Decision making is generally defined as a conscious and deliberate selection of one alternative (option, action) from a set of possible ones in order to satisfy the goals of one or more decision makers. Making a decision involves an irrevocable allocation of resources (time, money, effort, …), has consequences and is inherently subjective (subject to individual and/or societal values).
Decision AnalysisMethod DEX belongs to the class of multiple-criteria decision analysis methods.
Decision Analysis is a discipline popularly known as “Applied Decision Theory”. It provides a framework for analyzing decision problems by:
structuring and breaking them down into more manageable parts,
explicitly considering the possible alternatives, available information, involved uncertainties, and relevant preferences of the decision maker(s),
combining these in order to arrive at optimal or at least ‘sufficiently good’ decisions.
Decision Analysis is particularly interested in complex decision problems, that is, problems which are for some reason considered difficult by the decision maker and require careful elaboration and analysis. Complex decision problems are usually characterized by:
Novelty: the decision maker is confronted with the problem for the first time and has insufficient knowledge or skills to address the problem.
Uncertainty: existence of possible events that cannot be controlled by the decision maker, but can affect the decision or its consequences (for example: competition response, weather).
Imprecision: unclear understanding of the problem and its goals, unknown or incompletely defined alternatives, missing data.
Multiple and possibly conflicting goals.
Group decision-making: involvement of different decision-makers or groups that have different and possibly conflicting goals.
Important consequences of the decision (such as possible big financial losses or environmental impacts, lifetime choices).
Limited resources to conduct the decision process (most often: available time and expertise).
Decision Analysis is aimed at supporting people in making decisions rather than making decisions themselves. For this purpose, decision-support tools, including DEXiWin, provide methods and tools for developing decision models and using them for the evaluation and analysis of alternatives.
Decision ProblemIn Decision Analysis, a decision problem is understood primarily as a problem of choice or ranking, which is defined as follows:
Given a set of alternatives, which typically represent some objects or actions, either
choose an alternative that best satisfies the goals (objectives) of the decision maker, or
rank the alternatives accordingly to fulfill these goals.
A somewhat special cases of ranking are sorting and classification: assigning each alternative to one category among a family of predefined categories. These categories can be preferentially ordered (sorting) or unordered (classification). While method DEX can be used for general choosing and ranking, it is most suitable for sorting and classification tasks.
Making a choice usually occurs as part of a decision process.
Decision ProcessThe ultimate goal of a decision process is to solve a decision problem, that is, to make a decision. In Decision Analysis, the decision process is understood as a process of careful and in-depth analysis of the decision problem. It involves a systematic acquisition and organization of knowledge about the decision problem, which is done by participants of the decision process and typically includes:
assessing the problem,
collecting and verifying information,
identifying alternatives,
anticipating consequences of decisions,
making the choice using sound and logical judgment based on available information,
justification and informing others of the decision and its rationale,
evaluating decisions and their consequences.
In general, such a process should:
provide all the information needed for a ‘sufficiently good’ decision,
reduce the chance of overlooking important information and making other errors,
improve the effectiveness and efficiency of the decision-making, and
improve the quality of the decision itself.
Usually, the decision process involves at least the following steps:
Modeling: developing a decision model
Evaluation and analysis of alternatives
Choice: making the decision
Implementation of the decision
The DEXi Suite software is primarily used in the steps 2 and 3.
Decision Problem IdentificationThe identification of decision problem occurs at the beginning of a decision process. At this stage, the objective is to understand the decision problem and its components. Some typical questions asked in this stage are:
What is the decision problem about? Is it difficult and important? Why?
Is this a one-time or recurring decision?
Who is the stakeholder (decision owner)? Who is responsible, and who will be affected by the decision? Who are other possible participants in the decision process?
What in general are the alternatives in this case? Can we define some specific ones?
Which goals (objectives) should be achieved by the decision? Which are the criteria to be met by the decision?
What are the uncertainties involved?
