Fair Combinatorial Auction for Blockchain Trade Intents: Being Fair without Knowing What is Fair (2408.12225v2)

Abstract: Blockchain trade intent auctions currently intermediate approximately USD 5 billion monthly. Due to production complementarities, the auction is combinatorial: when multiple trade intents from different traders are auctioned off simultaneously, a bidder (here called solver) can generate additional efficiencies by winning a batch of multiple trade intents. However, sharing these additional efficiencies between traders is problematic: because of market frictions and fees (solvers' private information), the auctioneer does not know how much each trader would have received had its trade been auctioned off individually. We formalize this problem and study the most commonly used auction formats: batch auctions and multiple simultaneous auctions. We also propose a novel fair combinatorial auction that combines batch auction and multiple simultaneous auctions: solvers submit individual-trade bids and batched bids, but batched bids are considered only if they are better for all traders relative to the outcome of the simultaneous auctions constructed using the individual-trade bids. We find a trade-off between the fairness guarantees provided in equilibrium by the auction (i.e., the minimum each trader can expect to receive) and the expected value of the assets returned to the traders. Also, the amount that each trader receives in the equilibrium of the fair combinatorial auction may be higher or lower than what they receive in the equilibrium of the simultaneous auctions used as a benchmark for fairness.

• The paper introduces a theoretical model that captures the unique dynamics of blockchain trade-intent auctions without a common numeraire.
• The paper analyzes batch versus simultaneous auctions, revealing how auction format influences competition and the equitable distribution of trader returns.
• The paper proposes a fair combinatorial auction mechanism that balances efficiency and fairness, highlighting the trade-off between maximizing value and delivering equitable outcomes.

Fairness in Combinatorial Blockchain Trade-Intent Auctions

The paper "Combinatorial Auctions without a Numeraire: The Case of Blockchain Trade-Intent Auctions" by Andrea Canidio and Felix Henneke examines the framework and mechanisms underlying blockchain trade-intent auctions. These auctions intermediate significant volumes, approximately USD 5 billion monthly, in decentralized markets facilitated by blockchain technology. The absence of a common numeraire and the emergence of production complementarities necessitate a combinatorial approach, deviating from traditional combinatorial auction literature.

Key Contributions

Formalizing Trade-Intent Auctions

The authors introduce a theoretical model to capture the unique dynamics of trade-intent auctions on blockchains. Participants, termed solvers, bid for the right to complete partial transactions, exploiting complementarities that arise from batching multiple trade intents. The researchers focus on the fairness implications of these mechanisms, highlighting the challenges posed by illiquid assets and the difficulty of redistributing the benefits from batching among different traders.

Auction Formats: Batch Auctions vs. Simultaneous Standard Auctions

Two prevalent auction formats are evaluated:

1. Simultaneous Standard Auctions: Each trade intent is auctioned individually. The analysis reveals a specialization equilibrium where solvers adept at specific trades dominate, leading to minimal competition and lower overall value for traders.
2. Batch Auctions: Multiple trade intents are auctioned together. These create competition among specialized solvers, resulting in higher aggregate returns for traders. Notwithstanding, equilibrium outcomes can disproportionately benefit certain traders, leading to perceptions of unfairness.

Introducing Fair Combinatorial Auctions

To address fairness concerns, the paper proposes the fair combinatorial auction. This novel mechanism blends batch and simultaneous standard auctions, enforcing fairness by allowing batched bids only if they improve upon the outcome of simultaneous standard auctions for all traders involved.

Equilibrium Analysis of Fair Combinatorial Auctions

• Second-Price Individual Auctions: The fair combinatorial auction with second-price individual auctions reduces trivially to the batch auction without providing additional fairness guarantees. Solvers' inability to influence the reference for fairness precludes any substantial deviation from the original batch auction.
• First-Price Individual Auctions: When individual auctions use the first-price format, solvers can manipulate fairness benchmarks, potentially leading to three possible regimes:
  • Specialization: Each solver focuses on the trade it can execute most efficiently, but still returns a higher fraction of assets to traders than in simultaneous standard auctions.
  • Competitive Batching: Strong solvers' bids ensure no batching occurs unless it surpasses the fairness benchmark, guaranteeing all traders better outcomes than simultaneous auctions.
  • Uncompetitive Batching: Large benefits from batching but one solver is particularly dominant, leading to weak competition.

Practical and Theoretical Implications

The research elucidates the trade-offs between fairness and efficiency in trade-intent auction design. The fair combinatorial auction shows that stronger fairness guarantees inevitably reduce the total value delivered to traders, particularly evident when the auction format shifts from second-price to first-price.

Future Directions

Future explorations might involve extending the theoretical model to include more complex scenarios with multiple traders and solvers. Additionally, analyzing the computational complexity implications of these fair combinatorial auctions as the number of trades increases could yield valuable insights. This line of inquiry is crucial for scalable and equitable decentralized marketplaces on blockchain platforms.

Conclusion

Canidio and Henneke's rigorous examination of blockchain trade-intent auctions highlights the intricate balance between maximizing efficiency and ensuring fairness. Their fair combinatorial auction model sets a foundational framework for further refinement and application in burgeoning blockchain-based financial markets.