Robust interactive decision‐analysis (RID)
Behavioral results and implications
Bibliographic Data
| ID | 12165478 |
|---|---|
| Authors | Herbert Moskowitz (Purdue University West Lafayette, corresponding author), Richard T Wong (0000-0001-5543-9975, Purdue University West Lafayette), Po‐Young Chu |
| Year | 1989 |
| Volume | 2 |
| Issue | 2 |
| Pages | 95-111 |
| Publication date | 1989-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Behavioral Decision Making (JOURNAL) |
| Journal identifiers | ISSN: 0894-3257 • E-ISSN: 1099-0771 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/bdm.3960020204 |
| OpenAlex | W2138171569 |
| Language | EN |
| References cited | 18 |
We have proposed a novel interactive procedure for performing decision analysis, called Robust Interactive Decision Analysis (RID), which permits a decision maker (DM) to voluntarily and interactively express strong ( viz , sure) binary preferences for actions, partial decision functions, and full decision functions, and only imprecise probability and utility function assessments. These serve as INPUTS TO operators to prune the state probability space and decision space until an optimal choice strategy is obtained. The viability of the RID approach depends on a DM's ability to provide such information consistently and meaningfully. On a limited scale we experimentally investigate the behavioral implications of the RID method in order to ascertain its potential operational feasibility and viability. More specifically, we examine whether a DM can (1) express strong preferences between pairs of vectors of unconditional and conditional payoffs or utilities consistently; (2) provide imprecise (ordinal and interval) state probabilities that are individually as well as mutually consistent with the state probabilities imputed from the expressed strong preferences. The results show that a DM can provide strong individually and mutually consistent preference and ordinal probability information. Moreover, most individuals also appear to be able to provide interval probabilities that are individually and mutually consistent with their strong preference inputs. However, the several violations observed, our small sample size, and the limited scope of our investigation suggest that further experimentation is needed to determine whether and/or how such inputs should be elicited. Overall, the results indicate that the RID method is behaviorally viable
Binary number · Conditional probability · Decision analysis · Decision maker · Decision tree · Expected utility hypothesis · Function (biology · Interval (graph theory · Machine learning · Operations research · Optimal decision · Preference · Scope (computer science · State space · Statistics · Subjective expected utility · Complex Systems and Decision Making · Computer Science · Decision-Making and Behavioral Economics · Economic and Environmental Valuation · Mathematics · Artificial Intelligence
| Highly cited | No |
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