanization as well as permanently higher per capita incomes in 1700, without technological change. Europe's pre­cocious rise to economic riches can be explained as the result of complex inter­actions of the plague shock with the belligerent political environment and the nature of post-medieval cities. Other related approaches to the computational investigation of the social, political and economic effects of historical epidemics are Duncan et al. 1993; Lagerlof 2003; Monecke et al. 2009; Gaudart et al. 2010; Kausrud et al. 2010.

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