Showing papers on "Bidding published in 2010"

False Data Injection Attacks in Electricity Markets

Abstract: We present a potential class of cyber attack, named false data injection attack, against the state estimation in deregulated electricity markets. With the knowledge of the system configuration, we show that such attacks will circumvent the bad data measurement detection equipped in present SCADA systems, and lead to profitable financial misconduct such as virtual bidding the ex-post locational marginal price (LMP). We demonstrate the potential attacks on an IEEE 14-bus system.

Strategic Herding Behavior in Peer-to-Peer Loan Auctions

Abstract: Online Peer-to-Peer (P2P) loan auctions enable individual consumers to borrow and lend money directly to one another. We study herding behavior, defined as a greater likelihood of bidding in auctions with more existing bids, in P2P loan auctions on Prosper.com. The results of an empirical study provide evidence of strategic herding behavior by lenders such that they have a greater likelihood of bidding on an auction with more bids (a 1% increase in the number of bids increases the likelihood of an additional bid by 15%), but only to the point at which it has received full funding. After this point, herding diminishes (a 1% increase in bids increases the likelihood of an additional bid by only 5%). We also find a positive association between herding in the loan auction and its subsequent performance, that is, whether borrowers pay the money back on time. Unlike eBay auctions where herding impacts bidders adversely, our findings reveal that strategic herding behavior in P2P loan auctions benefits bidders, individually and collectively.

A Dynamic Model of Sponsored Search Advertising

Abstract: Sponsored search advertising is ascendant -- Jupiter Research reports expenditures rose 28% in 2007 to $8.9B and will continue to rise at a 26% CAGR, approaching 1/2 the level of television advertising and making it one of the major advertising trends to affect the marketing landscape. Yet little empirical research exists to explore how the interaction of various agents (searchers, advertisers, and the search engine) in keyword markets affects consumer welfare and firm profits. The dynamic structural model we propose serves as a foundation to explore these outcomes. We fit this model to a proprietary data set provided by an anonymous search engine. These data include consumer search and clicking behavior, advertiser bidding behavior, and search engine information such as keyword pricing and website design.With respect to the advertisers, we find evidence of dynamic bidding behavior. Advertiser value for clicks on their sponsored links averages about 26 cents. Given the typical $22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.2%, well within common estimates of 1-2% (gamedaily.com). With respect to consumers, we find that frequent clickers place a greater emphasis on the position of the sponsored advertising link. We further find that about 10% of consumers do 90% of the clicks.We then conduct several policy simulations to illustrate the effects of changes in search engine policy. First, we find the search engine obtains revenue gains of 1% by sharing individual level information with advertisers and enabling them to vary their bids by consumer segment. This also improves advertiser revenue by 6% and consumer welfare by 1.6%. Second, we find that a switch from a first to second price auction results in truth telling (advertiser bids rise to advertiser valuations). However, the second price auction has little impact on search engine profits. Third, consumer search tools lead to a platform revenue increase of 2.9% and an increase of consumer welfare by 3.8%. However, these tools, by reducing advertising exposures, lower advertiser profits by 2.1%.

The Race for Sponsored Links: Bidding Patterns for Search Advertising

Abstract: Paid placements on search engines reached sales of nearly $11 billion in the United States last year and represent the most rapidly growing form of online advertising today. In its classic form, a search engine sets up an auction for each search word in which competing websites bid for their sponsored links to be displayed next to the search results. We model this advertising market, focusing on two of its key characteristics: (1) the interaction between the list of search results and the list of sponsored links on the search page and (2) the inherent differences in attractiveness between sites. We find that both of these special aspects of search advertising have a significant effect on sites' bidding behavior and the equilibrium prices of sponsored links. Often, sites that are not among the most popular ones obtain the sponsored links, especially if the marginal return of sites on clicks is quickly decreasing and if consumers do not trust sponsored links. In three extensions, we also explore (1) heterogeneous valuations across bidding sites, (2) the endogenous choice of the number of sponsored links that the search engine sells, and (3) a dynamic model where websites' bidding behavior is a function of their previous positions on the sponsored list. Our results shed light on the seemingly random order of sites on search engines' list of sponsored links and their variation over time. They also provide normative insights for both buyers and sellers of search advertising.

A Study of the Internal Organization of a Bidding Cartel

Abstract: This paper examines bidding in over 1,700 knockout auctions used by a bidding cartel (or ring) of stamp dealers in the 1990s. The knockout was conducted using a variant of the model studied by Daniel Graham, Robert Marshall, and Jean-Francois Richard (1990). Following a reduced form examination of these data, damages, induced inefficiency, and the ring's benefit from colluding are estimated using a structural model in the spirit of Emmanuel Guerre, Isabelle Perrigne, and Quang Vuong (2000). A notable finding is that nonring bidders suffered damages that were of the same order of magnitude as those of the sellers.

