This model investigates how anti-conformist intentions could be related to some biases on the perception of attitudes. It starts from two case studies, related to the adoption of organic farming, that show anti-conformist intentions. It proposes an agent-based model which computes an intention based on the Theory of Reasoned Action and assumes some biases in the perception of others’ attitudes according to the Social Judgement Theory.
It investigates the conditions on the model parameter values for which the simulations reproduce the features observed in the case studies. The results suggest that perception biases are indeed likely to contribute to anti-conformist intentions.

The model is based on Swann and Buhrmester’s Identity Fusion behavioural theory, which seeks to explain why an individual puts the group’s priorities above their personal expectations. In order to observe the theory and validate group behaviour, a case study was carried out focusing on scenarios of group violence in football stadiums in Brazil. For the modelling, each agent has a distribution of levels of identification with the group to which they belong, with their level of fusion varying between 1 and 5. According to behavioural theory, an individual’s degree of fusion with the group directly interferes with their behaviour of replicating actions and absorbing group beliefs.

At the heart of a study of Social-Ecological Systems, this model is built by coupling together two independently developed models of social and ecological phenomena. The social component of the model is an abstract model of interactions of a governing agent and several user agents, where the governing agent aims to promote a particular behavior among the user agents. The ecological model is a spatial model of spread of the Mountain Pine Beetle in the forests of British Columbia, Canada. The coupled model allowed us to simulate various hypothetical management scenarios in a context of forest insect infestations. The social and ecological components of this model are developed in two different environments. In order to establish the connection between those components, this model is equipped with a ‘FlipFlop’ - a structure of storage directories and communication protocols which allows each of the models to process its inputs, send an output message to the other, and/or wait for an input message from the other, when necessary. To see the publications associated with the social and ecological components of this coupled model please see the References section.

An agent-based model simulating West Nile Virus dynamics in a one host (American robin)-one vector (Culex spp. mosquito) system. ODD improved and code cleaned.

Ants in the genus Temnothorax use tandem runs (rather than pheromone trails) to recruit to food sources. This model explores the collective consequences of this linear recruitment (as opposed to highly nonlinear pheromone trails).

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

This is based off my previous Profiler tutorial model, but with an added tutorial on converting it into a model usable with BehaviorSpace, and creating a BehaviorSpace experiment.

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.

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.

The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).