Updating Beliefs When Evidence is Open to Interpretation: Implications for Bias and Polarization
Roland G. Fryer Jr.
Harvard University - Department of Economics; National Bureau of Economic Research (NBER); American Bar Foundation; University of Chicago
ETH Zurich - Department of Mathematics
Matthew O. Jackson
Stanford University - Department of Economics; Santa Fe Institute; Canadian Institute for Advanced Research (CIFAR)
We introduce a model in which agents observe signals about the state of the world, some of which are open to interpretation. Our decision makers use Bayes' rule in an iterative way: first to interpret each signal and then to form a posterior on the sequence of interpreted signals. This 'double updating' leads to confirmation bias and can lead agents who observe the same information to polarize: there can be a greater distance between their beliefs after observing a common sequence of signals than before. Such updating is optimal if agents have bounded memory and sufficiently discount the future. If they are very patient and have bounded memory, then a time-varying random interpretation rule (still double-updating) is optimal. We explore the model in an on-line experiment in which individuals interpret research summaries about climate change and the death penalty and report beliefs. Consistent with the model, there is a significant relationship between an individual's prior and their interpretation of the summaries. More than half of the subjects exhibit a polarizing behavior - shifting their beliefs further from the average belief after seeing the same summaries as all other subjects - something that is inconsistent with standard Bayesian updating, or even naive updating, but consistent with our model.
Number of Pages in PDF File: 41
Keywords: beliefs, polarization, learning, updating, Bayesian updating, biases, discrimination, decision making
JEL Classification: D10, D80, J15, J71, I30
Date posted: June 5, 2013 ; Last revised: May 4, 2015
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