Designing all-pay auctions using deep learning and multi-agent simulation

Area of Science:

• Computational Economics
• Artificial Intelligence
• Game Theory

Background:

• Designing optimal crowdsourcing contests and All-Pay auctions is complex.
• Traditional methods often rely on tractable game-theoretic equilibrium analyses.
• Manual design by economists may not explore the full design space effectively.

Purpose of the Study:

• To develop a novel multi-agent learning approach for designing crowdsourcing contests and All-Pay auctions.
• To overcome limitations of traditional methods in complex or analytically intractable settings.
• To optimize auctioneer utility through automated design exploration.

Main Methods:

• Utilizing a multi-agent learning simulation to explore the contest design space.
• Employing neural networks to predict outcomes for untested contest designs.
• Applying mirror ascent optimization for achieving desirable design outcomes.

Main Results:

• The proposed method accurately matches optimal outcomes in known equilibrium settings.
• The approach generates high-quality designs for analytically unsolvable problems.
• Simulations demonstrate the effectiveness of agent learning in contest design.

Conclusions:

• Multi-agent learning offers a powerful framework for designing complex auctions and contests.
• The method provides a viable alternative when game-theoretic analysis is intractable.
• This approach enhances auctioneer utility by optimizing contest parameters through simulation.