An algorithm implemented in NetLogo that can be used for searching resources.

The purpose of this model is to explore the influence of integrating individuals’ behavioral dynamics in an agent-based model of COVID-19, on the dynamics of disease transmission. The model is an agent-based extention of an established large-scale Individual-based model called STRIDE. Four risk factors determine the individual’s perception of the risk and how they behave accordingly. It is assumed that individuals with higher levels of risk perception adopt higher levels of contact reduction in their daily routines. Individuals can assign different weights to any of the four different risk factors, i.e., the modeler can model different populations and explore how the transmission dynamics vary among them.

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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In order to test how prosocial strategies (compassionate altruism vs. reciprocity) grow over time, we developed an evolutionary simulation model where artificial agents are equipped with different emotionally-based drivers that vary in strength. Evolutionary algorithms mimic the evolutionary selection process by letting the chances of agents conceiving offspring depend on their fitness. Equipping the agents with heritable prosocial strategies allows for a selection of those strategies that result in the highest fitness. Since some prosocial attributes may be more successful than others, an initially heterogeneous population can specialize towards altruism or reciprocity. The success of particular prosocial strategies is also expected to depend on the cultural norms and environmental conditions the agents live in.

This paper develops a spatial agent-based model to examine how fertility regime shifts reshape population concentration and wealth distribution in an abstract urban system. Migration decisions combine population preference, cultural homophily, expected net income, and resource endowment through a standardised softmax utility. The design is deliberately stylised: it is not calibrated to a particular country or city system, but is intended to isolate the feedbacks linking migration, fertility, urban scaling, and accumulated wealth.
The simulations reveal robust directional asymmetry. When fertility shifts from low to high, population concentration responds rapidly; when fertility shifts from high to low, concentration declines only after a detectable delay and may temporarily continue in the previous direction. Wealth adds a second layer of hysteresis: cell total-wealth concentration follows population concentration with delay, cell mean-wealth inequality and system-level wealth indicators are slower still, and phase-space trajectories form loops rather than collapsing onto a single population–wealth curve. Robustness experiments indicate that longer fertility cycles, wider mobility neighbourhoods, and smoother resource landscapes change the magnitude of delay and overshoot, but do not remove the qualitative asymmetry. The paper argues that demographic decline should be understood not as the mirror image of demographic expansion, but as a path-dependent transition mediated by fast migration-income feedbacks and slower fertility, cohort, culture, and wealth mechanisms.

The purpose of the study is to unpack and explore a potentially beneficial role of sharing metacognitive information within a group when making repeated decisions about common pool resource (CPR) use.

We explore the explanatory power of sharing metacognition by varying (a) the individual errors in judgement (myside-bias); (b) the ways of reaching a collective judgement (metacognition-dependent), (c) individual knowledge updating (metacognition- dependent) and d) the decision making context.

The model (AgentEx-Meta) represents an extension to an existing and validated model reflecting behavioural CPR laboratory experiments (Schill, Lindahl & Crépin, 2015; Lindahl, Crépin & Schill, 2016). AgentEx-Meta allows us to systematically vary the extent to which metacognitive information is available to agents, and to explore the boundary conditions of group benefits of metacognitive information.

This is an interdisciplinary agent-based model with Monte Carlo simulations to assess the relative effects of broadcast and contagion processes in a multiplex social network. This multiplex approach models multiple channels of informal communication - phone, word-of-mouth, and social media - that vary in their attribute values. Each agent is an individual in a threatened community who, once warned, has a probability of warning others in their social network using one of these channels. The probability of an individual warning others is based on their warning source and the time remaining until disaster impact, among other variables. Default parameter values were chosen from empirical studies of disaster warnings along with the spatial aspects of Coos Bay, OR, USA and Seaside, OR, USA communities.

Interest-based compound economies generate monotonically increasing wealth inequality through multiplicative accumulation dynamics, yet the conditions under which gift-based reciprocal exchange outperforms such systems in collective well-being remain unquantified. We present Zensei Wago (全生和合), a seven-layer agent-based model comparing a Gift Resource Circulation (GRC) economy with a Compound Interest Circulation (CIC) economy under identical initial conditions. Across N = 5000 Monte Carlo replications (T = 700 ticks, N = 100 agents), GRC produced significantly higher collective resonance than CIC (p < 0.001, Cohen’s d = +0.171), above a critical prosocial threshold pm ≈ 0.698. Cohen’s d grows monotonically with duration — d = +1.943 at T = 1500 and d = +4.126 at T = 3000 — driven primarily by structural collapse of CIC resonance as inequality exceeds a critical Gini threshold (G > 0.333), while GRC resonance remains stable. The gift mechanism further decouples collective well-being from distributional outcomes, generating resonance through relational quality rather than material redistribution. Network topology analysis across seven configurations — combining a Watts-Strogatz rewiring sweep and a T = 1500 longitudinal replication — reveals that ring topology maximises GRC advantage (d = +1.17), that most topology-dependent reversals are transient (sparse and small-world both transition to significantly positive by T = 1500), and that a critical rewiring threshold of p ≈ 0.10–0.20 separates GRC-advantaged from GRC-disadvantaged network configurations. Scale-free networks remain persistently adverse (d = -7.24*), requiring structural redesign for gift-economy viability.

SERDUX-MARCIM simulates the propagation of a cyberattack over the computational network of an organization in the maritime sector, at the strategic level of decision-making. It is the instantiation of ABM-MARCIM, the agent-based model of the MARCIM framework for the modeling and simulation of maritime cyberdefense.

Every computational asset of the target organization – servers, endpoints, routers, gateways, vessel systems, radars – is an agent that occupies one of six states at each time step: Susceptible, Exposed, Resistant, Degraded, Unavailable or Destroyed, the initials of which give the model its name. The states Degraded, Unavailable and Destroyed are associated with the D5 cyberattack effects (disrupt, degrade, deny, destroy, deceive) as a function of the degree and the duration of the attack. Thirteen transitions between states are admissible.

Unlike a conventional agent-based model, the local update function is not an individual behavioral rule. It is a system of six ordinary differential equations with eight time-dependent transition rates – propagation, cyberattack (degraded), cyberattack (unavailable), cyberattack (destroyed), recovery, sanitation, loss of resistance, and unavailability by other causes. The values of those rates derive from the capabilities of the target organization, the capabilities of the attacker, and the degree and duration of the cyberattack, computed through a cyber risk approach aligned with the OWASP Risk Rating Methodology, the ISACA categorization of security controls and the IMO Guidelines on Maritime Cyber Risk Management.

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Ge, J., & Polhill, G. (2016). Exploring the Combined Impact of Factors Influencing Commuting Patterns and CO2 Emission in Aberdeen Using an Agent-Based Model. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/11.html
We develop an agent-based transport model using a realistic GIS-enabled road network and the car following method. The model can be used to study the impact of social interventions such as flexi-time and workplace sharing, as well as large infrastructure such as the construction of a bypass or highway. The model is developed in Netlogo version 5 and requires road network data in GIS format to run.