Social influence makes self-interested crowds smarter: an optimal control perspective
Yu Luo (1), Garud Iyengar (2), Venkat Venkatasubramanian (1) ((1), Department of Chemical Engineering, Columbia University, (2) Department of, Industrial Engineering, Operations Research, Columbia University)

TL;DR
This paper demonstrates that social influence, when optimally calibrated, can enhance crowd performance and robustness, challenging the belief that social influence always diminishes collective wisdom.
Contribution
It introduces a control-theoretic approach to determine the optimal level of social influence for improving crowd decision-making performance.
Findings
Optimal social influence level estimated at 30% improves performance by 29%.
Crowd self-organizes to near-optimal influence levels in practice.
Method applicable to diverse fields like economics and public health.
Abstract
It is very common to observe crowds of individuals solving similar problems with similar information in a largely independent manner. We argue here that crowds can become "smarter," i.e., more efficient and robust, by partially following the average opinion. This observation runs counter to the widely accepted claim that the wisdom of crowds deteriorates with social influence. The key difference is that individuals are self-interested and hence will reject feedbacks that do not improve their performance. We propose a control-theoretic methodology to compute the degree of social influence, i.e., the level to which one accepts the population feedback, that optimizes performance. We conducted an experiment with human subjects (), where the participants were first asked to solve an optimization problem independently, i.e., under no social influence. Our theoretical methodology…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Game Theory and Applications
