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When we come up with simple rules to get things done, we consciously or unconsciously engage fast thinking. The reason, Nobel Prize winning psychologist Daniel Kahneman says, is that:
The automatic operations of System 1 generate surprisingly complex patterns of ideas, but only the slower System 2 can construct thoughts in an orderly series of steps.
We go with what our gut tells us for expediency-sake. The “good enough” option. As our more process-oriented ability runs in the background, we go about getting things done, simply. A more complete definition of the two systems Kahneman talks about illuminates how they operate differently:
System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control
System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration
In Thinking Fast and Slow Kahneman provides multiple examples from research to help us understand how while we identify with System 2, “the conscious, reasoning self that has beliefs, makes choices, and decides what to think about and what to do,” in reality most of the time we use System 1 to form quick and automatic “impressions and feelings.” These automatic feelings are “the main sources of explicit beliefs and deliberate choices of System 2.”
“rather than expending conscious cognitive effort,” we “adopt universal heuristics that are cognitively easy, like representativeness (Pick what is usual) and availability (Pick what first comes to mind).”
When we lack time and have insufficient information, we either adopt automatic and obvious rules, or no rules at all to get things done.
However, when we try to figure out why some things work and others don’t over time, we need to make those rules more effective.
Because simple rules tend to be fairly automatic, they are typically less strategic and accurate than we would like them to be. Or are they? The best way to learn if improving them leads to ore successful outcomes is to test them. Eisenhardt and a colleague engaged in a facilitated experiment to find out.
The experiment consisted of providing a common scenario and a few data points of a problem to solve to three groups of people, then adding one unique rule for each group. This was the additional rule. Two groups were told to “Listen to others,” and “Share your information.” The researchers categorized these two rules as helpful. The third group, however, received what the researchers thought was a useless simple rule, “Watch your Time.”
What happened next will surprise us as it did the researchers.
Predictably, the “Listen to others” group was the most successful. But, it was the “Watch your Time” useless rule group that came in second. The “Share your information” group came in last.
Why did a rule intended as helpful hold the group back?
Deconstructing the exercise, the researchers learned that while the instructions of the first two groups encouraged pauses and reflection or re-strategizing, those of the third group generated unhelpful behavior — more talking.
Doing the right things, and doing things right
To be effective, simple rules need to encourage useful behavior. Less talk, more reflection and adjustments lead to accelerating the process for improving simple rules. This is helpful behavior. But it’s not enough. To get to better rules Sull and Eisenhardt say we need to “figure out the logic behind the simple rules” and tie that logic back to real world success:
Simple rules seem to improve in a predictable pattern.
Over time, three things happen:
1/ their content shifts from superficial and convenient rules to strategic and abstract ones that prove more effective over a broader range of activities and decisions
2/ the different types of rules are learned in a specific, sequential order — boundary and how-to rules first, then other rules that are more difficult to learn
3/ the rules go through simplification cycling, which means they grow in number and then shrink as circumstances change
This process of improvement benefits from “learning to do the right things:”
Key learning processes like consciously reflecting on past experience and engaging in varied but related experiences to accelerate improvement, and combining multiple learning processes is the most potent approach to improvement of all.
Doing the right things, tying together data points from multiple experiences, also needs to be coupled with doing things right. For example, Sull and Eisenhardt say:
A hallmark of experts is their efficient cognitive organization of relevant information into larger patterns or chunks than novices perceive.
Organization of information into patterns enables experts to handle more information at once and link different pieces of information together faster than novices can.
We can learn to become better, even expert, at seeing the patterns through Deliberate Practice, which doesn’t mean practicing deliberately as the term might suggest, but practicing by doing things right.
According to Michael J. Mauboussin, experts perform well when the domain is rule-based with a wide range of outcomes — for example, like in chess — and perform equal or worse than collectives in probabilistic domains with a limited range of outcomes — for example, like in poker.
In Think Twice, Mauboussin says that in rule-based domains that have a limited range of outcomes — like for example in credit scoring or simple medical diagnosis — once experts have investigated a problem based on past patterns and extrapolated the rules to guide decisions, computers are more reliable and cheaper to boot.
As we go through the process of improving simple rules and learn how to expand our options we want to keep an eye on keeping the rules simple enough to use consistently while we keep adapting them to changed circumstances. We may use a combination of experts to identify patterns and computers to churn the information thereafter, until the context changes and requires recalibrating the rules.