Multi-Criteria Decision-Making (MCDM)

 

Definition of MCDM

"Multi-Criteria Decision Making (MCDM) is the study of methods and procedures by which concerns about multiple conflicting criteria can be formally incorporated into the management planning process", as defined by the International Society on Multiple Criteria Decision Making

MCDM is also referred as:

  • Multi-Criteria Decision Analysis (MCDA)
  • Multi-Dimensions Decision-Making (MDDM)
  • Multi-Attributes Decision Making (MADM)

The Decision Theory

Decision analysis looks at the paradigm in which an individual decision maker (or decision group) contemplates a choice of action in an uncertain environment. The decision theory helps identify the alternative with the highest expected value (probability of obtaining a possible value). The theory of decision analysis is designed to help the individual make a choice among a set of pre-specified alternatives. The decision making process relies on information about the alternatives. The quality of information in any decision situation can run the whole gamut from scientifically-derived hard data to subjective interpretations, from certainty about decision outcomes (deterministic information) to uncertain outcomes represented by probabilities and fuzzy numbers. This diversity in type and quality of information about a decision problem calls for methods and techniques that can assist in information processing. Ultimately, these methods and techniques (MCDA, MCDM) may lead to better decisions. 

Decision Methods

The Decision Paradigm


Our values, beliefs and perceptions are the force behind almost any decision-making activity. They are responsible for the perceived discrepancy between the present and a desirable state. Values are articulated in a goal, which is often the first step in a formal (supported by decision-making techniques) decision process. This goal may be put forth by an individual (decision- maker) or by a group of people (for example, a family). The actual decision boils down to selecting "a good choice" from a number of available choices. Each choice represents a decision alternative. In the multi-criteria decision-making (MCDM) context, the selection is facilitated by evaluating each choice on the set of criteria. The criteria must be measurable - even if the measurement is performed only at the nominal scale (yes/no; present/absent) and their outcomes must be measured for every decision alternative. Criterion outcomes provide the basis for comparison of choices and consequently facilitate the selection of one, satisfactory choice.

Mathematics Applied To Decision

Criterion outcomes of decision alternatives can be collected in a table (called decision matrix or decision table) comprised of a set of columns and rows. The table rows represent decision alternatives, with table columns representing criteria. A value found at the intersection of row and column in the table represents a criterion outcome - a measured or predicted performance of a decision alternative on a criterion. The decision matrix is a central structure of the MCDA/MCDM since it contains the data for comparison of decision alternatives.

Different Decision Methods

At a practical level, mathematical programming under multiple objectives has emerged as a powerful tool to assist in the process of searching for decisions which best satisfy a multitude of conflicting objectives, and there are a number of distinct methodologies for multi-criteria decision-making problems that exist. The decision theory is descriptive when it shows how people take decisions, and prescriptive when it tells people how they should take decisions. These methodologies can be categorized in a variety of ways, such as form of model (e.g. linear, non-linear, stochastic), characteristics of the decision space (e.g. finite or infinite), or solution process (e.g. prior specification of preferences or interactive). For an example of a multi-objective methodology for the management of water resources integrating climate change and climate variability data, look at the article on Climate and Water in the West: Science, Information, and Decision-Making.

Taking The Future Into Account

The true goal in integrated decision-making support is to provide the decision-maker with the ability to look into the future, and to make the best possible decision based on past and present information and future predictions. In the case of sustainable development, this means to be able to predict in advance the risk and vulnerability of populations and infrastructure to hazards, both natural and man-induced. This requires that data be transformed into knowledge, and that the consequences of information use, as well as decision-making and participatory processes, be analyzed carefully (see Science, vulnerability and the search for equity: El Niсo events, forecasts and decision making in Peru and Brazil).

Decision Support Systems

Decision support systems (DSS) are computerized information systems that support decision making activities.

The Computer-Aided Decision

A decision involves making a selection from a set of alternative choices. Broadly speaking, a decision-support systems (DSS) is simply a computer system that helps you make a decision by leveraging the multi-criteria decision-making model. DSS provide a means for decision-makers to make decisions on the basis of more complete information and analysis. Among the main advantages of the use of DSS are the following:

  1. Increased number of alternatives examined 
  2. Better understanding of the business 
  3. Fast response to unexpected situations 
  4. Improved communication 
  5. Cost savings 
  6. Better decisions
  7. More effective teamwork
  8. Time savings
  9. Better use of data resources 

When Theory Meets Practice

There is a need for approaches that combine available quantitative data with the more subjective knowledge of experts. Decision-theory techniques applied by high-end knowledge professionals have been successfully used for contrasting expert judgments and making educated choices. The multi-criteria decision-making model, by coupling theory and knowledge, provides an analytical approach to expert consultation and is adapted for a variety of technology and business fields aiming at suitability assessments.

Web Resources About MCDM/MCDA

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