An agent-based model of the scapegoat mechanism of René Girard’s mimetic theory on a coevolving network. Agents accumulate tension from a shared stressor and from contagion; an agent whose whole neighbourhood is saturated accuses another; ties reorganise around accusers; once the marked exceed a tenth of the living, a threshold rule expels one of them and the group’s tension is discharged. The model asks when a stressed population reaches the collective act, what it costs and what it leaves behind: without the accusation rules tension saturates and most of the population dies; with them tension stays low and the cost falls on a few; closure (the reluctance to form new ties) decides whether expulsion occurs and skepticism decides when; the act’s lasting mark is a lower division into communities. A null model, the ablation of each rule, six initial networks and controls on the reach of the relief test the results. Release 1.1.0 is the package behind a manuscript submitted to JASSS (November 2026): six model versions, the per-tick output of every series, daily aggregates of eight anonymised episodes of online hostility and the scripts that regenerate every table and figure. Releases 1.0.x hold an earlier version of the model alone.

Artificial Anasazi is an agent-based model of farming households in the Long House Valley, Arizona, from 800 to 1350 CE. Each household chooses where to farm and settle based on expected harvest, water sources and stored maize, and simulated household counts are compared with the archaeological record.

This release adapts the NetLogo Models Library version (Stonedahl & Wilensky 2010) of Janssen’s (2009) NetLogo replication of the original Ascape model (Dean et al. 2000; Axtell et al. 2002). It contains four configurations in one NetLogo 7.0.4 file, selected with two switches:

• Both switches off: the baseline, which is the Models Library model with small fixes that make it work as intended (the arable-uplands zone name, the setup base yield, the initial household count, one-tick-lag capacity reporting, and seed control with observation-only instruments).
• soil-on?: dynamic soil quality that depletes under cultivation and regrows, following Janssen (2010, eq. 4).
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This model is an extended version of the original MERCURY model (https://www.comses.net/codebases/4347/releases/1.1.0/ ) . It allows for experiments to be performed in which empirically informed population sizes of sites are included, that allow for the scaling of the number of tableware traders with the population of settlements, and for hypothesised production centres of four tablewares to be used in experiments.

Experiments performed with this population extension and substantive interpretations derived from them are published in:

Hanson, J.W. & T. Brughmans. In press. Settlement scale and economic networks in the Roman Empire, in T. Brughmans & A.I. Wilson (ed.) Simulating Roman Economies. Theories, Methods and Computational Models. Oxford: Oxford University Press.

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The agent based model matches origins and destinations using employment search methods at the individual level.

What is it?

This model demonstrates a very simple bidding market where buyers try to acquire a desired item at the best price in a competitive environment

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Simulation-Framework to study the governance of complex, network-like sociotechnical systems by means of ABM. Agents’ behaviour is based on a sociological model of action. A set of basic governance mechanisms helps to conduct first experiments.

The dynamic agent based model of system which turn out the self-adjusting system, are considered in this text.

The model of market of one commodity , in which there are in each moment of time the same quantity and the same quantity of money was formulated and researched in this text. We also study this system as a game of automata.

Industrial clustering patterns are the result of an entrepreneurial process where spinoffs inherit the ideas and attributes of their parent firms. This computational model maps these patterns using abstract methodologies.

Manipulate[
Module[{fDot, mDot, poly, roots, stableRoots, rStar, rIso,
endPointStar, endPointIso},(1. Define the System Dynamics)
fDot = phi1(f/m) - phi2(m/f);
mDot = mu1(f/m) - mu2(m/f);
(*2. Find the Equilibrium Ratio r=f/
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