The natural selection of foresight, an accuracy at assess the environment, under degrees of environmental heterogeneity. The model is designed to connect local scale mobility, from foraging, with the global scale phenomenon of population dispersal.

This is a gender differentiation model in terms of reputations, prestige and self-esteem (presented in the paper https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236840). The model is based on the influence function of the Leviathan model (Deffuant, Carletti, Huet 2013 and Huet and Deffuant 2017) considering two groups.

This agent-based model studies how inequalities can be explained by the difference of open-mindness between two groups of interacting agents. We consider agents having an opinion/esteem about each other and about themselves. During dyadic meetings, agents change their respective opinion about each other and possibly about other agents they gossip about, with a noisy perception of the opinions of their interlocutor. Highly valued agents are more influential in such encounters. We study an heterogeneous population of two different groups: one more open to influence of others, taking less into account their perceived difference of esteem, called L; a second one less prone to it, called S, who designed the credibility they give to others strongly based on how higher or lower valued than themselves they perceive them.

We show that a mixed population always turns in favor to some agents belonging to the group of less open-minded agents S, and harms the other group: (1) the average group self-opinion or reputation of S is always better than the one of L; (2) the higher rank in terms of reputation are more frequently occupied by the S agents while the L agents occupy more the bottom rank; (3) the properties of the dynamics of differentiation between the two groups are similar to the properties of the glass ceiling effect proposed by Cotter et al (2001).

Positive feedback can lead to “trapping” in local optima. Adding a simple negative feedback effect, based on ant behaviour, prevents this trapping

The model employs an agent-based model for exploring the victim-centered approach to identifying human trafficking and the approach’s effectiveness in an abstract representation of migrant flows.

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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This project is an interactive agent-based model simulating consumption of a shared, renewable resource using a game-theoretic framework with environmental feedback. The primary function of this model was to test how resource-use among AI and human agents degrades the environment, and to explore the socio-environmental feedback loops that lead to complex emergent system dynamics. We implemented a classic game theoretic matrix which decides agents´ strategies, and added a feedback loop which switches between strategies in pristine vs degraded environments. This leads to cooperation in bad environments, and defection in good ones.

Despite this use, it can be applicable for a variety of other scenarios including simulating climate disasters, environmental sensitivity to resource consumption, or influence of environmental degradation to agent behaviour.
The ABM was inspired by the Weitz et. al. (2016, https://pubmed.ncbi.nlm.nih.gov/27830651/) use of environmental feedback in their paper, as well as the Demographic Prisoner’s Dilemma on a Grid model (https://mesa.readthedocs.io/stable/examples/advanced/pd_grid.html#demographic-prisoner-s-dilemma-on-a-grid). The main innovation is the added environmental feedback with local resource replenishment.

Beyond its theoretical insights into coevolutionary dynamics, it serves as a versatile tool with several practical applications. For urban planners and policymakers, the model can function as a ”digital sandbox” for testing the impacts of locating high-consumption industrial agents, such as data centers, in proximity to residential communities. It allows for the exploration of different urban densities, and the evaluation of policy interventions—such as taxes on defection or subsidies for cooperation—by directly modifying the agents’ resource consumptions to observe effects on resource health. Furthermore, the model provides a framework for assessing the resilience of such socio-environmental systems to external shocks.
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The model simulates the spatial patterns of secondary forest succession above the current alpine tree line in the context of land use and climate change. Three scenarios are offered: (1) climate change, (2) land use change, (3) species composition.

Our aim is to demonstrate how conversational AI systems, exemplified by ChatGPT, can support the conceptualisation of Agent-Based Social Simulation (ABSS) models, leading to a full ABSS model design document. Through advanced prompt engineering and adherence to the Engineering ABSS framework (Siebers and Klügl 2017), we have constructed a comprehensive script that is easy to use and that supports the design of ABSS models with or even by AI. The performance of the script is demonstrated through an illustrative case study related to the use of adaptive architecture in museums. The repository contains (1) the comprehensive script in a format that allows copying and pasting prompts for use with ChatGPT, (2) the results of the illustrative case study in the form of two conceptual ABSS models, the ground truth and the autogenerated version.

This model is a part of an ongoing research project on Multiagent Reinforcement Learning (MARL). The ODD protocol is included in the model. In this version of the model, Proximal Policy Optimization (PPO) is designed in the agent behaviors. It also includes a designed experiment in its Behavior Space which is used in the Response Surface Methodology and training of an Artificial Neural Network (ANN) based Recommender System.

Industrial clustering patterns are the result of an entrepreneurial process where spinoffs inherit the ideas and attributes of their parent firms. This computational model maps these patterns using abstract methodologies.