This is a guest post from Nic Hall from Bend, OR. A passionate outdoorsmen, backcountry skier, and medical professional. Nic is currently a faculty member at the School of Medicine OHSU with a research focus on decision making in high stakes environments. This is the first part in a series of blogs with Nic that will dive deep into decision making for backcountry skiers. If a sharp mind is the most important thing to bring with you into the mountains, think of these cognitive tools as an edge-file for your backcountry brain. As backcountry use has exploded over the past five years, we have seen a huge influx of new users in technical terrain. While the death rate has remained fairly stable, 15 +/- 11 deaths a year, for the past several years; a great proportion of the deaths have been attributed to both backcountry skiers and snowmobile access. Near misses have also increased which go largely uncounted and are poorly examined. We have all heard the axiom “it takes 10,000 hours to be an expert”, spending 10,000 hours learning from mistakes is a sure-fire way to be exposed to dangerous events in the backcountry. While there is no replacement for experience and education, researchers have been distilling the processes to give novices the tools to see complex situations, like backcountry travel, through the eyes of a seasoned expert. Over this season, I want to take you through the research of intuitive decision making. Hopefully, we can distill a few methods you can apply to your everyday decision making in the back country and reduce your risk by thinking about the way you assess information and make judgements. Most of the research traditionally compiled around human decision making was approached from the basis of heuristics and biases, or the inherent human conditions that make us poor decision makers.2 But in 1973 a group of researchers sat down with chess grand masters to see how they planned out their moves when playing other grand masters compared to novice players. They found that grand masters formed judgements in a matter of seconds, quickly recognizing patterns out of potentially thousands of moves. These new insights led to the formation of the naturalistic decision making model, explaining how people can make correct and favorable decisions under pressure, with limited information despite their shortcomings. Gary Klein wanted to know how these grand master chess players were making correct decisions so quickly and processing staggering amounts of information. He started looking at battlefield commanders, fire captains, and medical experts, all fields that had to make complex decisions with limited information under pressure. He found that all these experts had a few things in common: they avoided analytical thinking and comparative analysis, instead opting for rapid situational assessment, and mentally simulated only one or two plausible actions. There is another side of decision making though: the traditional field of biases and heuristics. Humans like patterns, we are easily distracted, we do not gather all the information before making decisions, and we generally suck at not involving our emotions in decisions. These are important pitfalls we will look at, as well as tools to avoid them as best as possible. In the next few blog posts and videos throughout the season, I will break down cognitive skills training techniques to sharply reduce that adage of 10,000 hours to mastery. Being aware of biases, using techniques of experts, and being cognizant of the reasons we are making decisions, can potentially decrease our risk exposure and hopefully decrease the number of near misses and maybe even deaths we see in the backcountry. https://www.wemjournal.org/article/S1080-6032(15)00428-7/pdf https://pdfs.semanticscholar.org/ee58/5835323852ef2c0197714df91c665fab3d18.pdf DeGroot, A. D. (1978).Thought and choice in chess.The Hague: Mouton. Klein, G. A. (1993). A recognition-primed decision (RPD) model of rapid decision making. In G. A. Klein, J. Orasanu, R. Calderwood, & C. E. Zsambok (Eds.), Decision making in action: Models and methods (p. 138–147). Ablex Publishing