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Decision analysis is a systematic and analytical approach that supports informed decision-making in problems involving multiple alternatives and criteria. It provides a transparent means of combining data about the impacts of the alternatives on different criteria and stakeholders’ preferences regarding the criteria. The aim is to support well-informed decisions about the most appropriate practice or policy-oriented actions.
Elements of decision analysis problems
- Decision maker(s) (people making the decision).
- Stakeholders (people who are affected by the decision)
- Experts (people knowledgeable about the issues related to the problem and
facilitators providing support for the process). - Objectives (things that the people involved want to achieve).
- Alternatives (available options when trying to reach the objectives).
- Criteria (dimensions for measuring how well the objectives are met).
- Impacts (consequences of alternatives in terms of different criteria).
- Values (underlying preferences and priorities that shape the decision).
- Uncertainties (external factors affecting the outcomes).
To analyse these problems, decision analysis provides:
- Decision rules (means of structuring these elements to support transparent, reasoned, and defensible decision-making).
- Decision process (a structured pathway to reach the decision).
- Support for making trade-offs between the criteria (various methods to elicit the trade-offs).
- Support for participation (means of involving stakeholders and decision-makers).
Decision analysis is most useful in complex decision problems, such as large environmental problems, where multiple and diverse dimensions are otherwise very difficult to deal with.
Note: Decision analysis, used here in its broadest sense, as an umbrella term for a range of systematic and analytical approaches that support rational decision-making.
Decision analysis approaches are often divided into two broad categories:
- Approaches based on expected utility theory, which support decision-making under uncertainty through the explicit use of probabilities. In some fields, the term decision analysis is used specifically to refer to these approaches rather than to decision analysis as a whole, which can cause confusion.
- Multi-criteria decision analysis (MCDA)-based approaches focusing on trade-offs between multiple objectives. In these approaches, uncertainties are typically not explicitly integrated into the model as probabilities, but they are analysed afterwards, for example, with sensitivity analyses.
Outcome of the phase: An understanding of the basic principles and possibilities of applying decision analysis to support environmental decision-making.
Follow up Questions for the AI
Ask the following questions to gather more information from the AI:
- Can you elaborate the typical elements of decision analysis (understood in a broad sense) problem?
- What are the key differences with decision analysis based on probability-based uncertainty modelling and multi-criteria decision analysis?
- Can you give examples of applying decision analysis process in environmental decision-making and references to these examples?
- Can you elaborate the terminology around decision analysis (DA, MCDA, Decision trees, MAVT, etc.)?
- Can you elaborate the differences and similarities of decision analysis and other similar approaches (such as CBA, influence diagrams, scenario analysis, etc.)?
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Benefits and Risks of Using AI
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Benefits (++)
- Elaborating the understanding of the basic elements of decision analysis.
- Providing examples of the application process of DA in environmental decision making.
- Understanding the terminology related to decision analysis.
- Understanding the relationship of decision analytical approaches to other similar approaches
Benefit Scoring: ++ Medium
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Risks (!)
- Superficial understanding of the approach and incorrect explanations (e.g. mixing “decision analysis” as an umbrella term and as an explicit term specifically used for approaches based on expected utilities).
- Reduced critical evaluation of the possibilities of different decision analytical approaches - AI can be overwhelmingly positive about the possibilities.
- Lack of analytical competence in understanding principles of decision analysis as a whole.
Risk Scoring: ! Low
Questions to Better Understand the Benefits and Risks of AI
Questions to make use of the benefits
- See follow up questions above
- How can AI support understanding of the basics of decision analysis and what are the main risks of using AI?
Questions to avoid the risks
- What are the risks of using AI to support understanding the basics of decision analysis? How can these risks be avoided?
- How do you interpret the term decision analysis in your answers?
Questions NOT to ask
- How can AI enhance decision analysis?
AI typically answers overly positive to questions that are formed in a positive way of asking opportunities and might thus only lists the positive sides of using AI. If these kinds of questions are asked, they should always be followed by questions of type “what are the risks of using AI…”