Frame-dependent Random Utility

TL;DR

This paper introduces a novel frame-dependent random utility model (FRUM) that captures how framing influences decision-making, providing testable conditions and a parametric version for practical analysis.

Contribution

It develops a new choice function and a comprehensive revealed preference analysis for frame-dependent utility, advancing understanding of framing effects in decision models.

Findings

FRUM captures observed framing effects in choices

Testable conditions characterize the model with limited data

A parametric version improves tractability

Abstract

We explore the influence of framing on decision-making, where some products are framed (e.g., displayed, recommended, endorsed, or labeled). We introduce a novel choice function that captures observed variations in framed alternatives. Building on this, we conduct a comprehensive revealed preference analysis, employing the concept of frame-dependent utility using both deterministic and probabilistic data. We demonstrate that simple and intuitive behavioral principles characterize our frame-dependent random utility model (FRUM), which offers testable conditions even with limited data. Finally, we introduce a parametric model to increase the tractability of FRUM. We also discuss how to recover the choice types in our framework.