Dynamical system model predicts when social learners impair collective performance

Vicky Chuqiao Yang (), Mirta Galesic, Harvey McGuinness and Ani Harutyunyan
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Vicky Chuqiao Yang: Santa Fe Institute, Santa Fe, NM 87501
Mirta Galesic: Santa Fe Institute, Santa Fe, NM 87501; Complexity Science Hub Vienna, A-1080 Vienna, Austria; Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405
Harvey McGuinness: Zanvyl Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD 21218

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 35, e2106292118

Abstract: A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that, when the proportion of social learners exceeds a critical threshold, a bistable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

Keywords: collective decisions; dynamical system modeling; social learning; collective intelligence (search for similar items in EconPapers)
Date: 2021
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