A simple theoretical agent-based model intending to represent anxiety in students is presented. In this, agents make two decisions each simulation tick, whether to: socialise and go to school (on school days). Circular causation can result in some agents entering a negative loop of high-anxiety and avoidance, even whilst most improve their skills and develop lower levels of anxiety. We compare three different mechanisms that may lie behind decision-making under anxiety: expect the worst, avoid uncertainty and avoid anxiety. Results indicate that, although these are often indistinguishable in terms of some obvious aggregate measures on the outcomes, they do exhibit different underlying dynamics.

This agent-based model simulates the implementation of a Transfer of Development Rights (TDR) mechanism in a stylized urban environment inspired by Dublin. It explores how developer agents interact with land parcels under spatial zoning, conservation protections, and incentive-based policy rules. The model captures emergent outcomes such as compact growth, green and heritage zone preservation, and public cost-efficiency. Built in NetLogo, the model enables experimentation with variable FSI bonuses, developer behavior, and spatial alignment of sending/receiving zones. It is intended as a policy sandbox to test market-aligned planning tools under behavioral and spatial uncertainty.

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.

…

Final project version - still needs a bit of work for being completly operational

Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.

The TechNet_04 is an abstract model that embeds a simple cultural tranmission process in an environment where interaction is structured by spatially-situated networks.

This is a replication of the altruistic trait selection model described in Pepper & Smuts (2000, 2002).

The computer model simulates the development of a social network (i.e. formation of friendships and cliques), the (dyadic) interactions between pupils and the development of similarities and differences in their behavioral profiles.

LUXE is a land-use change model featuring different levels of land market implementation. It integrates utility measures, budget constraints, competitive bidding, and market interactions to model land-use change in exurban environment.

Building upon the distance-based Hotelling’s differentiation idea, we describe the behavioral experience of several prototypes of consumers, who walk a hypothetical cognitive path in an attempt to maximize their satisfaction.