Wiser than the Wisest of Crowds: The Asch Effect Revisited under Friedkin-Johnsen Opinion Dynamics

TL;DR

This paper investigates how introducing a few influential agents, called stooges, into interconnected opinion networks affects the accuracy of collective estimations, using Friedkin-Johnsen dynamics, and develops algorithms to optimize this influence.

Contribution

It formulates the problem of selecting stooges to maximize or minimize the wisdom of crowds under Friedkin-Johnsen dynamics and proposes scalable heuristics with practical evaluations.

Findings

Selecting a small number of stooges can significantly alter the MSE of collective estimates.

Maximizing polarization often nearly optimizes the MSE for influencing crowd wisdom.

The problem of selecting stooges to optimize MSE is NP-hard, and the proposed heuristics perform well in practice.

Abstract

In 1907, Sir Francis Galton independently asked 787 villagers to estimate the weight of an ox. Although none of them guessed the exact weight, the average estimate was remarkably accurate. This phenomenon is known as wisdom of crowds. In a clever experiment, Asch employed actors to demonstrate the human tendency to conform to others' opinions. The question we ask is: what would Sir Francis Galton have observed if Asch had interfered by employing actors? Would the wisdom of crowds become even wiser or not? The problem becomes intriguing when considering the inter-connectedness of the villagers, which is the central theme of this work. We examine a scenario where $n$ agents are interconnected and influence each other. The average of their opinions provides an estimator of a certain quality for some unknown quantity. How can one improve or reduce the quality of the original estimator in terms of the MSE by utilizing Asch's strategy of hiring a few stooges? We present a new formulation of this problem, assuming that nodes adjust their opinions according to the Friedkin-Johnsen opinion dynamics. We demonstrate that selecting $k$ stooges for maximizing and minimizing the MSE is NP-hard. We also demonstrate that our formulation is closely related to maximizing or minimizing polarization and show NP-hardness. We propose an efficient greedy heuristic that scales to large networks and test our algorithm on synthetic and real-world datasets. Although MSE and polarization objectives differ, we find in practice that maximizing polarization often yields solutions that are nearly optimal for minimizing the wisdom of crowds in terms of MSE. Our analysis of real-world data reveals that even a small number of stooges can significantly influence the conversation on the war in Ukraine, resulting in a relative increase of the MSE of 207.80% (maximization) or a decrease of 50.62% (minimization).