Hybrid Approaches for Classification Under Information Acquisition Cost Constraint

The practical use of classification systems may be limited because the current classification systems do not allow decision makers to incorporate cost constraint. For example, in several financial applications (loan approval, credit scoring, etc.) an applicant is asked to submit a processing fee with the application (Mookerjee and Mannino 1997). The processing fee may be used to validate the information entered in the application. From an economic standpoint, it is important that the cost of validating the information not exceed the processing fee. Traditional classification systems do not allow the decision maker to incorporate information acquisition cost constraint. We term the problem of designing a classification system, where information acquisition costs are considered,astheproblemofclassificationwithinformationacquisitioncostconstraint(CIACC). TheCIACCproblemisaNP hard problem and is very difficult to solve to optimality

The Sequencing Problem in Sequential Investigation Processes

Many decision problems in various fields of application can be characterized as diagnostic problems trying to assess the true state (of the world) of given cases. The investigation of assessment criteria improves the initial information according to observed signal outcomes, which are related to the possible states. Such sequential investigation processes can be analyzed within the framework of statistical decision theory, in which prior probability distributions of classes of cases are updated, allowing for a sorting of particular cases into ever smaller subclasses. However, receiving such information causes investigation costs. Besides the question about the set of relevant criteria, this defines two additional problems of statistical decision problems: the optimal stopping of investigations and the optimal sequence of investigating a given set of criteria. Unfortunately, no solution exists with which the optimal sequence can generally be determined. Therefore, the paper characterizes the associated problems and analyzes existing heuristics trying to approximate an optimal solution.Decision-Making, Uncertainty, Information, Bayesian Analysis, Statistical Decision Theory

Optimal Sequential Investigation Rules in Competition Law

Although both in US antitrust and European competition law there is a clear evolution to a much broader application of "rule of reason" (instead of per-se rules), there is also an increasing awareness of the problems of a case-by-case approach. The "error costs approach" (minimizing the sum of welfare costs of decision errors and administrative costs) allows not only to decide between these two extremes, but also to design optimally differentiated rules (with an optimal depth of investigation) as intermediate solutions between simple per-se rules and a fullscale rule of reason. In this paper we present a decision-theoretic model that can be used as an instrument for deriving optimal rules for a sequential investigation process in competition law. Such a sequential investigation can be interpreted as a step-by-step sorting process into ever smaller subclasses of cases that help to discriminate better between pro- and anticompetitive cases. We analyze both the problem of optimal stopping of the investigation and optimal sequencing of the assessment criteria in an investigation. To illustrate, we show how a more differentiated rule on resale price maintenance could be derived after the rejection of its per-se prohibition by the US Supreme Court in the "Leegin" case 2007.Law Enforcement, Decision-Making, Competition Law, Antitrust Law

An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.

The Sequencing Problem in Sequential Investigation Processes

Many decision problems in various fields of application can be characterized as diagnostic problems trying to assess the true state (of the world) of given cases. The investigation of assessment criteria improves the initial information according to observed signal outcomes, which are related to the possible states. Such sequential investigation processes can be analyzed within the framework of statistical decision theory, in which prior probability distributions of classes of cases are updated, allowing for a sorting of particular cases into ever smaller subclasses. However, receiving such information causes investigation costs. Besides the question about the set of relevant criteria, this defines two additional problems of statistical decision problems: the optimal stopping of investigations and the optimal sequence of investigating a given set of criteria. Unfortunately, no solution exists with which the optimal sequence can generally be determined. Therefore, the paper characterizes the associated problems and analyzes existing heuristics trying to approximate an optimal solution

Optimal Sequential Investigation Rules in Competition Law

Although both in US antitrust and European competition law there is a clear evolution to a much broader application of "rule of reason" (instead of per-se rules), there is also an increasing awareness of the problems of a case-by-case approach. The "error costs approach" (minimizing the sum of welfare costs of decision errors and administrative costs) allows not only to decide between these two extremes, but also to design optimally differentiated rules (with an optimal depth of investigation) as intermediate solutions between simple per-se rules and a fullscale rule of reason. In this paper we present a decision-theoretic model that can be used as an instrument for deriving optimal rules for a sequential investigation process in competition law. Such a sequential investigation can be interpreted as a step-by-step sorting process into ever smaller subclasses of cases that help to discriminate better between pro- and anticompetitive cases. We analyze both the problem of optimal stopping of the investigation and optimal sequencing of the assessment criteria in an investigation. To illustrate, we show how a more differentiated rule on resale price maintenance could be derived after the rejection of its per-se prohibition by the US Supreme Court in the "Leegin" case 2007

Flexibility in highway noise management

The problem of noise management was taken as a key element of our life comfort. An adequate noise mitigation plan must be elaborated and implemented for a number of potential noise sources. The general idea of FLENO‐MA2 (FLExibility NOise MAnagement for A2 POZNAN BY‐PASS) advisory system was based on flexibility options situated on different management levels. In the recent years, two significant noise sources have been established in Poznan agglomeration: POZNAN BY‐PASS A2 as a part of A2 highway (BERLIN‐MOSCOW) and the NATO aircraft base in Krzesiny. Both are situated in the urban area of Poznan city. Special emphasis will be placed on flexibility noise mitigation options as an example of operational decision‐aid. However, finding a compromise solution based on diffused (using the Internet) information about the noise map (will be present in the near future) is the second goal of this system. First published online: 27 Oct 201

A Neuro-Expert Approach for Decision -Making in Welding Environment.

Decision making in welding is very important for achieving a good quality welded joint for the least possible cost. Of particular interest is decision making involving the selection of process, parameters, weld procedure specification, defect analysis and trouble shooting. This research has provided a means of capturing the planning knowledge in a Neuro-Expert System in a form that is capable of learning new information, correcting old information and automating the decision-making process in a welding environment. A strategy is formulated for the representation of knowledge in the form of a neural links and the translation of rules into neural link weights. After training those weights were converted back into rules to find out the inconsistent rules and capture new rules using a new approach. The various job variables affecting the process of welding are identified in detail and a Neuro-Expert system for the selection of process, parameters and weld procedure specification is developed. The neural networks are integrated with an expert system for decision making in welding environment. Apart from providing the initial parameters of welding, the expert system is used to validate the output of the neural network and served as a user-friendly interface for the neural network. Defect Analysis is performed in welding domain by mapping the welding parameters and defect patterns in a neural network. A neural network based approach for representing the knowledge in expert system is utilized for this purpose as the modification and updating of the knowledge was easier