Sequential auctions are an important mechanism for buying/selling multiple objects. Existing work has studied sequential auctions for objects that are exclusively either common value or private value. However, in many real-world cases an object has both features. Also, in such cases, the common value component (which is the same for all bidders) depends on how much each bidder values the object. Moreover, an individual bidder generally does not know the true common value, since it may not know how much the other bidders value it. On the other hand, a bidder’s private value is independent of the others ’ private values. Given this, we study settings that have both common and private value elements by treating each bidder’s information about the common value as uncertain. We first determine equilibrium bidding strategies for each auction in a sequence using English auction rules. On the basis of this equilibrium, we analyse the efficiency of auctions. Specifically, we show that the inefficiency that arises as a result of uncertainty about the common values can be reduced if the auctioneer makes its information about the common value known to all bidders. Moreover, our analysis also shows that the efficiency of auctions in an agent-based setting is higher than that in an all-human setting.

Auctions generally do not lead to efficient outcomes when the expected value of the object for sale depends on both private and common value information. We report a series of first-price auction experiments to test three key predictions of auctions with private and common values: (i) inefficiencies grow with the uncertainty about the common value while revenues fall, (ii) increased competition results in more efficient outcomes and higher revenues, and (iii) revenues and efficiency are higher when information about the common value is publicly released. We compare the predictions of several bidding models, including Nash, when examining these issues. A model in which a fraction of the bidders falls prey to a winner's curse and decision-making is noisy, best describes bidding behavior. We find that revenues and efficiency are positively affected by increased competition and a reduction in uncertainty about the common value. The public release of high-quality information about the commo...

Online auctions have become a popular method for business transactions. The variety of different auction rules, the restrictions in supply or demand, and the agents ’ com-binatorial preferences for the different commodities, have led to the creation of a very complex multi-agent “environment ” and a number of strategic tradeoffs. Designing an agent that deals efficiently with these tradeoffs has been a multi-pronged effort. Us-ing game-theoretic approaches, some equilibria have been computed for relatively sim-ple auctions. However, since these equilibria have limited practical application, due to the significant number of varying auctions that take place simultaneously, empiri-cal approaches and experimental evaluations of various strategies have also been used. Furthermore, progress has been made into designing better agent architectures. This dissertation presents results in all of these directions (theoretical and empiri-cal). We present a methodology for designing trading agents, and deciding their bidding strategy, when they participate in a large number of simultaneous auctions with a variety of rules. We use a modular, adaptive, scalable and robust agent architecture, combining principled methods and empirical knowledge. We decompose the problem faced by the

Abstract. Sequential auctions are an important mechanism for buying/selling multiple objects. Now existing work in the area has studied sequential auctions for objects that are exclusively either common value or private value. However, in many real-world cases an object has both features. Also, in such cases, the common value depends on how much each bidder values the object. Moreover, a bidder generally does not know the true common value (since it may not know how much the other bidders value it). Given this, our objective is to study settings that have both common and private value elements by treating each bidder’s information about the common value as uncertain. Each object is modelled with two signals: one for its common value and the other for its private value. The auctions are conducted using English auction rules. For this model, we first determine equilibrium bidding strategies for each auction in a sequence. On the basis of this equilibrium, we find the expected revenue and the winner’s expected profit for each auction. We then show that even if the common and private values of objects are distributed identically across all objects, the revenue and the winner’s profit are not the same for all of them. We show that, in accordance with Ashenfelter’s experimental results [1], the revenue for our model can decline in later auctions. 1

This paper analyzes sequential auctions for objects that have both common and private values. Existing work has studied sequential auctions for objects that are either exclusively common or private value. However, in many cases, an object has both features. Now, in such cases, the common value (which is the same for all bidders) depends on each bidder’s valuation of the object. But, generally speaking, a bidder cannot know the true common value, since it does not know how much the other bidders value it. On the other hand, a bidder’s private value is independent of the others ’ private values. Given this, our objective is to study sequential auctions for heterogeneous objects that have both common and private values by treating each bidder’s information about the common value as uncertain. In so doing, we first determine equilibrium bidding strategies for each auction in a sequence. Then, since the total revenue from all the auctions in a sequence depends on the auction agenda (i.e., the order in which the objects are auctioned) we determine the optimal agenda (i.e., the one that maximizes total expected revenue across all the auctions in a sequence). Finally, we show that, for a given agenda, the total revenue is the same for first-price, second-price, and English auction rules. 1

We analyze competitive pressures in a sequence of auctions with a growing number of bidders, in a model that includes private and common valuations as special cases. We show that the key determinant of bidders' surplus (and implicitly auction revenue) is how the goods are distributed. In any setting and sequence of auctions where the allocation of good(s) is concentrated among a shrinking proportion of the population, the winning bidders enjoy no surplus in the limit. If instead the good(s) are allocated in a dispersed manner so that a non-vanishing proportion of the bidders obtain objects, then in any of a wide class of auctions bidders enjoy a surplus that is bounded away from zero. Moreover, under dispersed allocations, the format of the auction matters. If bidders have constant marginal utilities for objects up to some limit, then uniform price auctions lead to higher revenue than discriminatory auctions. If agents have decreasing marginal utilities for objects, then uniform price auctions are asymptotically efficient, while discriminatory auctions are asymptotically inefficient. Finally, we show that in some cases where dispersed allocations are efficient, revenue may increase by bundling goods at the expense of efficiency.

We study the effect of information on market outcomes using a field experiment. By randomly manipulating the availability of information about wholesale automobile quality, we measure the effect of information on market outcomes. As the theoretical literature predicts, more information increases expected revenues. However, the biggest gains in revenue are for the lowest and highest quality grades of automobiles. This suggests that the increase in revenues is due to better matching of buyers to vehicles, and is less a consequence of lower information rents. Finally, we quantify the value of gathering information and releasing it to buyers in this setup. JEL classifications C93, D44, D82, L15

Exploring the effects of competition for railway markets Contact

Monotone ratio orderings are refinements of first order stochastic dominance that allow monotone comparative statics results in games of incomplete information. We develop analogous refinements for second order stochastic dominance based on the monotonicity of the cumulative probability ratio and the unimodality of the likelihood and probability ratios. We go on to investigate comparative statics in first price auctions, both private and common value, of the effects of more precise information in the sense of the new orderings. We find that almost all types bid more aggressively under the new distribution than they did under the old, but the highest types may bid less. This leads to higher expected revenue in a simple common value auction, but to an ambiguous result in the private value case.

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