Diversity has become a defining feature of most Western societies, where boundaries of membership extend beyond ethnicity to encompass multiple intersecting categories, increasing the complexity of social integration. The aim of this model is to adapt the classical Schelling model of spatial segregation to this context and to identify mechanisms that may generate the hybrid segregation patterns where individuals are segregated from out-groups along one dimension while remaining integrated along others.

We extend the Schelling model using a discrete choice framework that incorporates dominant and secondary preferences among agents from two ethnic groups. Liberal agents prioritize cross-ethnic shared values over ethnicity, whereas conservative agents prioritize ethnic membership over shared values, although both retain some preference for the secondary dimension. The model is designed to explore how heterogeneous preferences and intersecting group memberships interact under different group compositions and initial distributions.

An agent-based model of how public space conditions shape the collective formation of local sports communities in high-mobility cities. A small gathering is already exercising in an abstract, systematically configurable public space; the model asks how strangers join them and whether the loose gathering consolidates into a stable community. Three conditions are manipulated, each acting through exactly one channel: spatial legibility governs whether a passer-by notices the activity, routine compatibility governs whether a member attends in a given week, and social anchor availability governs how much one episode of co-presence builds a tie. Ties carry a continuous strength with two thresholds, so that relationships of differing strength cross their thresholds at different condition levels. Because the model does not represent the origin of an activity, dissolution is irreversible and survival is a clean binary outcome. Across 32,790 runs in eight experiments the model yields two well separated thresholds along routine compatibility — one for survival, one for consolidation — with a wide band between them in which a space is used continuously but no community forms. The tie submodel is adapted from Jin, Girvan and Newman (2001) and verified against it by docking. Robustness is established against the seeded gathering, the pre-existing social network, a joint ±20 per cent perturbation of the behavioural parameters, and the parameters of the tie machinery.

This is a NetLogo 7.0.1 agent-based model of interdependent team productivity under O-ring production logic. The model asks when collective problem-solving capacity (CPS) improves team output and when its effect is constrained by trust, burnout, specialization diversity, weak-link quality, and team-formation rules. Agents are heterogeneous in skill, CPS, trust, burnout, effort, learning rate, openness, specialization, adaptability, and aspiration. Each tick forms temporary teams, computes individual contribution quality, combines contributions through a geometric O-ring production function with an explicit weakest-link term, records output and success, and updates agents through feedback, learning, trust change, burnout, recovery, and turnover. The paper follows the ODD protocol, reports exact implementation equations, and analyzes six BehaviorSpace experiments with 50 stochastic repetitions per condition. Scenario results show that CPS is beneficial but not sufficient: productivity is highest when CPS is combined with trust, low burnout, effective coordination, and a reliable weakest-link floor. Diversity yields only modest gains at low CPS but larger gains when CPS is high. Formation-mode results are especially informative because mixed CPS-skill formation maximizes total output, whereas random formation has the highest binary success rate, showing that output magnitude and threshold success can diverge. The study contributes a transparent computational mechanism linking collective intelligence with O-ring production and identifies boundary conditions under which high-CPS teams can still be limited by weak links and interdependence.

MOSAIC is an agent-based NetLogo model of decentralized mission coordination among heterogeneous robots operating under partial observability, limited energy, spatially variable risk, dynamic communication, and individual and cooperative task requirements. Robots discover tasks locally, exchange task information through temporary communication links, submit capability-, energy-, deadline-, and risk-aware bids, compete for individual contracts, and form temporary coalitions for cooperative tasks.

The model integrates decentralized auctions, greedy capability-based coalition formation, contract release and reassignment, four reward regimes, reputation, adaptive bidding strategies, failure traceability, and mission-, network-, information-, inequality-, and coalition-level metrics. It operates without a centralized mission planner or global combinatorial assignment solver.

Seven paired-seed BehaviorSpace experiments comprising 690 official simulation runs evaluate baseline mission viability, reward regimes, communication structure, capability heterogeneity, cooperative-task demand, reputation and adaptive strategies, and mission-incentive strength. The results indicate that structural coordination capacity—particularly information reach, capability compatibility, and feasible coalition construction—has a stronger effect on mission completion than increasing incentive intensity within the tested architecture and parameter ranges.

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STiMUS-HAI (Stigmergic–Mutualistic IMOI Model, Human-AI extension) is an agent-based model of teamwork in socio-technical systems where human and AI contributors collaborate through shared digital artefacts — wiki pages, code files, issue tickets, project cards, Scratch projects — represented as patches in a NetLogo world. It extends the human-only base model STiMUS v2.2, which established that two coordination mechanisms — stigmergy (indirect coordination through traces left in the environment) and mutualism (mutual benefit between contributors and the artefacts they tend) — can be decoupled: stigmergy decides where a contributor works, mutualism decides with what effort. STiMUS-HAI preserves this decoupling unchanged and adds two further theoretical questions: whether mixing AI agents into a human team distorts human coordination in ways that aggregate indicators hide, and whether AI’s cost to team outcomes depends on the type of work AI performs, not only on how much of it is present.

Two breeds of turtle — humans and ai-agents — follow identical target-selection, pheromone, and mutualism rules, so that any behavioural difference is attributable to team composition rather than a built-in advantage. The one designed asymmetry: AI agents never accumulate shared-mental-model and their motivation is fixed rather than adaptive. On top of this v3.0 baseline, v3.1 adds a task-type dimension to artefacts (“prediction” versus “judgment”, set via a judgment-share slider) that scales down AI edit-power specifically on judgment-requiring artefacts, and an ai-trust mechanic: humans build or lose trust in AI contributions based on the population-relative percentile rank of observed AI work quality (bottom-quartile work counts as an observed “error”), and that trust gates how much mutualistic benefit a human derives from continuing an AI’s work. Trust erodes quickly on a single error and recovers only after a streak of confirmed successes — an intentional asymmetry.

Party Competition with Costly Voting builds upon the baseline model in Chapter 5 from Laver and Sergenti’s Party Competition (2011); it adds voting costs to generate variable turnout.

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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This model is a part of an ongoing research project on Multiagent Reinforcement Learning (MARL). The ODD protocol is included in the model. In this version of the model, Proximal Policy Optimization (PPO) is designed in the agent behaviors. It also includes a designed experiment in its Behavior Space which is used in the Response Surface Methodology and training of an Artificial Neural Network (ANN) based Recommender System.

An agent-based microsimulation of insecticide-treated net (ITN) distribution and adoption in Kenya (2003–2024), integrating the Theory of Planned Behaviour, Rogers diffusion, Weibull net decay, and a GPS-based two-layer social network. 8,561 household agents calibrated via Approximate Bayesian Computation to six DHS/MIS survey waves, achieving 2.42 pp mean absolute error on Kenya-level ownership. The analysis chain supports mechanism counterfactuals and policy experiments on equity outcomes of ITN distribution strategies.

This model visualizes gradient descent optimization - the fundamental algorithm used to train neural networks and other machine learning models. Agents represent different optimization algorithms searching for the minimum of a loss landscape (the “error surface” that ML models try to minimize during training).

The model demonstrates how different optimizer types (SGD, Momentum with different parameters) behave on various loss landscapes, from simple bowls to the notoriously difficult Rosenbrock “banana valley” function. This helps build intuition about why certain optimization algorithms work better than others for different problem geometries.

HOW IT WORKS

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