Working Papers:

Well Excuse Me! Replicating and Connecting Excuse-Seeking Behaviors

(Beatriz Ahumada, Yufei Chen, Neeraja Gupta, Kelly Hyde, Marissa Lepper, Will Mathews, Neil Silveus, Lise Vesterlund, Taylor Weidman, Alistair Wilson, K. Pun Winichakul and Liyang Zhou)

January 2022

Excuse-seeking behavior that facilitates replacing altruistic choices with selfinterested ones has been documented in several domains. In a laboratory study, we replicate three leading papers on this topic: Dana et al. (2007), and the use of information avoidance; Exley (2015), and the use of differential risk preferences; and Di Tella et al. (2015), and the use of motivated beliefs. The replications were conducted as part of a graduate course, attempting to embed one answer to the growing call for experimental replications within the pedagogic process. We fully replicate the simpler Dana et al. paper, and broadly replicate the core findings for the other two projects, though with reduced effect sizes and a failure to replicate on some secondary measures. Finally, we attempt to connect behaviors to facilitate the understanding of how each fit within the broader literature. However, we find no connections across domains.

Additional details:

Online Appendices (PDF)

The Experimenters' Dilemma: Inferential Preferences over Populations

(with Neeraja Gupta and Luca Rigotti)

April 2023

We examine the experimenter's preferences over different populations using statistical power under a fixed budget as the stand-in for the researcher's utility. We consider five populations commonly used in experiments by economists: undergraduate students at a physical location, undergraduate students in a virtual setting, Amazon MTurk "workers", a filtered MTurk subset from CloudResearch, and Prolific. Focusing on noise due to inattention, observation costs dominate the comparisons, with the larger online population samples superior to the smaller lab samples. However, once we factor in responsiveness to treatment, the lab samples have greater power than either MTurk or Prolific.

Preference Reversals between One-Shot and Repeated Decisions: The Case of Regret

(with Alex Imas and Felipe Araujo).

June, 2022, revise & resubmit at JPE:Micro

We demonstrate potential pitfalls when extrapolating behavioral findings identified from one-shot choices to repeated settings. As a case study, we examine the use of "regret lotteries" as a behavioral tool to boost motivation. Based on findings from one-shot settings, presenting counterfactual information generates the potential for regret, which can be used to increase a lottery's value. This result has motivated the increasing use of regret lotteries in the field to incentivize recurrent decisions like exercise and compliance with company directives. Using a controlled experiment we show that while regret lotteries are the superior motivational tool in one-shot decisions, for repeated decisions the effect is entirely reversed. These findings have implications for incentive and policy design, highlighting the scope for error when extrapolating one-shot findings to inherently repeated settings.

Additional details:


Costly Communication in Groups: Theory and an Experiment

February 2016

I develop a novel model of group-based communication in which group members communicate with one another. Communication is costly in the sense that group members who choose to send or listen to messages incur costs. Equilibrium strategies have an intuitive characterization - those with the best information send, those with the worst information receive. Free-riding leads to less information exchange than is optimal, but a simple system of transfers and subsidies can correct this. Examining the model's predictions with an experiment I find that subjects over-communicate when costs are high, but fail to benefit from this as much as they should. Additionally, I find that listening costs are more harmful to fare, in contrast with the theory which indicates sending costs.

Paired-Uniform Scoring: Implementing a binarized scoring rule with non-mathematical language

(with Emanuel Vespa).

October, 2017

We outline a mechanism for eliciting probabilities using two uniform random numbers that is equivalent to the binarized scoring rule (BSR). Though our implementation is simple to describe and has a non-mathematical explanation, it retains the desirable theoretical features of the BSR. Moreover, we show that a discretized version with evenly-spaced reporting intervals can be implemented in the field with no more equipment than a pair of dice.

Work in Progress:

Laws of Large Numbers and Risk Preferences

(with Felipe Araujo and Alex Imas)

Testing Qualitative Effects with Experimenter Demand

(with David Danz, Lise Vesterlund and graduate experimental class)

Honesty on the Margins

(with Simon Halliday and Jonathan Lafky)

Strategic Uncertainty in Dynamic Games

(with Emanuel Vespa)

Competition and Communication: An Experiment

(with Emanuel Vespa)

Replicating Moral wiggling across domains

(with David Danz, Lise Vesterlund and graduate experimental class)

An Elicitation Horse Race (Where the Blinkered Horse Win by a Nose)

(with David Danz, Lise Vesterlund and and graduate experimental class)

Paying it forward: An experiment on an experimental college financing

(with David Danz, David Huffman, Lise Vesterlund and Stephanie Wang)