The professor introduced fast and frugal trees (FFTs) as a specific and especially useful form of heuristic decision making. We saw that FFTs are fast, simple, and work quite well. They save valuable time while making critical decisions. They are powerful, easy to use, robust, and easily interpretable.
Then he proceeded to introduce the signal detection theory (SDT), and how a signal and a noise distribution, and a decision criterion together determine a decision. Sensitivity and decision criterion or bias are the two important concepts in SDT. An important message here is that a biased decision criterion is often completely rational.
We saw that the logic of the decision criterion from SDT can be applied in FFTs as well and is related to its exit structure. We also learned that adjusting the exit structure of FFTs according to the payoff structure of the task is the most important way to improve its ecological rationality. Sensitivity of FFTs can also be well defined with respect to the order and certain other properties of the cues.
We learned that FFTs are not just prescriptive, but can be used as descriptive models of decision making too. Finding parallels between SDT and FFTs was interesting and is a great step towards integrating two seemingly disparate models of decision making.