Showing papers on "Bidding published in 2012"

Modern Construction Management

Abstract: Preface Acknowledgements 1 Introduction Section One: 2 Planning Techniques 3 Work Study 4 Activity Sampling 5 Incentives 6 Cost Control 7 Plant Management Section Two: 8 Company Organization 9 Contractual Arrangements 10 Market Planning 11 Estimating and Tendering 12 Competitive Bidding 13 Budgetary Control 14 Cash Flow and Interim Valuations 15 Economic Assessments 16 Financial Management 17 Quality Management Section Three: Worked Examples Questions Solutions Index

System and method for social networking interactions using online consumer browsing behavior, buying patterns, advertisements and affiliate advertising, for promotions, online coupons, mobile services, products, goods and services, entertainment and auctions, with geospatial mapping technology

Abstract: Systems and methods are provided using combined technologies for social networking interactions using tracking, predicting, and implementing online consumer communications, browsing behavior, buying patterns, and advertisements and affiliate advertising and communications, for online coupons, mobile services, products, goods & services, entertainment shopping, auctions, bidding, bidding behavior, bidding results for targeting and filtering of promotions, online coupons, mobile services, products, goods & services, penny auctions or online auctions, advertisements and affiliate advertising or services on a three dimensional geospatial platform using geospatial mapping technology, company-local information, social networking, and social networking communities (“PGS-GM-CL/I-SN”).

Optimized Bidding of a EV Aggregation Agent in the Electricity Market

Abstract: An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naive forecast for all variables of the problem.

Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services

Abstract: Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks.

Bid optimizing and inventory scoring in targeted online advertising

Abstract: Billions of online display advertising spots are purchased on a daily basis through real time bidding exchanges (RTBs). Advertising companies bid for these spots on behalf of a company or brand in order to purchase these spots to display banner advertisements. These bidding decisions must be made in fractions of a second after the potential purchaser is informed of what location (Internet site) has a spot available and who would see the advertisement. The entire transaction must be completed in near real-time to avoid delays loading the page and maintain a good users experience. This paper presents a bid-optimization approach that is implemented in production at Media6Degrees for bidding on these advertising opportunities at an appropriate price. The approach combines several supervised learning algorithms, as well as second price auction theory, to determine the correct price to ensure that the right message is delivered to the right person, at the right time.

Optimal bidding in spot instance market

Abstract: Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker's perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1−∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users.

When cloud meets eBay: Towards effective pricing for cloud computing

Abstract: The rapid deployment of cloud computing promises network users with elastic, abundant, and on-demand cloud services. The pay-as-you-go model allows users to be charged only for services they use. Current purchasing designs, however, are still primitive with significant constraints. Spot Instance, the first deployed auction-style pricing model of Amazon EC2, fails to enforce fair competition among users in facing of resource scarcity and may thus lead to untruthful bidding and unfair resource allocation. Dishonest users are able to abuse the system and obtain (at least) short-term advantages by deliberately setting large maximum price bids while being charged only at lower Spot Prices. Meanwhile, this may also prevent the demands of honest users from being satisfied due to resource scarcity. Furthermore, Spot Instance is inefficient and may not adequately meet users' overall demands because it limits users to bid for each computing instance individually instead of multiple different instances at a time. In this paper, we formulate and investigate the problem of cloud resource pricing. We propose a suite of computationally efficient and truthful auction-style pricing mechanisms, which enable users to fairly compete for resources and cloud providers to increase their overall revenue. We analytically show that the proposed algorithms can achieve truthfulness without collusion or (t; p)-truthfulness tolerating a collusion group of size t with probability at least p. We also show that the two proposed algorithms have polynomial complexities O(nm + n2) and O(nm), respectively, when n users compete for m different computing instances with multiple units. Extensive simulations show that, in a competitive cloud resource market, the proposed mechanisms can increase the revenue of cloud providers, especially when allocating relatively limited computing resources to a potentially large number of cloud users.

Learning on a budget: posted price mechanisms for online procurement

Abstract: We study online procurement markets where agents arrive in a sequential order and a mechanism must make an irrevocable decision whether or not to procure the service as the agent arrives. Our mechanisms are subject to a budget constraint and are designed for stochastic settings in which the bidders are either identically distributed or, more generally, permuted in random order. Thus, the problems we study contribute to the literature on budget-feasible mechanisms as well as the literature on secretary problems and online learning in auctions.Our main positive results are as follows. We present a constant-competitive posted price mechanism when agents are identically distributed and the buyer has a symmetric submodular utility function. For nonsymmetric submodular utilities, under the random ordering assumption we give a posted price mechanism that is O(log n)-competitive and a truthful mechanism that is O(1)-competitive but uses bidding rather than posted pricing.

