Simulating Economic, Social and Cultural Change in Prehistory. Why Humans Have Made Life so Complex and Difficult

Cultural diversity, social division of work and social hierarchization can be studied in terms of the complex (non-linear) accumulative consequence of relatively simple social mechanisms acting along time on non-isolated and dynamic aggregates of social agents.

Isern and Fort contribution to this volume (Chap. 7) focus on a specific kind of cultural shift: language shift. The birth of a new language is a slow process which usually includes several successive minor processes that spread throughout the population over the course of millennia, until eventually the language has diverged enough from the original language as for them to be mutually unintelligible. These are often considered random processes, analogous to genetic drift, which may include the invention of new words—e.g., for innovations—, acquisition of loan­words from other languages in contact, phonetic changes. The other process of language shift, the displacement of the local language by a foreign one that becomes the new prominent language in the region, once started, is usually a much faster process, which can take place in as short a time as a single generation.

Beyond language evolution, the study of social transitions comprises global changes with crucial impact on the evolution of human history, which besides the technical changes directly related to the adoption of agriculture, entailed as well changes in using organization, social structures and belief systems that may be the initial seed of the present sociocultural organization. The adoption of agriculture, herding and stockbreeding is one of the traditional domains for understanding the complex dynamics in cultural shift. Archaeologically known as the Neolithic, in this period human populations began to produce their own food substituting predator and forager practices that were in use for the most part of human history. There are many hypotheses about why this could have happened in a precise place and time (Gremillion et al. 2014). The suggested explanations are a mix of natural (environmental) factors affecting evolutionary behavior and adaptation to new environments, or even the creative nature of human minds, able to learn from natural process of biological reproduction, interfere with them in an intentionally way and building as a result a new artificial environment.

This is the obvious domain for computer simulations. Can agriculture and related practices of animal control emerge “mechanically” in a group of agents originally defined as foragers and predators? The technological side of this transition is not the result of the “intelligence” of some individuals who “invented” something new. As computational simulations have proved (Cribb 1987; Grosman 2005; Ch 'ng and Stone 2006; Conolly et al. 2007; Pearsall 2007; Allaby et al. 2010, 2015; Schreinemachers et al. 2011; Fuller et al. 2012; Gerbault et al. 2014; Larson and Burger 2013; Smith 2015a, b; Perrot et al. 2016; van Vliet et al. 2016), domesti­cation of plants and animals is an evolutionary emergent result. Therefore it seems that there are some possibilities that one of the most relevant transitions in the history of humanity had also a mechanical basis.

The following case studies are a good example of the way early agriculture can be simulated computationally. Lancelotti et al. (2014) have created a simple Agent Based Model in which agents relied on a pure subsistence strategy based on domesticated plants and animals. The model explores the role of climate, agricul­tural production and surplus, and animal availability on the resilience of agro-pastoralists communities on a simplified version of a semi-arid environment. The world where the agents move is divided in three randomly distributed types (dune, interdune and water). The environment state is tracked by the entity World, which takes care of generating the rain, updating the biomass quantity of the cells (depending on their state and type). Interdune type cells can be in one of three states: wild, crop and fallow. The agents derive their caloric intake form crop cells. The relationship rain-biomass-crop-calories is derived by ethnographic and eco­logical sources and it is based on species of small-millets. The data considered regard rain-fed, manual agriculture, which is believed to be the closest to incipient cultivation system.

Barton (2014) has simulated prehistoric swidden cultivation. The model can be run in controlled and adaptive modes. In the controlled mode, the researcher controls all the parameters that govern land-use, and sets them prior to running the model. These land-use parameters include: (1) the initial number of households that start a simulation, (2) the minimum amount of accumulated resources for a household to fission and form a new household, (3) the maximum distance farmers travel to cultivate fields, and (4) the level of low resource returns at which a household will decide to abandon a farm and move to a new locale. All households begin with an arbitrary 100 energy units. These energy units serve as the currency for land use costs, returns, and decisions. The researcher also controls a number of environmental parameters, including: (1) harvest return, (2) costs to clear land, and (3) costs to farm—all expressed as percentages of the initial energy units—along with (4) the rate at which fertility is lost when a parcel of land is farmed, and (5) is regained by soil when a patch is left fallow (in energy percentage lost/gained per time unit). A percentage of bad years can be set during which harvests are only half the normal.

