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ABSTRACT Transparency in computing is an important precondition to ensure the trust of users. One concrete way of delivering transparency is to provide explanations of computing results. To this end, we introduce a method for explaining the results of various linear and hierarchical multi‐criteria decision‐making (MCDM) techniques such as the weighted sum model (WSM) and the analytic hierarchy process (AHP). The two key ideas are (A) to maintain a fine‐grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high‐level comparison of two alternatives in an MCDM problem, presumably an optimal and a non‐optimal one, illuminating why one alternative was preferred over the other. We show the usefulness of our techniques by generating explanations for two well‐known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.
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MADMAX: a DSL for explanatory decision making
MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An important feature of this DSL is the ability to generate explanations of why a computed optimal solution is better than its alternatives. The functional approach and Haskell's type system support a high-level formulation of decision-making problems, which facilitates a number of innovations, including the gradual evolution and adaptation of problem representations, a more user-friendly form of sensitivity analysis based on problem domain data, and fine-grained control over explanations.
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- PAR ID:
- 10339412
- Date Published:
- Journal Name:
- ACM SIGPLAN Conference on Generative Programming: Concepts & Experiences
- Page Range / eLocation ID:
- 144 to 155
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3486609.3487206
