Agent-based model of hunting behavior of Ache hunter-gatherers from Paraguay. We evaluate the effect of group size and cooperative hunting

This model allows simulating the impacts of floods on a population. Floods are described by their intensity (flood height) and date of occurrence. Households are more or less severely hit by floods according to their geographical situation. Impacts are measured in terms of reductions in household wealth. Households may take up protection measures against floods, depending on their individual characteristics, a social network and information campaigns. If such measures are taken, flood impacts (wealth reduction) are less severe. Information campaigns increase the probability that households adopt protection measures. Two types of information campaigns are modeled: top-down policies which are the same for all households, people-centered policies, which adapt to the individual characteristics of each household.

This study presents a System Dynamics (SD) model that explores the “trajectories of homelessness” among youth outside of the formal care system. Unlike traditional approaches that view runaway behavior as a discrete choice, this model reinterprets it as a neurobiological adaptation to chronic resource deprivation and systemic neglect.
​The model incorporates key mechanisms such as ‘Allostatic Load’ accumulation, ‘PFC-Amygdala Switching’, and the ‘Iatrogenic Effects’ of shelter policies. It utilizes Monte Carlo simulations to demonstrate how structural factors create a “probabilistic vulnerability,” trapping youth in cycles of survival crime and isolation regardless of individual resilience.
​The uploaded code includes a Python implementation of the model to ensure reproducibility of the stochastic analysis presented in the paper.

This is a complex “Data Integration Model”, following a “KIDS” rather than a “KISS” methodology - guided by the available evidence. It looks at the complex mix of social processes that may determine why people vote or not.

This generic agent-based model allows the user to simulate and explore the influence of servicising policies on the uptake of servicising and on economic, environmental and social effects, notably absolute decoupling.

The purpose of this model is to examine equity and efficiency in crop production across a system of irrigated farms, as a function of maintenance costs, assessed water fees, and the capacity of farmers to trade water rights among themselves.

Innovation a byproduct of the intellectual capital, requires a new paradigm for the production constituents. Human Capital HC,Structural capital SC and relational capital RC become key for intellectual capital and consequently for innovation.

To our knowledge, this is the first agent-based simulation of continuous-time PGGs (where participants can change contributions at any time) which are much harder to realise within both laboratory and simulation environments.

Work related to this simulation has been published in the following journal article:
Vu, Tuong Manh, Wagner, Christian and Siebers, Peer-Olaf (2019) ‘ABOOMS: Overcoming the Hurdles of Continuous-Time Public Goods Games with a Simulation-Based Approach’ Journal of Artificial Societies and Social Simulation 22 (2) 7 http://jasss.soc.surrey.ac.uk/22/2/7.html. doi: 10.18564/jasss.3995

Abstract:
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STiMUS-HAI (Stigmergic–Mutualistic IMOI Model, Human-AI extension) is an agent-based model of teamwork in socio-technical systems where human and AI contributors collaborate through shared digital artefacts — wiki pages, code files, issue tickets, project cards, Scratch projects — represented as patches in a NetLogo world. It extends the human-only base model STiMUS v2.2, which established that two coordination mechanisms — stigmergy (indirect coordination through traces left in the environment) and mutualism (mutual benefit between contributors and the artefacts they tend) — can be decoupled: stigmergy decides where a contributor works, mutualism decides with what effort. STiMUS-HAI preserves this decoupling unchanged and adds two further theoretical questions: whether mixing AI agents into a human team distorts human coordination in ways that aggregate indicators hide, and whether AI’s cost to team outcomes depends on the type of work AI performs, not only on how much of it is present.

Two breeds of turtle — humans and ai-agents — follow identical target-selection, pheromone, and mutualism rules, so that any behavioural difference is attributable to team composition rather than a built-in advantage. The one designed asymmetry: AI agents never accumulate shared-mental-model and their motivation is fixed rather than adaptive. On top of this v3.0 baseline, v3.1 adds a task-type dimension to artefacts (“prediction” versus “judgment”, set via a judgment-share slider) that scales down AI edit-power specifically on judgment-requiring artefacts, and an ai-trust mechanic: humans build or lose trust in AI contributions based on the population-relative percentile rank of observed AI work quality (bottom-quartile work counts as an observed “error”), and that trust gates how much mutualistic benefit a human derives from continuing an AI’s work. Trust erodes quickly on a single error and recovers only after a streak of confirmed successes — an intentional asymmetry.

Comparing 7 alternative models of human behavior and assess their performance on a high resolution dataset based on individual behavior performance in laboratory experiments.