Aagesen and Dragicevic (2014) have developed a model to examine the spatio-temporal land-use changes and population responses of early agricultural communities under a variety of environmental and cultural conditions. Complex systems theory and geographical information systems (GIS) are integrated into the design of the model. The resulting Early Agricultural Resources and Land-use Investigation (EARLI) model couples agent-based modeling (ABM) and cellular automata (CA) techniques within a GIS framework. The model examines how both cultural and environmental factors influence land use change under multiple scenarios.

A naturalistic explanation of the origins of plant domestication and agriculture, not making any reference to human motivations nor intentions has been presented by Lemmen and colleagues (Lemmen and Wirtz 2006; Lemmen et al. 2011a, b, 2015; Lemmen 2012, 2015a, b). GLUES, a computer program simulating human population density, technological change and agricultural activity directly, based on the concept of gradient adaptive dynamics, where adoption of a subsistence life­style, e.g., Neolithic agriculture, by any given group of people at any particular time depends on endogenous environmental and social factors, e.g., potential produc­tivity, population density, and exogenous factors, including the presence of farming people in neighbouring regions.

If Lemmen's model does not take into account human rationality in the origin of agriculture, Tisdell and Svizzero (2016) and Sterelny (2015) have explore more behavioral approaches, such as satisficing types of behavior. Particular attention is given to social embedding as a constraint on economic change and to non-marginal limitations to economic evolution. The authors assume rational optimizing, and argue that satisficing theories provide a superior explanation of transition (and non-transition) by some hunter-gatherers. They conclude that many of the concepts associated with neoclassical economics are shown to be inadequate for analyzing the choice problems involved. Behavioral models take into account the relationship between human behavior and economic evolution paying attention to the way that decision-making is embedded in social structures.

There is a lot of interest modeling the transition towards agriculture as a wave of advance generated by the spatio-temporal spread of a new population (see Fort et al., Chap. 5). Based on the pioneering work by Cavalli-Sforza and Ammermann (1979), Cohen (1992), Ackland et al. (2007, 2014), Cohen and Ackland (2012a, b) has developed the original model based on fundamental concepts of food produc­tion, birth and death rates for various cultures. He and his collaborators showed how some cultures could expand at the expense of others. In the case of Neolithic farming, the form of the equation is similar to Fisher's, but since it was derived rather than postulated, it was also possible to deduce more subtle features. In particular, the equations only allow for a wave of advance of farming if the farmers have a birthrate higher than that of the Mesolithic hunters and gatherers they are displacing or absorbing. This may be due to their more sedentary lifestyle making childcare easier, a hypothesis which is borne out by observation in contemporary farming and nomadic societies. More unexpectedly, the model required that farmers should be less well-nourished and have shorter lifespans than the hunter gatherers they displaced, showing that the strategy more successful for advancing the culture may not be better for the individuals practicing it.

Parisi et al. (2003), Cecconi et al. (2006) follow a different approach within the same problem using cellular automata, which can be seen as a simplified version of an agent-based model. They have simulated the agricultural colonization of Europe from the VII to the IV millennium BC, and its possible similarity with the pre­historic differentiation of European languages. A similar simulation has been developed by Drechsler and Tiede (2007) in the case of the spread of Neolithic herders within the Near East, towards the Arabian Peninsula. In the model, envi­ronmental local features influence a global innovation diffusion pattern. Here, computational agents represent mobile populations. The spreading process itself is simulated by a repeated generation of random agents in space. The random com­ponent represents the archaeology incomprehensible decisions that lead to human displacements. Because it is more likely that “wandering groups” populate nearby places than faraway places, the possibility for the adoption on an innovation like agriculture is highest in the direct neighborhood of prior acceptance of innovation. Therefore, the random agents cluster spatially more frequently around the “parent” nodes. The spreading surface represents a combination of environmental parameters that are considered fundamental to the dispersal of Neolithic herders across the Arabian Peninsula. These parameters were evaluated for their influence on the movement of human groups, reclassified, and combined to obtain a spreading surface that represents local resistance to the process of spreading. As a result:

• Every place in each generation decreases the underlying raster value simulating the drain on resources and its exploitation value.

