arXiv subjects

Avner Seror

Publications and source records attributed to Avner Seror.

5 recordsLinked to original sources

arXiv2026

A Random Rule Model

We model stochastic choice as environment-dependent switching among a small library of deterministic decision rules. A Random Rule Model generates menu-level choice probabilities via named, interpretable rules weighted by observable menu characteristics. Identification has a two-step structure: within-feature decisive-side variation identifies relative rule weights; cross-feature richness identifies the gate. Applied to binary lottery choices, the estimated weights concentrate on a small subset of rules and shift systematically with complexity and dispersion asymmetry. The model closes nearly all of the prediction gap to a flexible neural-network benchmark, while remaining interpretable, restrictive under permutation diagnostics, and portable to an independent dataset.

econ.GN↗

arXiv2026

How Many Mechanisms? Measuring Parsimony in Risky Choice

Behavioral theories rest on parsimony: a small number of mechanisms organizing many decisions. We define a Maximum Rule Concentration Index that measures how parsimoniously a dataset of risky choices can be organized through a library of simple, parameter-free decision rules drawn from canonical behavioral theories: salience, regret, disappointment, modal-payoff focusing, extreme-outcome screening, and limited attention. Applied to three lottery-choice datasets, the data exhibit detectable parsimony: for a majority of subjects, observed concentration exceeds what standard utility models generate on the same menus. The concentration organizes around salience thinking, modal-payoff focusing, and regret.

econ.GN↗

arXiv2025

Measuring Hidden Consumer Heterogeneity with Revealed Preferences

Consumer heterogeneity in revealed-preference data is larger than bilateral rationality tests can reveal. We construct a continuous nonparametric metric of this hidden heterogeneity by repeatedly subsampling choices, partitioning agents into groups whose pooled data are jointly rationalisable under a chosen consistency criterion and recording how often each pair is co-classified. The resulting kernel is positive semi-definite, embeds the population in a Hilbert space, and induces a metric with the triangle inequality. Under a necessary-and-sufficient contrast-rank condition, its spectral structure recovers latent preference types. Inference on demographic correlates proceeds via a Monte-Carlo-conditional test and a finite-sample-valid permutation test. Applied to US grocery scanner data, the construction reveals a joint-rationality gap of 0.62 between near-saturated pairwise compatibility and population-level co-typing; binary lottery data yield a comparable gap of 0.38. Standard demographics organise only a modest part of the scanner kernel structure.

econ.TH↗

arXiv2024

The Moral Mind(s) of Large Language Models

As large language models (LLMs) increasingly participate in tasks with ethical and societal stakes, a critical question arises: do they exhibit an emergent "moral mind" - a consistent structure of moral preferences guiding their decisions - and to what extent is this structure shared across models? To investigate this, we applied tools from revealed preference theory to nearly 40 leading LLMs, presenting each with many structured moral dilemmas spanning five foundational dimensions of ethical reasoning. Using a probabilistic rationality test, we found that at least one model from each major provider exhibited behavior consistent with approximately stable moral preferences, acting as if guided by an underlying utility function. We then estimated these utility functions and found that most models cluster around neutral moral stances. To further characterize heterogeneity, we employed a non-parametric permutation approach, constructing a probabilistic similarity network based on revealed preference patterns. The results reveal a shared core in LLMs' moral reasoning, but also meaningful variation: some models show flexible reasoning across perspectives, while others adhere to more rigid ethical profiles. These findings provide a new empirical lens for evaluating moral consistency in LLMs and offer a framework for benchmarking ethical alignment across AI systems.

cs.CY↗

arXiv2024

Concave Rationalization with an Ideal Point: An Afriat Theorem and an Application to Survey Design

This paper develops an Afriat-type characterization of concave rationalization with an unknown ideal point. We show that, for each candidate peak, a finite system of linear inequalities is necessary and sufficient for the existence of a continuous concave utility with an ideal point that rationalizes choices from linear budget sets anchored at different corners of the choice space. A stronger characterization adds the requirement that supergradients at observed choices point coordinatewise toward the peak, a necessary condition for single-peaked rationalizability. The resulting peak-oriented system has a transparent geometry - budgets anchored at different corners triangulate the ideal point - and yields a nonparametric set of candidate ideal points. This provides the theoretical foundation for the Priced Survey Methodology (PSM), in which respondents complete the same survey under different linear constraints. We apply the PSM to study political preferences in a sample of French respondents.

econ.TH↗