This spatially explicit agent-based model addresses how effective foraging radius (r_e) affects the effective size–and thus the equilibrium cultural diversity–of a structured population composed of central-place foraging groups.

The model measures drivers of effectiveness of risk assessments in risk workshops regarding the correctness and required time. Specifically, we model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use Bayesian networks to assess a given risk individually and as a group.

An agent-based model simulating West Nile Virus dynamics in a one host (American robin)-one vector (Culex spp. mosquito) system. ODD improved and code cleaned.

Considering that two of the three avoider species could not reach the target area in the inittial scenario, five alternative corridor scenarios were created. In all cases, we generated a greater amount of cover area under ‘Urban forest’, including elements such as scattered trees, woody plants, wooded areas, and rows of trees. This covered type was selected since all three species use it as a regular habitat. That is the second sceneario where those ecological parks and other areas inside the capital city were boostered into “urban forest patches” or buffer points, with the idea of improving the survive of the three bird species and their movement. However one of the most restrictive specie was still having movement and survival issues.

An ABM, derived from a case study and a series of surveys with greenhouse growers in the Westland, Netherlands. Experiments using this model showshow that the greenhouse horticulture industry displays diversity, adaptive complexity and an uneven distribution, which all suggest that the industry is an evolving system.

The Communicating Hazard Information in the Modern Environment (CHIME) agent-based model (ABM) is a Netlogo program that facilitates the analysis of information flow and protective decisions across space and time during hazardous weather events. CHIME ABM provides a platform for testing hypotheses about collective human responses to weather forecasts and information flow, using empirical data from historical hurricanes. The model uses real world geographical and hurricane data to set the boundaries of the simulation, and it uses historical hurricane forecast information from the National Hurricane Center to initiate forecast information flow to citizen agents in the model.

An Agent-Based Model to simulate agent reactions to threatening information based on the anxiety-to-approach framework of Jonas et al. (2014).
The model showcases the framework of BIS/BAS (inhibitory and approach motivated behavior) for the case of climate information, including parameters for anxiety, environmental awareness, climate scepticism and pro-environmental behavior intention.

Agents receive external information according to threat-level and information frequency. The population dynamic is based on the learning from that information as well as social contagion mechanisms through a scale-free network topology.

The model uses Netlogo 6.2 and the network extension.
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This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.

This research article presents an agent-based simulation hereinafter called COMMONSIM. It builds on COMMONISM, i.e. a large-scale commons-based vision for a utopian society. In this society, production and distribution of means are not coordinated via markets, exchange, and money, or a central polity, but via bottom-up signalling and polycentric networks, i.e. ex-ante coordination via needs. Heterogeneous agents care for each other in life groups and produce in different groups care, environmental as well as intermediate and final means to satisfy sensual-vital needs. Productive needs decide on the magnitude of activity in groups for a common interest, e.g. the production of means in a multi-sectoral artificial economy. Agents share cultural traits identified by different behaviour: a propensity for egoism, leisure, environmentalism, and productivity. The narrative of this utopian society follows principles of critical psychology and sociology, complexity and evolution, the theory of commons, and critical political economy. The article presents the utopia and an agent-based study of it, with emphasis on culture-dependent allocation mechanisms and their social and economic implications for agents and groups.

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.