• The number of descendants at each place in each generation depends on the value of the underlying raster. The higher the value (“better conditions”), the greater will be the number of descendants in the next generation.

• The actual spreading distance (“how far a new generation will go”) also depends on the underlying raster value. The lower the raster value at a specific point, the higher the spreading distance.

The origin of agriculture and production economies as a consequence of the combination of demic processes and cultural transmission mechanisms is now a relatively popular subject of research. Joaquim Fort and Neus Isern have published extensively on this point (Fort 2011, 2012, 2015; Fort and Mendez 1999; Isern and Fort 2008, 2010, 2012; Isern et al. 2012). Fort et al. (Chap. 5) suggest a combi­nation of demic processes of population substitution and cultural transmission, that is, the spread of ideas (hunter-gatherers becoming farmers) instead of populations. They consider an abstract population of preindustrial farmers, initially located in some region, and assume they can disperse into other regions that are also suitable for farming but initially empty of farmers. The idea is that the next generations of farmers will disperse away from their parents and agriculture will propagate to neighbor areas as a wave of advance. The authors modify the classical Fisher's reactive model to predict the specific dynamics of such a wave of advance, taking also into consideration an integro-difference cohabitation model between new­comers (farmers) and indigenous populations (hunter-gatherers) in which cultural transmission from farmers to hunter-gatherers leads to a more complicated model.

Other relevant work exploring many alternative hypothesis on the causes of human movement and the spread of innovation with such historic contexts have been published by Barbujani et al. (1995), Di Piazza and Pearthree (1999), Excoffier et al. (2008), Cabana et al. (2008), Connolly et al. (2008), Boquet-Appel et al. (2009), Barton et al. (2010a, b), Baggaley et al. (2012), Hervella et al. (2012), Rasteiro et al. (2012), Currat and Silva (2013), Düring (2013), Gerbault et al. (2013), Ullah (2013), Guedes et al. (2014), Le Nechet et al. (2015), Silva and Steele (2015), Bernabeu et al. (2015), Gordo et al. (2015).

Sakahira and Terano (Chap. 10) also deal with a similar issue. These authors analyze the arrival of Chinese-Korean immigrants during the establishment of the agrarian culture of the Yayoi period (300 BC-250 AD) in Japan. The agrarian culture is reported to have been imported from China-Korea. Thus, the presence of Chinese-Korean immigrants was evidently of importance during the establishment of the Yayoi culture when agriculture became the social and economic foundation of society. However, several factors pertaining to these immigrants remain unclear within Japanese anthropology and archaeology. Specifically, these relate to the immigrants’ place of origin, the initial immigrant population size, the sex ratio of the immigrants, and whether native hunter-gatherer people or farmer immigrants played a formative role in the establishment of agrarian culture during the Yayoi period. This contribution focus on two issues: (1) the sex ratio of the immigrants, and (2) the question of who played a formative role in the development of the agrarian culture during the Yayoi period. This simulation model demonstrates that in the event that most of the initial immigrants were male, and that an agrarian culture was widely adopted by native hunter-gatherers people during the early stage of its development, it is probable that after 300 years, the majority of people shared the same traits as the immigrants. The authors have simulated three possible sce­narios. In the first, immigrants were polygamous and the agrarian culture was only inherited from a parent agent (not diffused from neighboring agents). In this case, the descendants of agriculturalists at an early stage were either immigrants or both immigrants and native hunter-gatherers people. Thus, immigrants played a forma­tive role in the establishment of an agrarian culture. In the second case, immigrants were polygamous and the agrarian culture was inherited from a parent agent as well as diffused from neighboring agents. However, the diffusion of the agrarian culture occurred slowly. In this case, the descendants of the agriculturalists at an early stage were mostly immigrants with few native hunter-gatherer people. As in the first case, immigrants played a formative role in the establishment of an agrarian culture. In the last case, the diffusion of the agrarian culture was significantly more rapid. In this case, the majority of descendants of agriculturalists were immigrants at the earliest stage, but shortly thereafter, native hunter-gatherer descendants were evi­dent during a subsequent early stage. Here, mostly Jomon people and a few immigrants played a formative role in the establishment of an agrarian culture. Of these three probable cases, the last is the most consistent with anthropological and archaeological evidence for the following reasons. In the first case, the diffusion rate of agriculture was too low.

