An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.

The model explores the emergence of inequality in cognitive and socio-emotional skills at the societal level within and across generations that results from differences in parental investment behavior during childhood and adolescence.

RaMDry allows to study the dynamic use of forage ressources by herbivores in semi-arid savanna with an emphasis on effects of change of climate and management. Seasonal dynamics affects the amount and the nutritional values of the available forage.

This is an extended replication of Abelson’s and Bernstein’s early computer simulation model of community referendum controversies which was originally published in 1963 and often cited, but seldom analysed in detail. This replication is in NetLogo 6.3.0, accompanied with an ODD+D protocol and class and sequence diagrams.

This replication replaces the original scales for attitude position and interest in the referendum issue which were distributed between 0 and 1 with values that are initialised according to a normal distribution with mean 0 and variance 1 to make simulation results easier compatible with scales derived from empirical data collected in surveys such as the European Value Study which often are derived via factor analysis or principal component analysis from the answers to sets of questions.

Another difference is that this model is not only run for Abelson’s and Bernstein’s ten week referendum campaign but for an arbitrary time in order that one can find out whether the distributions of attitude position and interest in the (still one-dimensional) issue stabilise in the long run.

This computational model accompanies the article “The Informational Assumptions of Schelling Segregation: An Agent-Based Decomposition of Cue Inference, Cultural Schemas, and Residential Sorting.” It implements an agent-based model in which agents infer latent neighborhood-type classes from noisy non-demographic cues through schema-specific diagnostic mappings, update beliefs, and relocate when satisfaction on a preferred latent class falls below a threshold.

The model serves as a mechanism-isolation device for studying the informational architecture underlying Schelling-style residential sorting. It includes the principal sweep configuration (14,400 runs across a seven-parameter grid), a disagreement-metric sub-sweep with permutation-minimized Jensen-Shannon divergence recorded natively, controls (positive, negative, and frozen-belief), a paired-seed cue-channel perturbation experiment, and selected-cell sensitivity sweeps for cue persistence and home-biased mobility.

The full ODD protocol, parameter manifests, deterministic seed schedules, processed outputs, regenerable figure scripts, the verification test suite, and the satisfaction-mapping audit document are included. Every reported run is deterministic given a (config, seed) pair, and an included audit script verifies bit-for-bit replay on sampled runs.

An agent-based model of rule-governed (religious) communities in which networked agents observe one another’s conduct, sanction deviation,
and decide whether to remain. The model separates two structural conditions for durable capture. First, whether an enforcement apparatus
activates: agents punish deviation only where conduct is observable (the code-geometry parameter sigma) and where sanctioning is
institutionally rewarded (pi), a joint threshold in which neither condition alone suffices. Second, whether members’ exit is foreclosed: an
outside-option degradation (delta) that can be imposed exogenously or allowed to drift with enforcement activity. Punishment concentrates in
a small, endogenously recomputed enforcer cadre, but that concentration is produced by an institutional privilege bundle (a punishment
…

This model is intended to explore the effectiveness of different courses of interventions on an abstract population of infections. Illustrative findings highlight the importance of the mechanisms for variability and mutation on the effectiveness of different interventions.

A computational model of a classic small group study by Alex Bavelas. This computational model was designed to explore the difficulty in translating a seemingly simple real-world experiment into a computational model.

A more complete description of the model can be found in Appendix I as an ODD protocol. This model is an expansion of the Hemelrijk (1996) that was expanded to include a simple food seeking behavior.

ABMIND, the Agent-Based Model of Individual Psychological Distance, is a modeling framework developed to examine how psychological distance influences environmental protection behavior in coastal farming communities in southern China. Using household survey data and empirically estimated behavioral pathways, the model represents how uncertainty shapes four dimensions of psychological distance, namely temporal, spatial, social and hypothetical distance, and how these dimensions guide protection and degradation decisions. Agents include households, government actors and mangrove ecosystem patches, connected through social networks and ecological feedbacks that affect learning, expectations and perceived benefits. Policy interventions such as rewards, penalties and publicity guidance efforts work by modifying uncertainty and psychological distance rather than directly controlling behavior. ABMIND is implemented as a spatially explicit model following the ODD protocol, and a concise user guide is provided. In developing ABMIND we introduce a structured validation workflow that links statistical mediation analysis with simulation-based diagnostics, allowing empirical cognitive mechanisms to be systematically embedded and tested within the ABM. This integrated approach strengthens the credibility of psychological-mechanism models and supports their use in policy evaluation. The framework offers a methodological platform for integrating cognitive mechanisms into agent-based environmental behavior modeling and for evaluating policy strategies that support ecosystem protection.
Model paper:
ABMIND: An empirically informed agent-based model of psychological distance and environmental protection behaviour
Ecological Modelling
https://doi.org/10.1016/j.ecolmodel.2026.111700