We study first-and second-order subjective expectations (beliefs) in strategic decisionmaking. We propose a method to elicit probabilistically both first-and second-order beliefs and apply the method to a Hide-and-Seek experiment. We study the relationship between choice and beliefs in terms of whether observed choice coincides with the optimal action given elicited beliefs. We study the relationship between first-and second-order beliefs under a coherence criterion. Weak coherence requires that if an event is assigned, according to first-order beliefs, a probability higher/lower/equal to the one assigned to another event, then the same holds according to second-order beliefs. Strong coherence requires the probability assigned according to first-and second-order beliefs to coincide. Evidence of heterogeneity across participants is reported. Verbal comments collected at the end of the experiment shed light on how subjects think and decide in a complex environment that is strategic, dynamic and populated by potentially heterogeneous individuals.
This paper introduces a new general model of boundedly rational observational learning: Quasi-Bayesian updating. The approach is applicable to any environment of observational learning and is rationally founded. We conduct a laboratory experiment and find strong supportive evidence for Quasi-Bayesian updating. We analyze the theoretical long run implications of Quasi-Bayesian updating in a model of repeated interaction in social networks with binary actions. We provide a characterization of the environment in which consensus and information aggregation is achieved. The experimental evidence is in line with our theoretical predictions. Finally, we establish that for any environment information aggregation fails in large networks.
We study first-and second-order subjective expectations (beliefs) in strategic decisionmaking. We propose a method to elicit probabilistically both first-and second-order beliefs and apply the method to a Hide-and-Seek experiment. We study the relationship between choice and beliefs in terms of whether observed choice coincides with the optimal action given elicited beliefs. We study the relationship between first-and second-order beliefs under a coherence criterion. Weak coherence requires that if an event is assigned, according to first-order beliefs, a probability higher/lower/equal to the one assigned to another event, then the same holds according to second-order beliefs. Strong coherence requires the probability assigned according to first-and second-order beliefs to coincide. Evidence of heterogeneity across participants is reported. Verbal comments collected at the end of the experiment shed light on how subjects think and decide in a complex environment that is strategic, dynamic and populated by potentially heterogeneous individuals.
We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we provide a normative foundation for quasi‐Bayesian updating, where a quasi‐Bayesian agent treats others' actions as if they were based only on their private signal. Quasi‐Bayesian updating induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly asymmetric environments. In all other environments, learning fails in networks with a diameter larger than 4. Finally, we consider a richer class of updating behavior that allows for nonstationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems that induce learning for most networks.
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