Matsumoto and Sasakura (Chap. 11) develop the same case study as Sakahira and Terano (Chap. 10), that is, hunter-gatherer to farmer transitions in Japan as a consequence of the arrival of new populations with a new economy and the hybridizing with local populations. Drastic socio-cultural changes in subsistence, material culture and settlement structure occurred in the northern part of Kyushu Island around 10th-8th centuries BC., and they seem related with the arrival of new populations, and the consequent pattern of interaction between populations and cultural transmission between newcomers and local settlers, which ended with the acculturation of indigenous populations. The authors consider that decisions concerning cultural integration, transformation and adoption/rejection of cultural elements were important factors in this transition, but they only focus on cultural transmission aspects for the sake of simplicity. A simulation of 500 years of accumulated changes shows that cultural skill could have spread quickly without much loss in the case of biased transmission, even in case the migration rate was very low, and that the spread of cultural skill without significant genetic influence was possible even when cultural transmission was restricted to between relatives. The result gives an inspiration for possible explanatory models of hunter-gatherer/farming transition in Japan in which indigenous people play more significant roles in areas remote from the locus of Yayoi cultural origin. Among their main results, we can quote:

• The rate of population increase can considerably vary due to chance factors, while the spread of genetic value is almost constant as the same marriage rules and move rate were applied to all runs.

• Cultural skill can spread quickly without much loss in the case of biased transmission, even when migration rate is maintained very low.

• The spread of cultural skill without significant genetic influence is possible even when cultural transmission is restricted within relatives.

• Nonrandom migration based on family relationship may facilitate the spread of g-value.

These models of social transition built on the assumption of population spread rely on complex mechanisms of population growth. Important work has been done on this aspect, trying to model the mechanisms of social reproduction, fertility, marriage and mortality in small-scale early agricultural societies (Artzrouni and Komlos 1985; Komlos and Nefedov 2002; Low et al. 2002; Jager and Janssen 2003; Fletcher et al. 2011; Baggaley et al. 2012; Machalek et al. 2012; Rasteiro et al. 2012; Rogers et al. 2012; Geard et al. 2013; Seguy and Buchet 2013; Lemmen 2014; Puleston et al. 2014; Diachenko and Zubrow 2015; Shennan 2015; Sajjad et al. 2016; Winterhalder et al. 2015). As an example of these kind of investigations we can quote Iwamura et al. (2014), who have developed a holistic model framework with agent-based modeling to examine interactions between demo­graphic growth, hunting, subsistence agriculture, land cover change, and animal population in a particular geographical area, investigating the conditions under which indigenous communities relying on hunting and subsistence agriculture alter their impacts on an ecological system through land use change. This is a spatially-explicit household simulation mode, and it is meant to analyze the feed­back between human activities and natural resource systems. The authors use an extensive field dataset from social surveys, animal observation records and hunting kill locations along with satellite images. The model exhibits feedback loops between a growing human population and depletion of local natural resources. The model can reproduce the population size of two different villages along with landscape patterns without further calibration. This model has been used for understanding the conditions of sustainability for indigenous communities relying on subsistence agriculture and hunting, and for scenario analyses to examine the implications of external interventions.

