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Canessa, E., & Chaigneau, S. (2014). The dynamics of social agreement according to Conceptual Agreement Theory. Qual. Quant., 48(6), 3289–3309.
Abstract: Many social phenomena can be viewed as processes in which individuals in social groups develop agreement (e.g., public opinion, the spreading of rumor, the formation of social and linguistic conventions). Conceptual Agreement Theory (CAT) models social agreement as a simplified communicational event in which an Observer and Actor exchange ideas about a concept , and where uses that information to infer whether 's conceptual state is the same as its own (i.e., to infer agreement). Agreement may be true (when infers that is thinking and this is in fact the case, event ) or illusory (when infers that is thinking and this is not the case, event ). In CAT, concepts that afford or become more salient in the minds of members of social groups. Results from an agent-based model (ABM) and probabilistic model that implement CAT show that, as our conceptual analyses suggested would be the case, the simulated social system selects concepts according to their usefulness to agents in promoting agreement among them (Experiment 1). Furthermore, the ABM exhibits more complex dynamics where similar minded agents cluster and are able to retain useful concepts even when a different group of agents discards them (Experiment 2). We discuss the relevance of CAT and the current findings for analyzing different social communication events, and suggest ways in which CAT could be put to empirical test.
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Canessa, E., & Riolo, R. L. (2006). An agent-based model of the impact of computer-mediated communication on organizational culture and performance: an example of the application of complex systems analysis tools to the study of CIS. J. Inf. Technol., 21(4), 272–283.
Abstract: Organizations that make use of computer information systems (CIS) are prototypical complex adaptive systems (CAS). This paper shows how an approach from Complexity Science, exploratory agent-based modeling (ABM), can be used to study the impact of two different modes of use of computer-mediated communication (CMC) on organizational culture (OC) and performance. The ABM includes stylized representations of (a) agents communicating with other agents to complete tasks; (b) an OC consisting of the distribution of agent traits, changing as agents communicate; (c) the effect of OC on communication effectiveness (CE), and (d) the effect of CE on task completion times, that is, performance. If CMC is used in a broad mode, that is, to contact and collaborate with many, new agents, the development of a strong OC is slowed, leading to decreased CE and poorer performance early on. If CMC is used in a local mode, repeatedly contacting the same agents, a strong OC develops rapidly, leading to increased CE and high performance early on. However, if CMC is used in a broad mode over longer time periods, a strong OC can develop over a wider set of agents, leading to an OC that is stronger than an OC which develops with local CMC use. Thus broad use of CMC results in overall CE and performance that is higher than is generated by local use of CMC. We also discuss how the dynamics generated by an ABM can lead to a deeper understanding of the behavior of a CAS, for example, allowing us to better design empirical longitudinal studies.
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Canessa, E., Chaigneau, S. E., & Marchant, N. (2023). Use of Agent-Based Modeling (ABM) in Psychological Research. In Trends and Challenges in Cognitive Modeling (Vol. Early Access, pp. 7–20).
Abstract: In this chapter, we introduce the general use of agent-based modeling (ABM) in social science studies and in particular in psychological research. Given that ABM is frequently used in many disciplines in social sciences, as the main research tool or in conjunction with other modeling approaches, it is rather surprising its infrequent use in psychology. There are many reasons for that infrequent use of ABM in psychology, some justified, but others stem from not knowing the potential benefits of applying ABM to psychological research. Thus, we begin by giving a brief overview of ABM and the stages one has to go through to develop and analyze such a model. Then, we present and discuss the general drawbacks of ABM and the ones specific to psychology. Through that discussion, the reader should be able to better assess whether those disadvantages are sufficiently strong for precluding the application of ABM to his/her research. Finally, we end up by stating the benefits of ABM and examining how those advantages may outweigh the potential drawbacks, thus making ABM a valuable tool to consider in psychological research.
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