International Journal of Information Technology & Decision Making

The widespread use of the Internet fostered the establishment of hundreds of sites that support on-line auctions of products and services, some of which also support combinatorial auctions. Finding the optimal combination of winning bids in combinatorial auctions is a difficult task. This paper presents the combinatorial auction problem, classifies combinatorial auctions by the different procedures used during the bidding process, presents some of the applications of combinatorial auctions, and develops mathematical models of combinatorial auctions. The mathematical models are used to identify some of the problem properties and prove that winner determination in combinatorial auctions has the same level of difficulty as solving the allocation problem facing an auctioneer. The paper concludes with the discussion on some of the complexities involved in combinatorial auctions.

This paper is devoted to a laboratory study of human behavior in a multi-criteria choice problem. The specific feature of the experimental study is the creation of an individually adjusted instance of a general task for each subject in accordance with his/her preferences over each criterion. Human behavior is studied in a specially constructed choice situation based on the decomposition of the alternatives of a multi-criteria problem. The procedure is based on multiple steps of pair-wise comparisons involving only some (two or three) of the original components of the alternatives. Abilities of subjects to use such comparisons and to answer the questions in a logical way are tested. The experiment was carried out in two countries: Finland and Russia.

There are different methods of statistical hypotheses testing.^1–4 Among them, is Bayesian approach. A generalization of Bayesian rule of many hypotheses testing is given below. It consists of decision rule dimensionality with respect to the number of tested hypotheses, which allows to make decisions more differentiated than in the classical case and to state, instead of unconstrained optimization problem, constrained one that enables to make guaranteed decisions concerning errors of true decisions rejection, which is the key point when solving a number of practical problems. These generalizations are given both for a set of simple hypotheses, each containing one space point, and hypotheses containing a finite set of separated space points.

In this paper, we develop a model-based approach to examine the trade-offs between short- and long-term supplier contracts. Specifically, we consider learning and technology breakthroughs leading to cost improvements and develop cost models to characterize these effects. A deterministic environment is first considered in which demand can be constant or dynamic and learning effects and technology breakthroughs are dynamic. Then uncertainty in market prices is considered. Interesting managerial insights and strategies in selecting suppliers are discussed based on the analytical results of the models.

In credit scoring, it is sometimes desirable to implement a new score in place of an existing score. Let Y denote the existing score and let X denote the new score. It is almost always the case that the score ranges for X and Y are different. For example, the score range for X might be 0 to 100, while the score range for Y might be 300 to 600. It follows from this difference in score ranges that a major difficulty in implementing any new score will be in training those who use the existing score to use the new score.

Score calibration is the process of constructing a calibration function g (·) in such a way that the calibrated score, g (X), is a score which mimics the behavior of Y. In applications, it is commonly found that problems in the distribution of g (X) arise as the result of the calibration process. There is a great need in production for an agorithm which automates the task of discovering these problems and correcting them. In this paper, we propose such a fully automatic algorithm. This algorithm has been successfully tested in many applications.

The rapid growth of the network-centered (Internet and Intranet) computing environments requires new architectures for information gathering systems. Typically, in these environments, the information resources are dynamic, heterogeneous and distributed. In addition, these computing environments are open, where information resources may be connected or disconnected at any time. This paper presents an architecture for a multi-agent information gathering system. The architecture includes three types of agents: interface, broker and resource agents. The interface agents interact with the users to fulfill their interests and preferences. The resource agents access and capture the content of the information resources. The broker agents facilitate cooperation among the information and the resource agents to achieve their desired goals. This paper provides the agents' architecture, design and implementations that enable them to cooperate, coordinate and communicate with each other to gather information in an open and dynamic environment.

In this paper, we present a concept based on the similarity of categorical attribute values considering implicit relationships and propose a new and effective clustering procedure for mixed data. Our procedure obtains similarities between categorical values from careful analysis and maps the values in each categorical attribute into points in two-dimensional coordinate space using multidimensional scaling. These mapped values make it possible to interpret the relationships between attribute values and to directly apply categorical attributes to clustering algorithms using a Euclidean distance. After trivial modifications, our procedure for clustering mixed data uses the k-means algorithm, well known for its efficiency in clustering large data sets. We use the familiar soybean disease and adult data sets to demonstrate the performance of our clustering procedure. The satisfactory results that we have obtained demonstrate the effectiveness of our algorithm in discovering structure in data.

This article investigates the market reaction to a sample of announcements of business service outsourcing arrangements made by UK quoted companies between 1991 and 1997. Event study methodology is applied to daily stock returns to measure the reaction, in the form of excess returns, immediately prior to and at the date of an outsourcing contract announcement. The conclusion is that initial announcements tend to enjoy positive and significant reaction and that the larger companies in the sample show a more positive reaction than smaller companies. Overall outsourcing announcements appear to be associated with excess returns but the absence of any recognized basis for disclosure prevents a complete analysis of such events.