An agent-based model of irregular warfare in which civilians adapt their alignment in response to local violence, security presence, and territorial control. The simulation explores how decentralized interactions generate spatial patterns of loyalty, conflict dynamics, and stabilization.

This archive contains the SAFE-ABM computational model developed to evaluate targeted intervention strategies for protecting essential workers during epidemic conditions, accompanying the published article “Essential Workers at Risk: An Agent-Based Model (SAFE-ABM) with Uncertainty Quantification” in the Journal of Artificial Societies and Social Simulation, 29(4), Article 7 (2026). https://doi.org/10.18564/jasss.6276. SAFE-ABM is an agent-based model that simulates disease transmission through structured interactions across households, workplaces, and schools, while incorporating stochastic disease progression and uncertainty in transmission, recovery, and mortality parameters.

The model evaluates five intervention scenarios: Scenario 1, no restrictions; Scenario 2, school-from-home; Scenario 3, essential-workers-only movement; Scenario 4, rotational shifts among essential workers with quarantine of symptomatic active workers; and Scenario 5, rotational shifts among essential workers without quarantine. The archive includes scenario-specific simulation scripts, comparison-analysis scripts, saved derived outputs, README documentation, software requirements, and reproducibility notes.

The included comparison scripts reproduce the main numerical summaries and manuscript outputs, including median epidemic trajectories, predictive-uncertainty plots, cumulative recovery and death summaries, inflection-point summaries, Welch-test comparisons, and mutual-information sensitivity rankings. Saved derived outputs are included so that the main figures and tables can be reproduced without rerunning all large-scale simulations.

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Subjective biases and errors systematically affect market equilibria, whether at the population level or in bilateral trading. Here, we consider the possibility that an agent engaged in bilateral trading is mistaken about her own valuation of the good she expects to trade, that has not been explicitly incorporated into the existing bilateral trade literature. Although it may sound paradoxical that a subjective private valuation is something an agent can be mistaken about, as it is up to her to fix it, we consider the case in which that agent, seller or buyer, consciously or not, given the structure of a market, a type of good, and a temporary lack of information, may arrive at an erroneous valuation. The typical context through which this possibility may arise is in relation with so-called experience goods, which are sold while all their intrinsic qualities are still unknown (such as untasted bottled fine wines). We model this “private misvaluation” phenomenon in our study. The agents may also be mistaken about how their exchange counterparties are themselves mistaken. Formally, they attribute a certain margin of error to the other agent, which can differ from the actual way that another agent misvalues the good under consideration. This can constitute the source of a second-order misvaluation. We model different attitudes and situations in which agents face unexpected signals from their counterparties and the manner and extent to which they revise their initial beliefs. We analyse and simulate numerically the consequences of first-order and second-order misvaluation on market equilibria.

This is an extension of the basic Suceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in two spaces, one a treatment, and one a control. Through the modeling options, one can explore how changing assumptions about the number of susceptible people, starting number of infected people, the disease’s infection probability, and average duration impacts the outcome. In addition, this version allows users to explore how public health interventions like social distancing, masking, and isolation can affect the number of people infected. The model shows that the interactions of agents, and the interventions can drastically affect the results of the model.

We used the model in our course about COVID-19: https://www.csats.psu.edu/science-of-covid19

Infantry Company Engagement model including command and control functions for the scenario of an offensive operation of the blue force

This is an agent-based model of a population of scientists alternatively authoring or reviewing manuscripts submitted to a scholarly journal for peer review. Peer-review evaluation can be either ‘confidential’, i.e. the identity of authors and reviewers is not disclosed, or ‘open’, i.e. authors’ identity is disclosed to reviewers. The quality of the submitted manuscripts vary according to their authors’ resources, which vary according to the number of publications. Reviewers can assess the assigned manuscript’s quality either reliably of unreliably according to varying behavioural assumptions, i.e. direct/indirect reciprocation of past outcome as authors, or deference towards higher-status authors.

This is a model of innovation implementation inside an organization. It characterizes an innovation as a set of distributed and technically interdependent tasks performed by a number of different and socially interconnected frontline workers.

The teamCognition model investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. The agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions.

A demonstration model showing how modellers can create a multi regional tram network with commuters, destinations and houses. The model offers options to create a random tram network made from modeller input or to load shapefiles for the Greater Manchester Metrolink.

The model uses NetLogo with gis, nw an csv extensions.

An agent-based model designed as a tool to assess and plan free-ranging dog population management programs that implement Animal Birth Control (ABC). The time, effort, financial resources and conditions needed to successfully control dog populations and achieve rabies control can be determined by performing virtual experiments using DogPopDy.