The decision space is the set of all possible outcomes for the problem and the limits on them. This world of possibilities is defined by the alternatives (i.e. the variables) that are the potential solutions for the problem and the criteria or constraints that measure or limit the solution. In all decision problems these two pieces of information are central to the problem definition. The preference model represents the goal of the problem or value of each potential outcome. In other words, preference is a model of what the decision maker(s) care about. This is often quantified by an objective, cost or utility function. Ullmans Advanced Decision Matrix (ADM) method. Similar to Pugh's method but incorporates "belief". where belief is based on knowledge and confidence. The belief model is a representation of the likelihood of success for a particular solution to the problem. The belief model expresses what the decision maker(s) knows or accepts to be the basis for making the decision. This can be measured by the knowledge about the alternatives and the confidence in them satisfying the criteria. a.k.a decision-matrix method 1 1 1 The comparison basis is a measure of the type of comparison made in the evaluation of an alternative. An alternative is either compared absolutely to a criterion or relatively to another alternative. Relative comparisons are made along dimensions set by criteria. Each design issue may be independent or dependent on other issues. The level of dependence affects the number of times an issue is reconsidered. There are three types of dependency considered here: In most engineering decision making problems all the alternatives are not evaluated against all the criteria by everyone on the design team. This is especially true if the team is multi-disciplinary or if some team members have little knowledge about some of the criteria. This measure focuses on the completeness of the team evaluation of the information. In the example, if everyone on the team gives an evaluation of each alternative relative to each criteria then the belief model would be complete. If everyone on the team does not give an evaluation of each alternative relative to each criteria then the belief model would be complete. The information used in the problem may be deterministic or distributed. While most analysis is performed on point-valued variables it has long been recognized that all design parameters are actually distributions. Often the exact distribution of information is unknown. 1 Satisfaction of an alternative. The satisfication is the weighted sum of an decision makets belief multiplied by the importnace weighting for each criteria. NOTE: the only properties that should be populated are "alternative to consider" and "Evaluator". A SWRL rule needs to be created to identify all the criteria associated with that alternative and the belief of the Evaluator for each criteria. Iinformation describing the alternatives and criteria, which may be complete or incomplete. The level of support offered to the decision makers can be classified as either: representation, outcome determination, or decision analysis. The focus of each design issue is either on the product being designed or on one of the processes that support the design, manufacture or distribution of the product. The alternatives and criteria may be refined or abstracted.Generally, refined information is quantitative, whereas abstract information is qualitative.In some decision problems, the Abstraction level of the information is mixed. It is well recognized that much early design information is qualitative and that the final manufacturing specifications are quantitative. A key feature of all decision making methods is a mechanism for evaluating how well alternatives meets the criteria. This evaluation is quantified through an objective, utility or cost function, a measure of preference of one alternative relative to others. In many problems the objective function is based on the values for parameters that characterize the alternatives and the desire to maximize, minimize or find an internal optimum point for some parametric characteristic as in formal optimization. In other problems, the objective function is a weighted preference of criteria that call for judgement or satisficing rather than analysis. 1 1 1 needs properties: knowledge, confidence, alternative considering, criteria considering, who's belief map? When a team of designers is making design problem decisions, there may be many different viewpoints on the importance of criteria and the evaluation of alternatives, thus, preference models may vary within the team. If the problem is being solved by a single individual, a team with a unified view, or if a methodology can only represent a single viewpoint then the decision making problem is considered consistent. If there are conflicting viewpoints, then the preference model is inconsistent. Knowledge is a measure of how much the evaluator knows about the alternative/criteria space. Confidence is a measure of how well the evaluator believes the alternative actually meets the criteria. Decision problems or methods are classed by the number of dimensions of information involved: none; 1 (confidence or knowledge); or 2 (knowledge and confidence). requriements or specifications N/A relatively absolutely Confidence level, similar to knowledge level. Must be between 0 and 1. confidence knowledge none knowledge and confidence Incomplete Complete 2 4 5 3 1 0 optimum judgement none product process product or process calculated based on knowledge and confidence Type1: Independent Type3: Interdependent Type2: Dependent consistent N/A inconsistent Name of decision methodology distributed deterministic or distributed deterministic -1 0 1 Qualitative Quantitative Mixed Level 1: Representation Level 3: Decision Analysis Level 2: Outcome Determination Ullman states: "Knowledge is a measure of the information held by a decision-maker about a feature of an alternative defined by a criterion. One way to meauser knowledge is to consider the words that are generally used to describe knowledge and compare them with the probability of having the correct information. This must be between 0 and 1. Ullman recommends that "an individual with no knowledge would have a 50/50 chance of guessing correct information so that individual would have a knowledge level of 0.5. NOTE: there are many different ways to estimate this knowledge level. complete incomplete N/A