# A Simple and Approximately Optimal Mechanism for an Additive Buyer

arXiv: https://arxiv.org/abs/1405.6146 · HTML: https://mlsanity.com/paper/1405.6146/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2014.

## Built on by (most cited first, 9 of 9)

- [A Context-Integrated Transformer-Based Neural Network for Auction Design](https://mlsanity.com/paper/2201.12489/) (2022)
- [GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning](https://mlsanity.com/paper/2406.07428/) (2024)
- [Deep Contract Design via Discontinuous Networks](https://mlsanity.com/paper/2307.02318/) (2023)
- [Recommender Systems meet Mechanism Design](https://mlsanity.com/paper/2110.12558/) (2021)
- [Learning Consumer Preferences from Bundle Sales Data](https://mlsanity.com/paper/2209.04942/) (2022)
- [Learning Optimal Posted Prices for a Unit-Demand Buyer](https://mlsanity.com/paper/2506.02284/) (2025)
- [Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design](https://mlsanity.com/paper/2601.13489/) (2026)
- [Refined Mechanism Design for Approximately Structured Priors via Active Regression](https://mlsanity.com/paper/2310.07874/) (2023)
- [Cogentic: Multi-Agent Orchestration for Automated Proof Discovery](https://mlsanity.com/paper/2609.40324/) (2026)

## Abstract (arXiv)

We consider a monopolist seller with n heterogeneous items, facing a single buyer. The buyer has a value for each item drawn independently according to (non-identical) distributions, and her value for a set of items is additive. The seller aims to maximize his revenue. We suggest using the a-priori better of two simple pricing methods: selling the items separately, each at its optimal price, and bundling together, in which the entire set of items is sold as one bundle at its optimal price. We show that for any distribution, this mechanism achieves a constant-factor approximation to the optimal revenue. Beyond its simplicity, this is the first computationally tractable mechanism to obtain a constant-factor approximation for this multi-parameter problem. We additionally discuss extensions to multiple buyers and to valuations that are correlated across items.