C-Causal Blindness
This addresses a hypothetical cognitive bias problem for researchers in AI, cognitive science, and logic, but it appears incremental as it builds on existing concepts of blindness and Markov models.
The paper tackles the problem of C-Causal Blindness, where a policy to achieve an objective inadvertently causes the opposite outcome, as illustrated by Gödel's starvation example, and proposes a computational framework using a Weighted Hidden Markov Model to demonstrate its isomorphic relationships across brain, logic, and computer computations.
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 Gödel'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 Gödel wanted to avoid to begin with. Gödel 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.