On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.     

Investment and pricing strategies of competing firms: A computational approach

We analyze the classical investment and pricing problem of a dominant firm faced with competition from substitute industries or marginal firms in the same field. The firm owns a finite level of a resource (e.g. the stock of an exhaustible one), the consumption of which is to be divided optimally over a finite planning horizon. The competitors' measures affect the demand for the resource towards the dominating firm. Rising crude oil prices and investments in forms of alternative energy are representative examples of the strategic questions which involve competitive and contradictory interests among firms within an industry. The investment and pricing problem can be solved analytically only with strong, simplifying assumptions. To make the analysis simpler and to relax these restrictions, we combine a series of numerical tools, computerize them, and build up a user-oriented, computerized decision aid, which we call a ‘computerized approach’. We solve the problem under different sets of theoretical assumptions. This chosen incremental theory building allows us to study the theoretical sensitivity of the original problem.