Conversion in Progress

Chapter 5
The Concerns of the Decision Sciences

While it may be difficult to establish a definition which unifies the vast field of the decision sciences, there are some characteristics of the methods commonly practiced in this field upon which most practitioners would agree. These include: a) The methods are principally algorithmic. b) Their purposes are primarily to optimize and secondarily to satisfice objectives. c) The methods are primarily numeric and quantitative.These characteristics are commonly applied to methods within the major area disciplines of; 1) Decision Analysis (DA) and a subset Multiple Criteria Decision Making (MCDM), 2) Statistics, 3) Forecasting, and 4) Mathematical Programming.Other areas contained within the decision sciences but which are emphasized less include Production Quality Control & Scheduling, Markovian Analysis, Project Management, Simulation, Game Theory, Queuing Theory, Inventory Control, Material Requirements Planning, Influence Diagramming, and Financial Modeling. Often the study of these techniques appear within the disciplines of management science and operations research. We will now look at the four major areas outlined above with a particular eye toward demonstrating how the methods in each area contribute to problem solving, decision making, and overcoming cognitive weaknesses. Decision Analysis/MCDM MethodsThere are a number of decision techniques which have been developed to provide a rational model for decision making. The term for the body of these techniques is Decision Analysis (DA). Multiple Criteria Decision Making (MCDM) was a term originally ascribed to the body of techniques known as linear programming, but now MCDM and DA are largely used interchangeably. These techniques may contain some or all the elements of the following general decision model. ÚÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ¿ ³ Goal/Problem ³ ³ Alternatives ³ ³ Criteria or Attributes (may include sub-criteria) ³ ³ Preference or Likelihood of Occurrence (Uncertainty) ³ ³ Measurement Scales (e.g. \$, yards, horsepower,yes/no) ³ ³ Synthesis Technique ³ ³ ³ ³ Figure 5.1 Elements of The General Decision Model ³ ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÙAs an example a goal may be to select a new automobile. Alternatives involved with this goal might include a BMW 325i, an Acura Legend, and a Oldsmobile Cutlass. Criteria would include such common characteristics as cost, performance, obsolescence period, and styling. Preference would relate to a criteria such as styling. Likelihood would relate to a criteria such as maintenance. The comparison scales would differ depending on the criteria. For example \$ would apply for costs, an ordinal scale such as great/good/fair/poor for styling, and a scale such as years for obsolescence. Finally, a synthesis technique would dictate how all the above would be "combined" to rank or distance the alternatives. Methods here include additive, multiplicative, geometric mean, and vector processing. See Johnson and Huber[DA2] for a more in depth coverage. Researchers have developed a number of techniques for organizing, controlling and effecting these elements. Some of these follow. DA/MCDM methods are largely utilized for the summary, selection from, and synthesis of a set of alternatives. Eight of these techniques are listed and discussed below.ÚÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ¿³ Analytical Hierarchy Process ³³ Bayesian Updating ³³ Cost Benefit Analysis ³³ Cost Effectiveness Analysis ³³ Decision Trees ³³ Matrix ³³ Outranking ³³ Subjective Judgement Theory ³³ Utility Assessment ³³ ³³ Figure 5-2. Decision Analysis Techniques ³ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÙ More detail concerning each of these methods follows: Analytical Hierarchy Process (AHP) is largely a satisficing technique developed by Dr. Thomas Saaty[MC11]. It provides selection and ranking of alternatives using criteria and pairwise relative comparison. Synthesis technique utilizes eigenvectors & eigenvalues processing. It also has a normalized consistency check. Inherent in AHP is a cognitive ST memory 7+2 concept and a 1 to 9 numeric scale for evaluation. This scale was developed using human cognitive experiments. Bayesian Updating is a "a posteriori" technique postulated in the 18th Century by Rev. Thomas Bayes. It combines a users beliefs with evidence and hypotheses. It follows the basic tenets of mathematical probability to help a user evaluate network paths for subsequent action. It was partially developed because humans tend to understate changes in position based upon new information.Cost Benefit Analysis (CBA) is a relatively simple technique where a dollar assignment is made to a list of benefits and costs. Final evaluation is made through an additive or ratio comparison of costs and benefits. A major complaint of this method is the difficulty in determining benefits.Cost Effectiveness Analysis is a technique in the same genre as CBA. An effectiveness measure is created for each criterion. The ratio of cost to effectiveness then provides a ranking of alternatives. Example: Measure of Effectiveness = time to reach 