arXiv subjects

Elchin Suleymanov

Publications and source records attributed to Elchin Suleymanov.

5 recordsLinked to original sources

arXiv2026

A Revealed Preference Framework for AI Alignment

Human decision makers increasingly delegate choices to AI agents, raising a natural question: does the AI implement the human principal's preferences or pursue its own? To study this question using revealed preference techniques, I introduce the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's. I show that the AI's alignment (similarity of human and AI preferences) can be generically identified in two settings: the laboratory setting, where both human and AI choices are observed, and the field setting, where only AI choices are observed.

econ.TH↗

arXiv2025

Robust Maximum Likelihood Updating

There is a large body of evidence that decision makers frequently depart from Bayesian updating. This paper introduces a model, robust maximum likelihood (RML) updating, where deviations from Bayesian updating are due to multiple priors/ambiguity. Using the decision maker's prior and posteriors as the primitives of the analysis, I axiomatically characterize a representation where the decision maker's probability assessment can be described by a benchmark prior, which is interpreted as an initial best guess, and a set of plausible priors, which represents all the priors that cannot be ruled out. When new information is received, the decision maker revises her benchmark prior within the set of plausible priors via the maximum likelihood principle in a way that ensures maximally dynamically consistent behavior, and updates the new benchmark prior using Bayes' rule. I demonstrate how the set of plausible priors can be uniquely identified by comparing ex ante and ex post beliefs and show how most commonly observed updating biases can be accommodated within the model in a unified framework.

econ.TH↗

arXiv2025

Entropy Regularized Belief Reporting

This paper investigates a model of partition dependence, a widely reported experimental finding where the agent's reported beliefs depend on how the states are grouped. In the model, called Entropy Regularized Belief Reporting (ERBR), the agent is endowed with a latent benchmark prior that is unobserved by the analyst. When presented with a partition, the agent reports a prior that minimizes Kullback-Leibler divergence from the latent benchmark prior subject to entropy regularization. This captures the intuition that while the agent would like to report a prior that is close to her latent benchmark prior, she may also have a preference to remain noncommittal. I provide the structural properties of the model that allow for identification of the latent benchmark prior and apply the model to the experimental data from Benjamin et al. (2017).

econ.TH↗

arXiv2023

Reference Dependence and Random Attention

We explore the ways that a reference point may direct attention. Utilizing a stochastic choice framework, we provide behavioral foundations for the Reference-Dependent Random Attention Model (RD-RAM). Our characterization result shows that preferences may be uniquely identified even when the attention process depends arbitrarily on both the menu and the reference point. The RD-RAM is able to capture rich behavioral patterns, including frequency reversals among non-status quo alternatives and choice overload. We also analyze specific attention processes, characterizing reference-dependent versions of several prominent models of stochastic consideration.

econ.TH↗

arXiv2019

A Random Attention Model

This paper illustrates how one can deduce preference from observed choices when attention is not only limited but also random. In contrast to earlier approaches, we introduce a Random Attention Model (RAM) where we abstain from any particular attention formation, and instead consider a large class of nonparametric random attention rules. Our model imposes one intuitive condition, termed Monotonic Attention, which captures the idea that each consideration set competes for the decision-maker's attention. We then develop revealed preference theory within RAM and obtain precise testable implications for observable choice probabilities. Based on these theoretical findings, we propose econometric methods for identification, estimation, and inference of the decision maker's preferences. To illustrate the applicability of our results and their concrete empirical content in specific settings, we also develop revealed preference theory and accompanying econometric methods under additional nonparametric assumptions on the consideration set for binary choice problems. Finally, we provide general purpose software implementation of our estimation and inference results, and showcase their performance using simulations.

econ.EM↗