Sequential License Buyback Auctions: An Experimental Analysis

Fisheries managers use licenses as a method of capping the size of a fishing industry, but as management goals change and the size of fishery stocks fluctuate, managers may be faced with the decision to buy back licenses. The vast majority of economic literature on license buyback programs focuses on the changes to economic efficiency of the fleet, often citing changes to the composition of fleet size. However, managers have little guidance in deciding how to structure a buyback auction, despite the fact that the auction structure plays a key role in determining which licenses are retired and in the composition of the remaining fleet. With the Texas Park and Wildlife Department’s Inshore Shrimp License Buyback Program as a basis for auction design, this research uses three experimental treatments to analyze how individuals respond to various reverse auction structures. In terms of the quickest license expiration, our experiments suggest that fisheries managers should select a binding auction with no sequential quality. However, we find that managers would see higher average bids from fishers in comparison to the two sequential auctions. The results are also relevant to other environmental programs in which environmental services are purchased over time in a sequential reverse auction.Fisheries management, license buyback, reverse auction, Institutional and Behavioral Economics, Research and Development/Tech Change/Emerging Technologies, Research Methods/ Statistical Methods, Q22, Q28, C9,

Reducing Risk by Leveraging Information for a Competitive Advantage in Bidding

Sales management is responsible for securing business by submitting bids for contracts to supply their products to wholesalers and retailers. In this case study, the bidding information was organized to improve the process for evaluating each contract using a model. The model is used in daily operations to price products in a frequently repeated, sequential auction environment for contracts awarded to the lowest price. This methodology quantifies three factors in developing a probability distribution describing the potential of winning the contract. These factors include the competitor\u27s reaction to the last winning bid, the second lowest bid and their own bid. Other factors that influence the pricing decision, such as quantity, geography, and number of competitors, are included in the selecting of contracts to build the model. For over a decade, management utilized this technique to maintain over $300 million in sales each year

PARTICIPATION AND LEARNING IN AUCTIONS: BIDDING DECISIONS IN EGYPTIAN OILSEED AUCTIONS

Auctions are common mechanisms for identifying prices and suppliers of commodities and are particularly important in agricultural marketing. Information asymmetries among bidders may be ameliorated over time through some form of learning. In this study, we incorporate prior decisions to participate, information from previous auctions, and firm-specific attributes to explain both the decision to bid and the level of the bid. Our analysis uses data from Egyptian oilseed tenders, an important market both for oilseeds and tendering. Because of the unbalanced nature of the panel data, we are able to evaluate the effects of signals received from previous tenders. We find that firms learn from previous auctions and can gain an informational advantage through some form of representation (e.g., by having an agent and/or direct sales agent to the country). Our results provide strong evidence that learning-by-doing affects the decision to participate and that learning affects the bid value. We also find that firms use outcomes of previous auctions to update information in both their decisions to participate in a market as well as determining the bid level. Finally, we find that firms with representation have a higher probability of participating in auctions and some evidence that they submit higher bids (earning higher returns).auction, bidding, tenders, optimal bids, learning, Marketing,

TRANSPARENCY AND BIDDING COMPETITION IN INTERNATIONAL WHEAT TRADE

One of the major trade policy problems identified by U.S. interests, including grower groups, traders, and policymakers, is that of pricing transparency. This has been a gnawing issue generally related to the pricing practices of competitor exporting countries with state trading enterprises (STEs). The transparency problem generally refers to the inability to observe rivals' terms of trade (including price, quality, credit, etc.) and is normally associated with commercial exporters competing against STE rivals. The perception being the less transparent competitors (STEs) would have a strategic advantage. A game theory model of bidding competition was developed to simulate the effects of information asymmetry amongst rivals. A Bayes-Nash equilibrium was used to derive equilibrium solutions. Several stylized examples were used to illustrate aspects of competition and to analyze effects on bidding strategies. Results indicate that: 1) anything that reduces uncertainties among rivals would reduce equilibrium bids and prices; 2) bidding situations in which there is less transparency have the effect of increasing bids and prices to buyers, and payoffs to sellers; and 3) increases in the number of rivals have the effect of reducing bids and mitigating the informational advantages of STEs. In all cases, less transparent sellers have an advantage in bidding competition relative to more transparent sellers. That advantage in our stylized case was in the area of 1-2$/mt. However, that advantage is mitigated with an increase in the number of transparent rivals and in the case where more transparent players have acted as agents for an STE and have more information about costs of an STE. Further, cessation of exports under U.S. EEP programs should have decreased the transparency of U.S. firms, increasing their competitiveness in the international grain trade.Price Transparency, Strategic Bidding, Game Theory, Bayesian-Nash, State Trading Enterprises, Export Enhancement Program, Wheat, International Relations/Trade,

On the distribution of bids for construction contract auctions

The statistical distribution representing bid values constitutes an essential part of many auction models and has involved a wide range of assumptions, including the Uniform, Normal, Lognormal and Weibull densities. From a modelling point of view, its goodness is defined by how well it enables the probability of a particular bid value to be estimated – a past bid for ex-post analysis and a future bid for ex-ante (forecasting) analysis. However, there is no agreement to date of what is the most appropriate form and empirical work is sparse. Twelve extant construction datasets from four continents over different time periods are analysed in this paper for their fit to a variety of candidate statistical distributions assuming homogeneity of bidders (ID not known). The results show there is no one single fit-all distribution, but that the 3p Log-Normal, Fréchet/2p Log-Normal, Normal, Gamma and Gumbel generally rank the best ex-post, and the 2p Log-Normal, Normal, Gamma and Gumbel the best ex-ante – with ex-ante having around three to four times worse fit than ex-post. Final comments focus on the results relating to the third and fourth standardised moments of the bids and a post-hoc rationalisation of the empirical outcome of the analysis

