Fixed Price Approximability of the Optimal Gain From Trade
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Abstract (arXiv)
Bilateral trade is a fundamental economic scenario comprising a strategically acting buyer and seller, each holding valuations for the item, drawn from publicly known distributions. A mechanism is supposed to facilitate trade between these agents, if such trade is beneficial. It was recently shown that the only mechanisms that are simultaneously DSIC, SBB, and ex-post IR, are fixed price mechanisms, i.e., mechanisms that are parametrised by a price p, and trade occurs if and only if the valuation of the buyer is at least p and the valuation of the seller is at most p. The gain from trade is the increase in welfare that results from applying a mechanism; here we study the gain from trade achievable by fixed price mechanisms. We explore this question for both the bilateral trade setting, and a double auction setting where there are multiple buyers and sellers. We first identify a fixed price mechanism that achieves a gain from trade of at least 2/r times the optimum, where r is the probability that the seller's valuation does not exceed the buyer's valuation. This extends a previous result by McAfee. Subsequently, we improve this approximation factor in an asymptotic sense, by showing that a more sophisticated rule for setting the fixed price results in an expected gain from trade within a factor O(log(1/r)) of the optimal gain from trade. This is asymptotically the best approximation factor possible. Lastly, we extend our study of fixed price mechanisms to the double auction setting defined by a set of multiple i.i.d. unit demand buyers, and i.i.d. unit supply sellers. We present a fixed price mechanism that achieves a gain from trade that achieves for all epsilon > 0 a gain from trade of at least (1-epsilon) times the expected optimal gain from trade with probability 1 - 2/e^{#T epsilon^2 /2}, where #T is the expected number of trades resulting from the double auction.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
8 papers.
- 2021Bilateral Trade: A Regret Minimization Perspectivecited by 29
- 2021A Regret Analysis of Bilateral Tradecited by 19
- 2022An α-regret analysis of Adversarial Bilateral Tradecited by 16
- 2023An Online Learning Theory of Brokeragecited by 8
- 2024A Parametric Contextual Online Learning Theory of Brokeragecited by 4
- 2026Contextual Online Bilateral Tradecited by 3
- 2025A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokeragecited by 2
- 2024Trading Volume Maximization with Online Learningcited by 0
- 2026Contextual Online Bilateral Trade13 Feb 2026
- 2025A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage3 Mar 2025
- 2024A Parametric Contextual Online Learning Theory of Brokerage22 May 2024
- 2024Trading Volume Maximization with Online Learning21 May 2024
- 2023An Online Learning Theory of Brokerage18 Oct 2023
- 2022An α-regret analysis of Adversarial Bilateral Trade13 Oct 2022
- 2021Bilateral Trade: A Regret Minimization Perspective8 Sep 2021
- 2021A Regret Analysis of Bilateral Trade16 Feb 2021