Polynomial-time method tests peak-oriented preference rationality
A Polynomial-Time Test for Peak-Oriented Rationalizability
Computational Complexity
Summary
Understanding how people choose things can be studied by looking at their most preferred options, or peaks. The authors found a way to quickly check if such peak preferences make sense under a certain theory, and to figure out the ratings behind them. However, another measure related to these preferences is much harder to compute. Their work clarifies exactly which parts of this analysis can be done easily and which cannot.
What this means in practice
- •For behavioral economists: Efficiently verify if consumer choices align with peak preference rationality models in large datasets.
- •For market analysts: Use tractable methods to assess preference consistency when consumers’ top choices drive decision-making.
A theory result. No direct application yet.
Authors
Taotao He, Runfa Hu
Abstract
We study the computational complexity of peak-oriented rationalizability, a survey based revealed-preference test introduced by Seror (2026). We provide a polynomial-time algorithm for testing rationalizability and recovering a utility function, establishing that peak-oriented preference elicitation is computationally tractable. In contrast, we show that computing the peak-oriented Houtman-Maks index is NP-hard. These results delineate the precise computational boundaries of peak-oriented revealed-preference analysis.