In the same way as Chaps. 3 and 4 tried to reproduce in silico hunting and gathering ways of living in prehistory, we can reproduce computationally the social life of first farmers. Figueiredo and Velho (2001) have programmed a system based on three different kinds of agents: cattle, hunters, and farmers. These agents com­pete for natural resources (plants). The success of each type of agent is determined not only by the availability of the natural resource but also by the capability of other agents to gather those resources for themselves. Running the model consists of creating a landscape and introducing initial populations of animal and hunters. The initial group of hunters follows the cattle around killing them whenever possible. The killing rule relates the energy of the animal to the number of humans in cells around it. So the kills are determined by the patterns of movements of animals and hunters. As the animals follow the concentration of plants, and hunters the con­centration of animals, the two groups move close together. Farming disturbs the natural availability of resources. Farmers are located in the same locations were animals eat. Cattle are competitors for farmers, hunters are competitors for hunters, farming increase the number of cattle. Vaart et al. (2006) used a similar approach to understand the consequences of the different social mechanisms related to the management of wild preys and domesticated cereals. Saqalli et al. (2014) describe another spatialized Agent-Based model, reconstructing the society system of the oldest central European farming communities (Linear Band Keramik, circa 5500­4500 BC) functioning at the village level. The idea was to reconstruct in the same model the functioning along a local grid level (1 ha/cell) of village societies. The goal of this combination of scale was that small variations at the farming/livestock keeping/hunting-gathering system do have exponential effects on a larger scale.

Saqalli and Baum (Chap. 8) discuss different “Terroir”-based environmentally constrained models based on information from local archaeological data regarding environmental characteristics (soil, vegetation, local climate, distance to village) and cropping and livestock-keeping practices, to evaluate the environmental impact of human settlements over several village territories, along several farming sce­narios (shifting, intensive garden and non-intensive cultivation) and diet assumptions.

Tilman Baum (Chap. 9, see also Baum 2014) analyses some aspects of the economic and social life of first farmers in Europe. He presents WELASSIMO, an Agent-Based Model simulating land use of Neolithic wetland settlements in the Swiss and German pre-alpine forelands. Its aims are to test whether any of the existing hypotheses would justify a settlement relocation for systemic reasons. It is shown that for relatively small communities, the non-finite resources related to their land-use most likely have not been limiting and thus did not determine the observed settlement pattern. Instead, it is argued that the continuous duration of moderate land-use did increase the economic value of the evolving landscape. Thus, it is proposed, that relocations happened mostly inside of the relevant landscapes as a combined consequence of the poor durability of wooden houses in waterlogged environs and the spatiotemporal variability of suitable timber. This does not exclude the possibility, that also cultural/social reasons may have been involved. The aim of WELASSIMO to fill this gap and, more specifically, to answer the following questions:

• What implications and systemic feedbacks go alongside with the published hypotheses on land-use systems?

• What was the spatial and temporal availability of non-finite resources?

• Can excessive resource use have caused the observed dynamic settlement pattern?

Within the research domain of agricultural societies, the VIRTUAL ANASAZI project (Dean et al. 2000; Axtell et al. 2002) is another example of agent-based modeling designed to investigate where early agricultural prehistoric communities of the American Southwest would have situated their households based on both the natural and social environments in which they lived. The idea was to define nuclear families (households, the smallest social unit consistently definable in the archae­ological record) as agents, and loosed them on landscapes, which have been archaeologically studied for different historical periods, and plenty of paleo-productivity data exist. The model has been used to predict individual household responses to changes in agricultural productivity in annual increments based on reconstructions of yearly climatic conditions, as well as long-term hydrologic trends, cycles of erosion and deposition, and demographic change. The performance of the model is evaluated against archaeological data of population, settlement, and organizational parameters. By manipulating numbers and attributes of households, climate patterns, and other environmental variables, it is possible to evaluate the roles of these factors in prehistoric culture change. Here the household is a theoretical construct, but it moves on a historically defined environment, which is the most precise available archaeological data allow. Simulated population levels closely follow the historical trajectory. In the first 200 years, the model understates the historical population, whereas the peak population just after A.D. 1100 is somewhat too high in the model. The historical clustering of settlements along the valley zonal boundaries is nicely reproduced. Although the ability of the model to predict the actual location of settlements varies from year to year, the progressive movement of the population northward over time, clear in the historical data, is also reproduced in the simulation. Long House Valley was abandoned after A.D. 1300. The agent model suggests that even the degraded environment between 1270 and 1450 could have supported a reduced but substantial population in small settle­ments dispersed across suitable farming habitats located primarily in areas of high potential crop production in the northern part of the valley. The fact that in the real world of Long House Valley, the supportable population chose not to stay behind but to participate in the exodus from the valley indicates the magnitude of socio-cultural “push” or “pull” factors that induced them to move. Thus, comparing the model results with the actual history helps differentiate external (environmental) from internal (social) determinants of cultural dynamics. It also provides a clue—in the form of the population that could have stayed but elected to go—to the relative magnitude of those determinants.

