Some Thoughts on MCDM: Myths and Ideas
Decision making is a most important function and process of management. It is one of the most pervasive problems of management and a pervasive problem in general. Multiple criteria decision making is an approach to decision making. It is the making of decisions taking into consideration multiple conflicting objectives. How do we make decisions? What is involved? There are many simple paradigms, and until recently about all that could be said was that you should list the alternatives, score them on certain dimensions, and then somehow aggregate the scores, and choose an alternative. Unfortunately, many excellent decision makers simply choose an alternative, based on instinct, intuition, or feel. Then they go on. Though that is fine in many cases; in some cases it is not. In this paper I consider some myths of decision making. I then propose some steps that we, as decision analysts and scientists, should consider taking to try to sensitize decision makers to thinking more about their decisions and making better decisions.
A fuzzy compromise approach for solving multi-objective stratified sampling design
In this paper, we established a framework for finding out the optimal allocation in the multivariate stratified sample using the fuzzy compromise method. The problem of multivariate stratified sample is formulated as an all integer nonlinear programming problem, and a solution is obtained by using the criterion of “Minimizing the sum of the squares of coefficient of variation for different characteristics.” There is also a quantitative illustration being carried out to explain the statistical nature of the approaches and solved through the LINGO program. We have also studied different techniques for comparison.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.