C-Causal Blindness

TL;DR

This paper introduces the concept of C-Causal Blindness, a form of cognitive bias where policies to avoid negative outcomes lead to those outcomes, and proposes a computational framework using a Weighted Hidden Markov Model to analyze it.

Contribution

It defines C-Causal Blindness and presents a novel computational model to analyze this cognitive bias across brain, logic, and computer systems.

Findings

C-CB can cause policies to backfire, leading to undesired outcomes.

A new algorithm based on Weighted Hidden Markov Models models C-CB.

The framework demonstrates isomorphic relationships across disciplines.

Abstract

This text is concerned with a hypothetical flavour of cognitive blindness referred to in this paper as \textit{C-Causal Blindness} or C-CB. A cognitive blindness where the policy to obtain the objective leads to the state to be avoided. A literal example of C-CB would be \textit{Kurt G\"odel's} decision to starve for \textit{"fear of being poisoned"} - take this to be premise \textbf{A}. The objective being \textit{"to avoid being poisoned (so as to not die)"}: \textbf{C}, the plan or policy being \textit{"don't eat"}: \textbf{B}, and the actual outcome having been \textit{"dying"}: $\lnot$\textbf{C} - the state that G\"odel wanted to avoid to begin with. G\"odel pursued a strategy that caused the result he wanted to avoid. An experimental computational framework is proposed to show the isomorphic relationship between C-CB in brain computations, logic, and computer computations using a new proposed algorithm: a Weighted Hidden Markov Model.