Do Investors Learn from Experience? Evidence from Frequent IPO Investors

Abstract: We examine how experience affects the decisions of individual investors and institutions in IPO auctions to bid in subsequent auctions, and their bidding returns. We track bidding histories for all 31,476 individual investors and 1,232 institutional investors across all 84 IPO auctions during 1995-2000 in Taiwan. For individual bidders: (1) high returns in previous IPO auctions increase the likelihood of participating in future auctions; (2) bidders’ returns decrease as they participate in more auctions; (3) auction selection ability deteriorates with experience; and (4) bidders with greater experience bid more aggressively. These findings are consistent with naive reinforcement learning wherein individuals become unduly optimistic after receiving good returns. In sharp contrast, there is little sign that institutional investors exhibit such behavior.

Dynamic bidding and expected value

Abstract: A system for receiving data associated with a mobile content is configured to calculate an expected value of the mobile content based at least in part on the data received, and determine a bid amount for a sponsorship of the mobile content based at least in part on the expected value.

Notice of Violation of IEEE Publication Principles A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market

Abstract: In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. The choice of the forecasting model becomes the important influence factor on how to improve price forecasting accuracy. This paper provides a hybrid methodology that combines both autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models for predicting short-term electricity prices. This method is examined by using the data of Australian national electricity market, New South Wales, in the year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN, and hybrid models are presented. Empirical results indicate that a hybrid ARIMA-ANN model can improve the price forecasting accuracy.

A Greater Price for a Greater Good? Evidence That Consumers Pay More for Charity-Linked Products

Abstract: To study whether consumers will pay more for products that gener ate charitable donations, we analyze data from eBay on charity and noncharity auctions of otherwise identical products. Charity prices are 6 percent higher, on average, than noncharity prices. Bids below the closing price are also higher, as are bids by individuals bidding on identical charity and noncharity products. Bidders appear to value charity revenue at least partially as a public good, as they submit bids earlier in charity auctions, stimulating other bidders to bid more aggressively. Our results help explain why firms may pledge charitable donations, green production, or similar activities. (JEL D12, D44, D64, L81, M14, M31)

Serial Acquirer Bidding: An Empirical Test of the Learning Hypothesis

Abstract: Recent academic studies indicate that acquirers’ cumulative abnormal returns (CAR) decline from deal to deal in acquisitions programs. Does this pattern suggest hubristic CEO behaviors are significant enough to influence average CAR patterns during acquisitions programs? An alternative explanation is CEO learning. This study therefore tests for learning using successive acquisitions of large U.S. public targets undertaken by U.S. acquirers. A dynamic framework reveals that both rational and hubristic CEOs take on average investor reactions to their previous deals into account and adjust their bidding behavior accordingly. These results are consistent with a learning hypothesis.

The State of Solving Large Incomplete-Information Games, and Application to Poker

Abstract: Game-theoretic solution concepts prescribe how rational parties should act, but to become operational the concepts need to be accompanied by algorithms. I will review the state of solving incomplete-information games. They encompass many practical problems such as auctions, negotiations, and security applications. I will discuss them in the context of how they have transformed computer poker. In short, game-theoretic reasoning now scales to many large problems, outperforms the alternatives on those problems, and in some games beats the best humans.

Course Bidding at Business Schools

Abstract: Mechanisms that rely on course bidding are widely used at Business Schools in order to allocate seats at oversubscribed courses. Bids play two key roles under these mechanisms: Bids are used to infer student preferences and bids are used to determine who have bigger claims on course seats. We show that these two roles may easily consict and preferences induced from bids may signiÞcantly differ from the true preferences. Therefore while these mechanisms are promoted as market mechanisms, they do not necessarily yield market outcomes. The two consicting roles of bids is a potential source of efficiency loss part of which can be avoided simply by asking students to state their preferences in addition to bidding and thus “separating” the two roles of the bids. While there may be multiple market outcomes under this proposal, there is a market outcome which Pareto dominates any other market outcome.