Ultimately, “to explain” the settlement and farming dynamics of Anasazi society in Long House Valley is to identify rules of agent behavior that account for those dynamics (Dean et al. 2000). To “explain” an observed spatiotemporal history is to specify agents that generate—or grow—this history. By this criterion, this strictly environmental account of the evolution of this society during this period goes a long way toward explaining this history (Axtell et al. 2002). The simulation imitates the target data by computing the individual agents' behavior in response to some input environmental data, by computing the effects of the individual behaviors on the environment, and by computing the repercussions these environmental effects have on individual agents. As shown, this ‘best fit' still does not necessarily accurately replicate the historical findings. In particular, it simulates a higher population early on, and does not replicate the complete eclipse of the settlement in around 1300. The authors point out that better fits can be achieved by increasing the number of household attributes and their heterogeneity, possibly introducing non-uniform distributions.

The evolution of the Virtual Anasazi project can be seen in the similar but at a much higher scale “Village Ecodynamics” project by Kohler and his colleagues (Kohler 2003, 2013; Kohler and Carr 1997; Kohler and Yap 2003; Kohler et al. 2000, 2005, 2007; Kohler et al. 2012; Kohler and Varien 2012; Johnson et al. 2005; Crabtree 2015). Some interesting details of this model are also discussed in Saqalli and Baum contribution to this volume (Chap. 8), Kohler and associates began by entering paleo environmental data on a digitized map of the area, and then placed the agents—simulated households—randomly on the map. The primary area of research is the study of the effect of exchange relationships upon the formation of larger social groups. Since agricultural yields varied greatly from year to year, farmers needed to adapt mechanisms to reduce their uncertainty of future yields. One such mechanism thought to be important is reciprocity between households. After a reasonable model of agent planting was constructed, agents were endowed with balanced reciprocity behaviors and adaptive encodings of exchange, placing the households into a social and an economic network or other (related and unre­lated) households. This network is flexible enough to evolve according to agent interactions and changes in the world environment. The authors are also trying to include the natural production and human degradation of what they consider Critical Natural Resources into the agent-based simulation modeling of household settlement patterns. By demonstrating the ease with which populations could have depleted fuels in this environment, for instance, the simulation builds a context in which changes in food preparation, craft production, architecture, frequency of axes, and so forth, which might be responsive to fuel scarcity, become more plausibly interpreted as having been intended to do so (Johnson et al. 2005).

In recent simulations, the authors have extend the previous model by adding the ability of agents to perform symmetrically initiated or asymmetrically initiated generalized reciprocal exchange (Reynolds et al. 2004a, b, 2005a, b; Kobti and Reynolds 2005). According to this model, the decision made by the group is a not consensus based upon the weights and opinions of the members, but the individual knowledge is pooled and used by a central decision maker to produce a decision (Reynolds and Peng 2005). Selected individuals contribute to the cultural knowl­edge, which is stored and manipulated based on individual experiences and their successes or failures.

