The prescriptive (normative) Bayesian theory of decision making under uncertainty has reached a high level of maturity. The assumption that the decision maker is rational (i.e. that they optimize expected utility, in Savage’s formulation) is central to this theory. However, empirical research indicates that this central assumption is often violated by real decision-makers. This limits the ability of the prescriptive Bayesian theory to provide a descriptive theory of the real world. One of the reasons that have been proposed for why the assumption of rationality might be violated by real decision makers is the limited cognitive and computational resources of those decision makers, [1]-[5]. This workshop intends to inspect this core assumption and to consider possible ways to modify or complement it.

Many of the precise issues related to this theme – some of which will be addressed in the invited talks - can be formulated as questions:

• Does the concept of rationality require Bayesian reasoning?
• Does quantum probability theory (extending classical Kolmogorov probability) provide novel insights into the relation between decision making and cognition?
• Do the extensions of expected utility (which is a linear function of the relevant probabilities) to nonlinear functions of probabilities enhance the flexibility of decision-making task formulating while respecting the limited cognitive resources of decision makers?
• How can good (meta-)heuristics, so successfully used by real-world decision makers, be elicited?

The list is definitely not complete and we expect that contributed talks, posters and informal discussions will extend it. To stimulate the informal discussions, the invited talks will be complemented by discussants challenging them. Altogether, the workshop aims to bring together diverse scientific communities, to brainstorm possible research directions, and to encourage collaboration among researchers with complementary ideas and expertise. The intended outcome is to understand and diminish the discrepancy between the established prescriptive theory and real-world decision making.

The targeted audience is scientists and students from the diverse scientific communities (decision science, cognitive science, natural science, artificial intelligence, machine learning, social science, economics, etc.) interested in various aspects of rationality.

All accepted submissions will be published in a special issue of the Workshop and Conference Proceedings series of the Journal of Machine Learning Research (JMRL).

[1] H.A. Simon: Theories Of Decision-Making In Economics and Behavioral Science, The American Economic Review, XLIX, 253-283, 1959
[2] C.A. Sims Implications of Rational Inattention, J. of Monetary Economics, 50, 3, 665 -- 690, 2003
[3] A. Tversky, D. Kahneman: Advances in Prospect Theory: Cumulative Representation of Uncertainty, J. of Risk and Uncertainty, 5, 297-323, 1992
[4] 2011 NIPS Workshop on Decision Making with Multiple Imperfect Decision Makers
[5] 2015 NIPS Workshop on Bounded Optimality and Metareasoning