Bidding

About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.

Negotiation among self-interested computationally limited agents

Abstract: In multiagent systems, computational agents search for and make contracts on behalf of the real world parties that they represent. This dissertation analyses negotiations among agents that try to maximize payoff without concern of the global good. Such a self-interested agent will choose the best negotiation strategy for itself. Accordingly, the interaction protocols need to be designed normatively so that the desired local strategies are best for the agents--and thus the agents will use them--then certain desirable social outcomes follow. The normative approach allows the agents to be constructed by separate designers and/or to represent different parties. Game theory also takes a normative approach, but full rationality of the agents is usually assumed. This dissertation focuses on situations where computational limitations restrict each agent's rationality: in combinatorial negotiation domains computational complexity precludes enumerating and evaluating all possible outcomes. The dissertation contributes to: automated contracting, coalition formation, and contract execution. The contract net framework is extended to work among self-interested, computationally limited agents. The original contract net lacked a formal model for making bidding and awarding decisions, while in this work these decisions are based on marginal approximations of cost calculations. Agents pay each other for handling tasks. An iterative scheme for anytime task reallocation is presented. Next it is proven that a leveled commitment contracting protocol enables contracts that are impossible via classical full commitment contracts. Three new contract types are presented: clustering, swaps and multiagent contracts. These can be combined into a new type, CSM-contract, which is provably necessary and sufficient for reaching a globally optimal task allocation. Next, contracting implications of limited computation are discussed, including the necessity of local deliberation scheduling, and tradeoffs between computational complexity and monetary risk when an agent can participate in multiple simultaneous negotiations. Finally, issues in distributed asynchronous implementation are discussed. A normative theory of coalitions among self-interested, computationally limited agents is developed. It states which agents should form coalitions and which coalition structures are stable. These analytical prescriptions depend on the performance profiles of the agents' problem solving algorithms and the unit cost of computation. The prescriptions differ significantly from those for fully rational agents. The developed theory includes a formal application independent domain classification for bounded rational agents, and relates it precisely to two traditional domain classifications of fully rational agents. Experimental results are presented. Unenforced exchange methods are particularly desirable among computational agents because litigation is difficult. A method for carrying out exchanges without enforcement is presented. It is based on splitting the exchange into chunks that are delivered one at a time. Two chunking algorithms are developed, as well as a nontrivial sound and complete quadratic chunk sequencing algorithm. Optimal stable strategies for carrying out the exchange are derived. The role of real-time is also analyzed, and deadline methods are developed that do not themselves require enforcement. All of these analyses are carried out for isolated exchanges as well as for exchanges where reputation effects prevail. Finally, it is argued that the unenforced exchange method hinders unfair renegotiation. The developed methods in all three subareas are domain independent. The possibility of scaling to large problem instances was shown experimentally on an ${\cal N}P$-complete distributed vehicle routing problem. The large-scale problem instance was collected from five real-world dispatch centers. (Abstract shortened by UMI.)

A Statistical Approach for Interval Forecasting of the Electricity Price

Abstract: Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we construct a statistical model by introducing a heteroscedastic variance equation for the SVM. Maximum likelihood estimation (MLE) is used to estimate model parameters. Results from the case studies on real-world price data prove that the proposed method is highly effective compared with existing methods such as GARCH models.

Interactive Dispatch Modes and Bidding Strategy of Multiple Virtual Power Plants Based on Demand Response and Game Theory

Abstract: This paper examines operation models of multiple virtual power plants (multiVPPs), aiming at unified management of the multiVPP through VPP central controller, which reveals the controllability of the VPP as source and load in general. Two operation models will be introduced in this paper: 1) VPP dispatch model; and 2) the game theoretic model for multiVPP dispatch. In the VPP dispatch model, considering interactive coordination between VPP and energy consumers, a demand response model based on time-of-use pricing mechanism and interruptible loads is employed. And then the optimal results of VPP dispatch model are applied in the game theoretic model for multiVPP dispatch. During the process of market competition, the bidding strategy of each VPP is determined by its affordable power output and fuel cost. To solve the inherent variability and unpredictability of the renewable generation sources, the multitime scale rolling scheduling strategy is adopted. Finally, taking the multiVPP, which consists of various distributed generations and battery storage devices, as an example, variables including transferred load, compensation capacity, optimal bidding strategy, and profits for each VPP are obtained. From the analysis of the results, the method serves as a foundation for dispatch of multiVPP.

The Spite Motive and Equilibrium Behavior in Auctions

Abstract: We study auctions where bidders have independent private values but attach a disutility to the surplus of rivals, and derive symmetric equilibria for rst-price, second-price, English, and Dutch auctions. We nd that equilibrium bidding is more aggressive than standard predictions. Indeed, in second-price auctions it is optimal to bid above one’s valuation; that is, bidding \frenzies" can arise in equilibrium. Further, revenue equivalence between second-price and rst-price auctions breaks down, with second-price outperforming rst-price. We also nd that strategic equivalence between second-price and English auctions no longer holds, although they remain revenue equivalent. We conclude that spiteful bidding rationalizes anomalies observed in laboratory experiments across the four auction forms better than the leading alternatives.

Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design

Abstract: Ad exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a prespecified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in ad exchanges and the implications for publishers' decisions. The presence of budgets introduces dynamic interactions among advertisers that need to be taken into account when attempting to characterize the bidding landscape or the impact of changes in the auction design. To this end, we introduce the notion of a fluid mean-field equilibrium FMFE that is behaviorally appealing and computationally tractable, and in some important cases, it yields a closed-form characterization. We establish that an FMFE approximates well the rational behavior of advertisers in these markets. We then show how this framework may be used to provide sharp prescriptions for key auction design decisions that publishers face in these markets. In particular, we show that ignoring budgets, a common practice in this literature, can result in significant profit losses for the publisher when setting the reserve price. This paper was accepted by Dimitris Bertsimas, optimization.