California Management Review
California Management Review is a premier academic management journal published at UC Berkeley
by Brian T. McCann
One of the major challenges plaguing managerial decision making is that it often requires a commitment of resources. These decisions involve uncertainty and it is difficult to forecast whether or not a choice will lead to success. Being able to effectively analyze risk and think in probabilistic terms is an essential skill that is becoming more important as predicting outcomes becomes even harder due to growing levels of uncertainty. This article introduces you to a more systematic approach that will lead to more accurate estimates and better decisions.
Objective probability statements express properties of long-run outcomes of well-defined, repeatable processes. Subjective probability aligns more accurately with the issues and decisions that managers face in the real world. It recognizes that managers must make forecasts and decisions even when they do not have exact statistics to calculate an objective, frequency-based probability. The subjective probability view is a more expansive definition of probability, as it can be applied to many different events. However, subjective probability heavily relies on an individual’s knowledge and is prone to being influenced by outside decisions.
Bayesian approaches tell you what information you need and how to use it to update your existing probability estimate. This article examines Bayesian updating and details how it can aid in decision making. Bayesian updating involves combining existing or prior beliefs with an assessment of the strength of new evidence. This all combines to serve as an updated estimate for decision making.
The advantage to using a Bayesian approach is the generality it provides. It can be applied to any field of managerial practice. For instance, market researchers can use Bayesian approaches to improve decisions related to pricing, distribution logistics, new product development, and promotional campaigns. They have also been used to help firms operating in the US movie industry learn as they enter multiple international markets.
In many respects, Bayesian approach is similar to how humans adapt when given new information. We are always updating our beliefs about the world when presented with new data. However, this process is subject to a lot of human error, which is why we use Bayesian updating to achieve more accurate probability estimates. The increased levels of accuracy in estimation has the potential to be highly significant in influencing decision-making.
Using a Bayesian approach helps us think more open-mindedly by shifting our thinking from dichotomous terms (true v. false) to continuous terms (degrees of confidence in the truth of a particular belief). It can also help reduce cognitive bias. This is most evident in outcome bias, shifting the focus from results of decisions to the quality of the process used in making them. Moreover, a Bayesian approach can provide insight to what distinguishes evidence of greater value. It teaches us that the strength of evidence can depend on probability of certain events occurring. Finally, it allows us to communication with more clarity. Communicating using numerical estimates, instead of descriptive words, allows for a greater understanding regarding decision-making.
Bayesian approaches are most effective in situations that call for probabilistic thinking where beliefs are updated as new evidence is discovered. These approaches are considerably diverse and can be applied to many fields as a result. This is crucial because many decisions can be decided on very small margins – where accuracy is important. Adopting a Bayesian-like mindset will improve managerial decision-making by making your beliefs more accurate.
To find out more, please read the full article in California Management Review, Volume 63, Issue 1.