An empirical-response agent-based model of how many personalized feeds execute a shared low-exposure creator-discovery objective. Built from the KuaiRec dataset: the big interaction matrix initializes a transparent rank-8 matrix-factorization platform learner and the activity schedule, while the near-complete small matrix returns observed viewing responses only after a user-video pair is exposed. Four exploration policies (synchronous low-exposure targeting, uniform exploration, per-user random tie-breaking, capacity-balanced coordination) are compared over 28 rounds at a nominal 10% exploration budget, across 30 paired seeds (core) and 10 paired seeds (bias-only probe), with slot-level redundancy, cross-user collision, and coverage diagnostics.
This release accompanies an anonymised manuscript under review at the Journal of Artificial Societies and Social Simulation.

The model represents 1,411 users, 3,327 videos, 2,031 authors, and an adaptive platform over 28 discrete rounds derived from the KuaiRec big-matrix activity calendar. Exploration policies differ only in how a fixed 10% slot budget is allocated; all policies share the opportunity schedule, response oracle, initial checkpoints, and online update rule.

Archive contents: analysis pipeline scripts (01-16), frozen machine-readable protocols with input hashes, initial model checkpoints, aggregate result tables, the complete ODD protocol record, and publication figures. Raw KuaiRec files are not redistributed; obtain them from the official dataset repository and verify against the input hashes in data_contract/. Row-level oracle tables are excluded by design.
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This paper develops a spatial agent-based model to examine how fertility regime shifts reshape population concentration and wealth distribution in an abstract urban system. Migration decisions combine population preference, cultural homophily, expected net income, and resource endowment through a standardised softmax utility. The design is deliberately stylised: it is not calibrated to a particular country or city system, but is intended to isolate the feedbacks linking migration, fertility, urban scaling, and accumulated wealth.
The simulations reveal robust directional asymmetry. When fertility shifts from low to high, population concentration responds rapidly; when fertility shifts from high to low, concentration declines only after a detectable delay and may temporarily continue in the previous direction. Wealth adds a second layer of hysteresis: cell total-wealth concentration follows population concentration with delay, cell mean-wealth inequality and system-level wealth indicators are slower still, and phase-space trajectories form loops rather than collapsing onto a single population–wealth curve. Robustness experiments indicate that longer fertility cycles, wider mobility neighbourhoods, and smoother resource landscapes change the magnitude of delay and overshoot, but do not remove the qualitative asymmetry. The paper argues that demographic decline should be understood not as the mirror image of demographic expansion, but as a path-dependent transition mediated by fast migration-income feedbacks and slower fertility, cohort, culture, and wealth mechanisms.

Agent-based model simulating 60 undergraduate CS students over 8 semesters to investigate how differential AI tool access affects academic inequality, performance distributions, and dependency formation. Implements a three-phase AI dependency mechanism (effort reduction, metacognitive miscalibration, capability erosion) under five policy scenarios with a 4,050-run parameter sensitivity sweep.

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.

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.

MASTOC-LLM extends the classic Multi-Agent System Tragedy of the Commons (MASTOC) model by replacing hard-coded behavioral rules with autonomous decision-making powered by large language models (LLMs). Three heterogeneous agents manage herds of cows on a shared grassland commons. Each tick, an agent receives a structured prompt describing current resource levels, its own herd size, peer behavior, and — optionally — a rolling memory of recent rounds and messages from neighboring agents. The LLM returns a stocking decision (add, remove, or hold cows) together with a natural-language rationale and, when communication is enabled, a short message to broadcast to peers.

The model is designed to test whether LLM agents spontaneously develop Ostrom-style common-pool resource governance (mutual monitoring, graduated sanctions, graduated rule revision) or instead fall into identifiable failure modes. Preliminary experiments with Claude Haiku 4.5, GPT-5.4-mini, and DeepSeek R1:32b have revealed four recurring collapse patterns — Cooperative Paralysis, Defection Cascade, Overshoot-Panic, and Hybrid Architecture Failure — whose onset timing is sensitive to memory length, inter-agent communication, and the post-training alignment approach of the underlying model.

MASTOC-LLM is intended as a laboratory for generative agent-based modelling (GABM) methodology: it provides a clean, well-understood commons baseline against which LLM behavioral hypotheses can be systematically tested and compared across models, parameter sweeps, and alignment regimes.

This repository contains the Python implementation of an agent-based model investigating how localized boundary-crossing dynamics generate large-scale connectivity in structured multi-attractor landscapes.

Agents evolve in a continuous two-dimensional environment composed of attractor basins. A fraction of agents exhibits exploratory higher-mobility dynamics, while the remaining agents remain locally constrained. The model analyzes how localized configurational transitions accumulate into transition networks that progressively integrate the explored state space.

The repository includes:

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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.

Interest-based compound economies generate monotonically increasing wealth inequality through multiplicative accumulation dynamics, yet the conditions under which gift-based reciprocal exchange outperforms such systems in collective well-being remain unquantified. We present Zensei Wago (全生和合), a seven-layer agent-based model comparing a Gift Resource Circulation (GRC) economy with a Compound Interest Circulation (CIC) economy under identical initial conditions. Across N = 5000 Monte Carlo replications (T = 700 ticks, N = 100 agents), GRC produced significantly higher collective resonance than CIC (p < 0.001, Cohen’s d = +0.171), above a critical prosocial threshold pm ≈ 0.698. Cohen’s d grows monotonically with duration — d = +1.943 at T = 1500 and d = +4.126 at T = 3000 — driven primarily by structural collapse of CIC resonance as inequality exceeds a critical Gini threshold (G > 0.333), while GRC resonance remains stable. The gift mechanism further decouples collective well-being from distributional outcomes, generating resonance through relational quality rather than material redistribution. Network topology analysis across seven configurations — combining a Watts-Strogatz rewiring sweep and a T = 1500 longitudinal replication — reveals that ring topology maximises GRC advantage (d = +1.17), that most topology-dependent reversals are transient (sparse and small-world both transition to significantly positive by T = 1500), and that a critical rewiring threshold of p ≈ 0.10–0.20 separates GRC-advantaged from GRC-disadvantaged network configurations. Scale-free networks remain persistently adverse (d = -7.24*), requiring structural redesign for gift-economy viability.

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.