Altruistic observational learning

Abstract

We report two information cascade game experiments that directly test the impact of altruism on observational learning. Participants interact in two parallel sequences, the observed and the unobserved sequence. Only the actions of the observed entail informational benefits to subsequent participants. We find that observed contradict their private information significantly less often than unobserved when the monetary incentives to herd are moderately weak. Long laboratory cascades accumulate substantial public information which increases the earnings of participants. In Experiment 2, participants have better opportunities to learn about the strategies played by observed which amplifies the impact of altruism on observational learning.

A question about the existence of Russian economic thought

“…by Galperti and Strulovici [2017]. Of particular relevance to our argument are the experiments by March and Ziegelmeyer [2020] and Peng, Rao, Sun, and Xiao [2017], who find evidence of altruistic motives when testing standard models of observational learning. Also related is the work by 7 The decentralized nature of decision-making and communication in our model relates us to the literature on social learning on networks, which are inherently decentralized.…”

An experimental investigation of the “follow own signal” decision rule under increased information uncertainty

“…One possible explanation is that early decision makers may believe that their predecessors are more prone to make errors and hence rely more on own signals (Kubler and Weizsacker, 2004). Interestingly, the tendency to follow one's own signal has been reported to be correlated with altruistic preferences (March and Ziegelmeyer, 2020). March and Ziegelmeyer (2020) conjecture that participants with altruistic preferences are aware of the welfare benefit of their actions on subsequent decision makers, and that the welfare benefit of following their own signals outweighs the individual costs [6].…”