🤖 AI Summary
This paper investigates the behavioral mechanisms underlying stochastic subset selection from a menu of alternatives. Addressing the proliferation of ad hoc functional forms and unclear behavioral foundations in existing menu-choice models, we develop a rigorous axiomatic framework that systematically characterizes the probabilistic structure of multi-item choice. Our method employs formal axiomatic analysis to derive necessary and sufficient conditions for key classes of parametric models. The main contributions are threefold: (i) a unified conceptualization of prominent models—including Random Utility, generalized Luce’s Axiom, and Sequential Elimination—clarifying their logical interconnections and fundamental distinctions; (ii) explicit identification of the behavioral assumptions embedded in each model and their domain of applicability; and (iii) derivation of empirically testable, high-discriminatory-power theoretical propositions. This framework advances the theoretical understanding of multi-item choice and provides a solid axiomatic foundation for empirical model testing and selection.
📝 Abstract
This paper studies choice situations in which a decision maker can choose multiple alternatives. Given a menu of available options, the decision maker selects a subset of the menu with certain probabilities. We employ an axiomatic approach to characterize various parametric models in the literature. Our results elucidate the implications of the functional form assumptions and shed light on the distinctions between models. The behavioral postulates offer simple tools for testing and falsifying the choice procedures used by the decision maker and reveal a close connection between models that are seemingly unrelated.