Robust Bidding in First-Price Auctions: How to Bid without Knowing What Others Are Doing

Kasberger, Bernhard; Schlag, Karl H.

doi:10.2139/ssrn.3044438

Cited by 8 publications

(17 citation statements)

References 48 publications

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“…Indeed, if any bid distribution is deemed possible, then in particular the actually faced. 11 Kasberger and Schlag [2020] illustrate empirically that loss-minimizing bids perform well in first-price auctions despite bidders having very coarse beliefs about competitors' behavior. From a more descriptive perspective, group decision making serves as a motivation for minimax loss.…”

Section: Introductionmentioning

confidence: 95%

“…As not even the Bayesian approach delivers ex post optimality in games of incomplete information, we prefer the interim perspective. Moreover, note that the interim but not the ex post perspective allows to meaningfully incorporate belief restrictions as in Kasberger and Schlag [2020] and Kasberger [2020]. Following Schlag and Zapechelnyuk [2021] and Kasberger and Schlag [2020], we refer to the interim concept as loss and to the ex post equivalent as regret.…”

Section: Introductionmentioning

confidence: 99%

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Bidding in Multi-Unit Auctions under Limited Information

Kasberger¹,

Woodward²

2021

Preprint

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We study multi-unit auctions in which bidders have limited knowledge of opponent strategies and values. We characterize optimal prior-free bids; these bids minimize the maximal loss in expected utility resulting from uncertainty surrounding opponent behavior. Optimal bids are simply computable despite bidders having multi-dimensional private information, and in certain cases admit closed-form solutions. In the pay-as-bid auction the minimax-loss bid is unique; in the uniform-price auction the minimax-loss bid is unique if the bidder is allowed to determine the quantities for which they bid, as in many practical applications. Payments to the seller may be higher in either auction format, but minimax-loss bids are never uniformly higher in the pay-as-bid auction.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Bidding in Multi-Unit Auctions under Limited Information

Kasberger¹,

Woodward²

2021

Preprint

Self Cite

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“…Renou and Schlag (2010) analyze strategic uncertainty using the minimax regret criterion. Besides Kasberger and Schlag (2017), I am the only one addressing distributional and strategic uncertainty. They use the minimax regret criterion and allow for the possibility that a bidder can impose bounds on the other bidders' bids or valuation distributions.…”

Section: Relation To the Literaturementioning

confidence: 99%

“…In order to solve these issues, one could also exogenously restrict the set of possible strategies. For example, Kasberger and Schlag (2017) apply the minimax regret criterion to first-price auctions and assume common knowledge of an exogenously given restriction of the players' bidding strategies, for instance, in form of a lower bound of the highest bid.…”

Section: Possible Distributions and Strategiesmentioning

confidence: 99%

Strategies under Strategic Uncertainty

Mass

2018

SSRN Journal

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I investigate the decision problem of a player in a game of incomplete information who faces uncertainty about the other players' strategies. I propose a new decision criterion which works in two steps. First, I assume common knowledge of rationality and eliminate all strategies which are not rationalizable. Second, I apply the maximin expected utility criterion. Using this decision criterion, one can derive predictions about outcomes and recommendations for players facing strategic uncertainty. A bidder following this decision criterion in a first-price auction expects all other bidders to bid their highest rationalizable bid given their valuation. As a consequence, the bidder never expects to win against an equal or higher type and resorts to win against lower types with certainty.

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“…They measure insensitivity under small deviations as performance being close to that of the optimal policy. The same approach has been applied to large deviations, where the performance is evaluated under a large class of distributions, as in statistical treatment choice (Manski (2004), Schlag (2006), and Stoye (2009)), auctions (Kasberger and Schlag (2017)), and search in markets (Bergemann and Schlag (2011b) and Parakhonyak and Sobolev (2015)). The term “robustness” has been used in the same spirit, to achieve an objective independently of modeling details, in robust mechanism design (Bergemann and Morris (2005)), and in the field of control theory (Zhou et al (1995)).…”

Section: Introductionmentioning

confidence: 99%

Robust sequential search

Schlag

Zapechelnyuk

2021

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We study sequential search without priors. Our interest lies in decision rules that are close to being optimal under each prior and after each history. We call these rules robust. The search literature employs optimal rules based on cutoff strategies, and these rules are not robust. We derive robust rules and show that their performance exceeds 1/2 of the optimum against binary independent and identically distributed (i.i.d.) environments and 1/4 of the optimum against all i.i.d. environments. This performance improves substantially with the outside option value; for instance, it exceeds 2/3 of the optimum if the outside option exceeds 1/6 of the highest possible alternative.

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Robust Bidding in First-Price Auctions: How to Bid without Knowing What Others Are Doing

Cited by 8 publications

References 48 publications

Bidding in Multi-Unit Auctions under Limited Information

Bidding in Multi-Unit Auctions under Limited Information

Strategies under Strategic Uncertainty

Robust sequential search

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