Computer-implemented method and system for managing keyword bidding prices

Abstract: A computer-implemented method and system for managing keyword bidding prices are disclosed An example system embodiment includes an automatic keyword bidding module, operably coupled with a processor and a memory, operable to determine a revenue per click value associated with a keyword, obtain bidding information associated with the keyword, obtain automatic bid controls associated with the keyword, and automatically generate a bid value for the keyword based on the revenue per click value, the bidding information, and the automatic bid controls

Optimal Demand-Side Bidding Strategies in Electricity Spot Markets

Abstract: This paper proposes a methodology for determining the optimal bidding strategy of a retailer who supplies electricity to end-users in the short-term electricity market. The aim is to minimize the cost of purchasing energy in the sequence of trading opportunities that provide the day-ahead and intraday markets. A genetic algorithm has been designed to optimize the parameters that define the best purchasing strategy. The proposed methodology has been tested using real data from the Spanish day-ahead and intraday markets over a period of two years with a significant cost reduction with respect to trading solely in the day-ahead market.

The Economics of Collusion: Cartels and Bidding Rings

Abstract: An examination of collusive behavior: what it is, why it is profitable, how it is implemented, and how it might be detected. Explicit collusion is an agreement among competitors to suppress rivalry that relies on interfirm communication and/or transfers. Rivalry between competitors erodes profits; the suppression of rivalry through collusion is one avenue by which firms can enhance profits. Many cartels and bidding rings function for years in a stable and peaceful manner despite the illegality of their agreements and incentives for deviation by their members. In The Economics of Collusion, Robert Marshall and Leslie Marx offer an examination of collusive behavior: what it is, why it is profitable, how it is implemented, and how it might be detected. Marshall and Marx, who have studied collusion extensively for two decades, begin with three narratives: the organization and implementation of a cartel, the organization and implementation of a bidding ring, and a parent company's efforts to detect collusion by its divisions. These accounts-fictitious, but rooted in the inner workings and details from actual cases-offer a novel and engaging way for the reader to understand the basics of collusive behavior. The narratives are followed by detailed economic analyses of cartels, bidding rings, and detection. The narratives offer an engaging entree to the more rigorous economic discussion that follows. The book is accessible to any reader who understands basic economic reasoning. Mathematical material is flagged with asterisks.

Bidding Wars Over R&D Intensive Firms: Knowledge, Opportunism and the Market for Corporate Control

Abstract: There are number of competing (and complementary) theories of the firm seeking to explain the scope and boundaries of organizations. Two prominent competitors are the knowledge-based theory of the firm (KBTF) and transaction cost economics (TCE). These differ in that the KBTF seeks to explain the scope and existence of firms independent of opportunism, the driving force behind transaction cost economics (TCE). The KBTF implies that, as knowledge intensity increases, organizational boundary decisions are increasingly driven by knowledge management concerns, rather than by opportunism. For example, there may be a need to co-locate functions to facilitate knowledge transfer or communication. If this is true, then as R&D-intensity increases, the KBTF should gain explanatory power over TCE. In contrast, the results of this study suggest that problems of opportunism may increase with R&D intensity. That is, as R&D intensity went up, managers actively discouraged bidding wars (e.g., by granting lockup agreements). Managers may even be able to buy the firm themselves at a discount since rivals are unlikely to emerge. This is generally thought to run counter to shareholder interests and thus represents a problem of opportunism that is best addressed with a TCE or agency theoretic approach. As such, TCE seems to gain explanatory power as knowledge intensity grows. Implications for further research are suggested.

A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems

Abstract: In real manufacturing environments, the control of system elements such as automated guided vehicles has some difficulties when planning operations dynamically. Multi agent-based systems, a newly maturing area of distributed artificial intelligence, provide some effective mechanisms for the management of such dynamic operations in manufacturing environments. This paper proposes a multi-agent based scheduling approach for automated guided vehicles and machines within a manufacturing system. The proposed multi-agent based approach works under a real-time environment and generates feasible schedules using negotiation/bidding mechanisms between agents. This approach is tested on off-line scheduling problems from the literature. The results show that our approach is capable of generating good schedules in real time comparable with the optimization algorithms and the frequently used dispatching rules.

