Most real world problems involve uncertain information. Although uncertainty can often be reduced, it can be seldom completely eliminated and whether we are dealing with scientific, engineering, or personal problems, we are forced to make decisions that are based on incomplete knowledge. Even a deliberation of whether more information should be collected before making an actual decision is itself a decision under uncertainty. Decision making under uncertainty has been addressed in mathematics by probability theory and expected utility theory. These two together are known as decision theory. The art and practice of decision theory is known as decision analysis. Decision analysis offers a set of structured procedures that assist decision makers in structuring decision problems and developing creative decision options quantifying their uncertainty (his includes combining available statistics with expert judgments, and their own beliefs to arrive at estimates of the probabilities of various outcomes) quantifying their preferences (this includes structuring their value tradeoffs and examining their attitude towards risk) combining their uncertainty and preferences to arrive at optimal decisions. This course provides an introductory treatment of decision analysis, along with elements of human cognition under uncertainty.
Prerequisites: none
Recitations: none
Expected class size:
Frequency:
Credits: 3