Three diverse motives for information sharing

Valentina Vellani, Moshe Glickman, Tali Sharot
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Abstract

Knowledge is distributed over many individuals. Thus, humans are tasked with informing one another for the betterment of all. But as information can alter people’s action, affect and cognition in both positive and negative ways, deciding whether to share information can be a particularly difficult problem. Here, we examine how people integrate potentially conflicting consequences of knowledge, to decide whether to inform others. We show that participants (Exp1: N = 114, Pre-registered replication: N = 102) use their own information-seeking preferences to solve complex information-sharing decisions. In particular, when deciding whether to inform others, participants consider the usefulness of information in directing action, its valence and the receiver’s uncertainty level, and integrate these assessments into a calculation of the value of information that explains information sharing decisions. A cluster analysis revealed that participants were clustered into groups based on the different weights they assign to these three factors. Within individuals, the relative influence of each of these factors was stable across information-seeking and information-sharing decisions. These results suggest that people put themselves in a receiver position to determine whether to inform others and can help predict when people will share information. Individuals consider the usefulness, emotional valence, and prior uncertainty when deciding both when to seek information for themselves and when to share information with others.

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信息共享的三种不同动机。
知识分布在许多人身上。因此,人类的任务是相互通报信息,以造福全人类。但是,由于信息会以积极或消极的方式改变人们的行动、情感和认知,因此决定是否分享信息可能是一个特别困难的问题。在这里,我们研究了人们如何整合知识可能带来的冲突性后果,以决定是否告知他人。我们发现,参与者(Exp1: N = 114, Pre-registered replication: N = 102)会利用自身的信息搜寻偏好来解决复杂的信息共享决策。特别是,在决定是否告知他人时,参与者会考虑信息对指导行动的有用性、信息的价值和接收者的不确定性水平,并将这些评估整合到信息价值的计算中,从而解释信息共享决策。聚类分析显示,根据参与者对这三个因素所赋予的不同权重,他们被分为不同的组别。在个体内部,每个因素的相对影响力在信息寻求和信息共享决策中都是稳定的。这些结果表明,人们在决定是否告知他人时,会将自己置于接收者的位置,这有助于预测人们何时会分享信息。
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