Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency
–Neural Information Processing Systems
The recent increasing adoption of autobidding has inspired the growing interest in analyzing the performance of classic mechanism with value-maximizing autobidders both theoretically and empirically. It is known that optimal welfare can be obtained in first-price auctions if autobidders are restricted to uniform bid-scaling and the price of anarchy is 2 when non-uniform bid-scaling strategies are allowed. In this paper, we provide a fine-grained price of anarchy analysis for non-uniform bid-scaling strategies in first-price auctions, demonstrating the reason why more powerful (individual) non-uniform bid-scaling strategies may lead to worse (aggregated) performance in social welfare. Our theoretical results match recent empirical findings that a higher level of non-uniform bid-scaling leads to lower welfare performance in first-price auctions.
Neural Information Processing Systems
Mar-27-2025, 13:23:47 GMT
- Country:
- North America > United States (0.14)
- Genre:
- Research Report > Experimental Study (0.93)
- Industry:
- Information Technology (0.46)
- Technology: