Ongoing Research

  1. Models of Risk Preferences and Higher-Order Risk Attitudes (with Glenn W. Harrison & Andre Hofmeyr)

    Economists have gained a richer understanding of the structure of risk preferences by defining key concepts using intuitive and directly observable choice tasks. These choice tasks define “behavioral reference lottery pairs” that can be used to identify risk preferences reflecting risk aversion, prudence and temperance. We show how to apply these ideas to a wide range of models of risk preference beyond Expected Utility Theory.

  2. The Hunt for Loss Aversion: A Bayesian Detective Story (with James Bland & Glenn W. Harrison)

    Cumulative Prospect Theory (CPT) is a flexible model of risk preferences that seeks to rationalize apparent deviations from Expected Utility Theory by proposing the concept of loss aversion. We develop a Bayesian Hierarchical Model to estimate a structural model of Cumulative Prospect Theory at the individual subject level.

  3. Bayesian Hierarchical Models of Risk Preferences (with James Bland & Glenn W. Harrison)

    We demonstrate how Bayesian Hierarchical Models can be applied to a wide range of popular models of risk preference and inferential questions. We develop methods for efficiently solving these models consistently, even with large numbers of agents, and provide flexible software and templates to implement these methods across popular statistical platforms.

  4. Accuracy and Confidence in Eyewitness Identification: A Reconsideration (with Quanita Adams, Glenn W. Harrison, J. Todd Swarthout & John Theilman)

    We redefine what accuracy and confidence should mean in eyewitness identification, design a controlled experiment to demonstrate and measure the two in a rigorous manner, and offer normative recommendations for better managing the future risks of testimony from witnesses of crimes.

  5. Structurally Estimating Joint Belief Distributions

    An existing belief estimation method is extended to estimate joint belief distributions, allowing one to draw inferences about the dependence structure individuals ascribe to multivariate events.

  6. Limitations of Eliciting Beliefs with a Bandwidth Task

    A bandwidth task is an incentivized belief elicitation task with several limitations. Proofs are provided that the response does not map to any moment of the distribution, and the consequences of using this approach over various domains of interest are reviewed.

  7. Massively Parallel Adaptive Markov Chain Monte Carlo

    A method is proposed for adaptively selecting the proposal distribution for MCMC simulation based on the current state of many simultaneous chains. Gains in efficiency are demonstrated to be several orders of magnitude faster in reaching the ergodic distribution.