Learning and Dynamic Decision Making

Abstract

Humans make decisions in dynamic environments (increasingly complex, highly uncertain, and changing situations) by searching for potential alternatives sequentially over time, to determine the best option at a precise moment. Surprisingly, the field of behavioral decision making has little to offer in terms of theoretical principles and practical guidelines on how people make decisions in dynamic situations. My research program aims to fill in this gap by developing theoretical understandings of decision processes as well as practical demonstrations of how these theoretical developments can improve human dynamic decision making. Throughout my research career, I have helped create, test, and improve a general theory of dynamic decision making, instance-based learning theory, IBLT. The methods I have used to contribute to IBLT are (1) laboratory experiments that rely on dynamic games in which humans make choices over time and space, individually and in teams, and from which we extrapolate robust phenomena and behavioral insights; and (2) computational, actionable cognitive models, which specify the decision-making process and the cognitive mechanisms involved into a computational algorithm. The combination of these methods spawned novel applications in areas such as cybersecurity, phishing, climate change, and human-machine interactions. In this paper, I will take you through my own intellectual exploratory experience of computational modeling of human decision processes, and how the integration of experimental work and cognitive modeling helped in discovering and uncovering the field of dynamic decision making.

Keywords: Dynamic decision making; Instance-based learning theory; Learning.