The Agent-Based Model for Multiple Team Membership (ABMMTM) simulates design teams searching for viable design solutions, for a large design project that requires multiple design teams that are working simultaneously, under different organizational structures; specifically, the impact of multiple team membership (MTM). The key mechanism under study is how individual agent-level decision-making impacts macro-level project performance, specifically, wage cost. Each agent follows a stochastic learning approach, akin to simulated annealing or reinforcement learning, where they iteratively explore potential design solutions. The agent evaluates new solutions based on a random-walk exploration, accepting improvements while rejecting inferior designs. This iterative process simulates real-world problem-solving dynamics where designers refine solutions based on feedback.

As a proof-of-concept demonstration of assessing the macro-level effects of MTM in organizational design, we developed this agent-based simulation model which was used in a simulation experiment. The scenario is a system design project involving multiple interdependent teams of engineering designers. In this scenario, the required system design is split into three separate but interdependent systems, e.g., the design of a satellite could (trivially) be split into three components: power source, control system, and communication systems; each of three design team is in charge of a design of one of these components. A design team is responsible for ensuring its proposed component’s design meets the design requirement; they are not responsible for the design requirements of the other components. If the design of a given component does not affect the design requirements of the other components, we call this the uncoupled scenario; otherwise, it is a coupled scenario.

Sahelian transhumance is a seasonal pastoral mobility between the transhumant’s terroir of origin and one or more host terroirs. Sahelian transhumance can last several months and extend over hundreds of kilometers. Its purpose is to ensure efficient and inexpensive feeding of the herd’s ruminants. This paper describes an agent-based model to determine the spatio-temporal distribution of Sahelian transhumant herds and their impact on vegetation. Three scenarios based on different values of rainfall and the proportion of vegetation that can be grazed by transhumant herds are simulated. The results of the simulations show that the impact of Sahelian transhumant herds on vegetation is not significant and that rainfall does not impact the alley phase of transhumance. The beginning of the rainy season has a strong temporal impact on the spatial distribution of transhumant herds during the return phase of transhumance.

The model is based on the influence function of the Leviathan model (Deffuant, Carletti, Huet 2013 and Huet and Deffuant 2017). We aim at better explaining some patterns generated by this model, using a derived mathematical approximation of the evolution of the opinions averaged.

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 show that the inequality of reputations among agents have a negative effect on the opinions about the agents of low status.The mathematical analysis of the opinion dynamic shows that the lower the status of the agent, the more detrimental the interactions are for the opinions about this agent, especially when gossip is activated, while the interactions always tend to increase the opinions about agents of high status.

3 simple models to illustrate diffusion of innovations.
The models are discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

WatASit is an agent-based model implemented in the CORMAS plateform. The model is developped to simulate irrigation situations at the operational level during a collective irrigation campaign.

A discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations focusing on possible risks that could materialize in the final phase of the epidemic.

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

MarPEM is an agent-based model that can be used to study the effects of policy instruments on the transition away from HFO.

This model simulates the mechanisms of evolution, or how allele frequencies change in a population over time.

This model was developed to study the combination of electric vehicles (EVs) and intermitten renewable energy sources. The model presents an EV fleet in a fictional area, divided into a residential area, an office area and commercial area. The area has renewable energy sources: wind and PV solar panels. The agents can be encouraged to charge their electric vehicles at times of renewable energy surplus by introducing different policy interventions. Other interesting variables in the model are the installed renewable energy sources, EV fleet composition and available charging infrastructure. Where possible, use emperical data as input for our model. We expand upon previous models by incorporating environmental self-identity and range anxiety as agent variables.