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.

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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.

Diversification is fundamental to optimal investing, enabling investors to reduce portfolio risk without sacrificing expected returns. Standard theory assumes that investors understand the joint distribution of asset returns and adjust how much to diversify in response to correlation, optimally exploiting the resulting hedging opportunities. However, empirical evidence on whether people do is mixed. I show experimentally that complexity drives both correlation insensitivity and correlation mis-response. I independently vary Cancellation Complexity (identifying decision-irrelevant states), and Tradeoff Complexity (evaluating cross-state tradeoffs) via a series of experiments. I find that both reduce correlation sensitivity, but Tradeoff Complexity additionally pushes negative-correlation allocations off the mean-variance frontier. A structural model identifies distinct channels: Cancellation Complexity operates through choice noise on a flatter evaluation landscape, while Tradeoff Complexity induces an allocation-level complexity penalty. Using U.S. stock data, I find that Tradeoff Complexity amplifies the behavioral, but not the rational, component of the comovement premium, particularly in small stocks.

We study how the format of belief elicitation shapes elicited beliefs. In two experiments on Prolific (N=251), we compare the slider-based Classical Method (CM), which requires an absolute magnitude judgment, to a Dynamic Binary Method (DBM) that elicits the same belief through a sequence of comparative judgments over nested subintervals. A cognitive-friction model nests both: CM updates a noisy signal against a prior mean and compresses reports toward the prior mean; DBM applies a fixed threshold rule to per-step raw signals that never references the prior. Aggregate medians are statistically tied, but DBM produces 25–47% higher variance. The mechanism is directly observable within the DBM trajectory: conditional on a correct first binary choice, the report-on-truth slope is 0.98 and indistinguishable from one; conditional on a wrong first choice, it is 0.79—precisely the compression CM exhibits unconditionally. DBM exit intervals function as calibrated imprecision measures, and DBM-recovered perceptual noise predicts the same participant's CM accuracy on held-out tasks. Elicitation format is an active input into belief formation, not a neutral measurement device.