Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids *

Despite the development of increasingly sophisticated and refined multicriteria decision-making (MCDM) methods, an examination of the experimental evidence indicates that users most often prefer relatively unsophisticated methods. In this paper, we synthesize theories and empirical findings from the psychology of judgment and choice to provide a new theoretical explanation for such user preferences. Our argument centers on the assertion that the MCDM method preferred by decision makers is a function of the degree to which the method tends to introduce decisional conflict. The model we develop relates response mode, decision strategy, and the salience of decisional conflict to user preferences among decision aids. We then show that the model is consistent with empirical results in MCDM studies. Next, the role of decisional conflict in problem formulation aids is briefly discussed. Finally, we outline future research needed to thoroughly test the theoretical mechanisms we have proposed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73461/1/j.1540-5915.1991.tb00371.x.pd

Multiple Criteria Decision Making and Multiattribute Utility Theory

T his paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields

Randomly generated polytopes for testing mathematical programming algorithms

Randomly generated polytopes are used frequently to test and compare algorithms for a variety of mathematical programming problems. These polytopes are constructed by generating linear inequality constraints with coefficients drawn independently from a distribution such as the uniform or the normal. It is noted that this class of 'random' polytopes has a special property: the angles between the hyperplanes, though dependent on the specific distribution used, tend to be equal when the dimension of the space increases. Obviously this structure of 'random' polytopes may bias test results

Medial collateral ligament injuries of the knee: current treatment concepts

The medial collateral ligament is one of the most commonly injured ligaments of the knee. Most injuries result from a valgus force on the knee. The increased participation in football, ice hockey, and skiing has all contributed to the increased frequency of MCL injuries. Prophylactic knee bracing in contact sports may prevent injury; however, performance may suffer. The majority of patients who sustain an MCL injury will achieve their pre-injury activity level with non-operative treatment alone; however, those with combined ligamentous injuries may require acute operative care. Accurate characterization of each aspect of the injury will help to determine the optimum treatment plan

Random polytopes: Their definition, generation and aggregate properties

The definition of random polytope adopted in this paper restricts consideration to those probability measures satisfying two properties. First, the measure must induce an absolutely continuous distribution over the positions of the bounding hyperplanes of the random polytope; and second, it must result in every point in the space being equally as likely as any other point of lying within the random polytope. An efficient Monte Carlo method for their computer generation is presented together with analytical formulas characterizing their aggregate properties. In particular, it is shown that the expected number of extreme points for such random polytopes increases monotonically in the number of constraints to the limiting case of a polytope topologically equivalent to a hypercube. The implied upper bound of 2 n where n is the dimensionality of the space is significantly less than McMullen's attainable bound on the maximal number of vertices even for a moderate number of constraints.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47911/1/10107_2005_Article_BF01585093.pd