Observational Learning with a Budget
This work addresses the challenge of efficient resource allocation in social learning systems, which is incremental as it builds on existing observational learning models.
The paper tackles the problem of improving decision accuracy in a Bayesian observational learning model by optimally allocating a limited budget to enhance signal quality across agents, showing that at least one proposed strategy maximizes the probability of achieving a correct information cascade.
We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a decision based on its own signal and its observations of previous agents. A central planner seeks to improve the accuracy of these signals by allocating a limited budget to enhance signal quality across agents. We formulate and analyze the budget allocation problem and propose two optimal allocation strategies. At least one of these strategies is shown to maximize the probability of achieving a correct information cascade.