Working Papers
Joint Correlated Stochastic Choice
This paper introduces a stochastic choice framework where interdependent decision environments shape attention allocation. We develop two models—Connected Random Consideration with Limited Attention (CRC-LA) and Sparsely Validated Random Consideration with Limited Attention (SVRC-LA)—which extend the Random Consideration Sets framework to incorporate cross-menu information spillovers. The CRC-LA model expands consideration sets through exposure to another decision-maker’s menu, while the SVRC-LA model captures socially validated choices. These models disentangle attention constraints from preference structures, offering insights into peer effects and networked decision-making. We further explore extensions that incorporate asymmetric information and sequential choice, with implications for consumer behavior, voting, and organizational decision-making.
Random Boolean Choice with Complementarity
This paper introduces the Ascending Random Boolean Choice (ARBC) model, a stochastic choice framework for multidimensional decision-making in lattice-structured environments. Building on the Random Utility Maximization (RUM) tradition, ARBC extends monotone comparative statics to unordered alternatives by incorporating Strictly Superextremal (SSE) relations, which capture complementarity in stochastic choice. The model is axiomatized through Monotonicity and a novel SSE-Crossing condition, generalizing the single-crossing property to lattice structures. We establish a combinatorial random utility representation and demonstrate its implications for stochastic monotone choice. The results provide a foundation for analyzing economic decisions involving interdependent attributes, with applications to consumer behavior, demand systems, and strategic decision-making.
A Note on Single-Crossing Random Utility Models
This note examines the uniqueness of Single-Crossing Random Utility Models (SCRUM) in rationalizing stochastic choice data. While Apesteguia et.al. (2017) show that datasets satisfying Centrality and Monotonicity have a unique SCRUM representation, we demonstrate that they can also be rationalized by a continuum of Random Utility Models (RUMs) that do not satisfy single-crossing. This challenges the necessity of single-crossing preferences in stochastic monotone comparative statics and highlights probabilistic indeterminacy in RUM representations. Unlike single-peaked or single-dipped RUMs, which permit multiple representations, SCRUM uniquely identifies support preferences, offering a more parsimonious structure for stochastic choice analysis.