Showing papers on "Bidding published in 2007"

Decoupling CEO Wealth and Firm Performance: The Case of Acquiring CEOs

Abstract: We explore how compensation policies following mergers affect a CEO's incentives to pursue a merger. We find that even in mergers where bidding shareholders are worse off, bidding CEOs are better off three quarters of the time. Following a merger, a CEO's pay and overall wealth become insensitive to negative stock performance, but a CEO's wealth rises in step with positive stock performance. Corporate governance matters; bidding firms with stronger boards retain the sensitivity of their CEOs' compensation to poor performance following the merger. In comparison, we find that CEOs are not rewarded for undertaking major capital expenditures.

Level-k auctions : Can a nonequilibrium model of strategic thinking explain the winner's curse and overbidding in private-value auctions?

Abstract: This paper proposes a structural nonequilibrium model of initial responses to incomplete-information games based on “level-k” thinking, which describes behavior in many experiments with complete-information games. We derive the model's implications in first- and second-price auctions with general information structures, compare them to equilibrium and Eyster and Rabin's (2005) “cursed equilibrium,” and evaluate the model's potential to explain nonequilibrium bidding in auction experiments. The level-k model generalizes many insights from equilibrium auction theory. It allows a unified explanation of the winner's curse in common-value auctions and overbidding in those independent-private-value auctions without the uniform value distributions used in most experiments.

Method for conducting an on-line bidding session with bid pooling

Abstract: The invention presents a method for conducting an on-line bidding session to accumulate a collective bid for a property. The bidding session is conducted over a computer network that includes a central computer, a number of remote computers, and communication lines connecting the remote computers to the central computer. According to the method, at least one bidding group is registered in the central computer. The bidding group can be an association, institution, or group of investors formed for the purpose of bidding together for the property. The bidding group has a total bid for the property which is tracked in the central computer. The central computer receives bids entered from the remote computers by members of the bidding group. Each bid includes an individual bid amount which is contributed to the total bid of the group to accumulate the collective bid for the property.

A General Framework for Wireless Spectrum Auctions

Abstract: We propose a real-time spectrum auction framework to distribute spectrum among a large number wireless users under interference constraints. Our approach achieves conflict-free spectrum allocations that maximize auction revenue and spectrum utilization. Our design includes a compact and yet highly expressive bidding language, various pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to compute revenue-maximizing prices and allocations. Both analytical and experimental results verify the efficiency of the proposed approach. We conclude that bidding behaviors and pricing models have significant impact on auction outcomes. A spectrum auction system must consider local demand and spectrum availability in order to maximize revenue and utilization.

Position Auctions with Consumer Search

Abstract: This paper examines a model in which advertisers bid for "sponsored-link" positions on a search engine. The value advertisers derive from each position is endogenized as coming from sales to a population of consumers who make rational inferences about firm qualities and search optimally. Consumer search strategies, equilibrium bidding, and the welfare benefits of position auctions are analyzed. Implications for reserve prices and a number of other auction design questions are discussed.

Strategic bidder behavior in sponsored search auctions

Abstract: We examine sponsored search auctions run by Overture (now part of Yahoo!) and Google and present evidence of strategic bidder behavior in these auctions. Between June 15, 2002, and June 14, 2003, we estimate that Overture's revenue might have been higher if it had been able to prevent this strategic behavior. We present an alternative mechanism that could reduce the amount of strategizing by bidders, raise search engines' revenues, and increase the efficiency of the market. We conclude by showing that strategic behavior has not disappeared over time; it remains present on both search engines.

Dynamics of bid optimization in online advertisement auctions

Abstract: We consider the problem of online keyword advertising auctions among multiple bidders with limited budgets, and study a natural bidding heuristic in which advertisers attempt to optimize their utility by equalizing their return-on-investment across all keywords. We show that existing auction mechanisms combined with this heuristic can experience cycling (as has been observed in many current systems), and therefore propose a modified class of mechanisms with small random perturbations. This perturbation is reminiscent of the small time-dependent perturbations employed in the dynamical systems literature to convert many types of chaos into attracting motions. We show that the perturbed mechanism provably converges in the case of first-price auctions and experimentally converges in the case of second-price auctions. Moreover, the point of convergence has a natural economic interpretation as the unique market equilibrium in the case of first-price mechanisms. In the case of second-price auctions, we conjecture that it converges to the "supply-aware" market equilibrium. Thus, our results can be alternatively described as a tâtonnement process for convergence to market equilibriumin which prices are adjusted on the side of the buyers rather than the sellers. We also observe that perturbation in mechanism design is useful in a broader context: In general, it can allow bidders to "share" a particular item, leading to stable allocations and pricing for the bidders, and improved revenue for the auctioneer.