What are the goals of the decision process? Should we select a single alternative, or evaluate or rank more of them?
What are the expected consequences of this decision process?
Do we need to justify the decision? To whom and how?
To be suitable for multi-criteria modeling, a decision problem must have some specific properties. Primarily, it should deal with alternatives, which need to be evaluated, analyzed and compared with each other. It is important that the decision problem can be decomposed into smaller, less complex sub-problems, and that the alternatives can be described by their basic features that correspond to the problem decomposition. Thus, we should also ask questions such as:
Can we think of decomposing the problem into sub-problems? Can we define the relationship between factors that affect the decision?
Can we think about representing alternatives with their basic features? Which are these features?
In general, a typical decision process involves up to four types of participants, either individuals or groups:
Stakeholders (also called decision problem owners): individuals or organizations that have a legitimate interest in the decision-making problem. Usually, these are the ones that need to make the final decision, and are also responsible for that decision. They may, but need not, be familiar with the requirements and consequences of the decision problem at hand, and with the possible ways to approach the problem.
Experts: People knowledgeable in the field so that they can provide information and advice relevant for the decision. They may contribute to the overall decision problem identification, to the definition of alternatives, goals and criteria, and to the decision model development.
Decision analyst(s): Methodologists with experience in Decision Analysis, that is, the underlying methodology and tools. Often, they take the role of moderators or mediators of the decision-making team.
Users: People affected by the decision.
The Decision Analysis approach is characterized by the use of decision models. In general, a decision model encodes knowledge and information that is relevant for solving the decision problem at hand. Decision models are usually developed by participants of the decision process using tools such as DEXiWin. Typical models used in Decision Analysis are:
decision trees,
influence diagrams,
Among these, DEXi Suite employs qualitative multi-criteria models.
Once developed, the decision model is used to:
perform various analyses, such as what-if or sensitivity analysis.
The obtained evaluation and analysis results provide the basis for decision maker’s assessment and ranking of alternatives, and possible choice of the best one.
Multi-Criteria ModelMulti-criteria models (also called multi-attribute models) represent a class of models used in Decision Analysis that evaluate alternatives according to several, possibly conflicting, goals or objectives. In principle, a multi-criteria model represents a decomposition of a decision problem into smaller and less complex sub-problems.
AlternativesAlternatives are basic entities studied in a decision problem. Depending on the problem, they can represent different objects, solutions, courses of action, etc., which are evaluated and analyzed using a multi-criteria model.
Evaluation of AlternativesWith multi-criteria models, alternatives are evaluated in the following way:
Each alternative is represented by a vector of basic attribute values.
The values of each alternative are aggregated in a bottom-up way according to the defined structure of the model and corresponding functions.
The overall evaluation of an alternative is finally obtained as the value of one or more root attributes of the model.
On this basis, the decision maker can compare and rank the alternatives, and possibly identify and select the best one.
Analysis of AlternativesAnalysis is one of the key concepts in Decision Analysis. In contrast with evaluation, which is merely a calculation directed from inputs (alternative’s descriptions) to outputs (evaluation results), analysis is understood as an active involvement of participants who are trying to find answers to questions such as:
Are evaluations of alternatives in accordance with expectations? Are they ‘correct’? If not, why?
How do the alternatives compare with each other? Which one is the best and why?
Can we explain and justify the evaluations? What are the most important strong and weak points of individual alternatives?
What if something changes: What if we try a new alternative? What if an alternative becomes unavailable? What if some characteristics of alternatives change?
How sensitive is the evaluation to small changes of the model (such as addition or deletion of an attribute, modification of some decision rules)?
Which properties of an alternative should change so that the overall evaluation becomes better? Which changes of an alternative could substantially worsen is evaluation?
In other words, analysis is a creative and possibly repetitive application of decision models aimed at better understanding of the decision problem, better understanding of alternatives, their characteristics and consequences, and better justification of the decision. In general, this involves techniques such as: what-if analysis, sensitivity analysis, and stability analysis.
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