I have been intrigued recently by the question of when to use primarily intuition in decision making versus when to use primarily analysis. Clearly at the extremes there are obvious incentives to use one or the other. A decision to swerve when a child runs onto the road, or the decision to take a defensive posture when attacked are clearly instinctive. A decision about whether to undergo a major surgery is typically largely analytical.
Where it gets tricky is in the “middle”, and specifically what are the parameters in deciding how to decide? After reading through a variety of opinions on the subject. I surmised that some of the key influencers in decision approach comes down to “size of decision”, “amount of time to make a decision” and “depth of experience” in the topic of the decision. Using these three parameters, I came up with the following matrix of preferred decision types.
Part of what makes this topic interesting to me as an analyst is helping individuals or businesses make the right choice about when to use different decision types. Using the chart above, I believe there are several biases that lead to incorrect decision models.
Can Do Bias
First, for there is the miscalculation of the level of internal experience. Entrepreneurs fall into this trap because they develop a varied skill set in order to survive, and have a habit of acquiring knowledge that they lack. Ironically, while this is partly what helps an entrepreneur thrive early on, it also can be a significant barrier to growth. Entrepreneurs are trained to act instinctively, and have a difficult time switching to analysis. A typical first step into analytics for entrepreneurs is the establishment of decision support systems. These systems do not usually challenge the “ability” of the entrepreneur, but help provide targeted information quickly to support decision making.
Risk Aversion Bias
Second, there is the miscalculation of the significance of a decision. Corporations with long histories of custom analytics by consultants or in-house research departments typically fall into this trap. When the go-to decision becomes custom analytics, then the risk of over analysis goes way up. Suddenly, the company is spending $100k to research where they should have the holiday party, and what the appetizers should be. Companies with a culture of custom analytics need to complete a quick scenario analysis and determine if the marginal benefit of making the right decision justify the cost of identifying the right decision.
Finally, there is what I call the procrastinator's bias. Some people simply wait until the last minute to address key decisions, and then are forced to rely on intuition. The earlier key decisions can be identified, the more likely they can be properly analyzed. If you are always relying on intuition to make decisions, ask yourself if that is because you always wait until the last minute to decide.
What other biases do you think impact how we approach decisions?