We present a model elucidating wishful thinking, which comprehensively incorporates both the costs and benefits associated with biased beliefs. Our findings reveal that wishful thinking behavior can be characterized as equivalent to superquantile-utility maximization within the domain of threshold beliefs distortion cost functions. By leveraging this equivalence, we establish WT as driving decision-makers to exhibit a preference for choices characterized by skewness and increased risk. Furthermore, we discuss how our framework facilitates the study of optimistic stochastic choice and optimistic risk aversion.

We develop a model of wishful thinking that incorporates the costs and benefits of biased beliefs. We establish the connection between distorted beliefs and risk, revealing how wishful thinking can be understood in terms of risk measures. Our model accommodates extreme beliefs, allowing wishful-thinking decision-makers to assign zero probability to undesirable states and positive probability to otherwise impossible states.

Motivated reasoning plays a critical role in competitive, high-stakes environments such as job applications. This paper develops a game-theoretic framework in which workers, influenced by motivated reasoning, decide whether to apply for jobs, while a strategic firm determines its hiring standards. Workers derive ego utility ex ante and disappointment-based utility ex post, both of which shape their subjective confidence levels. These psychological incentives distort beliefs, influencing application behavior. Building on the literature on statistical discrimination, I show that firms respond systematically: they lower standards for less confident groups to offset self-selection and induce applications. The model yields testable implications, revealing how statistical discrimination leads to higher-quality matches with underconfident workers.

This paper presents a simple model of motivated reasoning

where decision-makers combine observational evidence with psychologically manufactured evidence. By adopting a revealed-preference approach, I derive bounds on the psychological desires that can rationalize data without making restrictive assumptions about observational evidence acquisition. This facilitates testing for the presence and direction of agents’ psychological desires using choice data, providing a practical tool for identifying motivated reasoning through observed choices.

I present a model of stochastic choice in which an additive perturbation term penalizes deviations from an anchoring choice vector. This framework generalizes logit choice while capturing richer substitution patterns, including the endogenous formation of choice sets. I extend the model to environments with uncertainty, introducing a novel approach to information acquisition that I term risky learning. Risky learning is an intrapersonal equilibrium where acquiring information enables agents to take risks that would otherwise be untenable. The framework admits Shannon-entropy-based rational inattention as a special case, but extends well beyond it, allowing state-dependent choice sets, novel aggregation properties, and insightful violations of IIA.

We consider decision makers who evaluate actions according to beliefs distorted at a cost from some posited probabilistic model: pessimists, whose preferences generalize the multiplier preferences of Hansen and Sargent (2001, 2008); and optimists, whose preferences generalize the wishful thinking described in Caplin and Leahy (2019). An action is justifiable if it is a best-reply to some model in the distorted-beliefs problem. We show that, for pessimists, greater pessimism and, for optimists, less optimism both expand the set of justifiable actions.

In a recent note, Strzalecki [2024] discusses a model of belief updating denoted as variational Bayes. We extend  Strzalecki’s framework by using the class of statistical distances known as ϕ-divergences, generating a broader and more versatile class of non-Bayesian updating rules. Our approach enables belief updating rules that censor unlikely state realizations, diminishing the influence of highly improbable observations. From a behavioral perspective, we show that this new class includes updating rules that are both more conservative, retaining beliefs closer to priors, and more exploratory, allowing for significant updates in response to meaningful signals, compared to traditional Bayesian updating.

Through advertising, firms transmit messages to consumers, influencing how they view a product. The content of these messages can be simple or quite complex, and firms must decide what type of content to use. Complex messaging motivates the development of a novel notion of obfuscation in advertising content. I show that obfuscating content is preferable for complex products, and comparative statics for the optimal advertising level are provided. A comparison is drawn between this obfuscating content view of advertising and Nelson's standard “advertising to signal quality” view. I show they have many similarities, but that obfuscating content explains, separate from a higher willingness to pay, investing more in advertising to consumers who view the good as high quality.

Here, you can find a link to slides that overview our contributions in the papers Wishful Thinking is Risky Thinking and Wishful Thinking and Censored Beliefs.

https://drive.google.com/file/d/18c2QGjpxiVPpVuGJodudnb8p_ylnOFax/view?usp=share_link