Probability Matching, a classical violation of expected utility maximization, refers to people's tendency to randomize, or even match their choice frequency to the outcome probability, when choosing over binary lotteries that differ only in their probabilities. Why? I present an experiment designed to distinguish between several broad classes of explanations: (1) models of Correlation-Invariant Stochastic Choice --- randomizing due to factors orthogonal to the correlation between lotteries, such as non-standard preferences or errors, and (2) models of Correlation-Sensitive Stochastic Choice --- deliberately randomizing due to misperceived hedging opportunities, especially when lotteries are negatively correlated. My experimental design differentiates between their testable predictions by varying the correlation between lottery outcomes. The findings indicate that the first class, despite being home to most existing theories, has limited explanatory power. Using additional treatment, I rule out Similarity Heuristics as a competing explanation with the second class. The results indicate that a vast majority of individuals deliberately randomize due to misperceived hedging opportunities.

[Published Version]

Optimal portfolios depend on the correlation among asset returns, yet evidence on whether investors respond to it is mixed. I show that a key driver is the complexity of two distinct cognitive operations that forming a portfolio requires, mirroring the two-step structure of the expected-utility axioms: canceling common-return states, which are irrelevant to the allocation (Cancellation Complexity, CC), and evaluating the cross-state tradeoff that remains (Tradeoff Complexity, TC). Diagnostically switching each friction on and off in experimental portfolio allocation tasks, I find that both frictions amplify mis-response to correlation --- participants over-diversify even when the hedging opportunity vanishes --- but through different channels: CC attenuates the response by diluting the stakes, whereas TC reverses it by shifting choice toward portfolios that are easier to evaluate. Outside the lab, the same channel appears in U.S. stock returns: the tradeoff complexity of a stock's return distribution relative to the market's amplifies the behavioral component of the comovement premium, with no detectable effect on its rational component. 

[Draft]

The same underlying data can be communicated or perceived in different formats. A natural question is whether people prefer the formats that serve them best. We study five different summary reports of a series of binary signals in the canonical belief updating setting: Majority (which signal type appeared more often), Percentage (the percentage of each signal type), Difference (how many more of the dominant type), Count (the number of each type), and Sequence (the raw sequence of the signals). These reports differ in two key dimensions: instrumental value (IV) and information richness (IR). In an online experiment (N=200), we elicit participants’ preference (WTP) for the reports and measure their belief-updating performance across the reports. Our central finding is a systematic misalignment between preference and performance. WTP rises with both IV and IR, although the standard economy theory predicts only IV should matter. Performance, in contrast, improves with IV but deteriorates with IR conditional on IV. That is, participants perform worse with the very reports they pay more to receive. We also find substantial heterogeneity in participants' preferences, but the misalignment between preference and performance is robust across subgroups. Together, these results indicate that demand for information is shaped by features that go beyond instrumental value, and that people have imperfect meta-cognition about their own belief-updating behavior.

(Soon)

Elicited beliefs compress toward the midpoint of the response scale, even under incentive-compatible mechanisms. The literature contributes it to either the reporting format or subjects’ beliefs. We show it is mostly neither. Most of the bias reflects imprecise perception: subjects cannot cleanly extract the truth from a noisy internal signal. This part distorts reports under any format. A smaller part is specific to the slider. The Dynamic Binary Method (DBM) separates the two. Participants halve the response scale step by step and stop when they choose. Each binary comparison thus reveals the perception before the format can act on it. In two experiments, the per-step choices fit a Gaussian-signal psychometric that a Bayesian-with-noise alternative cannot reproduce. The first comparison is prior-free, so conditioning on a correct one isolates the format-specific part of the bias. That part is robustly positive in sign, though its size depends on how compression is measured. Being prior-free, the trajectory also recovers each subject’s perceptual precision, separately from the default weight that a slider blends it with. The recovered precision predicts the same subject’s slider accuracy on held-out tasks. The two formats are therefore complements. A slider spreads the cost of imprecision across all reports through compression. DBM concentrates it in a few identifiable failures and, in exchange, measures the imprecision itself.

(Soon)