Agents' Behavior in Crisis: Can Quantum Decision Modeling Be a Better Answer?
Over the last decade, there has been a significant interest in the application of quantum decision theory (QDT, a mathematical theory of probabilities based on Hilbert space dynamics and operator formalism in quantum field theory) in social sciences. The motivation has been several paradoxes emerging from real data sets of agents behavior, which cannot be resolved via classical decision theory (expected Utility theory based on classical set theory and Boolean logic theory) which can be resolved fully through the use of this emerging paradigm. QDT has been applied to the financial decision making of late, where an attempt has been made to explain agents' behavior under ambiguity, or uncertainty based on quantum probability modeling. There have been attempts to derive new option pricing theory based on path integral mechanics used widely in quantum field theory. Here, the burning issue is whether a new decision theory approach is required to explain the behavior of financial markets in this era of globalization, specifically when there is an unprecedented negative effect of financial integration on asset prices? The last financial crisis, for example, has been criticized as an outcome of a failure in theoretical, as well as policymaking levels to understand agents' behavior when asset market crisis become endemic and creates contagions. It is also under this scenario that a new decision making theory is called for. There are many advantages of such a novel theory, for example, resolution of aforementioned paradoxes (order effects, disjunction and conjunction fallacies, Dutch book effect, etc. prediction of asset prices which are a more close match than the neoclassical predictions, and also modeling belief updations of agents under uncertainty (where results are fundamentally different from the Bayesian learning models) The current chapter provides a thorough review of the new revolution in social science, and attempts to extend the emerging QDT formalism to model the behavior of agents under financial crisis which still eludes neoclassical regime. The current paper is rather a nontechnical summary of this novel budding field, and for more mathematically rigorous introduction, readers may turn to the references in the appendix. More specifically, this paper would attempt to investigate whether QDT is able to investigate drastic changes in agents/investors behavior when large negative and Black Swan events like global financial crisis erupts. This has been an area which is less studied, since standard Bayesian learning models are inapt to explain such large jumps in behavioral patterns of investors. QDT has a novel non Bayesian updating mechanism through which such drastic behavioral changes can be modeled, which would be a new insight into the ways in which global crisis work.