(Aside: those Sox just got thrown out twice at home plate, and now Cano just hit a two-run homer to tie it. Grrrr.)
In our last episode, we said that "Recognition-Primed Decisions" (RPD) rely on expertise and experience to make good decisions by considering options in order, taking the first acceptable one that comes to mind. This is called "satisficing", as opposed to "optimizing."
Here are some applications of this theory:
- Be skeptical of shortcuts to effective decision-making
- Analytical methods may by helpful for inexperienced people
- Consider which decisions are worth making (in a zone of indifference, don't kill yourself deciding between two nearly-equal options)
- Do not teach the RPD model, since it just shows what experienced decision makers already do.
- Improve decision skills. Teaching people to think like experts may be too difficult, but try to teach people to learn like experts. More on this below.
- Use decision requirements for designing software systems. Ok, not much use for ultimate, but this is vaguely work-related, so I'm keeping it in.
Here is how experts learn.
- They engage in deliberate practice, so that each opportunity for practice has a goal and evaluation criteria.
- They compile an extensive experience bank.
- They obtain feedback that is accurate, diagnostic, and reasonably timely.
- They enrich their experiences by reviewing prior experiences to derive new insights and lessons from mistakes.
(6-3, Yankees, after Sheffield's three-run homer. No outs still. Maybe I should stop typing so they stop scoring.)
So, I think this is just what we were talking about the other day. You fast-track by going out of your way to analyze your performances soon after they happen, or else you just end up with one year of experience, 10 times. And it's not enough just to count successes or failures, but you have to assess the thought processes and the contingencies.