Ordered Reference Dependent Choice
This paper studies how violations of structural assumptions like expected utility and exponential discounting can be connected to basic rationality violations, even though these assumptions are typically regarded as independent building blocks in decision theory. A reference-dependent generalization of behavioral postulates captures preference shifts in various choice domains. When reference points are fixed, canonical models hold; otherwise, reference-dependent preference parameters (e.g., CARA coefficients, discount factors) give rise to "non-standard" behavior. The framework allows us to study risk, time, and social preferences collectively, where seemingly independent anomalies are interconnected through the lens of reference-dependent choice.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Single-Crossing Differences in Convex Environments
An agent's preferences depend on an ordered parameter or type. We characterize the set of utility functions with single-crossing differences (SCD) in convex environments. These include preferences over lotteries, both in…
Combinatorial Bandits with Relative Feedback
We consider combinatorial online learning with subset choices when only relative feedback information from subsets is available, instead of bandit or semi-bandit feedback which is absolute. Specifically, we study two reg…
Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study
Reference-based graph compression encodes each vertex's neighbor list relative to a recent vertex, exploiting locality to compress large directed graphs. The dominant tool, WebGraph's BVGraph, fixes a single encoding pip…
Community DetectionIdentification of Nonlinear Dynamic Panels under Partial Stationarity
This paper studies identification for a wide range of nonlinear panel data models, including binary choice, ordered response, and other types of limited dependent variable models. Our approach accommodates dynamic models…
Rushes: A Human Preference Dataset for Pluralistic Alignment
We introduce Rushes, a dataset and benchmark for studying revealed human engagement preferences in interactive narrative environments. Rushes is collected through a game interface where users interact with AI-generated b…