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

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