On optimal bidding in sequential procurement auctions

a b s t r a c t We investigate the problem of optimal bidding for a firm that in each period procures items to meet a random demand by participating in a finite sequence of auctions. We develop a new model for a firm where its item valuation derives from the sale of the acquired items via their demand distribution, sale price, acquisition cost, salvage value and lost sales. We establish monotonicity properties for the value function and the optimal dynamic bid strategy and we present computations

A rank-and-compare algorithm to detect abnormally low bids in procurement auctions

3noDetecting abnormally low bids in procurement auctions is a recognized problem, since their acceptance could result in the winner not being able to provide the service or work awarded by the auction, which is a significant risk for the auctioneer. A rank-and-compare algorithm is considered to detect such anomalous bids and help auctioneers in achieving an effective rejection decision. Analytical expressions and simulation results are provided for the detection probability, as well as for the false alarm probability. The suggested range of application of the detection algorithm leaves out the cases of many tenderers (more than 20) and quite dispersed bids (coefficient of variation larger than 0.15). An increase in the number of tenderers leads to contrasting effects, since both the false alarm probability and the detection probability are reduced. If the bids are spread over a large range, we have instead a double negative effect, with more false alarms and less detections. The presence of multiple anomalous bids worsens the performance of the algorithm as well. On the other hand, the method is quite robust to the presence of courtesy bids.reservedmixedL. De Giovanni; P. L. Conti; M. NaldiDE GIOVANNI, Livia; P. L., Conti; M., Nald

Private Information in Sequential Common-Value Auctions

We study an infinitely-repeated ?rst-price auction with common values. Initially, bid- ders receive independent private signals about the objects' value, which itself does not change over time. Learning occurs only through observation of the bids. Under one-sided incomplete information, this information is eventually revealed and the seller extracts es- sentially the entire rent (for large discount factors). Both players?payo¤s tend to zero as the discount factor tends to one. However, the uninformed bidder does relatively better than the informed bidder. We discuss the case of two-sided incomplete information, and argue that, under a Markovian re?nement, the outcome is pooling: information is revealed only insofar as it does not affect prices. Bidders submit a common, low bid in the tradition of collusion without conspiracy.repeated game with incomplete information; private information; ratchet effect; first-price auction; dynamic auctions

Competitive service market: modeling, storage and management

In order to capture the business dynamics underlying SOA-based service systems, we propose and formalize the concept of a competitive service market (CSM). A CSM is composed of a set of composite service providers, each managing a collection of atomic service providers. With the help of service composition protocol, composite service providers are able to invoke atomic services and aggregate them into value-added composite services for servicing various types of customers\u27 requests. Centering around the setting of a competitive service market, our research is separated into three parts: 1. Aiming to support the quantitative-based decision processes of different market players, we construct stochastic models to conduct performance analysis at various levels spanning vertically on the structural hierarchy of the service market. 2. In the context of requirements analysis, we classify the concept of service and service instance in terms of their respective functional and non-functional features. Hereafter, we identify the related storage issues and propose a counting Bloom filter-based hybrid storage architecture for the service registry design underlying the service market. A feature-based service discovery protocol is developed to demonstrate the usefulness of this design. 3. The business relationship between different market players are typically framed through the service level agreements (SLAs), which specify the attributes of QoS-based metrics and service costs for the realized service provisioning. SLAs constitute the backbone structure for managing the CSM. We identify several SLA design patterns in terms of different business scenarios that can occur in the life cycle of a service market. Against each pattern we study the corresponding SLA design scheme that can meet its unique requirements. In addition, we systematically investigate the application of Bayes estimator in these schemes, since the knowledge of their negotiation counterpart or market competitors is essential for reaching the goal of utility optimization. At the end, we cast the hybrid SLA design framework into a stochastic model that allows decision makers to obtain evaluations of performance of interest

Decision support and risk management system for competitive bidding in refurbishment work

This study is concerned with the management of risks in competitive bidding for refurbishment work (lump sum contracts). It investigates the main difficulties and risks faced by contractors when they are making decisions in competitive bidding as a result of the general lack of information both inside and outside a contractor's organisation. A decision support and risk management system model is developed which provides a systematic and objective approach to risk management in competitive bidding for refurbishment work. The model provides a framework whereby both quantitative (tender bid records) and qualitative (risk perception of contractors) information may be obtained to support the decisions of contractors during tendering. The research adopts a combination of both Archival and Opinion research methodologies to build up two main databases consisting of tender bid records and information on the risk perception of contractors during tendering. From the analysis, a decision support and risk management system is developed consisting of six modules namely: (i) Module 1 - Databases of tender bid records and Repertory grid data, (ii) Module 2 - General information of bidding characteristics, (iii) Module 3 - Contractor's analysis, (iv) Module 4 - Competitor's analysis, (v) Module 5 - Bidding models, and (vi) Module 6 - Risk management system. This study has demonstrated that past tender bid records of contractors may be organised in a systematic way to provide invaluable strategic information to enhance the understanding of contractors with respect to their competitive bidding environments, their own bidding performance and the bidding behaviour of their competitors, thereby enabling contractors to manage risks more effectively and efficiently