A Knapsack Secretary Problem with Applications

Abstract: We consider situations in which a decision-maker with a fixed budget faces a sequence of options, each with a cost and a value, and must select a subset of them online so as to maximize the total value. Such situations arise in many contexts, e.g., hiring workers, scheduling jobs, and bidding in sponsored search auctions. This problem, often called the online knapsack problem, is known to be inapproximable. Therefore, we make the enabling assumption that elements arrive in a randomorder. Hence our problem can be thought of as a weighted version of the classical secretary problem, which we call the knapsack secretary problem. Using the random-order assumption, we design a constant-competitive algorithm for arbitrary weights and values, as well as a e-competitive algorithm for the special case when all weights are equal (i.e., the multiple-choice secretary problem). In contrast to previous work on online knapsack problems, we do not assume any knowledge regarding the distribution of weights and values beyond the fact that the order is random.

Day-Ahead Electricity Price Forecasting in a Grid Environment

Abstract: Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. Market operators can also use electricity price forecasts to predict market power indexes for the purpose of monitoring participants' behaviors. Various forecasting techniques are applied to different time horizons for electricity price forecasting in locational marginal pricing (LMP) spot markets. Available correlated data also have to be selected to improve the short-term forecasting performance. In this paper, fuzzy inference system (FIS), least-squares estimation (LSE), and the combination of FIS and LSE are proposed. Based on extensive testing with various techniques, LSE provides the most accurate results, and FIS, which is also highly accurate, provides transparency and interpretability

Budget optimization in search-based advertising auctions

Abstract: Internet search companies sell advertisement slots based on users' search queries via an auction. While there has been previous work onthe auction process and its game-theoretic aspects, most of it focuses on the Internet company. In this work, we focus on the advertisers, who must solve a complex optimization problem to decide how to place bids on keywords to maximize their return (the number of user clicks on their ads) for a given budget. We model the entire process and study this budget optimization problem. While most variants are NP-hard, we show, perhaps surprisingly, that simply randomizing between two uniform strategies that bid equally on all the keywordsworks well. More precisely, this strategy gets at least a 1-1/e fraction of the maximum clicks possible. As our preliminary experiments show, such uniform strategies are likely to be practical. We also present inapproximability results, and optimal algorithms for variants of the budget optimization problem.

Risk-Constrained Bidding Strategy With Stochastic Unit Commitment

Abstract: This paper develops optimal bidding strategies based on hourly unit commitment in a generation company (GENCO) that participates in energy and ancillary services markets. The price-based unit commitment problem with uncertain market prices is modeled as a stochastic mixed integer linear program. The market price uncertainty is modeled using the scenario approach, Monte Carlo simulation is applied to generate scenarios, scenario reduction techniques are applied to reduce the size of the stochastic price-based unit commitment problem, and postprocessing is applied based on marginal cost of committed units to refine bidding curves. The financial risk associated with market price uncertainty is modeled using expected downside risk, which is incorporated explicitly as a constraint in the problem. Accordingly, the proposed method provides a closed-loop solution to devising specific strategies for risk-based bidding in a GENCO. Illustrative examples show the impact of market price uncertainty on GENCO's hourly commitment schedule and discuss the way GENCOs could decrease financial risks by managing expected payoffs

Multi-issue negotiation protocol for agents: exploring nonlinear utility spaces

Abstract: Multi-issue negotiation protocols have been studied widely and represent a promising field since most negotiation problems in the real world involve interdependent multiple issues The vast majority of this work has assumed that negotiation issues are independent, so agents can aggregate the utilities of the issue values by simple summation, producing linear utility functions In the real world, however, such aggregations are often unrealistic We cannot, for example, just add up the value of car's carburetor and the value of car's engine when engineers negotiate over the design a car These value of these choices are interdependent, resulting in nonlinear utility functions In this paper, we address this important gap in current negotiation techniques We propose a negotiation protocol where agents employ adjusted sampling to generate proposals, and a bidding-based mechanism is used to find social-welfare maximizing deals Our experimental results show that our method substantially outperforms existing methods in large non-linear utility spaces like those found in real world contexts

