# Learning Economic Parameters from Revealed Preferences

arXiv: https://arxiv.org/abs/1407.7937 · HTML: https://mlsanity.com/paper/1407.7937/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2014.

## Built on by (most cited first, 8 of 8)

- [Active Statistical Inference](https://mlsanity.com/paper/2403.03208/) (2024)
- [PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2308.02585/) (2023)
- [Contextual Inverse Optimization: Offline and Online Learning](https://mlsanity.com/paper/2106.14015/) (2021)
- [Learning in Online Principal-Agent Interactions: The Power of Menus](https://mlsanity.com/paper/2312.09869/) (2023)
- [Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements](https://mlsanity.com/paper/2205.00825/) (2022)
- [Agnostic learning with unknown utilities](https://mlsanity.com/paper/2104.08482/) (2021)
- [Online Learning for Equilibrium Pricing in Markets under Incomplete Information](https://mlsanity.com/paper/2303.11522/) (2023)
- [On the limitations of data-based price discrimination](https://mlsanity.com/paper/2204.12723/) (2022)

## Abstract (arXiv)

A recent line of work, starting with Beigman and Vohra (2006) and Zadimoghaddam and Roth (2012), has addressed the problem of em learning a utility function from revealed preference data. The goal here is to make use of past data describing the purchases of a utility maximizing agent when faced with certain prices and budget constraints in order to produce a hypothesis function that can accurately forecast the em future behavior of the agent. In this work we advance this line of work by providing sample complexity guarantees and efficient algorithms for a number of important classes. By drawing a connection to recent advances in multi-class learning, we provide a computationally efficient algorithm with tight sample complexity guarantees (Theta(d/ε) for the case of d goods) for learning linear utility functions under a linear price model. This solves an open question in Zadimoghaddam and Roth (2012). Our technique yields numerous generalizations including the ability to learn other well-studied classes of utility functions, to deal with a misspecified model, and with non-linear prices.