Bayesian decision making in human collectives with binary choices

TL;DR

This paper investigates how humans make binary choices in social settings, showing that a Bayesian model effectively explains their decision patterns and the influence of peer information, challenging Weber's law.

Contribution

It introduces a simple Bayesian framework for understanding binary decision-making in humans, supported by experimental data, and compares its performance to other models.

Findings

Bayesian model accurately predicts human binary choices

Peer influence is independent of question difficulty

Data contradicts Weber's law predictions

Abstract

Here we focus on the description of the mechanisms behind the process of information aggregation and decision making, a basic step to understand emergent phenomena in society, such as trends, information spreading or the wisdom of crowds. In many situations, agents choose between discrete options. We analyze experimental data on binary opinion choices in humans. The data consists of two separate experiments in which humans answer questions with a binary response, where one is correct and the other is incorrect. The questions are answered without and with information on the answers of some previous participants. We find that a Bayesian approach captures the probability of choosing one of the answers. The influence of peers is uncorrelated with the difficulty of the question. The data is inconsistent with Weber's law, which states that the probability of choosing an option depends on the proportion of previous answers choosing that option and not on the total number of those answers. Last, the present Bayesian model fits reasonably well to the data as compared to some other previously proposed functions although the latter sometime perform slightly better than the Bayesian model. The asset of the present model is the simplicity and mechanistic explanation of the behavior.