This article briefly tells the story of where the three schools arose and how they have interacted, beginning with the explosion of interest in the field during and after World War II and continuing to the present day, when companies such as Chevron have hundreds of decision analysts on staff.
We present and axiomatize several update rules for probabilities (and preferences) where there is no unique additive prior.
In the context of non-additive probabilities we define and axiomatize Bayesian update rules; in the context of multiple (additive) priors we define maximum likelihood rules.
We are therefore able to disentangle social learning from learning from a private signal.
Our main result is that subjects update on their private signal in an asymmetric way.
Each school of thought brings vital insights to bear.
Managers need to understand when to make decisions formally, when to make them by the seat of their pants, and when to blend those approaches.
But that isn’t the only useful way to think about making decisions.
The academic arena alone contains two other distinct schools of thought, one of which has a formal name—decision analysis—and the other of which can be characterized as demonstrating that we humans aren’t as dumb as we look.
HBR Reprint R1505F Research into how cognitive biases muck up decision making—a field perhaps best known for its offshoot, behavioral economics—is extremely popular among academics and the public alike.
Behavioral economics is just one perspective on decision making.
Managers need to understand when to make decisions formally, when to make them by the seat of their pants, and when to blend the two approaches. We all know this from personal experience, of course.