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.

Patagonia PSMED is an agent-based model designed to study a simple case of Evolution of Ethnic Differentiation. It replicates how can hunter-gatherer societies evolve and built cultural identities as a consequence of the way they interacted.

The simulation generates two kinds of agents, whose proposals are generated accordingly to their selfish or selfless behaviour. Then, agents compete in order to increase their portfolio playing the ultimatum game with a random-stranger matching.

This model allows for the investigation of the effect spatial clustering of raw material sources has on the outcome of the neutral model of stone raw material procurement by Brantingham (2003).

This NetLogo model illustrates the cultural evolution of pro-environmental behaviour patterns. It illustrates how collective behaviour patterns evolve from interactions between agents and agents (in a social network) as well as agents and the affordances (action opportunities provided by the environment) within a niche. More specifically, the cultural evolution of behaviour patterns is understood in this model as a product of:

1. The landscape of affordances provided by the material environment,
2. Individual learning and habituation,
3. Social learning and network structure,
4. Personal states (such as habits and attitudes), and
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Is the mass shooter a maniac or a relatively normal person in a state of great stress? According to the FBI report (Silver, J., Simons, A., & Craun, S. (2018). A Study of the Pre-Attack Behaviors of Active Shooters in the United States Between 2000 – 2013. Federal Bureau of Investigation, U.S. Department of Justice,Washington, D.C. 20535.), only 25% of the active shooters were known to have been diagnosed by a mental health professional with a mental illness of any kind prior to the offense.
The main objects of the model are the humans and the guns. The main factors influencing behavior are the population size, the number of people with mental disabilities (“psycho” in the model terminology) per 100,000 population, the total number of weapons (“guns”) in the population, the availability of guns for humans, the intensity of stressors affecting humans and the threshold level of stress, upon reaching which a person commits an act of mass shooting.
The key difference (in the model) between a normal person and a psycho is that a psycho accumulates stressors and, upon reaching a threshold level, commits an act of mass shooting. A normal person is exposed to stressors, but reaching the threshold level for killing occurs only when the simultaneous effect of stressors on him exceeds this level.
The population dynamics are determined by the following factors: average (normally distributed) life expectancy (“life_span” attribute of humans) and population growth with the percentage of newborns set by the value of the TickReprRatio% slider of the current population volume from 16 to 45 years old.Thus, one step of model time corresponds to a year.

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.

Organizations operate under conditions of imperfect performance in which human error is inevitable, yet errors are rarely examined as the triggers for the managerial interactions that shape organizational culture over time. The present research introduces an agent-based simulation of a work team completing a fixed sequence of tasks under varying degrees of managerial oversight and response policy. The model isolates the mechanical loss of throughput caused by errors from the psychological and cultural consequences of managerial reactions, which are categorized into ignoring, correcting or punishing. Furthermore, the model incorporates an autonomous self-notice mechanism, allowing workers to correct themselves in the absence of managerial intervention. By tracking the accumulation of worker resentment and the transient enhancements in learning, the simulation acts as a dynamic laboratory for observing delayed consequences, nonlinear tipping points and systemic organizational collapse. The results reveal a central paradox of organizational control. Highly monitored punitive environments generate high short-term throughput, yet they simultaneously accumulate interactional injustice and resentment that engineer a rapid cascading turnover and the highest probability of systemic collapse. Conversely, corrective policies combined with active monitoring achieve equivalent throughput while sustaining workforce viability. A comprehensive sensitivity analysis establishes that error accumulation is primarily determined by structural factors, namely agent-level mistake propensity and task difficulty, while resignation dynamics, resentment accumulation and collapse timing remain predominantly governed by managerial policy, a hierarchy independently corroborated by a surrogate model and shown to be stable across independent seeds and across stakeholder weighting scenarios. The study bridges the operational mechanics of task completion and the social dynamics of workplace mistreatment, illustrating how short-term punitive success often masks long-term structural fragility.

Overview

Purpose

Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..
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ForagerNet3_Demography is a non-spatial ABM for exploring hunter-gatherer demography. Key methods represent birth, death, and marriage. The dependency ratio is an imporant variable in many economic decisions embedded in the methods.