Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

TL;DR

This study uses controlled experiments and computational models to examine how sequential peer influences affect decision-making and rumor diffusion, revealing that peer effects can override individual uncertainty and accelerate sub-optimal information spread.

Contribution

It introduces an experimental approach to analyze sequential peer influence on decision-making and incorporates behavioral findings into simulations of real-world rumor diffusion.

Findings

Peer influence causes deviations from optimal choices despite risks and prior knowledge.

The quantity of social signals does not linearly increase influence responsiveness.

Sequential peer effects can accelerate rumor diffusion, especially when initial spread is slow.

Abstract

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.