Large-Market Discipline in Combinatorial Double Auctions: No Assembly, Bundle Selection, and Complementarities

2026-08-06

AI Summary

This study addresses the inefficiencies in pricing and resource allocation arising from complementarities under “No Assembly” constraints in combinatorial double auctions. The authors propose a constrained combinatorial buyer-bid double auction model that incorporates stability and price impact conditions, enabling bundled submarkets to inherit the competitive discipline of single-item large markets, thereby achieving efficient price discovery and eliminating strategic underbidding. Theoretically, in the two-good case, bundle bidding introduces no first-order strategic distortion, and complementarity mitigates frictions due to limited market size; clearing prices converge to competitive levels and track common values. Simulations reveal that welfare losses stem primarily from the “No Assembly” constraint rather than strategic behavior, are negligible in moderately thick markets, and further diminish as complementarity strengthens.

Abstract

We study double auctions for markets in which goods are valuable in bundles, such as data, model weights, and fine-tuned AI assets. A key friction in such markets is No Assembly: a platform may be unable, for legal or technical reasons, to combine components supplied by different sellers into a single bundle. We formulate a combinatorial buyer's-bid double auction under this constraint. Under explicit stability and price-influence conditions (maintained in general, and for two goods derived from local price-taking and a feedback bound), each bundle submarket inherits the large-market discipline of single-good double auctions: bid shading vanishes, and clearing prices concentrate on competitive levels and track the common value (price discovery). The key incentive step, that bidding on a bundle creates no first-order strategic distortion beyond the single-good logic, is proved for two goods; for larger item sets it remains a maintained condition. Multi-agent reinforcement-learning simulations decompose the welfare loss and indicate that No Assembly, not strategic shading, is the binding finite-market friction, with both losses small in moderately thick markets and declining with complementarity amongst goods.

Problem

• combinatorial double auctions
• No Assembly
• bundle selection
• complementarities
• market friction

Innovation

• No Assembly
• Combinatorial Double Auction
• Large-Market Discipline
• Price Discovery
• Bundle Complementarities