Paper Bidding

Paper bidding, encompassing various auction-based processes from advertising to reviewer assignment, aims to optimize resource allocation and incentivize truthful behavior. Current research focuses on developing strategy-proof mechanisms using deep learning architectures like neural networks (e.g., GemNet) and reinforcement learning, often incorporating techniques like conformal prediction to guarantee truthful bidding. These advancements have implications for improving efficiency in online advertising, energy markets, and peer review processes, while also addressing challenges like collusion detection and revenue maximization.