Artificial Anasazi is an agent-based model of farming households in the Long House Valley, Arizona, from 800 to 1350 CE. Each household chooses where to farm and settle based on expected harvest, water sources and stored maize, and simulated household counts are compared with the archaeological record.

This release adapts the NetLogo Models Library version (Stonedahl & Wilensky 2010) of Janssen’s (2009) NetLogo replication of the original Ascape model (Dean et al. 2000; Axtell et al. 2002). It contains four configurations in one NetLogo 7.0.4 file, selected with two switches:

• Both switches off: the baseline, which is the Models Library model with small fixes that make it work as intended (the arable-uplands zone name, the setup base yield, the initial household count, one-tick-lag capacity reporting, and seed control with observation-only instruments).
• soil-on?: dynamic soil quality that depletes under cultivation and regrows, following Janssen (2010, eq. 4).
  …

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
…

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.

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.

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.

…

This model tests whether local housing supply elasticity governs crash severity inside a single metropolitan housing market. Saiz (2010) established that across US metros, regions constrained by geography and regulation experience deeper boom-bust cycles than flexible ones. That finding is routinely applied downward to neighborhoods and ZIP codes as though the mechanism scaled without qualification.

The empirical record for the Washington DC and Northern Virginia region says it does not. Across 84 ZIP codes, measured supply elasticity ranges from 0.35 to 4.95 with a median of 1.21. The worst single-year price decline between 2007 and 2012 averaged 9.6 percent in constrained ZIP codes and 9.0 percent in flexible ones, a gap that cannot be distinguished from noise. Wide variation in the proposed cause, no meaningful separation in the proposed effect.

The model embeds households, houses and a metro-wide credit condition in the real ZIP geography of the region using three GIS layers and an empirical price panel. Local elasticity governs construction, exactly as theory predicts. Prices are driven by a shared macro drift schedule and, under the credit-amplified mode, by a leverage cycle with a financial accelerator and a deviation penalty. The design question is whether those shared forces are sufficient to override local supply differences at the sub-metropolitan scale.

…

MOSAIC is an agent-based NetLogo model of decentralized mission coordination among heterogeneous robots operating under partial observability, limited energy, spatially variable risk, dynamic communication, and individual and cooperative task requirements. Robots discover tasks locally, exchange task information through temporary communication links, submit capability-, energy-, deadline-, and risk-aware bids, compete for individual contracts, and form temporary coalitions for cooperative tasks.

The model integrates decentralized auctions, greedy capability-based coalition formation, contract release and reassignment, four reward regimes, reputation, adaptive bidding strategies, failure traceability, and mission-, network-, information-, inequality-, and coalition-level metrics. It operates without a centralized mission planner or global combinatorial assignment solver.

Seven paired-seed BehaviorSpace experiments comprising 690 official simulation runs evaluate baseline mission viability, reward regimes, communication structure, capability heterogeneity, cooperative-task demand, reputation and adaptive strategies, and mission-incentive strength. The results indicate that structural coordination capacity—particularly information reach, capability compatibility, and feasible coalition construction—has a stronger effect on mission completion than increasing incentive intensity within the tested architecture and parameter ranges.

…

This model is an extension of Wilensky’s (2003) Traffic Grid, a foundational NetLogo model of urban traffic flow. It embeds a dual-process cognitive architecture into each driver agent, transforming the original’s identical reactive units into cognitively heterogeneous individuals whose internal mental state evolves with experience; making the same intersection produce different decisions from different drivers, and different decisions from the same driver across occasions.
The core question the model addresses is the yellow-light dilemma zone: the seconds following amber onset in which a driver can neither stop safely nor clear the intersection before red. Field research documents that behavioral variance at this moment cannot be explained by geometry or legal obligation alone. This model provides the cognitive architecture that has been missing from traffic ABMs.
Each driver routes every amber-onset decision through either System 1 (fast, heuristic, automatic) or System 2 (slow, deliberative, prospect-theoretic), switching dynamically based on cognitive load, accumulated near-miss memory, and situational framing. The result is crash outcomes that are attributable, path-dependent, and sensitive to both driver disposition and signal infrastructure; none of which fixed-rule models can reproduce. Three signal control modes are included: fixed-cycle (replicating Wilensky’s original), adaptive-queue, and smart occupancy-based switching.

This repository contains the Python implementation of an agent-based model investigating how heterogeneous mobility and boundary-crossing dynamics generate large-scale connectivity in structured multi-attractor landscapes. A small fraction of higher-mobility agents explores the environment while the remaining agents stay locally constrained. The model compares pure random-walk exploration, interface-sensitive dynamics, novelty-biased exploration, and a flat-landscape control. The release includes the complete simulation framework, the experimental pipeline used in the associated paper, multi-K and population-scaling robustness analyses, network-snapshot and transition-slope analyses, and reproducibility documentation. Transition networks are analysed using graph density, connected components, and giant weakly connected components. The implementation is compatible with Python 3.11 and is released under the MIT License.

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.