Learning Prices for Repeated Auctions with Strategic Buyers

Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret --- the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus --- i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.Comment: Neural Information Processing Systems (NIPS 2013

Unilateral and Exclusionary/Strategic Effects of Common Agency: Price Impacts in a Repeated Common Value English Auction

The business justification for multiple principals to hire a common agent is efficiency. Our empirical study demonstrates that the creation of the common agent unilaterally depresses winning bids. Additionally, the common agent was not only observed to be the dominant bidder but also paid significantly lower prices than fringe competitors (price/quantity differential). The observed price/quantity differential is consistent with the almost common value English auction theory developed by Rose and Kagel (2008) in which a cost advantaged bidder is able to reduce competition by credibly raising the costs of disadvantaged rivals associated with the winner’s curse.Common Value Auctions, Common Agency, Antitrust, Industrial Organization, D44, K21, K23,

Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modifed Roth-Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier electricity study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htmagent-based computational economics; Wholesale electricity market; restructuring; repeated double auction; market power; efficiency; concentration; capacity; individual reinforcement learning; genetic algorithm social learning

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory mid-point pricing. Buyers and sellers use Roth-Erev individual reinforcement learning to determine their price and quantity offers in each auction round. It is shown that market microstructure is strongly predictive for the relative market power of buyers and sellers, and that high market efficiency is generally attained. These findings are robust for tested changes in individual learning parameters. It is also shown that similar relative market power findings are obtained if the electricity buyer and seller populations instead each engage in social mimicry learning via a genetic algorithm. However, market efficiency is substantially reduced.Wholesale electricity market, Electricity restructuring, Double auction, Market power, Efficiency, Concentration, Capacity, Agent-based computational economics, Roth-Erev reinforcement learning, Genetic algorithm social learning.

Low-Regret Algorithms for Strategic Buyers with Unknown Valuations in Repeated Posted-Price Auctions

We study repeated posted-price auctions where a single seller repeatedly interacts with a single buyer for a number of rounds. In previous works, it is common to consider that the buyer knows his own valuation with certainty. However, in many practical situations, the buyer may have a stochastic valuation. In this paper, we study repeated posted-price auctions from the perspective of a utility maximizing buyer who does not know the probability distribution of his valuation and only observes a sample from the valuation distribution after he purchases the item. We first consider non-strategic buyers and derive algorithms with sub-linear regret bounds that hold irrespective of the observed prices offered by the seller. These algorithms are then adapted into algorithms with similar guarantees for strategic buyers. We provide a theoretical analysis of our proposed algorithms and support our findings with numerical experiments. Our experiments show that, if the seller uses a low-regret algorithm for selecting the price, then strategic buyers can obtain much higher utilities compared to non-strategic buyers. Only when the prices of the seller are not related to the choices of the buyer, it is not beneficial to be strategic, but strategic buyers can still attain utilities of about 75% of the utility of non-strategic buyers.</p