Title

Analyzing Agent-Based Models Using Category Theory

Keywords

Agent-based modeling; Category theory; Markov-chain Monte Carlo

Abstract

Agent-based models are a useful technique for rapidly prototyping complex social systems; they are widely used in a number of disciplines and can yield theoretical insights that are different from those produced by a variable based analysis. However, it remains difficult to compare the results of two models and to validate the performance of an agent-based simulation. In this paper, we present a case study on how to analyze the relationship between agent-based models using category theory. Category theory is a powerful mathematical methodology that was originally introduced to organize mathematical ideas according to their shared structure. It has been successfully employed in abstract mathematical domains, but has also enjoyed some success as a formalism for software engineering. Here we present a procedure for analyzing agent-based models using category theory and a case study in its usage at analyzing two different types of simulations. © 2013 IEEE.

Publication Date

1-1-2013

Publication Title

Proceedings - 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013

Volume

2

Number of Pages

280-286

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/WI-IAT.2013.121

Socpus ID

84893251238 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893251238

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