污污草莓视频

Authors: Ernan Haruvy, Meisam Hejazi Nia, 脰zalp 脰zer, and A. Serdar 艦im艧ek

Publication:听Manufacturing & Service Operations Management; Volume 25, Issue 3, May-June 2023, Pages 1155-1175

Problem definition: Dynamic forecasting models in auctions have fallen short on two dimensions: (i) the lack of an equilibrium model for final bids and (ii) the lack of a winner鈥檚 curse (i.e., a tendency to overpay conditional on winning the auction) adjustment to allow bidders to account for a common value component in the auction item. In this paper, we develop a methodology to accurately predict equilibrium stage bids from the initial bidding dynamics and quantify the impact of the winner鈥檚 curse. This methodology allows us to conduct policy simulations to optimize auction design parameters.

Methodology/Results: Dynamic auctions typically have a stage of high exploratory activity, followed by an inactivity period, and then an equilibrium stage of last-minute bids with sharp jumps. With a Kalman filter approach, we use exploratory stage bids to predict an auction item鈥檚 valuation distribution. We feed this prediction into an equilibrium model and apply item-specific adjustments for winner鈥檚 curse, bidder heterogeneity, and inactivity period. We use the resulting equilibrium model to predict the equilibrium stage bids. Our methodology improves the forecast of equilibrium stage bids by 11.33%, on average, compared with a state-of-the-art benchmark. This improvement is even higher (18.99%) for common value auctions. We also find that (i) significantly more (respectively, fewer) bidders internalize the winner鈥檚 curse in common value (respectively, private value) auctions; (ii) bidders in common value auctions decrease their bids by 6.03% because of the winner鈥檚 curse; and (iii) the inactivity period has a lesser impact on the equilibrium stage bids in private value auctions.

Managerial implications: Our proposed methodology is intended to facilitate the need in academia and practice for real-time bid predictions that encompass different levels of the common value component in auctions. Using our methodology, auction platforms can support their choice of minimum bid increment policies and decide how to allocate resources across different auctions to mitigate the adverse effects of the winner鈥檚 curse.

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