Fictional narrative as a variational Bayesian method for estimating social dispositions in large groups
Date
2019
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Article
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Abstract
Modelling intentions in large groups is cognitively costly. Not alone must first order beliefs be tracked (’what does A think about X?’), but also beliefs about beliefs (’what does A think about B’s belief concerning X?’). Thus linear increases in group size impose non-linear increases in cognitive processing resources. At the same time, however, large groups offer coordination advantages relative to smaller groups due to specialisation and increased productive capacity. How might these competing demands be reconciled? We propose that fictional narrative can be understood as a cultural tool for dealing with
large groups. Specifically, we argue that prototypical action roles that are removed from real-world interactions function as interpretive priors in a form of variational Bayesian inference, such that they allow estimations can be made of unknown social motives. We offer support for this claim in two ways.
Firstly, by evaluating the existing literature on narrative cognition and showing where it anticipates a variational model; and secondly, by simulation, where we show that an agent-based model naturally converges on a set of social categories that resemble narrative across a wide range of starting points.
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Citation
James Carney, Cole Robertson, Tamás Dávid-Barrett, Fictional narrative as a variational Bayesian method for estimating social dispositions in large groups, Journal of Mathematical Psychology, Volume 93, 2019, 102279, ISSN 0022-2496, https://doi.org/10.1016/j.jmp.2019.102279.