The purpose of the model is to investigate how different factors affect the ability of researchers to reconstruct prehistoric social networks from artifact stylistic similarities, as well as the overall diversity of cultural traits observed in archaeological assemblages. Given that cultural transmission and evolution is affected by multiple interacting phenomena, our model allows to simultaneously explore six sets of factors that may condition how social networks relate to shared culture between individuals and groups:

1. Factors relating to the structure of social groups
2. Factors relating to the cultural traits in question
3. Factors relating to individual learning strategies
4. Factors relating to the environment
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UPDATE in V1.1.0: missing input data files added; relative paths to input data files changed to “../data/FILENAME”

A model that allows for representing key theories of Roman amphora reuse, to explore the differences in the distribution of amphorae, re-used amphorae and their contents.

This model generates simulated distributions of prime-use amphorae, primeuse contents (e.g. olive oil) and reused amphorae. These simulated distributions will differ between experiments depending on the experiment’s variable settings representing the tested theory: variations in the probability of reuse, the supply volume, the probability of reuse at ports. What we are interested in teasing out is what the effect is of each theory on the simulated amphora distributions.

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NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

The Archaeological Sampling Experimental Laboratory (tASEL) is an interactive tool for setting up and conducting experiments about sampling strategies for archaeological excavation, survey, and prospection.

This model aims to explore how gambling-like behavior can emerge in loot box spending within gaming communities. A loot box is a purchasable mystery box that randomly awards the player a series of in-game items. Since the contents of the box are largely up to chance, many players can fall into a compulsion loop of purchasing, as the fear of missing out and belief in the gambler’s fallacy allow one to rationalize repeated purchases, especially when one compares their own luck to others. To simulate this behavior, this model generates players in different network structures to observe how factors such as network connectivity, a player’s internal decision making strategy, or even common manipulations games use these days may influence a player’s transactions.

Style_Net_01 is a spatial agent-based model designed to serve as a platform for exploring geographic patterns of tool transport and discard among seasonally mobile hunter-gatherer populations. The model has four main levels: artifact, person, group, and system. Persons make, use, and discard artifacts. Persons travel in groups within the geographic space of the model. The movements of groups represent a seasonal pattern of aggregation and dispersal, with all groups coalescing at an aggregation site during one point of the yearly cycle. The scale of group mobility is controlled by a parameter. The creation, use, and discard of artifacts is controlled by several parameters that specify how many tools each person carries in a personal inventory, how many times each tool can be used before it is discarded, and the frequency of tool usage. A lithic source (representing a geographically-specific, recognizable source of stone for tools) can be placed anywhere in the geographic space of the model.

The Bronze Age Collapse model (BACO model) is written using free NetLogo software v.6.0.3. The purpose of using the BACO model is to develop a tool to identify and analyse the main factors that made the Late Bronze Age and Early Iron Age socio-ecological system resilient or vulnerable in the face of the environmental aridity recorded in the Aegean. The model explores the relationship between dependent and independent variables. Independent variables are: a) inter-annual rainfall variability for the Late Bronze Age and Early Iron Age in the eastern Mediterranean, b) intensity of raiding, c) percentage of marine, agricultural and other calorie sources included in the diet, d) soil erosion processes, e) farming assets, and d) storage capacity. Dependent variables are: a) human pressure for land, b) settlement patterns, c) number of commercial exchanges, d) demographic behaviour, and e) number of migrations.

AncientS-ABM is an agent-based model for simulating and evaluating the potential social organization of an artificial past society, configured by available archaeological data. Unlike most existing agent-based models used in archaeology, our ABM framework includes completely autonomous, utility-based agents. It also incorporates different social organization paradigms, different decision-making processes, and also different cultivation technologies used in ancient societies. Equipped with such paradigms, the model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics; and to assess the influence of social organization on agents’ population growth, agent community numbers, sizes and distribution.

AncientS-ABM also blends ideas from evolutionary game theory with multi-agent systems’ self-organization. We model the evolution of social behaviours in a population of strategically interacting agents in repeated games where they exchange resources (utility) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the progressive adoption of the most successful agent strategies. Agent population is not fixed, but fluctuates over time, while agents in stage games also receive non-static payoffs, in contrast to most games studied in the literature. To tackle this, we defined a novel formulation of the evolutionary dynamics via assessing agents’ rather than strategies’ fitness.

As a case study, we employ AncientS-ABM to evaluate the impact of the implemented social organization paradigms on an artificial Bronze Age “Minoan” society, located at different geographical parts of the island of Crete, Greece. 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 better sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, where a “heterarchical” social structure emerges. Results also demonstrate that successful agent societies adopt an evolutionary approach where cooperation is an emergent strategic behaviour. In simulation scenarios where the natural disaster module was enabled, we observe noticeable changes in the settlements’ distribution, relating to significantly higher migration rates immediately after the modeled Theran eruption. In addition, the initially cooperative behaviour is transformed to a non-cooperative one, thus providing support for archaeological theories suggesting that the volcanic eruption led to a clear breakdown of the Minoan socio-economic system.

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This model is an extension of the Artificial Long House Valley (ALHV) model developed by the authors (Swedlund et al. 2016; Warren and Sattenspiel 2020). The ALHV model simulates the population dynamics of individuals within the Long House Valley of Arizona from AD 800 to 1350. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. The present version of the model incorporates features of the ALHV model including realistic age-specific fertility and mortality and, in addition, it adds the Black Mesa environment and population, as well as additional methods to allow migration between the two regions.

As is the case for previous versions of the ALHV model as well as the Artificial Anasazi (AA) model from which the ALHV model was derived (Axtell et al. 2002; Janssen 2009), this version makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original AA model to estimate annual maize productivity of various agricultural zones within the Long House Valley. A new environment and associated methods have been developed for Black Mesa. Productivity estimates from both regions are used to determine suitable locations for households and farms during each year of the simulation.

This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).

As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.