Greedy bidding strategies for keyword auctions

Abstract: How should players bid in keyword auctions such as those used by Google, Yahoo! and MSN?allWe consider greedy bidding strategies for a repeated auction on a single keyword, where in each round, each player chooses some optimal bid for the next round, assuming that the other players merely repeat their previous bid. We study the revenue, convergence and robustness properties of such strategies. Most interesting among these is a strategy we call the balanced bidding strategy (BB): it is known that BB has a unique fixed point with payments identical to those of the VCG mechanism. We show that if all players use the BB strategy and update each round, BB converges when the number of slots is at most 2, but does not always converge for 3 or more slots. On the other hand, we present a simple variant which is guaranteed to converge to the same fixed point for any number of slots. In a model in which only one randomly chosen player updates each round according to the BB strategy, we prove that convergence occurs with probability 1.We complement our theoretical results with empirical studies.

A Reinforcement Learning Model to Assess Market Power Under Auction-Based Energy Pricing

Abstract: Auctions serve as a primary pricing mechanism in various market segments of a deregulated power industry. In day-ahead (DA) energy markets, strategies such as uniform price, discriminatory, and second-price uniform auctions result in different price settlements and thus offer different levels of market power. In this paper, we present a nonzero sum stochastic game theoretic model and a reinforcement learning (RL)-based solution framework that allow assessment of market power in DA markets. Since there are no available methods to obtain exact analytical solutions of stochastic games, an RL-based approach is utilized, which offers a computationally viable tool to obtain approximate solutions. These solutions provide effective bidding strategies for the DA market participants. The market powers associated with the bidding strategies are calculated using well-known indexes like Herfindahl-Hirschmann index and Lerner index and two new indices, quantity modulated price index (QMPI) and revenue-based market power index (RMPI), which are developed in this paper. The proposed RL-based methodology is tested on a sample network

The Demand Elasticity Impacts on the Strategic Bidding Behavior of the Electricity Producers

Abstract: Due to the oligopoly structure of the electricity markets and to the constraints imposed by the transmission network, the producers may exert market power by strategically bidding higher than their marginal costs. This brings market performance far from the perfect competition equilibrium, with higher market clearing price and extra surpluses obtained by the producers. Demand elasticity can significantly affect the market performance contributing to mitigate the strategic bidding behavior of the producers. Compared to other commodity markets, demand elasticity in the electricity markets is low, but even a small increase can result in appreciable improvement of the market performance. The network constraints of the power system play a specific role in determining the oligopoly equilibrium of the gaming behavior of the electricity producers. The model of supply function equilibrium is first applied to a simple three-bus test system for a conceptual analysis and then to a standard IEEE 30-bus test system. The effects of an increase of demand elasticity are assessed, resorting to a set of proper quantitative indexes

Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market

Abstract: In a deregulated electricity market, generators have to optimally bid to maximize their profit under incomplete information of other competing generators. This paper addresses an optimal bidding strategy of a thermal generator in a uniform price spot market considering a precise model of nonlinear operating cost function and minimum up/down constraints of unit commitment. The bidding behaviors of other competing generators are described using normal probability distribution function. Bidding strategy of a generator for each trading period in a day-ahead market is solved by fuzzy adaptive particle swarm optimization (FAPSO), where inertia weight is dynamically adjusted using fuzzy evaluation. FAPSO can dynamically follow the frequently changing market demand and supply in each trading interval. The effectiveness of the proposed approach is tested with examples and the results are compared with the solutions obtained using genetic algorithm (GA) approach and other versions of PSO.

