Decision quality (DQ) is the quality of a decision at the moment the decision is made, regardless of its outcome. Decision quality concepts permit the assurance of both effectiveness and efficiency in analyzing decision problems. In that sense, decision quality can be seen as an extension to decision analysis. Decision quality also describes the process that leads to a high-quality decision. Properly implemented, the DQ process enables capturing maximum value in uncertain and complex scenarios.
Fundamental to all decision quality concepts is the distinction between the decision and its outcome. They are different because of the uncertainties when making a choice—a high quality decision can still result in a poor outcome, and vice versa. In the face of uncertainty, the decision maker only has control over the decision, but no control over the outcome of external circumstances. Consequently, the outcome of a decision does not allow an assessment about its quality. A decision has quality at the time it is made, which is not changed by hindsight. Concepts of decision quality focus on measuring and improving the quality of the decision at the time it is being made.
The confidence a decision maker has in its choice, and related to it the commitment a decision maker has to act upon that choice, depends on the quality of the decision at the time of making the decision. A high-quality decision is characterized by the following elements:
A useful frame
Feasible and diverse alternatives
Meaningful and reliable information
Clear values, preferences, and trade-offs
Logically sound reasoning
Commitment to action
Quality in decision making requires quality in each of the elements listed above, and the overall quality of the decision is limited by the weakest element. Decision quality is achieved when for each element the cost to obtain additional information or insight to improve its quality exceeds the added value.
A variety of specific tools and processes exist to improve the quality of each element.
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The course deals with the methods and instruments supporting decision processes in the geographical space. The focus is on multi-criteria decision analysis, with the special requirements carried by sp
Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying machine learning at scale. The basic idea is that decisions are based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains.
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Elsevier2021
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