The tragedy of the commons in public resource governance is essentially the result of repeated interaction and adaptive learning among heterogeneous agents under dynamic resource constraints. Existing studies have generated rich insights into common-pool resource governance, institutional constraints, and cooperation, but they still rely mainly on theoretical deduction, static games, or econometric identification, making it difficult to jointly characterize resource dynamics, agent heterogeneity, behavioral learning, and policy scenarios. From the perspective of computational economics, this paper develops a multi-agent reinforcement learning simulation model for the governance of the tragedy of the commons. Specifically, the fish-pond resource system is formulated as a Markov decision process in discrete states, and adaptive decision-making under pure economic incentives, sustainability penalties, behavioral heterogeneity, and cooperation is characterized through the resource dynamics equation, harvesting equation, and differentiated reward functions. The model is further examined through sensitivity analysis, parameter calibration, and theoretical validation, and then used for policy simulation. The results show that pure economic incentives quickly induce resource collapse, sustainability penalties significantly reduce harvesting intensity and maintain a low but sustainable steady state, heterogeneous behavior parameters generate clear strategic divergence, and cooperation internalizes group harvesting constraints into individual payoffs and yields the strongest resource recovery and behavioral convergence.

This model is an agent-based simulation designed to explore how climate-induced environmental degradation can contribute to the emergence of social violence in coastal communities that depend heavily on ecosystem services for their livelihoods. The model represents a coupled social–ecological system in which environmental shocks—such as sea level rise and marine ecosystem decline—affect local economic conditions, food security, and community stability.

Agents in the model represent individuals whose livelihoods depend on coastal ecosystems. Environmental degradation reduces ecosystem productivity and increases economic hardship, which can lead to the formation of grievances among agents. The model incorporates behavioral thresholds that determine how individuals respond to hardship and perceived injustice. Under certain conditions—particularly when institutional capacity and law enforcement effectiveness are limited—these grievances may escalate into violent behavior.

The simulation allows users to explore how different climate scenarios, levels of ecosystem degradation, livelihood dependence, and institutional responses influence the probability of social instability and violence. By modeling the interactions between environmental stress, socio-economic vulnerability, and governance capacity, the model provides a computational framework for examining potential pathways linking climate change and conflict in coastal social–ecological systems.

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An agent-based model of urban travel behaviour in Dublin, Ireland, built in NetLogo and empirically grounded in 2016 travel survey data. Each agent represents a Dublin resident initialised with real socio-demographic attributes — including age, gender, household size and car ownership, income, driving licence status, and access to local amenities — alongside observed trip characteristics such as distance, travel time, and trip type (work, shopping, leisure).
At each time step, agents choose between four transport modes (car, public transport, cycling, and walking) across short, medium, and long trips. Mode choice is governed by a preference vector that weighs personal need satisfaction against social influence from neighbouring agents reflecting consumat framework. Satisfaction evolves dynamically based on cost (incorporating Irish motor tax bands and per-km operating rates), travel time, and trip-type suitability, with an uncertainty parameter capturing variability in perceived utility over time.
The model tracks aggregate modal shares and total CO2 emission at each tick, enabling exploration of how policy interventions — such as fuel taxation, public transport pricing, or active travel incentives — might shift the city’s travel demand profile over 100 simulated days.

An agent-based model of irregular warfare in which civilians adapt their alignment in response to local violence, security presence, and territorial control. The simulation explores how decentralized interactions generate spatial patterns of loyalty, conflict dynamics, and stabilization.

