Computational Model Library

Displaying 6 of 6 results arguments clear search

Myside Bias and Group Discussion

Edoardo Baccini | Published Monday, November 14, 2022 | Last modified Tuesday, September 05, 2023

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. This agent-based model in Netlogo simulates a group discussion among myside-biased agents, within a Bayesian setting. This model is designed to investigate the effects of the myside bias on the ability of groups to reach a consensus or collectively track the correct answer to a given binary issue.

This generic agent-based model simulates the evolution of agent’s opinions through their exchange of arguments.
The idea behind this model is to explicitly represent the process of mental deliberation of agents from arguments to an opinion, through the use of Dung’s argumentation framework complemented by a structured description of arguments. An application of the model on the diffusion of vegetarian diets is proposed.

This is a tool to explore the effects of groupsĀ“ spatial segregation on the emergence of opinion polarization. It embeds two opinion formation models: a model of negative (and positive) social influence and a model of persuasive argument exchange.

We seek to improve understanding of roles enzyme play in soil food webs. We created an agent-based simulation of a simple food web that includes enzymatic activity. The model was used in a publication, Moore et al. (in press; Biochemistry).

This ABM simulates opinions on a topic (originally contested infrastructures) through the interactions between paired agents and based on the sociopsychological assumptions of social judgment theory (SJT; Sherif & Hovland, 1961).

NarrABS

Tilman Schenk | Published Thursday, September 20, 2012 | Last modified Saturday, April 27, 2013

An agent based simulation of a political process based on stakeholder narratives

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept