Decision Theory

Published by Mario Oettler on

In this lesson, you will learn the basics of decision theory. They can be useful, if you decide upon a tool, blockchain, equipment, or project. Decision theory assumes that our decision doesn’t influence the decision of other persons. When putting this into a model, we typically say that we play against nature.

In this lesson, we deal with three major topics:

Decisions under certainty: Here, we know what the future will hold. The focus is on finding the best option. We know all payoffs and properties of each option. For that purpose, we look at different ways to aggregate different performance indicators into a single decision-relevant number.

Decision under uncertainty: Here, we know that there are different future events possible. These future events influence our payoffs for each option. The problem is that we don’t know which one will occur. We also don’t know with what probability each event might occur. The focus is on how to decide on a certain option, if we are uncertain about the probabilities.

Decision under risk: The difference to a decision under uncertainty is that we know the probability of occurrence of future events.