arXiv subjects

Jay Lu

Publications and source records attributed to Jay Lu.

5 recordsLinked to original sources

arXiv2026

Pure Risk

We introduce a behavioral notion of domain-specific risk aversion that separates attitudes toward risk from deterministic utility: an agent is more pure risk averse in one domain than in another if, for prizes that are indifferent under certainty, he is more averse to risk in the former domain than in the latter. We develop a model that goes beyond expected utility by allowing risk attitudes to vary across domains, while preserving expected utility within each domain. The domains are subjective and need not be specified in advance; they are identified from choice behavior. We establish uniqueness of the model's parameters and provide an axiomatic characterization.

econ.TH↗

arXiv2026

Creative Ownership in the Age of AI

Copyright law focuses on whether a new work is "substantially similar" to an existing one, but generative AI can closely imitate style without copying content, a capability now central to ongoing litigation. We argue that existing definitions of infringement are ill-suited to this setting and propose a new criterion: a generative AI output infringes on an existing work if it could not have been generated without that work in its training corpus. To operationalize this definition, we model generative systems as closure operators mapping a corpus of existing works to an output of new works. AI generated outputs are \emph{permissible} if they do not infringe on any existing work according to our criterion. Our results characterize structural properties of permissible generation and reveal a sharp asymptotic dichotomy: when the process of organic creations is light-tailed, dependence on individual works eventually vanishes, so that regulation imposes no limits on AI generation; with heavy-tailed creations, regulation can be persistently constraining.

econ.TH↗

arXiv2024

Did Harold Zuercher Have Time-Separable Preferences?

This paper proposes an empirical model of dynamic discrete choice to allow for non-separable time preferences, generalizing the well-known Rust (1987) model. Under weak conditions, we show the existence of value functions and hence well-defined optimal choices. We construct a contraction mapping of the value function and propose an estimation method similar to Rust's nested fixed point algorithm. Finally, we apply the framework to the bus engine replacement data. We improve the fit of the data with our general model and reject the null hypothesis that Harold Zuercher has separable time preferences. Misspecifying an agent's preference as time-separable when it is not leads to biased inferences about structure parameters (such as the agent's risk attitudes) and misleading policy recommendations.

econ.EM↗

arXiv2024

Algorithmic Fairness and Social Welfare

Algorithms are increasingly used to guide high-stakes decisions about individuals. Consequently, substantial interest has developed around defining and measuring the ``fairness'' of these algorithms. These definitions of fair algorithms share two features: First, they prioritize the role of a pre-defined group identity (e.g., race or gender) by focusing on how the algorithm's impact differs systematically across groups. Second, they are statistical in nature; for example, comparing false positive rates, or assessing whether group identity is independent of the decision (where both are viewed as random variables). These notions are facially distinct from a social welfare approach to fairness, in particular one based on ``veil of ignorance'' thought experiments in which individuals choose how to structure society prior to the realization of their social identity. In this paper, we seek to understand and organize the relationship between these different approaches to fairness. Can the optimization criteria proposed in the algorithmic fairness literature also be motivated as the choices of someone from behind the veil of ignorance? If not, what properties distinguish either approach to fairness?

econ.TH↗

arXiv2021

Algorithm Design: A Fairness-Accuracy Frontier

Algorithm designers increasingly optimize not only for accuracy, but also for the fairness of the algorithm across pre-defined groups. We study the tradeoff between fairness and accuracy for any given set of inputs to the algorithm. We propose and characterize a fairness-accuracy frontier, which consists of the optimal points across a broad range of preferences over fairness and accuracy. Our results identify a simple property of the inputs, group-balance, which qualitatively determines the shape of the frontier. We further study an information-design problem where the designer flexibly regulates the inputs (e.g., by coarsening an input or banning its use) but the algorithm is chosen by another agent. Whether it is optimal to ban an input generally depends on the designer's preferences. But when inputs are group-balanced, then excluding group identity is strictly suboptimal for all designers, and when the designer has access to group identity, then it is strictly suboptimal to exclude any informative input.

econ.TH↗