Working Papers

• When Are Decisions Improvable? An Evaluation of Diagnostic Methods (with B. Douglas Bernheim, Kirby Nielsen and Charles D. Sprenger). Submitted. [Abstract]
  We evaluate three methods for identifying improvable choices: documenting specific misconceptions (the Characterization Assessment method), gauging confidence in choices (the Decision Confidence method), and showing that specific behavioral patterns in the domain of interest also emerge in a related domain where they are objectively suboptimal (the Pattern Matching method). In experiments involving risky choice, the three methods imply that different choices are improvable and have conflicting implications regarding legitimate risk preferences. We clarify the assumptions underlying each method and reevaluate the evidence on risk-taking in light of their limitations.
• Robust Estimation of Risk Preferences (with Shunto J. Kobayashi). [Abstract]
  We develop a structural approach to measuring risk attitudes that permits any violation of the Independence Axiom. The foundation is a general representation theorem: any preference satisfying Weak Order, Continuity, and Weak Monotonicity is represented by a set of utility functions characterizing the EU core---its largest subrelation consistent with expected utility---together with a completion rule resolving utility disagreements. We estimate this set via a Bayesian partial-identification framework, remaining agnostic about the completion rule. We evaluate the approach along two dimensions---measuring risk aversion and predicting choice---in an experiment with 500 subjects. The resulting risk-attitude measures are parsimonious and interpretable, and correlate meaningfully with subjects' investment habits. For prediction, the set delivers a range of choice probabilities; we predict only when that range unambiguously favors one option. This approach enables reliable predictions across environments where economic models and machine learning algorithms fail to generalize.
• Deliberate Randomization under Risk (with Marco Loseto). [Abstract]
  We consider a decision-maker (DM) with convex preferences who faces a set of risky actions and can delegate his choice to a randomization device. Under convexity, the DM's preferences admit a conservative multi-utility representation: each utility generates an evaluation for each action, and actions are ranked according to the lowest evaluation. Building on this multi-utility representation, we characterize the set of optimal actions and propose an efficiency criterion to rank them. Next, we narrow our attention to deliberate randomization for a DM with two utilities. In this case, we show that the DM never needs to select more than two actions with positive probability and study when the desire to randomize reveals information about risk attitude. Finally, we apply our results to games where each player has two actions and two utility functions. We show that incentives to randomize extend to strategic settings and derive a new class of mixed Nash equilibria that we call ``strict" because players strictly prefer randomization. In general, convexity may lead to a multiplicity of mixed Nash equilibria. However, we show that when they exist, only strict equilibria are such that all the mixed actions are efficient.