Decision Analysis

The Decision Analysis area invites papers that enrich our understanding of how to make better decisions. Submitted manuscripts may be primarily either methodological or application driven.

A methodological contribution should extend, unify, or improve decision analysis methods. Decision analysis methods have traditionally addressed modeling uncertainty (Bayesian inference, subjective probability elicitation, combining expert opinions, scoring rules for eliciting and evaluating probability assessments, sensitivity analysis, information value); structuring preferences (utility and risk attitude, utility assessment, stochastic dominance, structuring objectives and attributes, multiattribute utility and value); and representing and solving decision problems (decision trees, influence diagrams, Bayesian networks, alternative generation, value trees, fault trees, dynamic programming). Decision analysis methods also draw on related fields such as the psychology of judgment and choice (heuristics and biases, prospect theory) or methods for dealing with multiple stakeholders (cooperative and noncooperative game theory, negotiation).

Although novel methods are encouraged, it is not enough to merely suggest an alternative approach. Authors must situate a novel approach within the decision-theoretic literature and demonstrate that the approach provides significant improvements or insights. It is important to illustrate novel approaches in a realistic setting.

An application-driven contribution should solve a specific operational problem or should otherwise demonstrate potential impact on practice, for instance with a realistically detailed example of potential use or documentation of an implementation. It should involve real decision makers making actual decisions. The paper may extend standard decision analysis methods to a novel application area or may illustrate how standard decision analysis methods need to be revised in creative or useful ways. In all cases, the contribution must clearly be significant, relevant, and conceptually sound and must meet the rigorous standards of the journal.

Associate Editors: Ali Abbas, Manel Baucells, David Bell, Jim Smith, Peng Sun, and Ilia Tsetlin