Towards a new brain science: lessons from the economic collapse

TL;DR

This paper explores the parallels between brain science and economics, proposing that complexity science and Bayesian methods can unify understanding of neural and economic systems.

Contribution

It introduces an integrative approach linking neural activity and economic behavior through complexity science and Bayesian inverse problems.

Findings

Neural and economic systems share systemic properties.

Bayesian inverse problem approach unifies neural and economic analysis.

Complex systems perspective enhances understanding of brain-economy interactions.

Abstract

Economies are complex man-made systems where organisms and markets interact according to motivations and principles not entirely understood yet. The increasing dissatisfaction with the postulates of traditional economics i.e. perfectly rational agents, interacting through efficient markets in the search of equilibrium, has created new incentives for different approaches in economics. The science of complexity may provide the platform to cross disciplinary boundaries in seemingly disparate fields such as brain science and economics. In this paper we take an integrative stance, fostering new insights into the economic character of neural activity. The objective here is to precisely delineate common topics in both neural and economic science, within a systemic outlook grounded in empirical basis that jolts the unification across the science of complex systems. It is argued that this mainly relies on the study of the inverse problem in complex system with a truly Bayesian approach.