Simulation and Pricing Mechanism Analysis of a Solar-Powered Electrical Microgrid

Abstract: A framework is proposed for an electrical power microgrid, such as for a colony or small township of homes that generate electrical power from solar energy and use it directly when possible, and via stored battery power at other times. The situation is described as a demand and supply problem in a multi-agent system with many consumers and suppliers and no explicit communication or coordination among the agents. Such a demand and supply problem is modeled as a Potluck Problem, a generalization of the Santa Fe Bar Problem. Power produced by PV panels and batteries may be used in the local market, in addition to being consumed locally. The proposed microgrid system model is able to determine the optimum operation of a solar-powered microgrid with respect to load demand, environmental requirements, PV panel and battery capacities. The results indicate the effect of various such parameters on the performance of these micro-grids. This paper also analyzes and proposes, based on auction theory, the most efficient and competing pricing mechanism in the proposed microgrid system model. Two important market bidding techniques, single bidding and discriminatory bidding, are considered. The microgrid is made to participate in the bidding process to serve the consumers at a reduced price and to provide better revenues. The viability of the model proposed is illustrated with analyses using realistic assumptions and published historical data.

Resource Pricing and Equilibrium Allocation Policy in Cloud Computing

Abstract: Cloud computing is a new emerging computing paradigm that advocates supplying users everything as a service. Compared with grid computing, the focus of resource management problem is transformed to resource virtualization and allocation rather than job decomposition and scheduling. It is more urgent to find better solutions for cloud resource allocation than ever before. Although there have been some research efforts in grid computing, most of them aim at maximizing utility of system and lack of analysis for competition between different users. Some researches consider competition analysis, but they assume that common knowledge is certain and known for every user, which is difficult to be applied in a global distributed cloud environment. In this paper, we hereby propose a new resource pricing and allocation policy where users can predict the future resource price as well as satisfy budget and deadline constraints. Experimental results prove that resource price can gradually converge to an equilibrium state by dynamic games and that cloud users can receive Nash equilibrium allocation proportion without other competitors' bidding information.

Borrower Decision Aid for people-to-people lending

Abstract: In setting up, and bidding in online auctions, people face difficult strategic decisions. In this study, a Borrower Decision Aid is introduced, which will help formalize the decision making process of the sellers, or borrowers in this case, in one particular P2P loan auction site, Prosper.com. The vast amount of real-life bidding data available in this online auction enables us to build new kinds of tools for decision makers. The Borrower Decision Aid helps the borrower to quantify her strategic options, such as starting interest rate, and the amount of loan requested. We identify which variables concerning the borrower are related to the probability of successfully securing a loan and the final interest rate.

A New Payment Rule for Core-Selecting Package Auctions

Abstract: We propose a new, easy-to-implement, class of payment rules, “Reference Rules,” to make core-selecting package auctions more robust. Small, almostriskless, profitable deviations from “truthful bidding” are often easy for bidders to find under currently-used payment rules. Reference Rules perform better than existing rules on our marginal-incentive-to-deviate criterion, and are as robust as existing rules to large deviations. Other considerations, including fairness and comprehensibility, also support the use of Reference Rules.

Multi‐attribute contractors ranking method by applying ordering of feasible alternatives of solutions in terms of preferability technique

Abstract: Contractor evaluation is a vital part of the project management cycle and deals with risk and risk management. One of the most important phases in the construction industry is the bidding process. In order to select the most appropriate contractor for the project and prepare the most realistic and accurate bid proposal, stakeholders have to know all financial, technical and general information about these contractors. The information can be determined as qualitative, quantitative or verbal data. This paper presents the multi‐attribute contractors ranking method bay applying Ordering of feasible alternatives of solutions in terms of preferability technique. This method allows dealing with qualitative and quantitative data as well as with data expressed in words (verbal data). Finally, an illustrative example of contractor selection is used to demonstrate the feasibility and practicability of the proposed model.

An Experimental Test of Flexible Combinatorial Spectrum Auction Formats

Abstract: The simultaneous multi-round (SMR) auction, introduced by the FCC in 1994, has been successfully applied in the sales of high-valued market licenses around the world. The FCC has recently developed a new auction format, SMRPB, which incorporates the possibility of fully flexible package bids. This paper reports laboratory experiments based on SMRPB and other combinatorial formats, including a “combinatorial clock,” which have been proposed to replace the SMR auction. In general, the interest of policy makers in combinatorial auctions is justified by the laboratory data; when value complementarities are present, package bidding yields improved performance. We find clear differences among the package formats, however, both in terms of efficiency and seller revenue. Notably, the combinatorial clock provides the highest revenue. The SMRPB auction performed worse than the other combinatorial formats, which is one of the main reasons why the FCC has decided not to implement SMRPB procedure for package bidding.

