I've been reading a collection of articles on the SunRockIce website. One that caught my eye is called Decision Making for Wilderness Leaders, by Ian McCammon, of the National Outdoor Leadership School. It's probably no secret that the avalanche education industry has undergone a crisis of confidence in recent years. Disturbing studies suggest not only that traditional avalanche courses may be ineffective, but that they may even increase the likelihood a person will expose themself to risk.
McCammon's article examines three ways we make decisions, and assesses them from the point of view of preventing avalanche fatalities.
The first method is called Analytic Decision Making. This is the process most often taught in avy courses, emphasizing the collection of data, using that data to compare alternatives, and then choosing a course of action.
We would expect Analytic Decision Making to be the most rational and therefore the most effective of the three methods. Surprise: in the topsy-turvey world of avalanches and mountaineering, the Analytic method is the least effective, particularly for novices.
To understand why, we need only look at the first step of the process: gathering data. This works well in situations where it is possible to collect and accurately assess all relevant data.
In the mountains, however, data is often incomplete and ambiguous. Additionally, it takes valuable time (itself a safety consideration) to collect the data. The likely result of trying to make an analysis of incomplete (or misleading) data is a bad decision, or the inability to even reach a decision.
The second mode is labeled Heuristic Decision Making. Mountaineers are probably familiar with the term Heuristic Trap, which refers to our tendency to apply rules of thumb inappropriately when making critical decisions.
Heuristics, however, can also be useful in the backcountry, provided those rules of thumb are appropriate to the situation. For example, a heuristic rule might be to avoid slopes steeper than 30 degrees whenever the avalanche danger is rated (by a professional forecast) considerable or higher.
Here, choice has been removed from the system in favor of a simple rule. For novices, this can be advantageous, provided the rule is adhered to—and provided the rule is appropriate to the context.
The last and most interesting method is called Expert Decision Making, or Recognition-Primed Decision Strategy. In this case, experts employ their extensive catalogue of knowledge to search for pattern matches.
That is, they gather information until they're able to recognize conditions as something they've seen before, at which point they're able to effectively predict what will happen, and make good choices accordingly.
RPD can be the most effective of the three methods. However, like heuristic decision making, experts can fool themselves into thinking they have expertise about a situation when in fact it is beyond their range of experience: the Expert Trap.
Ultimately, McCammon stresses the complexity of Human decision making processes, and how little we know about it. From these three examples, however, McCammon suggests ways to teach better backcountry decision strategies. If the topic interests you, do give the article a look.