The aim of this model is to study the dynamic propagation of individual climate adaptive behaviours in different scenarios within the analytical framework of conservation motivation theory, focusing on the impact of social and experiential learning on the adoption of climate adaptive behaviours by coastal farmers.
Model for paper “Promoting climate resilience through learning-based behavioural change: Insights from an agent-based model of a coastal farming community in Guangxi, China” in Environmental Science & Policy, Volume 179, May 2026, 104375, https://doi.org/10.1016/j.envsci.2026.104375

This model aims to study the dynamic propagation of individual behaviour within social networks, focusing on how normative expectations (NE) and experiential expectations (EE) jointly influence behavioural decisions. It also explores the long-term effects of different intervention scenarios (such as enhancing visibility, considering indirect social links, and education) on behavioural propagation patterns and the overall behaviour of the group.
The model was developed in NetLogo 6.4. It generates simulated groups based on large-scale survey data, utilizing NetLogo’s CSV, Table, and Matrix extensions. The model also employs the NW extension to enable network analysis functionality.
The model is designed for research “Shaping social norms to promote individual response behavior in public crises: An agent-based modeling approach” in Journal of Cleaner Production, Volume 554, 8 April 2026, 148014
https://doi.org/10.1016/j.jclepro.2026.148014

Negotiation Lab 1.0 is an agent-based model of peace negotiations that explores how the parties’ readiness — their motivation and optimism to engage in talks — evolves dynamically throughout the negotiation process. The model reconceptualizes readiness as an adaptive state variable that is continuously updated through feedback from negotiation outcomes, rather than a static precondition assessed at the onset of talks.
The model simulates two parties negotiating a multi-issue agenda. In each round, parties allocate effort to the current sub-issue; outcomes depend on their joint effort and a stochastic component representing external factors. Results feed back into each party’s readiness, shaping subsequent engagement. The negotiation ends either when all agenda items are resolved (agreement) or when a party’s readiness falls below a critical threshold (breakdown).
Key parameters include the initial readiness of each party, agenda structure (balanced, hard, easy, red, or random), type of negotiation (from highly cooperative to highly competitive), and each party’s effort strategy (always high, always low, random, or pseudo tit-for-tat). The model shows that while initial readiness is associated with negotiation outcomes, it is neither necessary nor sufficient to determine them: process variables — the type of interaction, agenda design, and adaptive effort strategies — exert comparatively larger effects on outcomes. Identical initial conditions can produce widely divergent trajectories, illustrating path dependence and sensitivity to feedback dynamics.
The model is implemented in NetLogo 7.0 and is documented using the ODD+D protocol. It is associated with the paper “Beyond Initial Conditions: How Adaptive Readiness Shapes Peace Negotiation Outcomes” (Arévalo, under review).

This model visualizes gradient descent optimization - the fundamental algorithm used to train neural networks and other machine learning models. Agents represent different optimization algorithms searching for the minimum of a loss landscape (the “error surface” that ML models try to minimize during training).

The model demonstrates how different optimizer types (SGD, Momentum with different parameters) behave on various loss landscapes, from simple bowls to the notoriously difficult Rosenbrock “banana valley” function. This helps build intuition about why certain optimization algorithms work better than others for different problem geometries.

HOW IT WORKS

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A reproducible NetLogo implementation of a spatial attraction-repulsion opinion model with eviction-driven relocation. Agents interact locally, converge with similar neighbors, diverge from dissimilar neighbors, and may evict the most dissimilar neighbor to a random empty location. Parameter sweeps reveal transitions among extremist, mixed, and consensus regimes, with outputs including phase diagrams, opinion distributions, and Moran’s I. The model is intended to reproduce and extend results on how exclusion frequency changes polarization outcomes.

This computational model is an agent-based model (ABM) developed to investigate how repeated failures of emerging niches accumulate and influence the trajectory of socio-technical transitions. Built in AnyLogic 8.7.11, the model simulates the dynamic interactions between a dominant regime and sequential niche entrants within a two-dimensional practice space. It models alignment, movement, and competition based on technological maturity and market penetration. The model utilizes a reinforcing feedback structure linking consumer support, output, resource accumulation, and capacity development (Physical and Institutional Capacity). A complete model specification following the ODD+D (Overview, Design concepts, Details, and Decision) protocol is included in the documentation.