Attitudes toward rejection play a critical role in competitive, high-stakes environments like job applications. This paper develops a framework, grounded in motivated reasoning, to explore how rejection aversion shapes labor market beliefs. These beliefs emerge from a combination of factual information and manufactured evidence. Strategic, profit-maximizing firms adapt their hiring strategies to workers’ cognitive biases, optimizing hiring standards to counteract rejection aversion. The framework provides testable implications, suggesting that motivated reasoning and rejection aversion distort workers’ perceptions of wages and job prospects.

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.

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.

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

I develop a strategic model of choice under risk and uncertainty, where these two separate selves contribute to a learning process. This game of learning takes place between a preparing self (The Bureaucrat) and a responding self (The Entrepreneur). The Bureaucrat is responsible for maintaining the status quo and preparing the Entrepreneur to optimally take valuable risks. The value of learning is the control over choice it yields to the Entrepreneur. The framework admits logit as a best response and Shannon-entropy-based rational inattention as a sequential equilibrium. Furthermore, the equilibrium generalizes to a large class of novel learning models. We demonstrate that this model can generate choice sets that vary depending on the underlying state, which is not feasible with rational inattention.

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.

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.