A small world social network emerged and the resultant agent populations were shown to be more resilient to environmental perturbations. When allowing agents more opportunities to exchange resources, the simulation produced more complex network structures, larger populations, and more resilient systems. Furthermore, allowing the agents to buffer their requests by using a finite state model improved the relative resilience of these larger systems. Introducing reciprocity that can be triggered by both requestors and donors produced the largest number of successful donations. This represents the synergy produced by using the information from two complementary situations within the network. Thus, the network has more infor­mation with which it can work and tended to be more resilient than otherwise (Crabtree 2015).

Cockburn et al. (2013), Crabtree (2015) have developed the original model by introducing a new model for agent specialization in small-scale human societies that incorporates planning based on social influence and economic state. Agents allocate their time among available tasks based on exchange, demand, competition from other agents, family needs, and previous experiences. Agents exchange and request goods using barter, balanced reciprocal exchange, and generalized reciprocal exchange. The authors use a weight-based reinforcement model for the allocation of resources among tasks. In the base model, agents represent households seeking to minimize their caloric costs for obtaining enough calories, protein, fuel, and water from a landscape which is always changing due to both exogenous factors (climate) and human resource use. Compared to the baseline condition of no specialization, specialization in conjunction with barter increases population wealth, global pop­ulation size, and degree of aggregation. Differences between scenarios for spe­cialization in which agents use only a weight-based model for time allocation among tasks, and one in which they also consider social influence, are more subtle. The networks generated by barter in the latter scenario exhibit higher clustering coefficients, suggesting that social influence allows a few agents to assume par­ticularly influential roles in the global exchange network.

The Virtual Anasazi and the Village Ecodynamics models are among the most influential computer simulations of prehistoric societies. This impact is easily observed in modern publications that model different aspects of social life in early agrarian societies (MacMillan and Huang 2008; Gabler 2012; Barton et al. 2014).

SimpopLocal is a stylized model describing an agrarian society in the Neolithic period, during the primary “urban transition” manifested by the appearance of the first cities (Schmitt et al. 2015). It is designed to study the emergence of a structured and hierarchical urban settlement system by simulating the growth dynamics of a system of settlements whose development remains hampered by strong environ­mental constraints. This exploratory model seeks to reproduce a particular structure of the Rank-Size distribution of settlements well defined in the literature as a generalized stylized-fact: for any given settlement system throughout the time and continents, the distribution of sizes is strongly differentiated, exhibiting a very large number of small settlements and a much smaller number of large settlements.

Ortega et al. (2014, 2016) examines an alternative approach to previously pro­posed models of prehistoric exchange to explain the distribution of obsidian across the Near East during the Neolithic period. Obsidian exchange is a complex system where multiple factors interact and evolve in time and space. Through Agent-Based Modelling simulations of an hypothetical exchange network where some agents (villages) are allowed to attain long-distance exchange partners through correlated random walks, the authors suggest that when additional variables (population density, degree of collaboration between villages...), a type of small-world exchange network could explain the breadth of obsidian distribution (up to 800 km from source) during the Near Eastern Neolithic.

In the same line, Cleuziou (2009) suggests modeling social evolution in con­junction with environmental changes by using non-linear multi-agent models is a much more fruitful way to understand the shift from coastal to inner environments by mid-3rd millennium BC and the apparent depopulation of the Oman Peninsula by 2000 BC. Rouse and Weeks (2011) have recently investigated the role of specialized production strategies in the development of socio-economic inequalities in Bronze Age south-eastern (SE) Arabia, and particularly, the ways in which a localized, internal exchange economy may have produced stress and instability in the SE Arabian socio-economic system. The agent-based model the authors have built with that perspective suggests the nature of the internal exchange economy in SE Arabia itself may have precipitated the social conditions necessary for change by allowing individuals to profit disproportionately.