60mph Measure of Cost = dollars time cost ratio Alternative A 5.0 sec \$20,000 250Alternative B 15.0 sec \$ 5,000 333One criticism of this method is the lack of consumer "preference" inherent in these ratios. For example in the above, cost may be a significant factor to one consumer but not another. This technique assumes cost "indifference." Another problem is the lack of a specific synthesis techniqueto combine the scaled criteria.Decision Trees utilize the application of probabilistic factors and "payoffs" to outcomes (alternatives). A tree is created representing the outcome of all possible states within the stages of a multifaceted decision. One criticism of the tree method is that it uses the "expected value" approach. This approach does not account for the element of risk, which varies from decision maker to decision maker. Matrix is likely the most commonly used technique. It is a satisficing technique which utilizes a simple matrix for the selection of a "best" alternative. It utilizes a subjective weight assignment for applying weights to the criteria, and for applying scores to each alternative's criteria. While simple, it fails two important criticisms of DA techniques - the accounting for interdependence between criteria, and establishing distance measures among alternatives on every criterion.Outranking created by B. Roy at the University of Paris. Outranking is less concerned with a method for applying weights to attributes, and more with a holistic comparison of Alternative A to Alternative B. Roy's utilizes both concordance and discordance measures for accomplishing this. The concordance measure is a ratio computed by summing the weights for those attributes for alternative A which are superior to the attributes for Alternative B divided by the weights for Alternative A as a whole. The closer this ratio is to 1.0, the more superior Alternative A is to Alternative B. The discordance measure looks the largest difference for the attribute sets of A over B compared to the largest difference over all alternatives.Subjective Judgement Theory - a statistically oriented technique which requires the user to evaluate "holistic" hypothetical combinations of criteria. SJT converts these evaluations into weights to be applied to each pre-defined criterion using the least squares method. One criticism is the large number of evaluations which must be performed to elicit these numbers.Utility Assessment encompasses several known techniques for extracting a decision maker's preferences. These include simple ranking, category methods, direct methods, gamble methods and indifference methods. There is considerable value to establishing what is known as a utility curve for each attribute of a decision. This curve establishes a utility score (e.g. a number from 1 to 10) over the range of values a criterion can assume. For example an automobile's acceleration (from 0 to 60 mph) may take a range of 4.0 to 25.0 seconds, being assigned respectively scores of 10 and 0. A linear curve for this attribute would appear as follows.ÚÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ¿³ ³ ³³ 10 ³ ³³ ³ ³³ ³ ³³ score ³ ³³ ³ ³³ ³ ³³ 0 ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ ³³ ³³ 4.0 25.0 ³³ ³³ seconds ³³ ³³ Figure 5-3. Example of a Utility Curve ³ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÙKeeney and Raiffa [DM4] proposed some characteristics for evaluating the ability of a technique to properly reflect the decision environment. These include; - expression of all dimensions of the problem (DIM), - a meaningful link between the alternatives and the criteria (LNK), - independence of certainty and preference in an attribute (IND), - clear independence in the measures (MEA), and - minimal expression of relationships (REL).It may be somewhat helpful to look at these characteristics in the context of the techniques just covered. An arbitrary scale of high, moderate and low is applied to indicate the degree of attribute presence within a technique. In addition to the stated criteria, two have been added. The first, valuation (VAL), has been created to evaluate the effectiveness within a technique for attaching value to a specific measure. For example how well is a specific \$ measure attached to a criterion, or how can a velocity measure be utilized within the context of the technique. A second addition, cognitive assistance (COG), has been added to allow for the inherent ability of a technique to contribute to cognitive weaknesses in decision making.

```TECHNIQUE           DIM    LNK    IND     MEA    REL    VAL    COG

AHP                 mod    hgh    hgh     mod    mod    mod    hgh

Bayesian            mod    mod    hgh     low    mod    hgh    hgh

CBA                 mod    mod    low     low    low    mod    low

CEA                 mod    mod    mod     hgh    mod    hgh    low

Decision Trees      low    mod    hgh     low    low    mod    low

Matrix              mod    mod    low     mod    low    hgh    low

Outranking          mod    mod    low     mod    mod    low    mod

SJT                 mod    mod    low     mod    mod    low    mod

Utility             low    mod    hgh     mod    mod    mod    mod```