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Agent-based modeling of ancient societies and their organization structure

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Abstract

Some of the most interesting questions one can ask about early societies, are about people and their relations, and the nature and scale of their organization. In this work, we attempt to answer such questions with approaches introduced by multiagent systems. Specifically, we developed a generic agent-based model (ABM) for simulating ancient societies. Unlike most existing ABMs used in archaeology, our model includes agents that are autonomous and utility-based. Our model can (and does) also incorporate different social organization paradigms and technologies used in ancient societies. Equipped with such paradigms, our model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics—i.e., the flexibility and evolution of power relationships depending on social context and time. As a case study, we employ our model to evaluate the impact of the implemented social and technological paradigms on an artificial Early Bronze Age “Minoan” society located at a particular region of the island of Crete. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate an impressive sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, and which was inspired by a recent framework for modern self-organizing agent organizations. This is the first time a self-organization approach is incorporated in an archaeology ABM system.

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Notes

  1. We will be using the acronym ABM to refer to both “agent-based modeling” and “agent-based model(s)”.

  2. We note that by doing so we do not mean to argue that utility is the main factor driving human behaviour or the advance of human societies. Nevertheless, utility-based agents and utility theory have long been adopted as useful tools in the MAS community [56, 68].

  3. A sketch of this model appeared in a short AAMAS-2014 paper [10].

  4. Mortality and fertility rates in [43] depend on age, rather than on production.

  5. These are the agricultural technologies in use at the period of interest for our case study [27, 35].

  6. In Eq. 1 we let the agents organization population P influence the amount of labour applied on a cell, even though any given cell contributes to the utility of a single agent only (cf. Eq. 3), since field cultivation was in many respects communal in those times [61]. Regardless of that assumption’s validity, this value is essentially normalized by the maximum possible population; thus the \(R_i\) function’s desired behaviour would have been entirely similar had we used the household size instead of the settlement population.

  7. Certainly, however, when a household agent’s utility and storage values reach zero, all individuals in the household inevitably “die”, and the agent is removed from the system (and organization).

  8. Others estimate growth rates in mainland Greece and the Aegean to be between 0.1 and 0.4 % per year, for long periods during the Neolithic Era and the Bronze Age [2, 41].

  9. More accurately: socio-economic.

  10. We note that the notion of “lineages” for agent organization evolution has actually been implicitly introduced in the order by which agents in need are given priority for asking for help. Specifically, the “older” an agent (in need) is within the community, the higher in the energy distribution priority queue is placed. This is a social norm mirroring an indirect “kinship” or “tradition” system, in use within the artificial families.

  11. Note that dissolving “improper” existing relations, improves the efficiency of the agents’ decision-making process, since there are fewer relations to consider when allocating tasks.

  12. There are other minor differences with the work of Kota et al. [44, 45]. For instance, in our model we replace the notion of the number of time-steps that an agent has waiting tasks, with that of an agent having \(U < u^{thres}\) (and storage = 0). We do not list these minor differences here.

  13. The “Eteocretans”, as they were called by Homer long time before the “Minoan” term that was coined by Arthur Evans after the mythic “King Minos”, were farmers as well as traders in the whole Aegean [66], who had survived a natural catastrophe, possibly an earthquake and an eruption of the Thera volcano (such an eruption is often identified as a catastrophic natural event leading to the Minoans’ rapid collapse [49]). Unlike what was the case in the Mellars model of the EOS project [20] (see Sect. 2), the wealth of environmental resources sustaining the Minoan civilization is not our focus of attention here.

  14. Archaeologists’ minimal definition of the Minoan “Palaces” describes them as regional centers or settlements that mobilized resources through secondary rural centers i.e. redistribution centers or perhaps exchange markets [5, 28, 52].

  15. It is important to note that the early Bronze Age society we model here, is one relying on farming within an environment that offered less than plentiful resources; and that, unlike modern “egalitarian” societies like these of Eskimos or Kalahari bushmen, the early Minoan society was one that in fact most probably transitioned from an originally segmentary society to one possessing a state-like organization.

  16. NetLogo can support thousands of agents, though RAM limitations are inherent in the underlying Java VM and/or operating system.

  17. http://resources.arcgis.com.

  18. http://www.aitia.ai/en/web/iaws/mass.

  19. We do not show error bars for Figs. 7 and 8b, c (depicting settlements and agents per settlement). This is to avoid overloading these figures, and because of the apparent overlaps. We can report however that the standard error observed in those results is at most 1.

  20. During the Early Minoan period (3000-2000 BCE), however, reviews of archaeological evidence for the Pre-palatial society visualize a “wholly undifferentiated” landscape, comprising “very small scale autonomous local units” of a “small-scale intensive farming model”, with no convincing evidence for “wealthy elites” [26]. This society later gave its place to the “Palaces” of the Middle/Late Minoan periods.

  21. The steady population growth rate r is achieved assuming agents are consuming adequate resources (cf. Sect. 3.3). In that case, the expected population size N after t (yearly) time steps is given by the equation \(N = N_0 * (1+r)^t\) (where \(N_0\) is the initial population).

  22. This is not unexpected, since, as the individuals’ population increases, soil erosion leads to a slow production decrease (cf. Eq. 1), and thus to a decay in utility.

  23. For interest, we note that this is also in agreement to genetic evidence regarding the continuity of the existence of a Minoan population at the Lassithi plateau [38].

  24. We do not claim that this is the most appropriate way to tackle non-stationarity. Solving non-stationary, multi-agent MDPs is not one of our goals in this paper.

  25. See, e.g., https://github.com/NetLogo/NetLogo/issues/402.

  26. Of course, several efforts could have been undertaken to speed-up the process of dynamically defining and solving the MDPs—e.g., via re-using MDPs already solved for agents operating in nearby regions and nearby time-steps. However, this is not the focus of our work here: our experiments in this section simply intended to demonstrate that our model can readily incorporate non-myopic agent deliberations.

  27. We have ran additional experiments which confirm that increasing the value of m can be beneficial when state transitions are non-deterministic. Moreover, we can observe improvement when agents plan ahead more often (e.g., every 5 yrs instead of 10). We do not report further on these findings in this paper.

  28. We have run simulations involving an initial population of 100 agents, and the results we obtained were similar to those reported in this paper. Thus, we do not anticipate that a higher initial population of agents will substantially change the conclusions drawn from our work here.

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Acknowledgments

We are grateful to the members of the Laboratory of Geophysical-Satellite Remote Sensing & Archaeo-environment (GeoSat ReSeArch) of the Institute for Mediterranean Studies (IMS)/Foundation of Research & Technology (FORTH) for providing archaeological data for our model. We particularly thank the anthropologist Dr. Tuna Kalayci and archaeologist Dr. Sylviane Déderix. Moreover, we extend our thanks to the anonymous reviewers for their constructive comments and suggestions.

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  1. School of Electronic and Computer Engineering, Technical University of Crete, Kounoupidiana Campus, 73100, Chania, Crete, Greece

    Angelos Chliaoutakis & Georgios Chalkiadakis

  2. GeoSat ReSeArch, IMS/FORTH, Rethymno, Greece

    Angelos Chliaoutakis

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  1. Angelos Chliaoutakis
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Correspondence to Angelos Chliaoutakis.

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Chliaoutakis, A., Chalkiadakis, G. Agent-based modeling of ancient societies and their organization structure. Auton Agent Multi-Agent Syst 30, 1072–1116 (2016). https://doi.org/10.1007/s10458-016-9325-9

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