In the Bronze/Iron Age, approximately 1500/500 years before our era, most human societies adopted production economies in the Old World, and some early forms of social complexity began to develop. Widgren (1979) was one of the very first researchers in modeling how those ancient economic systems worked. Kowarik et al. (2012, 2015) have modelled social life and ancient production techniques of the Bronze Age salt mining complex of Hallstatt/Austria (1458-1245 BC). The authors have addressed the complexity of production structures and especially their interaction with the natural and socioeconomic surroundings: what were the demands concerning workforce, means of production and subsistence? How many people had to be supplied with means of production and subsistence? Were the local resources sufficient? The authors have used Agent-Based Simulation to build a model of the working processes in one mining hall (breaking salt, collecting salt, transporting salt to the shaft), in order to gain insights into spatial organization, allocation of tasks and workload balance and to relate the time span of mining to the size of the workforce and the amount of mined salt. A System Dynamics Simulation was applied to correlate the size of the workforce (population dynamics) with food consumption and demand for mining tools. Through Process Simulation, the authors were able to display and analyze the workflow of an entire shaft system encompassing several mining halls.

Stekerova and Danielisova contribution to this book (Chap. 12) can also be regarded as an example of simulating farming economic systems before the full consolidation of social complexity. Authors approach computer modelling as a tool for understanding Celtic society and cultural changes at the end of the East European Iron Age. They focus on development of agent-based models of daily economic activities of inhabitants of Late Iron Age agglomerations (oppida), aiming to verify hypotheses about the probable self-subsistence of oppida by means of models of the population dynamics and socio-economic behavior of one par­ticular site, the Stare Hradisko oppidum in Bohemia. The core concept is the idea of society pursuing agro-pastoral activities within the given temporal and spatial scale which is tested against subsistence, surplus production and carrying capacity fac­tors. They aim to explore the dynamics of the food production and isolate possible crisis factors imposed either by environment or by unsustainability of the economic strategies pursued. Main questions throughout the chapter are:

• What is the maximum population that can be sustained in a given environment and when is this maximum reached?

• Using what cultivation strategies and labor input can the population most effectively exploit natural resources in order to be self-sufficient?

• What are the dynamics of production with constantly growing or declining population (subsistence-surplus-success rate-diminishing returns)?

In Stekerova and Danielisova's model, the whole Iron Age world despite its technological innovations, specialization and economic contacts, or its level of complexity, was still principally a world of the common farmer. It appears as part of a socio-economically advanced environment, together with a distinctive intensifi­cation of settlement patterns. Central places are programmed in historically reliable environments as “total consumers”. That generally means that they were too spe­cialized and hence engaged in other activities, so they were not capable of pro­ducing any foodstuffs. This fact should have eventually contributed decisively to the collapse of the Iron Age society in the 1st century BC. Some of these settle­ments surely had to overcome or accept some environmental constrains (imposed for example by higher altitude) or were forced to adapt their subsistence practices (e.g., develop an alternative approach to the exploitation of land). To answer these questions, they developed three models: the population dynamics model, subse­quent food production and land use model and workforce allocation model. The model of population dynamics generates data on synthetic population for four alternative depopulation scenarios, the model of food production and land use is designed to enable experimenting with carrying capacity of the environment with respect to alternative exploitation scenarios, and finally, the work-force model is used for studying allocation of working capacities during the harvest season which is understood to be one of “bottlenecks” of the agricultural year. The aim is especially to ascertain the resilience of the food production system (i.e., carrying capacity) of the oppida under the dynamically changing (increasing/decreasing) population. The models are designed to enable experimenting with alternative scenarios and strategies with the aim to test various upper limits of self-subsistence of the oppidum and to verify general theoretical hypotheses related to the func­tioning of the oppida within particular landscape environment and the ecological and economic rules that were shaping them.

The same authors have also explored related subjects, like the effects of popu­lation growth (Olsevicova-Stekerova et al. 2013), and the configuration of a set­tlement network (Olsevicova-Stekerova et al. 2015; Danielisova et al. 2015).

The work by Kim (2015) on the simulation of Bronze Age Korea can also be related with the economic and political evolution of prehistoric agricultural soci­eties. The author argues that sociopolitical development in the central and southern parts of the Korean Peninsula during the Early Bronze Age-Middle Bronze Age transition might have been closely related to economic intensification. This can be understood from a perspective that emphasizes elite control over basic economic resources as a significant factor in this development.

1.2.7