Systems and methods for cloud-based brokerage exchange of software entitlements

Abstract: Embodiments relate to systems and methods for cloud-based brokerage exchange of software entitlements. A user can host on-premise software applications on physical hardware, and extend those applications to the cloud based on a set of entitlements developed in conjunction with the vendor(s) of the software. The set of entitlements enjoyed by the user and/or offered by the vendor(s) can be exposed to a bidding marketplace via a brokerage engine and associated bidding service, which can be hosted on a Web site. Other users, and/or other vendors interesting in consuming or supplying premise or loud-based images of the software, or related services, can be to obtain or supply those resources through the brokerage service. The license terms including usage rates, number of users or images, security constraints, and/or other terms of software delivery and usage can be recorded in a dynamically updated entitlement database.

Spectrum auction framework for access allocation in cognitive radio networks

Abstract: In cognitive radio networks, there are two categories of networks on different channels: primary networks, which have high-priority access, and secondary networks, which have low-priority access. We develop an auction-based framework that allows networks to bid for primary and secondary access based on their utilities and traffic demands. The bids are used to solve the access allocation problem, which is that of selecting the primary and secondary networks on each channel either to maximize the auctioneer's revenue or to maximize the social welfare of the bidding networks, while enforcing incentive compatibility. We first consider the case when the bids of a network depend on which other networks it will share channels with. When there is only one secondary network on each channel, we design an optimal polynomial-time algorithm for the access allocation problem based on reduction to a maximum matching problem in weighted graphs. When there can be two or more secondary networks on a channel, we show that the optimal access allocation problem is NP-complete. Next, we consider the case when the bids of a network are independent of which other networks it will share channels with. We design a polynomial-time dynamic programming algorithm to optimally solve the access allocation problem when the number of possible cardinalities of the set of secondary networks on a channel is upper-bounded. Finally, we design a polynomial-time algorithm that approximates the access allocation problem within a factor of 2 when the above upper bound does not exist.

An agent-based market platform for Smart Grids

Abstract: The trend towards renewable, decentralized, and highly fluctuating energy suppliers (e.g. photovoltaic, wind power, CHP) introduces a tremendous burden on the stability of future power grids. By adding sophisticated ICT and intelligent devices, various Smart Grid initiatives work on concepts for intelligent power meters, peak load reductions, efficient balancing mechanisms, etc. As in the Smart Grid scenario data is inherently distributed over different, often non-cooperative parties, mechanisms for efficient coordination of the suppliers, consumers and intermediators is required in order to ensure global functioning of the power grid. In this paper, a highly flexible market platform is introduced for coordinating self-interested energy agents representing power suppliers, customers and prosumers. These energy agents implement a generic bidding strategy that can be governed by local policies. These policies declaratively represent user preferences or constraints of the devices controlled by the agent. Efficient coordination between the agents is realized through a market mechanism that incentivizes the agents to reveal their policies truthfully to the market. By knowing the agent's policies, an efficient solution for the overall system can be determined. As proof of concept implementation the market platform D'ACCORD is presented that supports various market structures ranging from a single local energy exchange to a hierarchical energy market structure (e.g. as proposed in [10]).

Optimal day-ahead trading and storage of renewable energies—an approximate dynamic programming approach

Abstract: A renewable power producer who trades on a day-ahead market sells electricity under supply and price uncertainty. Investments in energy storage mitigate the associated financial risks and allow for decoupling the timing of supply and delivery. This paper introduces a model of the optimal bidding strategy for a hybrid system of renewable power generation and energy storage. We formulate the problem as a continuous-state Markov decision process and present a solution based on approximate dynamic programming. We propose an algorithm that combines approximate policy iteration with Least Squares Policy Evaluation (LSPE) which is used to estimate the weights of a polynomial value function approximation. We find that the approximate policies produce significantly better results for the continuous state space than an optimal discrete policy obtained by linear programming. A numerical analysis of the response surface of rewards on model parameters reveals that supply uncertainty, imbalance costs and a negative correlation of market price and supplies are the main drivers for investments in energy storage. Supply and price autocorrelation, on the other hand, have a negative effect on the value of storage.

A new wholesale bidding mechanism for enhanced demand response in smart grids

Abstract: Calls to improve customer participation as a key element of smart grids have reinvigorated interest in demand-side features such as distributed generation, on-site storage and demand response. In the context of deregulated market structures, these features can improve flexibility of demand, but at the cost of added uncertainty. Therefore, how to implement these features under deregulated power markets is worth consideration. To address the problems induced by the demand-side participation features together with deregulated electricity markets, this paper presents a new bidding mechanism, which uses Price Elasticity Matrices (PEM) to model the concerned features. Three typical traditional bidding mechanisms are reviewed and compared with the proposed bidding mechanism. This paper also presents an algorithm guaranteeing better convergence to carry out the proposed bidding mechanism. The concept of a stepped supply curve's relative slope is defined in the algorithm. Multiple benefits induced are shown by numerical examples in a day-ahead wholesale electricity pool under real-time pricing.