Optimal Bids for Spot VMs in a Cloud for Deadline Constrained Jobs

Abstract: Spot virtual-machine (VM) instances, such as Amazon EC2 Spot VMs, are a class of VMs that are purchased through a market mechanism of price-bids submitted by cloud users. Spot VMs can be obtained at substantially lower cost than other VM classes such as Reserved and On-demand instances, but they do not have guaranteed availability since it depends on the submitted price bids and the fluctuating spot VM price. Many applications with large computing requirements but no real-time availability constraints, such as scientific computing, financial modelling and large data analysis, can be carried out at a significantly lower cost using spot VMs. For such jobs, an important question that arises is what should the submitted price bids be so that the computation is completed within a fixed time interval while the cost is minimized. Towards this goal, we model a job as a fixed computation request with a deadline constraint and formulate the problem of designing a dynamic bidding policy that minimizes the average cost of job completion. We obtain analytical and closed-form results for the optimal strategy under a Markov spot price evolution, and then evaluate the performance of the algorithms on the actual spot price history of Amazon EC2 Spot VMs.

Towards Optimal Bidding Strategy for Amazon EC2 Cloud Spot Instance

Abstract: With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies to minimize the cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance Price traces and workload models, we compare several adaptive check-pointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.

Using Internet Data for Economic Research

Abstract: The data used by economists can be broadly divided into two categories. First, structured datasets arise when a government agency, trade association, or company can justify the expense of assembling records. The Internet has transformed how economists interact with these datasets by lowering the cost of storing, updating, distributing, finding, and retrieving this information. Second, some economic researchers affirmatively collect data of interest. For researcher-collected data, the Internet opens exceptional possibilities both by increasing the amount of information available for researchers to gather and by lowering researchers' costs of collecting information. In this paper, I explore the Internet's new datasets, present methods for harnessing their wealth, and survey a sampling of the research questions these data help to answer. The first section of this paper discusses "scraping" the Internet for data—that is, collecting data on prices, quantities, and key characteristics that are already available on websites but not yet organized in a form useful for economic research. A second part of the paper considers online experiments, including experiments that the economic researcher observes but does not control (for example, when Amazon or eBay alters site design or bidding rules); and experiments in which a researcher participates in design, including those conducted in partnership with a company or website, and online versions of laboratory experiments. Finally, I discuss certain limits to this type of data collection, including both "terms of use" restrictions on websites and concerns about privacy and confidentiality.

Transaction costs and tradable mobility credits

Abstract: Artificial markets for mobility credits have been proposed as an alternative to conventional congestion pricing schemes. This paper examines the effects of transaction costs on two types of markets: an auction market and a negotiated market. In an auction market, users purchase all of the needed mobility credits through a competitive bidding process. In a negotiated market, the users initially receive certain amount of mobility credits from the government and trade with each other through negotiation to fulfill their needs. We assume that a brokerage service is built in both markets to facilitate transactions and accordingly, the users have to pay a commission fee proportional to the value of trade. The users are also given the option to purchase credits from the government if for some reasons they cannot use or wish to avoid the markets. Our analyses suggest that the auction market can achieve the desired equilibrium allocation of mobility credits as long as the government sets its price properly and the unit transaction cost is lower than the price that the market would reach in absence of transaction costs. However, in the negotiated market, transaction costs could divert the system from the desired equilibrium regardless of their magnitude. More importantly, the initial allocation of mobility credits may affect the final equilibrium even when marginal transaction costs are constant.

Demand Response Providing Ancillary Services A Comparison of Opportunities and Challenges in the US Wholesale Markets

Abstract: Interest in using demand response (DR) resources to supply low cost reliability products to the bulk electricity system is on the rise due to the uncertain impacts of increasing penetrations of intermittent generation and recent rulings supporting demand side participation in wholesale markets from the Federal Energy Regulatory Commission. However, organized electricity and ancillary services markets are just beginning to support DR resources for ancillary services, and the set of rules and requirements for participation are unique to each market. This paper discusses the opportunities that exist in the ancillary service markets in each US Independent System Operator territory and identifies challenges to market participation for demand response resources. It compares resource requirements, limits to aggregation, measurement and verification, bidding requirements, market timelines, and the types of organizations that can play in the markets. Additionally, it uses market clearing prices and market size to compare what value may be extracted from these markets, and identifies how these prices are currently determined. Using these criteria, PJM and ERCOT have the most favorable conditions for demand response participation in ancillary service markets, and changes to rules that effect aggregation and minimum resource size could promote more participation in other AS markets across the US.

