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Methods and sources

Decidient's tools rest on decision analysis. This field has more than half a century of research behind it. This page names the methods, the people who developed them, and where each one shows up in a Decidient result.

What kind of system this is

Sprague's framework describes a specific decision support system as one built to help a particular decision-maker with a particular kind of decision (Sprague, 1980). Each Decidient tool is built that way, for one decision. Power sorts decision support systems by the main component that drives them, such as data, models, knowledge or documents, and recognizes systems that combine several types (Power, 2002). A Decidient tool combines three of these: knowledge-driven (applying the rules that govern a decision), model-driven (scoring the options) and document-driven (writing a report). Research on recommender systems describes one family as knowledge-based. These systems reason from explicit knowledge about the options and about what the person needs (Burke, 2002). A Decidient tool works in the same way.

How a result is built

  • Start from the real decision. Each tool begins with the real structure of the decision: the options, the rules that apply and the deadlines. Starting from the decision follows the first phase of Herbert Simon's model, which he called intelligence (Simon, 1960). Starting there also follows the problem-first step of the PrOACT method (Hammond, Keeney and Raiffa, 1999).
  • Rules before weights. Eligibility rules remove options first. Each rule rests on a sentence quoted from a source page. The options that remain are scored against your answers. ELECTRE is a family of outranking methods that compare options using multiple criteria. Some ELECTRE methods use veto thresholds, so a sufficiently serious weakness on one criterion can prevent one option from outranking another (Roy, 1991).
  • Weighted scores. Each option gets a score on each criterion, and the scores are combined using weights. Scoring this way is a standard form of multi-attribute value theory (Keeney and Raiffa, 1976). The approach works when the criteria are independent of one another. Today, every criterion you answer counts equally, an answer of "no strong preference" counts for less, and when two options finish close, one this-or-that question asks which of their differences matters more to you.
  • Sensitivity. A result shows which of your answers the leading option is most sensitive to. Decision analysts call this sensitivity analysis (Phillips, 1984).
  • Facts come with sources. Each fact in a result links back to the page it came from. You can check the fact yourself.

Quality standard

Decision aids have a published quality checklist, the International Patient Decision Aid Standards. The 2026 update was set through a consensus process with 202 participants from 26 countries. The standards ask for balanced presentation, a funding statement, a production date and an update policy (Volk et al., 2026).

When people use a decision aid, more of them reach a choice that fits what matters to them. In 21 trials with 9,377 people, about 19 more in 100 did, compared with usual care. Knowledge scores also rose by about 12 points out of 100 (Stacey et al., 2024). Those trials were in health care. Decidient applies the same idea to other decisions: lay out the real options, then score them against your own answers.

References

  • Burke, R. (2002). Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331-370.
  • Dunn, E. W., Wilson, T. D. and Gilbert, D. T. (2003). Location, location, location: the misprediction of satisfaction in housing lotteries. Personality and Social Psychology Bulletin 29(11), 1421-1432.
  • Hammond, J. S., Keeney, R. L. and Raiffa, H. (1999). Smart Choices: A Practical Guide to Making Better Decisions. Harvard Business School Press.
  • Hsee, C. K. (1996). The evaluability hypothesis: an explanation for preference reversals between joint and separate evaluations of alternatives. Organizational Behavior and Human Decision Processes 67(3), 247-257.
  • Keeney, R. L. and Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley.
  • Miller, T. (2019). Explanation in artificial intelligence: insights from the social sciences. Artificial Intelligence 267, 1-38.
  • Phillips, L. D. (1984). A theory of requisite decision models. Acta Psychologica 56(1-3), 29-48.
  • Power, D. J. (2002). Decision Support Systems: Concepts and Resources for Managers. Quorum Books.
  • Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision 31(1), 49-73.
  • Simon, H. A. (1960). The New Science of Management Decision. Harper and Brothers.
  • Sprague, R. H. (1980). A framework for the development of decision support systems. MIS Quarterly 4(4), 1-26.
  • Stacey, D. et al. (2024). Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews, CD001431.pub6.
  • Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science 185(4157), 1124-1131.
  • Volk, R. J. et al. (2026). Updated International Patient Decision Aid Standards (IPDAS version 5.0). BMJ 393, e088116.

This page was written by AI from the sources cited here and reviewed by a person. Last updated 1 October 2026.

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Compare the optionsSee every option side by side, on the same scale.Test the assumptionsSee what happens when your priorities shift.Stress-test the resultCheck whether your top option really holds up.

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