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We present two models of how people form beliefs that are based on machine learning theory. We illustrate how these models give insight into observed human phenomena by showing how polarized beliefs can arise even when people are exposed to almost identical sources of information. In our first model, people form beliefs that are deterministic functions that best fit their past data (training sets). In that model, their inability to form probabilistic beliefs can lead people to have opposing views even if their data are drawn from distributions that only slightly disagree. In the second model, people pay a cost that is increasing in the complexity of the function that represents their beliefs. In this second model, even with large training sets drawn from exactly the same distribution, agents can disagree substantially because they simplify the world along different dimensions. We discuss what these models of belief formation suggest for improving people’s accuracy and agreement.

 

Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. While the United States sets refugee capacities for communities on an annual basis, refugees arrive and must be placed over the course of the year. We introduce a dynamic allocation system based on two-stage stochastic programming to improve employment outcomes. Our algorithm is able to achieve over 98 percent of the hindsight-optimal employment compared to under 90 percent of current greedy-like approaches. This dramatic improvement persists even when we incorporate a vast array of practical features of the refugee resettlement process including indivisible families, batching, and uncertainty with respect to the number of future arrivals. Our algorithm is now part of the Annie™ MOORE optimization software used by a leading American refugee resettlement agency. The full version of this paper is available at https://arxiv.org/pdf/2105.14388.pdf.