Auctions with Severely Bounded Communication

We study auctions with severe bounds on the communication allowed: each bidder may only transmit t bits of information to the auctioneer. We consider both welfare- and profit-maximizing auctions under this communication restriction. For both measures, we determine the optimal auction and show that the loss incurred relative to unconstrained auctions is mild. We prove non-surprising properties of these kinds of auctions, e.g., that in optimal mechanisms bidders simply report the interval in which their valuation lies in, as well as some surprising properties, e.g., that asymmetric auctions are better than symmetric ones and that multi-round auctions reduce the communication complexity only by a linear factor

Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

Bidding at Sequential First-Price Auctions with(out) Supply Uncertainty: A Laboratory Analysis

We report on a series of experiments that test the effects of an uncertain supply on the formation of bids and prices in sequential first-price auctions with private-independent values and unit-demands. Supply is assumed uncertain when buyers do not know the exact number of units to be sold (i.e., the length of the sequence). Although we observe a non-monotone behavior when supply is certain and an important overbidding, the data qualitatively support our price trend predictions and the risk neutral Nash equilibrium model of bidding for the last stage of a sequence, whether supply is certain or not. Our study shows that behavior in these markets changes significantly with the presence of an uncertain supply, and that it can be explained by assuming that bidders formulate pessimistic beliefs about the occurrence of another stage.sequential first-price auctions, independent private values, unit-demand, supply uncertainty, bidding behavior, price trends, experimental economics

Dynamic Auctions: A Survey

We survey the recent literature on designing auctions and mechanisms for dynamic settings. Two settings are considered: those with a dynamic population of agents or buyers whose private information remains fixed throughout time; and those with a fixed population of agents or buyers whose private information changes across time. Within each of these settings, we discuss both efficient (welfare-maximizing) and optimal (revenue-maximizing) mechanisms.Dynamic auctions and mechanisms, Random arrivals and departures, Changing private information, Incentive compatibility

An experimental comparison of sequential first- and second-price auctions with synergies

The presence of synergies in recurrent procurement auctions leads to an exposure problem and asymmetries among bidders. We consider sequential first- and second-price auctions with synergies in a setting with four bidders. In a series of experiments we compare the performance of the two pricing formats for three different sizes of the synergy. We find that for small synergies, the first-price auction performs better in terms of efficiency, revenue, and the probability on losses. However, once the synergy factor becomes very large the performance of the two different pricing formats becomes more similar. We also find that even though the potential total surplus that can be divided between buyers and seller increases in the synergy factor, subjects’ earnings within a pricing rule do not significantly change in the synergy factor. Finally, we observe that the two pricing formats give rise to different price trends within the auction sequence. In general, our results provide support for the common use of first-price instead of second-price auctions for public procurement.industrial organization ;

Opportunity costs calculation in agent-based vehicle routing and scheduling

In this paper we consider a real-time, dynamic pickup and delivery problem with timewindows where orders should be assigned to one of a set of competing transportation companies. Our approach decomposes the problem into a multi-agent structure where vehicle agents are responsible for the routing and scheduling decisions and the assignment of orders to vehicles is done by using a second-price auction. Therefore the system performance will be heavily dependent on the pricing strategy of the vehicle agents. We propose a pricing strategy for vehicle agents based on dynamic programming where not only the direct cost of a job insertion is taken into account, but also its impact on future opportunities. We also propose a waiting strategy based on the same opportunity valuation. Simulation is used to evaluate the benefit of pricing opportunities compared to simple pricing strategies in different market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization and delivery reliability