Arguing for Decisions: A Qualitative Model of Decision Making
This work addresses the need for more intuitive decision-making models in AI, though it is incremental as it builds on existing qualitative approaches without claiming broad superiority.
The authors tackled the problem of modeling simple human decision-making by proposing a qualitative model that uses rules, high probabilities, and lexicographical preferences to enable transparent reasoning, offering an alternative to complex existing methods like Decision Theory.
We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decisions Theory either ignore uncertainty and tradeoffs, or provide languages and algorithms that are too complex for this task. The proposed model provides a language based on rules, a semantics based on high probabilities and lexicographical preferences, and a transparent decision procedure where reasons for and against decisions interact. The model is no substitude for Decision Theory, yet for decisions that people find easy to explain it may provide an appealing alternative.