Heuristic Choice

From "TAMING UNCERTAINTY" (R, Hertwig et al., MIT Press. 2019), the 2nd paper, "The Robust Beauty of Heuristics in Choice under Uncertainty" (R, Hertwig, J, K. Woike, T, Pachur, and E, Brandstätter).

Heuristic means "without in-depth calculation or analysis" or "drawing conclusions from simple considerations." It can also mean "empirical" or "exploratory.

The paper reports that heuristic methods seem to make better choices under uncertain circumstances.

The key point of verification is "Which model can successfully calculate the expected value (reward)?

Traditionally, classical methods such as "Expected Utility Theory" and "Bayesian Decision Theory," which are well-known in game theory, have been used to calculate expected values in decision making. However, it has been pointed out that these methods are not very useful in practice because they cannot be used unless a great deal of prior information is available, and that heuristic methods may be useful in uncertain situations. For example, when making a decision under rather ambiguous circumstances, the classical method cannot be used without quantifying and incorporating the decision maker's own subjective expectations. However, if that forecast itself is vague, the result will be at an impossible level, and that risk has been pointed out.