Abstract
Nearly 30 years have passed since Naturalistic Decision-Making (NDM) was introduced in 1989, and NDM has developed rapidly in recent years. The purpose of this paper is to discuss all background information relevant to the field of Decision-Making (DM) research. It also introduces the latest developments in this field and proposes its military applications. The first part is a review of NDM, including foundations of Naturalistic Decision Making, traditional analytical DM models, Recognition-Primed Decision (RPD) making methods and Schemata and Mental model. The second part is a review of the nature of expertise-experience, of Teamwork and Decision Errors in NDM, in which Teamwork consists of defining and Shared Mental Models (SMM), and Decision Errors consist of defining and teamwork decision-making error. The third part proposes the military application of NDM, including guiding military systems design and evaluation, teamwork decision-making error, optimizing training, supporting team collaboration.
Highlights
An important achievement of the Naturalistic DecisionMaking community is the specification of the way common people reach decisions under real-world conditions [1], [2]
After thirty years since the emergence of Naturalistic Decision-Making (NDM) in 1989, it has been applied in several diverse areas, including health care, military services, aviation, command and control, complex engineering, and high-reliability industrial settings [3]
The fifth and sixth factors indicate the situational characteristics of naturalistic decision-making, which ‘‘high stakes’’ emphasizes that making decisions is high-risk and wrong decisions can lead to fatal consequences
Summary
An important achievement of the Naturalistic DecisionMaking community is the specification of the way common people reach decisions under real-world conditions (as opposed to the lab) [1], [2]. The Rational Choice Model (RACM) is the most common example, which emphasizes that high stress (especially time pressure) can affect the human decision-making process B. THE RECOGNITION-PRIMED DECISION APPROACH The RPD methodology (Fig. 1) aims to model the reasoning process that exploits human experience in order to perform pattern recognition and reach decisions without comparing options in realistic environments [23]. Expert decision makers (EXDM), have a larger knowledge base, but they organize its structure in a different way from the novice ones [32], [36] They are performing pattern recognition, they can quickly gain a clear image of the case and they can select responses to situations of greater diversity and complexity, without consuming energy in forward-looking and in deliberative thinking [37]. Human experts reach their decisions by self-regulating and monitoring their process in order to make judgments about the consistency, reliability, and completeness of the information to increase the quality of the decision
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