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Multiple-criteria decision analysis

Multiple-criteria decision analysis is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Multiple-criteria decision analysis rather than just read about it. In short: Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine). It is also known as multi-attribute decision making (MADM), multiple attribute utility theory, multiple attribute value theory, multip…

Multiple-criteria decision analysis — main illustration
Multiple-criteria decision analysis — illustration

Key takeaways

  • Multiple-criteria decision analysis belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Multiple-criteria decision analysis to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Multiple-criteria decision analysis from memory before moving on to harder problems.

Reference excerpt

Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine). It is also known as multi-attribute decision making (MADM), multiple attribute utility theory, multiple attribute value theory, multiple attribute preference theory, and multi-objective decision analysis. Conflicting criteria are typical in evaluating options: cost or price is usually one of the main criteria, and some measure of quality is typically another criterion, easily in conflict with the cost. In purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria we consider – it is unusual that the cheapest car is the most comfortable and the safest one. In portfolio management, managers are interested in getting high returns while simultaneously reducing risks; however, the stocks that have the potential of bringing high returns typically carry high risk of losing money. In a service industry, customer satisfaction and the cost of providing service are fundamental conflicting criteria. In their daily lives, people usually weigh multiple criteria implicitly and may be comfortable with the consequences of such decisions that are made based on only intuition. On the other hand, when stakes are high, it is important to properly structure the problem and explicitly evaluate multiple criteria. In making the decision of whether to build a nuclear power plant or not, and where to build it, there are not only very complex issues involving multiple criteria, but there are also multiple parties who are deeply affected by the consequences. Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions. There have been important advances in this field since the start of the modern multiple-criteria decision-making discipline in the early 1960s. A variety of approaches and methods, many implemented by specialized decision-making software, have been developed for their application in an array of disciplines, ranging from politics and business to the environment and energy.

Foundations, concepts, definitions MCDM or MCDA are acronyms for multiple-criteria decision-making and multiple-criteria decision analysis. Stanley Zionts helped popularizing the acronym with his 1979 article "MCDM – If not a Roman Numeral, then What?", intended for an entrepreneurial audience. MCDM is concerned with structuring and solving decision and planning problems involving multiple criteria. The purpose is to support decision-makers facing such problems. Typically, there does not exist a unique optimal solution for such problems and it is necessary to use decision-makers' preferences to differentiate between solutions. "Solving" can be interpreted in different ways. It could correspond to choosing the "best" alternative from a set of available alternatives (where "best" can be interpreted as "the most preferred alternative" of a decision-maker). Another interpretation of "solving" could be choosing a small set of good alternatives, or grouping alternatives into different preference sets. An extreme interpretation could be to find all "efficient" or "nondominated" alternatives (which we will define shortly). The difficulty of the problem originates from the presence of more than one criterion. There is no longer a unique optimal solution to an MCDM problem that can be obtained without incorporating preference information. The concept of an optimal solution is often replaced by the set of nondominated solutions. A solution is called nondominated if it is not possible to improve it in any criterion without sacrificing it in another. Therefore, it makes sense for the decision-maker to choose a solution from the nondominated set. Otherwise, they could do better in terms of some or all of the criteria, and not do worse in any of them. Generally, however, the set of nondominated solutions is too large to be presented to the decision-maker for the final choice. Hence we need tools that help the decision-maker focus on the preferred solutions (or alternatives). Normally one has to "tradeoff" certain criteria for others. MCDM has been an active area of research since the 1970s. There are several MCDM-related organizations including the International Society on Multi-criteria Decision Making, Euro Working Group on MCDA, and INFORMS Section on MCDM. For a history see: Köksalan, Wallenius and Zionts (2011). MCDM draws upon knowledge in many fields including:

Mathematics Decision analysis Economics Computer technology Software engineering Information systems

A typology There are different classifications of MCDM problems and methods. A major distinction between MCDM problems is based on whether the solutions are explicitly or implicitly defined.

… excerpt ends here. Continue reading the full article.

Illustrations

Multiple-criteria decision analysis: In this example a company should prefer product B's risk and payoffs under realistic risk preference coefficients.
In this example a company should prefer product B's risk and payoffs under realistic risk preference coefficients.
Multiple-criteria decision analysis: Figure 1. Demonstration of the decision space
Figure 1. Demonstration of the decision space
Multiple-criteria decision analysis: Figure 2. Demonstration of the solutions in the criterion space
Figure 2. Demonstration of the solutions in the criterion space
Multiple-criteria decision analysis: Figure 3. Projecting points onto the nondominated set with an Achievement Scalarizing Function
Figure 3. Projecting points onto the nondominated set with an Achievement Scalarizing Function

Worked examples

Example 1 — a first encounter with Multiple-criteria decision analysis

Start with the simplest possible case. Write down what Multiple-criteria decision analysis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Multiple-criteria decision analysis before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Multiple-criteria decision analysis ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Multiple-criteria decision analysis

In research
Multiple-criteria decision analysis appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Multiple-criteria decision analysis in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Multiple-criteria decision analysis is common in secondary-school and first-year university syllabi. It links to neighbouring topics Decision analysis, Management systems, Mathematical optimization, so understanding it makes those chapters shorter.
In everyday life
Look for Multiple-criteria decision analysis outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Multiple-criteria decision analysis in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Multiple-criteria decision analysis means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Multiple-criteria decision analysis out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multiple-criteria decision analysis in simple terms?

Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine). It is also kno…

Why does Multiple-criteria decision analysis matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Multiple-criteria decision analysis?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Multiple-criteria decision analysis.

Tags

  • Decision analysis
  • Management systems
  • Mathematical optimization
  • Multiple-criteria decision analysis
  • Utility

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