The model explores the impact of public disclosure on tax compliance among diverse agents, including individual taxpayers and a tax authority. It incorporates heterogeneous preferences and income endowments among taxpayers, captured through a utility function that considers psychic costs subtracted from expected pecuniary utility. These costs include moral, reciprocity, and stigma costs associated with norm violations, leading to variations in taxpayers’ risk attitudes and related parameters. The tax authority’s attributes, such as the frequency of random audits, penalty rate, and the choice between partial or full disclosure, remain fixed throughout the simulation. Income endowments and preference parameters are randomly assigned to taxpayers at the outset.

Taxpayers maximize their expected utility by reporting income, taking into account tax, penalty, and audit rates. They make annual decisions based on their own and their peers’ behaviors from the previous year. Taxpayers indirectly interact at the societal level through public disclosure conducted by the tax authority, exchanging tax information among peers. Each period in the simulation collects data on total reported income, average compliance rates per income group, distribution of compliance rates, counts of compliers, full evaders, partial evaders, and the numbers of taxpayers experiencing guilt and anger. The model evaluates whether public disclosure positively or negatively impacts compliance rates and quantifies this impact based on aggregated individual reporting behaviors.

This model reproduces the double auction experiments and explores the difference between short-term and long-term trading and pricing.

Interactions of players embedded in a closed square lattice are determined by distance and overall gains and they lead to shifts of reward payoff between temptation and punishment. A new winner balancing against threats is ultimately discovered.

LethalGeometry was developed to examine whether territory size influences the mortality risk for individuals within that territory. For animals who live in territoral groups and are lethally aggressive, we can expect that most aggression occurs along the periphery (or border) between two adjacent territories. For territories that are relatively large, the periphery makes up a proportionately small amount of the of the total territory size, suggesting that individuals in these territories might be less likely to die from these territorial skirmishes. LethalGeometry examines this geometric relationship between territory size and mortality risk under realistic assumptions of variable territory size and shape, variable border width, and stochastic interactions and movement.

The individuals (agents) are programmed to walk randomly about their environment, search for and eat food to obtain energy, reproduce if they can, and act aggressively toward individuals of other groups. During each simulation step, individuals analyze their environment and internal state to determine which actions to take. The actions available to individuals include moving, fighting, and giving birth.

This model builds on another model in this library (“diffusion of culture”).

This model illustrates a positive ‘transport’ feedback loop in which lines with different resistance to flows of material result in variation in rates of change in linked entities.

We construct a new type of agent-based model (ABM) that can simultaneously simulate land-use changes at multiple distant places (namely TeleABM, telecoupled agent-based model). We use soybean trade between Brazil and China as an example, where Brazil is the sending system and China is the receiving system because they are the world’s largest soybean exporter and importer respectively. We select one representative county in each country to calibrate and validate the model with spatio-temporal analysis of historical land-use changes and the empirical analysis of household survey data. The whole model is programmed on RePast Simphony. The most unique features of TeleABM are that it can simulate a telecoupled system and the flows between sending and receiving systems in this telecoupled system.

This repository contains the Python implementation of an agent-based model investigating how heterogeneous mobility and boundary-crossing dynamics generate large-scale connectivity in structured multi-attractor landscapes. A small fraction of higher-mobility agents explores the environment while the remaining agents stay locally constrained. The model compares pure random-walk exploration, interface-sensitive dynamics, novelty-biased exploration, and a flat-landscape control. The release includes the complete simulation framework, the experimental pipeline used in the associated paper, multi-K and population-scaling robustness analyses, network-snapshot and transition-slope analyses, and reproducibility documentation. Transition networks are analysed using graph density, connected components, and giant weakly connected components. The implementation is compatible with Python 3.11 and is released under the MIT License.

This Agent-Based model intends to explore the conditions for the emergence and change of land use patterns in Central Asian oases and similar contexts.

STiMUS (Stigmergic–Mutualistic IMOI Model) is an agent-based model of teamwork in socio-technical systems where contributors collaborate through shared digital artefacts — wiki pages, code files, issue tickets, project cards, Scratch projects — represented as patches in a NetLogo world. The model integrates two coordination mechanisms. Stigmergy is indirect coordination through traces left in a shared environment: each edit deposits a pheromone that diffuses to neighbouring patches and evaporates over time, so recent activity attracts further contributions. Mutualism is a reciprocal benefit loop in which valuable, well-maintained artefacts raise contributor motivation and shared understanding, while motivated contributors improve artefacts.

Contributors (turtles of the contributor breed) carry individual state: skill, motivation, shared-mental-model, specialty, benefit-gain, and an explicit-mode flag. At each tick every contributor selects a target artefact with an ant-colony-optimization-style rule weighing the artefact’s pheromone, incompleteness (1 - completeness), resource-value, and topic match between specialty and the artefact’s topic-tag; with probability p-explicit it instead takes the patch with the highest maintenance-need, modelling explicit task assignment. Each edit increases pheromone, quality, completeness and reuse-count, raises resource-value, lowers maintenance-need, and appends the editor to the artefact’s edit-authors list. When the previous last-editor-id differs from the current editor, the Edit Succession Ratio rises, the editor’s shared-mental-model grows, and a co-editing link is created — operationalising the idea that repeated cross-author succession on the same artefact builds shared understanding. Contributors’ motivation is updated from the benefit drawn from the visited artefact.

Each patch maintains a stigmergic layer (pheromone, quality, completeness, recentness, last-editor-id, edit-count, edit-authors) and a mutualistic layer (resource-value, reuse-count, maintenance-need, topic-tag), plus task flags (is-task?, task-complexity). Global monitors report the Edit Succession Ratio (ESR = cross-author-edits / total-edits, and an alternative esr-value = share of edited patches with more than one distinct author), mean-quality, mean-resource-value, a mutualism-index averaging contributor benefit and resource value, coediting-density (network density of the co-editing graph), active-pages-share, and task-completion-rate. The model logs every edit as a bipartite edge (tick, author_id, pageid, specialty, topic_tag, quality), exportable to CSV.

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