Bidding to Drive: Car License Auction Policy in Shanghai and Its Public Acceptance

Abstract: Increased automobile ownership and use in China have led to traffic congestion, high energy consumption, and severe air pollution, and an urgent need for congestion control policies in major cities. The countrywide growth in car ownership, however, conceals great variation among cities. For example, Shanghai and Beijing both had about 2 million private cars each in 2004, but by 2010, Beijing already had 4.8 million private cars whereas Shanghai had only 3.1 million. Shanghai’s growth rate was about half that of Beijing. Many factors have contributed to this divergence but one of them is Shanghai’s very active vehicle control policy, which uses monthly license auctions to limit the number of new cars. The policy appears to be effective: in addition to dampening growth in car ownership, it generates annual revenues up to 4 billion CNY. But important questions must be answered: Do Shanghai people accept the policy and to what degree? Can other Chinese or western cities learn from this policy which, in most western cities, would be deemed draconian? In this study, the authors surveyed 524 employees at nine Shanghai companies to investigate public acceptance of Shanghai’s license auction policy and factors that contribute to acceptance: perceived policy effectiveness, affordability, equity concerns, and policy implementation. Respondents perceived the policy to be effective to some extent, but were largely negative towards the policy themselves although they expected that others would accept the policy more than they did. Respondents were consistently negative about affordability, four aspects of equity concerns and the implementation process. There were clear problems with lack of transparency in revenue usage, the perception that government vehicles enjoyed various license advantages, the bidding process and technology, and difficulties in obtaining information about the auction policy. When asked to consider license auctions in relation to other policy options, respondents thought that license auctions and congestion charges were more effective and acceptable than parking charges and fuel taxes. To improve public acceptability of the policy, the authors make six recommendations: transparency in revenue usage; transparency in government vehicle licensing and use, categorizing licenses by vehicle type, implementation and technology improvements to increase bidding convenience; a designated policy information website; and consideration of policies that restrict vehicle usage in congested locations.

Commitment Problems or Bidding Wars? Rebel Fragmentation as Peace Building

Abstract: After highly fragmented civil wars, order is often secured through the selective co-optation of rebel field commanders and atomized insurgents. This paper presents a formal model of civil war settlement as a coalition formation game between various regime and rebel factions. This approach emphasizes the ability of installed civilian rulers to lure warlords into the state based on promises of future wealth, then use divide-and-rule tactics to pit different warlord factions against one another. Quantitative and qualitative data from Tajikistan, including an original data set of warlord incorporation and regime purges during wartime reconstruction, are used to evaluate the model.

Bayesian Games and the Smoothness Framework

Abstract: We consider a general class of Bayesian Games where each players utility depends on his type (possibly multidimensional) and on the strategy profile and where players' types are distributed independently. We show that if their full information version for any fixed instance of the type profile is a smooth game then the Price of Anarchy bound implied by the smoothness property, carries over to the Bayes-Nash Price of Anarchy. We show how some proofs from the literature (item bidding auctions, greedy auctions) can be cast as smoothness proofs or be simplified using smoothness. For first price item bidding with fractionally subadditive bidders we actually manage to improve by much the existing result \cite{Hassidim2011a} from 4 to $\frac{e}{e-1}\approx 1.58$. This also shows a very interesting separation between first and second price item bidding since second price item bidding has PoA at least 2 even under complete information. For a larger class of Bayesian Games where the strategy space of a player also changes with his type we are able to show that a slightly stronger definition of smoothness also implies a Bayes-Nash PoA bound. We show how weighted congestion games actually satisfy this stronger definition of smoothness. This allows us to show that the inefficiency bounds of weighted congestion games known in the literature carry over to incomplete versions where the weights of the players are private information. We also show how an incomplete version of a natural class of monotone valid utility games, called effort market games are universally $(1,1)$-smooth. Hence, we show that incomplete versions of effort market games where the abilities of the players and their budgets are private information has Bayes-Nash PoA at most 2.

