Strict certainty preference in the predictive brain: a new perspective on financial innovations and their role in the real economy

Abstract

The dominant paradigm in neuroscience considers the brain to be a prediction engine. The brain generates predictions first, which are then contrasted with information to generate error signals. Finite brain resources are subsequently spent in selectively processing the error signals based on their relative value with higher value signals getting a priority. In this way, the brain can be thought of as optimizing on its own internal resources before seeking to optimize on the resources available in the external world. We show that such considerations change the cost–benefit calculations of certain vs uncertain outcomes in the brain, giving rise to, what can be termed as, a strict certainty preference. A new perspective on prominent financial innovations (such as securitization, interest rate swaps, and credit default swaps) emerges, with a dark side that potentially leads to a misallocation of resources towards low NPV projects.