使用对照实验和计算模型测量顺序同伴暴露对决策的影响
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
摘要：
人们普遍认为，一个人的同龄人影响产品采用行为。这种关系与决策者在社交网络中收到的信号数量有关。但目前还不清楚，当它接收这些信号的模式发生变化时，以及当同伴影响指向非最佳选择时，这些原则是否成立。为了调查这一点，我们在一个在线控制实验中使用与人类参与者的游戏来操纵社交信号暴露。游戏中的每个参与者在具有不同效用的选择中做出决定。我们注意到以下情况：（1）即使存在货币风险和先前获得的选择知识，决策者也倾向于偏离明显的最优决策，当他们的同伴做出类似的决策时，我们称之为影响决策，（2）当社会信号的数量随时间变化时，影响决策的转发概率以及因此对社会影响的响应不一定与信号的绝对数量。为了更好地理解这些规则的同行影响力可以用于建模应用程序的现实世界中的扩散和网络环境中，我们用我们的行为研究结果来模拟传播动态在现实世界中的案例研究。我们特别试图看看累积的影响如何发挥在用户的不确定性和测量其结果的谣言扩散，我们作为一个例子，次优选择扩散模型。总之，我们的模拟结果表明，从影响决策的顺序同行效应克服了个人的不确定性，引导更快的谣言随着时间的推移扩散。然而，当扩散的速度是缓慢的，在开始时，用户的不确定性可以有相当大的作用相比，同行的影响，在决定采用轨迹的一块有问题的信息。
英文摘要：
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.
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