Optimization-Based Bidding in Day-Ahead Electricity Auction Markets: A Review of Models for Power Producers

Abstract: We review some mathematical programming models that capture the optimal bidding problem that power producers face in day-ahead electricity auction markets. The models consider both price-taking and non-price taking assumptions. The models include linear and non-linear integer programming models, mathematical programs with equilibrium constraints, and stochastic programming models with recourse. Models are emphasized where the producer must self-schedule units and therefore must integrate optimal bidding with unit commitment decisions. We classify models according to whether competition from competing producers is directly incorporated in the model.

A new genetic algorithm approach for optimizing bidding strategy viewpoint of profit maximization of a generation company

Abstract: This paper presents a new approach for bidding strategy in a day-ahead market from the viewpoint of a generation company (GENCO) in order to maximize its own profit as a participant in the market. It is assumed that each GENCO submits its own bid as pairs of price and quantity, and the sealed auction with a pay-as-bid market clearing price (MCP) is employed. The optimal bidding strategies are determined by solving an optimization problem with unit commitment constraints such as generating limitations. In this paper, the problem is solved from two different viewpoints including profit maximization of GENCO without considering rival's profit function, and profit maximization of GENCO by considering both rivals' bid and profit functions. Therefore, there is a multi-objective problem to be solved in this study. Since this problem is non-convex which is difficult to solve by traditional optimization techniques, hence, genetic algorithm (GA) has been employed to solve the problem. A simple test problem is designed to illustrate the efficiency of the proposed approach.

Effects of the Presence of Organic Listing in Search Advertising

Abstract: This paper analyzes how the presence of organic listing as a competing information source affects advertisers' sponsored bidding and the equilibrium outcomes in search advertising. We consider a game-theoretic model in which two firms bid for sponsored advertising slots provided by a monopolistic search engine and then compete for consumers in price in the product market. Firms are asymmetrically differentiated in market preference and are given different exposure in organic listing aligned with their market appeal. We identify two aspects of a firm's sponsored bidding incentive, namely, the promotive and the preventive incentives. The presence of organic listing alters firms' sponsored bidding incentives such that the stronger firm has primarily preventive incentive, whereas the weaker has mainly promotive incentive. We show that the preventive incentive decreases and the promotive incentive increases as the difference in firms' market appeal decreases, and as a result, even the weaker firm may outbid the stronger competitor under such a co-listing setting. We further examine how the presence of organic listing affects the equilibrium outcomes by comparing it with a benchmark case in which there is only a sponsored list. We show that the differentiated exposure in the organic list gives the weaker advertiser chances to win a better sponsored position, which improves the overall information structure the search engine provides. As a result, the equilibrium social welfare, sales diversity, and consumer surplus increase. Although the presence of the free exposure from the organic list may reduce advertisers' sponsored bidding incentive per se, the overall effect benefits the search engine's growth in the long run.

Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions

Abstract: Combinatorial auctions---in which bidders can bid on combinations of goods---can increase the economic efficiency of a trade when goods have complementarities. Recent theoretical developments have lessened the computational complexity of these auctions, but the issue of cognitive complexity remains an unexplored barrier for the online marketplace. This study uses a data-driven approach to explore how bidders react to the complexity in such auctions using three experimental feedback treatments. Using cluster analyses of the bids and the clicks generated by bidders, we find three stable bidder strategies across the three treatments. Further, these strategies are robust for separate experiments using a different setup. We also benchmark the continuous auctions against an iterative form of combinatorial auction---the combinatorial clock auction. The enumeration of the bidding strategies across different types of feedback, along with the analysis of their economic implications, is offered to help practitioners design better combinatorial auction environments. This paper was accepted by Lorin Hitt, information systems.

Why Can’t a Woman Bid More Like a Man?

Abstract: We investigate gender differences and menstrual cycle effects in first-price and second-price sealed-bid auctions with independent private values in a laboratory setting. We find that women bid significantly higher and earn significantly less than men do in the first-price auction, while we find no evidence of a gender difference in bidding or earnings in the second-price auction. Focusing on the first-price auction, we find that, while the gender gap in bidding and earnings persists over the entire course of the menstrual cycle, bidding of contraceptive pill users follows a sine-like pattern throughout the menstrual cycle, with higher than average bidding in the follicular phase and lower than average bidding in the luteal phase. In comparison, pill non-users have a flat bidding profile throughout the cycle.