Regret in auctions: theory and evidence

Abstract: The sealed-bid first-price auction of a single object in the case of independent privately-known values is the simplest auction setting and understanding it is important for understanding more complex mechanisms. But bidders bid above the risk-neutral Nash equilibrium theory prediction. The reasons for this “over bidding” remain an unsolved puzzle. Several explanations have been offered, including risk aversion, social comparisons, and learning. We present a new explanation based on regret and a model that explains not only the observed over bidding in sealed-bid first-price auctions, but also behavior in several other settings that is inconsistent with risk aversion.

Exclusivity bidding for mobile sponsored content

Abstract: In embodiments, the present invention provides a method and system for receiving a bid for an exclusive sponsored content item to be presented on a mobile communication facility, the bid including an amount and at least one exclusivity characteristic relating to a mobile subscriber characteristic and matching the at least one exclusivity characteristic with the exclusive sponsored content item based at least in part on a relevancy for presentation to a mobile communication facility.

A market-based multi-agent system model for decentralized multi-project scheduling

Abstract: We consider a multi-project scheduling problem, where each project is composed of a set of activities, with precedence relations, requiring specific amounts of local and shared (among projects) resources. The aim is to complete all the project activities, satisfying precedence and resource constraints, and minimizing each project schedule length. The decision making process is supposed to be decentralized, with as many local decision makers as the projects. A multi-agent system model, and an iterative combinatorial auction mechanism for the agent coordination are proposed. We provide a dynamic programming formulation for the combinatorial auction problem, and heuristic algorithms for both the combinatorial auction and the bidding process. An experimental analysis on the whole multi-agent system model is discussed.

A Dynamic Analysis of a Demand Curve-Based Capacity Market Proposal: The PJM Reliability Pricing Model

Abstract: Because of high generation adequacy standards in the power industry, some peaking capacity operates for a limited time during the year and may not receive sufficient energy revenues to meet its fixed costs. This is particularly true when energy prices are capped in order to mitigate market power. The northeastern U.S. independent system operators (ISOs) have responded to this issue by establishing capacity obligations for loads and markets for installed capacity, thus providing a capacity revenue stream to generators. The installed capacity (ICAP) markets in the northeastern U.S. markets are a response to this need for additional incentives to construct generation. The Federal Energy Regulatory Commission (FERC) has accepted the PJM Interconnection's (PJM) proposal to replace the present fixed ICAP requirement that is placed upon load serving entities (LSEs) with a demand curve-based system in which the ISO would be responsible for acquiring "residual" capacity on behalf of LSEs. The demand curve approach pays more when reserve margins are smaller and provides a reduced incentive for investment when installed reserves are above the target. Another goal is to make revenues more predictable for generators, making investment less costly and, ultimately, lowering prices for consumers. A dynamic representative agent model is presented for projecting effects upon reserve margins, generator profitability, and consumer costs and is applied to alternative demand curves proposed for the PJM market. The consumer costs resulting from a sloped demand curve are robustly lower compared to the present fixed requirement under a wide range of assumptions concerning behavior of generation owners, including risk attitudes, bidding behavior, and willingness to build capacity as a function of forecast profit. The cost savings arise from lower capital costs to generators due to reduced risk and risk premiums. Also, average installed capacity is less for the same level of reliability because of reduced fluctuations in installed reserves

Method and system for controlling the initiation and duration of overtime interval in electronic auctions

Abstract: A method and system for conducting electronic auctions is described. A dynamic lot closing extension feature avoids collisions in closing times of multiple lots by dynamically extending the closing time of a subsequent lot if a preceding lot's closing time is extended to be too close to the subsequent lot's then-currently scheduled closing time. Scheduled closing times can be extended with a flexible overtime feature, in which the properties of the event triggering the extension and the duration of the overtime period(s) can be tailored to a particular auction, particular lots of products within an auction, and to the particular time within an auction process. The bidding status of a lot can be set to a “pending” status after the nominal closing time for submission of bids to allow bidders to alert the auction coordinator of technical problems in submission of bids. This allows the possibility for a lot to be return to open status for further bidding by all bidders. The auction may be paused by the auction coordinator to correct technical, market and miscellaneous problems that may arise during the course of an auction. Individual bid ceilings can be set for each bidder so that they are required to bid lower than certain thresholds determined in advance of the auction. Failsafe error detection is performed to prevent erroneous bids from entering the auction. The auction coordinator has the ability to override any erroneous bids that are entered to prevent prejudice to the auction.