Measuring beliefs has always been a central aspect of economic decision-making. This presentation combines two research projects that develop and apply a new method to measure belief distributions:

The first paper introduces the method and applies it to the measurement of inflation expectations, showing how it improves upon traditional techniques and mitigates some of the biases of previous methods. Specifically, our approach yields higher mean inflation estimates and substantially reduces the standard deviations of the distributions, while using a method which respondents find both easier and more engaging.In the second paper, the method is applied within a broader theoretical framework to identify and disentangle multiple belief-updating biases. This framework is then tested in a laboratory experiment and find that, while all tested biases are present to a certain extent, sequence-related biases (gambler’s fallacy and hot hand fallacy) and motivated-belief biases (optimism and pessimism) are the most commonly exhibited biases.