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.

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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Negotiation plays a fundamental role in shaping human societies, underpinning conflict resolution, institutional design, and economic coordination. This article introduces E³-MAN, a novel multi-agent model for negotiation that integrates individual utility maximization with fairness and institutional legitimacy. Unlike classical approaches grounded solely in game theory, our model incorporates Bayesian opponent modeling, transfer learning from past negotiation domains, and fallback institutional rules to resolve deadlocks. Agents interact in dynamic environments characterized by strategic heterogeneity and asymmetric information, negotiating over multidimensional issues under time constraints. Through extensive simulation experiments, we compare E³-MAN against the Nash bargaining solution and equal-split baselines using key performance metrics: utilitarian efficiency, Nash social welfare, Jain fairness index, Gini coefficient, and institutional compliance. Results show that E³-MAN achieves near-optimal efficiency while significantly improving distributive equity and agreement stability. A legal application simulating multilateral labor arbitration demonstrates that institutional default rules foster more balanced outcomes and increase negotiation success rates from 58% to 98%. By combining computational intelligence with normative constraints, this work contributes to the growing field of socially aware autonomous agents. It offers a virtual laboratory for exploring how simple institutional interventions can enhance justice, cooperation, and robustness in complex socio-legal systems.

The Agent-Based Model for Multiple Team Membership (ABMMTM) simulates design teams searching for viable design solutions, for a large design project that requires multiple design teams that are working simultaneously, under different organizational structures; specifically, the impact of multiple team membership (MTM). The key mechanism under study is how individual agent-level decision-making impacts macro-level project performance, specifically, wage cost. Each agent follows a stochastic learning approach, akin to simulated annealing or reinforcement learning, where they iteratively explore potential design solutions. The agent evaluates new solutions based on a random-walk exploration, accepting improvements while rejecting inferior designs. This iterative process simulates real-world problem-solving dynamics where designers refine solutions based on feedback.

As a proof-of-concept demonstration of assessing the macro-level effects of MTM in organizational design, we developed this agent-based simulation model which was used in a simulation experiment. The scenario is a system design project involving multiple interdependent teams of engineering designers. In this scenario, the required system design is split into three separate but interdependent systems, e.g., the design of a satellite could (trivially) be split into three components: power source, control system, and communication systems; each of three design team is in charge of a design of one of these components. A design team is responsible for ensuring its proposed component’s design meets the design requirement; they are not responsible for the design requirements of the other components. If the design of a given component does not affect the design requirements of the other components, we call this the uncoupled scenario; otherwise, it is a coupled scenario.

The aim of our model is to investigate the team dynamics through two types of task allocation strategies, with a focus on the dynamic interplay between individual needs and group performance. To achieve this goal, we have formulated an agent-based model (ABM) to formalize Deci & Ryan’s self-determination theory (SDT) and explore the social dynamics that govern the relationship between individual and group levels of team performance.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

Agent-based models of organizational search have long investigated how exploitative and exploratory behaviors shape and affect performance on complex landscapes. To explore this further, we build a series of models where agents have different levels of expertise and cognitive capabilities, so they must rely on each other’s knowledge to navigate the landscape. Model A investigates performance results for efficient and inefficient networks. Building on Model B, it adds individual-level cognitive diversity and interaction based on knowledge similarity. Model C then explores the performance implications of coordination spaces. Results show that totally connected networks outperform both hierarchical and clustered network structures when there are clear signals to detect neighbor performance. However, this pattern is reversed when agents must rely on experiential search and follow a path-dependent exploration pattern.

This ABM simulates problem solving agents as they work on a set of tasks. Each agent has a trait vector describing their skills. Two agents might form a collaboration if their traits are similar enough. Tasks are defined by a component vector. Agents work on tasks by decreasing tasks’ component vectors towards zero.

The simulation generates agents with given intrapersonal functional diversity (IFD), and dominant function diversity (DFD), and a set of random tasks and evaluates how agents’ traits influence their level of communication and the performance of a team of agents.

Modeling results highlight the importance of the distributions of agents’ properties forming a team, and suggests that for a thorough description of management teams, not only diversity measures based on individual agents, but an aggregate measure is also required.

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The model presented here is extensively described in the paper ‘Talk less to strangers: How homophily can improve collective decision-making in diverse teams’ (forthcoming at JASSS). A full replication package reproducing all results presented in the paper is accessible at https://osf.io/76hfm/.

Narrative documentation includes a detailed description of the model, including a schematic figure and an extensive representation of the model in pseudocode.

The model develops a formal representation of a diverse work team facing a decision problem as implemented in the experimental setup of the hidden-profile paradigm. We implement a setup where a group seeks to identify the best out of a set of possible decision options. Individuals are equipped with different pieces of information that need to be combined to identify the best option. To this end, we assume a team of N agents. Each agent belongs to one of M groups where each group consists of agents who share a common identity.
The virtual teams in our model face a decision problem, in that the best option out of a set of J discrete options needs to be identified. Every team member forms her own belief about which decision option is best but is open to influence by other team members. Influence is implemented as a sequence of communication events. Agents choose an interaction partner according to homophily h and take turns in sharing an argument with an interaction partner. Every time an argument is emitted, the recipient updates her beliefs and tells her team what option she currently believes to be best. This influence process continues until all agents prefer the same option. This option is the team’s decision.

This version 2.1.0 of the uFunk model is about setting a business strategy (the S in the name) for an organization. A team of managers (or executives) meet and discuss various options on the strategy for the firm. There are three aspects that they have to agree on to set the strategic positioning of the organization.
The discussion is on market, stakeholders, and resources. The team (it could be a business strategy task force) considers various aspects of these three elements. The resources they use to develop the discussion can come from a traditional approach to strategy or from non-traditional means (e.g., so-called serious play, creativity and imagination techniques).
The S-uFunk 2.1.0 Model wants to understand to which extent cognitive means triggered by traditional and non-traditional resources affect the making of the strategy process.