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 agent-based model simulates the lifecycle, movement, and satisfaction of teachers within an urban educational system composed of multiple universities and schools. Each teacher agent transitions through several possible roles: newcomer, university student, unemployed graduate, and employed teacher. Teachers’ pathways are shaped by spatial configuration, institutional capacities, individual characteristics, and dynamic interactions with schools and universities. Universities are assigned spatial locations with a controllable level of centralization and are characterized by academic ratings, capacity, and alumni records. Schools are distributed throughout the city, each with a limited number of vacancies, hiring requirements, and offered salaries. Teachers apply to universities based on the alignment of their personal academic profiles with institutional ratings, pursue studies, and upon graduation become candidates for employment at schools.
The employment process is driven by a decentralized matching of teacher expectations and school offers, taking into account factors such as salary, proximity, and peer similarity. Teachers’ satisfaction evolves over time, reflecting both institutional characteristics and the composition of their colleagues; low satisfaction may prompt teachers to transfer between schools within their mobility radius. Mortality and teacher attrition further shape workforce dynamics, leading to continuous recruitment of newcomers to maintain a stable population. The model tracks university reputation through the academic performance and number of alumni, and visualizes key metrics including teacher status distribution, school staffing, university alumni counts, and overall satisfaction. This structure enables the exploration of policy interventions, hiring and training strategies, and the impact of spatial and institutional design on the allocation, retention, and happiness of urban educational staff.