Collective Adoption of Max-Min Strategy in an Information Cascade Voting Experiment

TL;DR

This paper investigates how herders in an information cascade adopt a max-min strategy in a voting experiment, demonstrating that they collectively follow this strategy to maximize expected returns when choosing options with a variable multiplier.

Contribution

The study introduces the concept of an analog herder adopting a max-min strategy and shows its effectiveness in a real voting experiment with human subjects.

Findings

Herders' choice probability is inversely proportional to the multiplier m for 4/3 < m < 4.

Herders collectively adopt the max-min strategy within this range.

The system achieves near-perfect correct choice probability when the informed accuracy q is one.

Abstract

We consider a situation where one has to choose an option with multiplier m. The multiplier is inversely proportional to the number of people who have chosen the option and is proportional to the return if it is correct. If one does not know the correct option, we call him a herder, and then there is a zero-sum game between the herder and other people who have set the multiplier. The max-min strategy where one divides one's choice inversely proportional to m is optimal from the viewpoint of the maximization of expected return. We call the optimal herder an analog herder. The system of analog herders takes the probability of correct choice to one for any value of the ratio of herders, p<1, in the thermodynamic limit if the accuracy of the choice of informed person q is one. We study how herders choose by a voting experiment in which 50 to 60 subjects sequentially answer a two-choice quiz. We show that the probability of selecting a choice by the herders is inversely proportional to m for 4/3 < m < 4 and they collectively adopt the max-min strategy in that range.