The model represents a large, traditional firm competing with small, sustainable food producers. The large firm operates under conventional assumptions, pursuing monopoly profits maximized through standard economic principles. Its behavior is driven by economies of scale, which compel small firms to strategically differentiate themselves to deliver added value to consumers. Consequently, small firms focus on offering higher-quality products—a factor that matters significantly to consumers.

The gains achieved by small firms represent social profits that hinge upon establishing co-participation schemes with farmers and building trustworthy relationships with consumers. Cooperation with farmers directly shapes production processes and product quality. Because a firm trusted by one consumer becomes visible to neighbors, trust can diffuse and evolve into a collective identity. Ultimately, population-level legitimation occurs when the organizational form of the organic food producer gains widespread acceptance within the consumer community.

The model’s behavior depends on three key factors: a trust parameter, a legitimation intensity parameter, and an aspiration-level weight (for the large firm).

An agent-based model in which fourteen European Union member states each carry a cooperation
propensity in [0,1], updated by a logistic link applied to a latent index that combines the unit’s
own previous state, a weighted average of the other units’ states, and four min-max normalised
exogenous indicators (cooperative benefit, opportunistic temptation, systemic exit cost,
intertemporal confidence). The logistic centres are anchored so that the observed 2024
configuration is an exact fixed point of the baseline map, the device that makes such models
…

Norms@Risk ABM aim is to develop an explanation for the conditions under which people jointly take action to prevent disasters from happening, e.g. impacts from climate crisis, pandemic. The aim of the Norms@Risk work is to explore understudied dynamics between risk, social norms and cooperative behaviour and their role in overcoming collective threats. More specifically, Norms@Risk seeks to refine existing theory on cooperation by unpacking the role of collective risk interacting with social norms in ‘Collective Risk Social Dilemmas’*.

This simulation study takes on the next scientific iteration after a series of behavioural experiments. With the model we target to refine existing theory by capturing the heterogeneity in contributions that existing theories cannot fully explain as the behavioural responses are assumed to be more homogenous, particularly diversity regarding sensitivity to risk and norms.

*Collective Risk Social Dilemmas reflect a special type of social dilemma, unlike typical public goods, the collective risk dilemma involves individual contribution not to realise a gain, but to avoid a collective loss.

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Agent-based model of cooperation among fourteen EU member states under geopolitical shocks, 2024-2040. States are adaptive agents on the observed intra-EU trade network, updating a cooperation propensity through inertia, network influence, and a repeated-game payoff structure built from verified Eurostat, Eurobarometer and IMF data. An anchoring device makes the observed 2024 configuration exactly stationary absent shocks, so results are read as deviations from an empirical baseline. Five scenarios of increasing severity are simulated with Monte Carlo replication, up to an extreme counterfactual crisis in the Taiwan Strait. Includes six robustness batteries and a full ODD description.

An empirically calibrated agent-based model of cooperation among 14 EU member states. Adaptive state-agents update their cooperation propensity through behavioural inertia, influence along the observed intra-EU trade network (IMF bilateral flows), and repeated-game payoff indicators built from verified Eurostat, Eurobarometer and IMF data (2021-2024). An anchored logistic mapping makes the observed configuration stationary in the absence of shocks, so outcomes read as deviations from the empirical baseline. The model stress-tests European cooperation to 2040 under five scenarios of increasing severity, from a baseline to a Taiwan Strait crisis counterfactual, with 1,000 Monte Carlo replications and a full sensitivity suite (one-factor-at-a-time, joint parameter sampling, breaking-point analysis, alternative functional form). Documented with the ODD protocol; self-testing and fully reproducible under fixed seeds.

Exploring learned cooperation, coevolution and free-riding. Learning is achieved through Multi-Agent Deep Reinforcement Learning (MADRL) in an ecological environment. The environment emits no other than sparse reproduction rewards. No reward shaping, no explicit cooperation signal.

This is a model that explores how a few fishermen sharing a common fishery learn their harvesting strategies under different incentive settings, and how individual greed, cooperation, and sustainability penalties shape resource depletion and the tragedy of the commons.

ARMM is a theoretical agent-based model that formalizes Murra’s Theory of Verticality (Murra, 1972) to explore how multi-zonal resource management systems emerge in mountain landscapes. The model identifies the social, political, and economic mechanisms that enable vertical complementarity across ecological gradients.
Built in NetLogo, ARMM employs an abstract 111×111 grid divided into four Andean ecological zones (Altiplano, Highland, Lowland, Coast), each containing up to 18 resource types distributed according to ecological suitability. To test general theoretical principles rather than replicate specific geography, resource locations are randomized at each model initialization.
Settlement agents pursue one of two economic strategies: diversification (seeking resource variety, maximum 2 units per type) or accumulation (maximising total quantity, maximum 30 units). Agents move between adjacent zones through hierarchical decision-making, first attempting peaceful interactions—coexistence (governed by tolerance) and trading (governed by cooperation)—before resorting to conflict (theft or takeover, governed by belligerence).
The model demonstrates that vertical complementarity can emerge through fundamentally different mechanisms: either through autonomous mobility under political decentralization or through state-coordinated redistribution under centralization. Sensitivity analysis reveals that belligerence and economic strategy explain approximately 25% of outcome variance, confirming that structural inequalities between zones result from political-economic organization rather than environmental constraints alone.
As a preliminary theoretical model, ARMM intentionally maintains simplicity to isolate core mechanisms and generate testable hypotheses. This foundational framework will guide future empirically-calibrated versions that incorporate specific archaeological settlement data and geographic features from the Carangas region (Bolivia-Chile border), enabling direct comparison between theoretical predictions and observed historical patterns.

Project Selene ABM Suite

Agent-Based Scenario Exploration for Consortium Cooperation Dynamics

Version: 2.1 (Revised)
Date: January 2026
Status: Exploratory Analysis Tool

An Agent Based Model that explores the deployment of hydrogen among a regional industrial cluster in the Netherlands, consisting of 15 companies. The companies seek to decarbonize by replacing their natural gas by hydrogen.
The model integrates technical characteristics as well as company motivations to transition to hydrogen. The baseline model only considers individual investments where company can locally produce hydrogen. If they reach the backbone threshold, companies can also consider buying hydrogen through a connection to the national hydrogen network. The second scenario considers that companies can participate in a joint investment to get an electrolyzer to locally produce the hydrogen.
Two experiments look at the impact of the sectoral configuration and at the impact of subsidy conditions on the region’s hydrogen transition