Quantum ideas in micro level decision making and macro level financial system analysis

Vortrag

Non equilibrium systems, like financial markets, can be approached from both a micro and a macro level. On the micro level of financial decision making, an agent needs to act in an environment with multitudes of dynamically changing pieces of information. In a very simple case, a Bayesian decision agent, who is keen to make a certain investment, is confronted with two pieces of new information that both individually reduce the agent's inclination to make that investment. In such a situation, it would be natural to assume that the simultaneous consideration of both pieces of information should further reduce the agent's initial enthusiasm. Surprisingly, general statements like this about the double conditional probability require substantial additional assumptions. We investigate schemes of assumptions in classical Bayesian probability and quantum probability that allow `reasonable` conclusions about the double conditional probability. On the macro level traditional quantitative methods typically consist of multivariate linear regression models that offer limited insight into causal mechanisms. Since the seminal work of John Bell, causal models are used in quantum foundations to understand the constraints for hidden variable models that aim at reproducing quantum statistics. Notably, such causal approach allows to address counterfactual questions. It can be fully formalized and then used as a test to rule out competing causal models of price formation on the macro level.