Uncertainty is everywhere. At any point in time we may be called on to decide among actions, deciding whether to buy a house or rent, deciding whether to find a job or complete graduate school, deciding whether to change lanes on a crowded high speed roadway late on a rainy, cold night.
Are people choosing the most beneficial action in the face of uncertainty?
While classical decision making studies have witnessed numerous human failures, I, however, hold an optimistic view on human ability to deal with uncertainty. After all, our ancestors had lived millions of years in the wild where neither food nor safety is certain.
My approach: Context-Specific Human Decision Making
First, I am testing human decisions under all kinds of specific uncertainties, rather than an abstract general uncertainty. The poor acuity of our peripheral vision, the inaccurate aiming of our speedy movements, as well as the fast-changing environment, could be sources of uncertainty. There is no reason to presume that we automatically translate all of these into numerical lotteries (as used by classical decision making studies) before we make decisions (especially for folks who hate numbers!).
Second, I am investigating how human decisions are subjective to the decision maker’s ongoing needs in the context. The action that maximizes expected gain is not always the most beneficial action. For example, if, to paraphrase Keynes, in the long run we are all dead, then it matters little that, in the long run, we are all rich.
I use Bayesian Decision Theory (see Maloney & Zhang, 2010) to model ideal decisions under uncertainty and compare human performances to the ideal.
My collaborators (alphabetically by last name): Nathaniel Daw, Louis-Alexandre Etezad-Heydari, Xiaolan Fu, Qicheng Jing, Soumya V. Maddula, Laurence T. Maloney, Camille Morvan, Zenon Pylyshyn, Friederike Schüür, Shih-Wei Wu, Yuming Xuan