Review of Economic Theories of Regulation

This paper reviews the economic theories of regulation. It discusses the public and private interest theories of regulation, as the criticisms that have been leveled at them. The extent to which these theories are also able to account for privatization and deregulation is evaluated and policies involving re-regulation are discussed. The paper thus reviews rate of return regulation, price-cap regulation, yardstick regulation, interconnection and access regulation, and franchising or bidding processes. The primary aim of those instruments is to improve the operating efficiency of the regulated firms. Huge investments will be needed in the regulated network sectors. The question is brought up if regulatory instruments and institutions primarily designed to improve operating efficiency are equally well-placed to promote the necessary investments and to balance the resulting conflicting interests between for example consumers and investors.Regulation, Deregulation, Public Interest Theories, Private Interest Theories, Interest Groups, Public Choice, Market Failures, Price-cap Regulation, Rate of Return Regulation, Yardstick Competition, Franchise Bidding, Access Regulation.

Blending Words & Numbers: Towards a Framework for Combining Quantitative and Qualitative Strategies for Organizational Research

Blending qualitative and quantitative research methods is widely propagatedas a strategy for both quality control and enrichment of organizationresearch. This has been recognized in the organization literature for morethan twenty years. However, during the last decade the progress in thepractice of research has not been altogether impressive. Ambiguity is one ofthe key problems in this respect. This paper tries to clarify the discussion onblended methods, by (1) clarifying concepts used to describe blendeddesign, (2) inventoririzing and categorizing the different forms and objectivesof blended design, and (3) developing a provisional framework. The studydeparts from the research practice, the sequences of action in concretestudies. The focus is on research as a process, rather than on specificmethods. Finally, the paper suggest some directions for a developmentprogram for blending methods.economics of technology ;

Robust Counterparts of Inequalities Containing Sums of Maxima of Linear Functions

This paper adresses the robust counterparts of optimization problems containing sums of maxima of linear functions and proposes several reformulations. These problems include many practical problems, e.g. problems with sums of absolute values, and arise when taking the robust counterpart of a linear inequality that is affine in the decision variables, affine in a parameter with box uncertainty, and affine in a parameter with general uncertainty. In the literature, often the reformulation that is exact when there is no uncertainty is used. However, in robust optimization this reformulation gives an inferior solution and provides a pessimistic view. We observe that in many papers this conservatism is not mentioned. Some papers have recognized this problem, but existing solutions are either too conservative or their performance for different uncertainty regions is not known, a comparison between them is not available, and they are restricted to specific problems. We provide techniques for general problems and compare them with numerical examples in inventory management, regression and brachytherapy. Based on these examples, we give tractable recommendations for reducing the conservatism.robust optimization;sum of maxima of linear functions;biaffine uncertainty;robust conic quadratic constraints

Kriging Models That Are Robust With Respect to Simulation Errors

In the field of the Design and Analysis of Computer Experiments (DACE) meta-models are used to approximate time-consuming simulations. These simulations often contain simulation-model errors in the output variables. In the construction of meta-models, these errors are often ignored. Simulation-model errors may be magnified by the meta-model. Therefore, in this paper, we study the construction of Kriging models that are robust with respect to simulation-model errors. We introduce a robustness criterion, to quantify the robustness of a Kriging model. Based on this robustness criterion, two new methods to find robust Kriging models are introduced. We illustrate these methods with the approximation of the Six-hump camel back function and a real life example. Furthermore, we validate the two methods by simulating artificial perturbations. Finally, we consider the influence of the Design of Computer Experiments (DoCE) on the robustness of Kriging models.Kriging;robustness;simulation-model error

Safe Approximations of Chance Constraints Using Historical Data

This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach uses the available historical data for the uncertain parameters and is based on goodness-of-fit statistics. It guarantees that the probability that the uncertain constraint holds is at least the prescribed value. Compared to existing safe approximation methods for chance constraints, our approach directly uses the historical-data information and leads to tighter uncertainty sets and therefore to better objective values. This improvement is significant especially when the number of uncertain parameters is low. Other advantages of our approach are that it can handle joint chance constraints easily, it can deal with uncertain parameters that are dependent, and it can be extended to nonlinear inequalities. Several numerical examples illustrate the validity of our approach.robust optimization;chance constraint;phi-divergence;goodness-of-fit statistics

On Markov Chains with Uncertain Data

In this paper, a general method is described to determine uncertainty intervals for performance measures of Markov chains given an uncertainty region for the parameters of the Markov chains. We investigate the effects of uncertainties in the transition probabilities on the limiting distributions, on the state probabilities after n steps, on mean sojourn times in transient states, and on absorption probabilities for absorbing states. We show that the uncertainty effects can be calculated by solving linear programming problems in the case of interval uncertainty for the transition probabilities, and by second order cone optimization in the case of ellipsoidal uncertainty. Many examples are given, especially Markovian queueing examples, to illustrate the theory.Markov chain;Interval uncertainty;Ellipsoidal uncertainty;Linear Programming;Second Order Cone Optimization