The naturalistic decision making (NDM) framework emerged as a means of studying how people make decisions and perform cognitively complex functions in demanding, real-world situations. These include situations marked by limited time, uncertainty, high stakes, team and organizational constraints, unstable conditions, and varying amounts of experience.
Origins of the movement
The NDM movement originated at a conference in Dayton, Ohio in 1989, which resulted in a book by Gary Klein, Judith Orasanu, Roberta Calderwood, and Caroline Zsambok. Since then, NDM conferences have been held every 2–3 years, alternating between U.S. and European venues. A series of NDM books have been published, and in 1995 the Human Factors and Ergonomics Society established a new technical group, Cognitive Engineering and Decision Making, that has built on the NDM tradition.
Origins of the framework
Decision researchers had conducted experiments and developed models for decades prior to the emergence of NDM in 1989. The first NDM researchers went against the norm because they knew there were better ways of making decisions. The heuristics and biases paradigm (e.g., Kahneman, Slovic, & Tversky, 1982) proves this. It demonstrated how people did not adhere to the principles of optimal performance; respondents relied on heuristic as opposed to algorithmic strategies even when these strategies generated systematic deviations from optimal judgments as defined by the laws of probability, the axioms of expected utility theory, and Bayesian statistics. This proves that people defy the laws of probability when making decisions. The first NDM researchers went with this approach. Instead of beginning with formal models of decision making, they began by conducting field research to try to discover the strategies people used. Instead of looking for ways that people were suboptimal, they wanted to find out how people were able to make tough decisions under difficult conditions.
The NDM framework focuses on cognitive functions such as decision making, sensemaking, situational awareness, planning – which emerge in natural settings and take forms that are not easily replicated in the laboratory. For example, it is difficult to replicate high stakes, or provide for problem detection, or to achieve extremely high levels of expertise, or to realistically incorporate team and organizational constraints. Therefore, NDM researchers rely on cognitive field research methods such as task analysis to observe and study skilled performers. From the perspective of scientific methodology, NDM studies usually address the initial stages of observing phenomena and developing descriptive accounts. In contrast, controlled laboratory studies emphasize the testing of hypotheses. NDM and controlled experimentation are thus complementary approaches. NDM provides the observations and models, and controlled experimentation provides the testing and formalization.
Recognition-Primed Decision (RPD)
The recognition-primed decision (RPD) model describes how people use their experience in the form of patterns. These patterns highlight the relevant cues, provide expected outcomes, identify plausible goals, and suggest typical types of reactions in that type of situation. When people need to make a decision, they can quickly match the situation to the patterns they have learned and experienced in the past. Doing this, people can successfully make rapid decisions. The RPD model explains how people can make good decisions without comparing options. However, there is more to the RPD model than pattern matching. How can a person evaluate an option without comparing it with others? It has been found that fireground commanders evaluate a course of action by using mental simulation to imagine how a situation would play out within the context of the current situation. If it would work, then the commanders could initiate the action. If it almost worked, they could try to adapt it or else consider other actions that were somewhat less typical, continuing until they found an option that felt comfortable. This process exemplifies Herbert Simon’s (1957) notion of satisficing – looking for the first workable option rather than trying to find the best possible option. Because fires grow exponentially, the faster the commanders could react, the easier their job. Therefore, the RPD model is a blend of intuition and analysis. The pattern matching is the intuitive part, and the mental simulation is the conscious, deliberate, and analytical part. Intuitive strategy relying only on pattern matching would be too risky because sometimes the pattern matching generates flawed options. Also, a completely deliberative and analytical strategy would be too slow; the fires would be out of control by the time the commanders finished deliberating. In-depth interviews with fireground commanders who recently experienced challenging incidents show that the percentage of RPD strategies used in those situations generally ranged from 80% to 90% (Klein, 1989). Other researchers have replicated these findings (see Klein, 1998). The first moves that occurred to them were much better than would be expected by chance. These findings support the RPD hypothesis that the first option considered is usually satisfactory. These results were later replicated by Johnson and Raab (2003).
Some of the first funding into NDM research came from the U.S. Army and Navy in the mid 1980s. The U.S. Navy became interested in naturalistic decisions following the 1988 USS Vincennes shoot-down incident, in which a U.S. Navy Aegis cruiser destroyed an Iranian commercial airliner, mistaking it for a hostile attacker. Both the Army and the Navy wanted to help people make high-stakes decisions under extreme time pressure and under dynamic and uncertain conditions. The NDM researchers studied people in field settings, such as Navy commanders and army small unit leaders. From this perspective, making a decision means committing oneself to a course of action where plausible alternatives exist, even if the person does not identify or compare these alternatives. The NDM movement shifted our conception of human decision making from a domain independent general approach to a knowledge based approach exemplified by decision makers who had substantial experience. The decision making process was expanded to include a prior stage of perception and recognition of situations, as well as generation of appropriate responses, not just choice from among given options. This perspective took advantage of advances in cognitive psychology such as knowledge representation concepts of scripts, schemas, and mental models, to contrast expert versus novice behavior. NDM has even affected Army doctrine. The current edition of the Army Field Manual on Command and Control (FM 101-5) includes for the first time a section on intuitive decision making, largely influenced by research on the RPD model. The field of NDM has also provided guidance for training decision making and related cognitive skills. Cannon-Bowers and Salas (1998) have described the range of lessons learned from the TADMUS (Tactical Decision Making Under Stress) project initiated by the Navy following the USS Vincennes shoot-down decision. These include methods for providing stress inoculation along with approaches for individual and team decision training.
- Klein. G, Orasanu J., Calderwood R., Zsambok C. E., (1993), "Decision Making in Action - Models and Methods", Ablex Publishing (January 1, 1993), ISBN 0893919438, http://www.macrocognition.com/LOCKED%20PDF/Decision%20Making%20in%20Action-Models%20and%20Methods.pdf
- Todd, P. and Gigerenzer, G, Putting Naturalistic Decision Making into the Adaptive Toolbox, Journal of Behavioral Decision Making, Vol. 14, 381-383, 2001.
- Zsambok, C.E. and Klein, G (1997) Naturalistic Decision Making. Lawrence Erlbaum Associates, Mahwah, NJ.
- Johnson, J.G. and Raab, M Take the First: Option Generation and Resulting Choices. Elsevier Science, San Diego, CA.
- Gary Klein, . N.p.. Web. 2 Dec 2013. <http://www.utexas.edu/law/journals/tlr/sources/Volume 92/Issue 3/Tor/Tor.fn311.Klein.NaturalisticDecisionMaking.pdf>.