In many auctions the valuation structure involves both private and common value elements. Existing experimental evidence (e.g. Goeree and Offerman in Am. Econ. Rev. 92(3):625–643, 2002) demonstrates that first-price auctions with this valuation structure tend to be inefficient, and inexperienced subjects tend to bid above the break-even bidding threshold. In this paper, we compare first-price auctions with an alternative auction mechanism: the least-revenue auction. This auction mechanism shifts the risk regarding the common value of the good to the auctioneer. Such a shift is desirable when ex post negative payoffs for the winning bidder results in unfulfilled contracts, as is often the case in infrastructure concessions contracts. We directly compare these two auction formats within two valuation structures: (1) pure common value and (2) common value with a private cost. We find that, relative to first-price auctions, bidding above the break-even bidding threshold is significantly less prevalent in least-revenue auctions regardless of valuation structure. As a result, revenue in first-price auctions is higher than in least-revenue auctions, contrary to theory. Further, when there are private and common value components, least-revenue auctions are significantly more efficient than first-price auctions.

We propose a simple adaptive learning model to study behavior in the call market. The laboratory environment features buyers and sellers who receive a new random value or cost in each period, so they must learn a strategy that maps these random draws into bids or asks. We focus on buyers’ adjustment of the “mark-down” ratio of bids relative to private value and sellers’ adjustment of the corresponding “mark-up” ratio of asks relative to private cost. The learning model involves partial adjustment of these ratios towards the ex post optimum each period. The model explains a substantial proportion of the variation in traders’ strategies. Parameter estimates indicate strong recency effects and negligible autonomous trend, but strongly asymmetric response to different kinds of ex post error. The asymmetry is only slightly attenuated in “observational learning” from other traders’ ex post errors. Simulations show that the model can account for the main systematic deviations from equilibrium predictions observed in this market institution and environment.

Attracting bidders to an auction is a key factor in determining revenue. We experimentally investigate entry and bidding behavior in first-price and English clock auctions to determine the revenue implications of entry. Potential bidders observe their value and then decide whether or not to incur a cost to enter. We also vary whether or not bidders are informed regarding the number of entrants prior to placing their bids. Revenue equivalence is predicted in all four environments. We find that, regardless of whether or not bidders are informed, first-price auctions generate more revenue than English clock auctions. Within a given auction format, the effect of informing bidders differs. In first-price auctions, revenue is higher when bidders are informed, while the opposite is true in English clock auctions. The optimal choice for an auction designer who wishes to maximize revenue is a first-price auction with uninformed bidders.

In many contest situations, such as R&D competition and rent seeking, participants’ costs are private information. We report the results of an experimental study of bidding in contests under different information and symmetry conditions about players’ costs of effort. The theory predicts qualitatively different comparative statics between bids under complete and incomplete information in contests of two and more than two players. We use a 2×3 experimental design, (n=2, n=4)×(symmetric complete information, asymmetric complete information, incomplete information), to test the theoretical predictions. We find the comparative statics of bids across the information and symmetry conditions, and the qualitative differences in comparative statics across group sizes, in partial agreement with the theory.

In an experiment using two-bidder first-price sealed bid auctions with symmetric independent private values and 400 subjects, we scan also the right hand of each subject. We study how the ratio of the length of the index and ring fingers (2D:4D) of the right hand, a measure of prenatal hormone exposure, is correlated with bidding behavior and total profits. 2D:4D has been reported to predict competitiveness in sports competition (Manning and Taylor in Evol. Hum. Behav. 22:61–69, 2001, and Hönekopp et al. in Horm. Behav. 49:545–549, 2006), risk aversion in lottery tasks (Dreber and Hoffman in Portfolio selection in utero. Stockholm School of Economics, 2007; Garbarino et al. in J. Risk Uncertain. 42:1–26, 2011), and the average profitability of high-frequency traders in financial markets (Coates et al. in Proc. Natl. Acad. Sci. 106:623–628, 2009). We do not find any significant correlation between 2D:4D on either bidding or profits. However, there might be racial differences in the correlation between 2D:4D and bidding and profits.

Exploration is a fundamental problem in robotics, and multi-robot systems exploration has been extensively studied in this field. In order to overcome the problem of a non-optimal target being selected in the exploration process, a revised single linkage clustering frontier cell (CFC) algorithm is proposed to calculate the exploration benefit of all available frontier cells. Moreover, there exist unexplored islands for most of the bidding-based multi-robot coordination algorithms in the exploration of unknown environments. To deal with this problem, some rules switched by emotion states are proposed. So, the proposed bidding coordination algorithm with CFC and an emotion switch has a hierarchical architecture. The upper level is modeled as an automaton, which is used to represent emotion status, and the emotion variables decide whether a robot will participate in a bid and explore an unknown area abiding by the walking rules. In the lower level, the robots perform bidding activities with CFC and the walking rules according to the emotion variables. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots. The experiments results demonstrate that the exploration efficiency is improved, and our algorithm has a greater coverage rate than classic bidding-based coordination algorithms.

Auction experiments are commonly used to determine consumers’ willingness to pay for various food items. While their non-hypothetical nature is a positive, market substitutes create a probable confounding of bids by field prices. This study examines the influence of field prices on bids for four foods in two versions by conducting auctions before and after large price increases in 2007. Results show that bids were capped at given field prices and were significantly higher in sessions conducted after store prices increased. Percentage premiums, however, were not significantly different across sessions, suggesting that effects of field prices could be reduced. Overall, researchers must be conscious of how field prices affect bids.