C-Causal Blindness

TL;DR

C-Causal Blindness (C-CB) is framed as a self-defeating intrapersonal bias bundle where a policy to avoid a negative state can paradoxically cause that state. The paper formalizes C-CB with a novel Weighted Hidden Markov Model (WHMM) that incorporates emotional and heuristic weights into state transitions and emissions, linking Bayesian brain concepts and predictive processing to decision dysfunction. Through examples such as the ir rational student and the war on drugs, it demonstrates how relaxed causal chains can invert outcomes, and it uses possible-worlds and modal logic to formalize the reliability of causal inferences. The work outlines an experimental pathway, neural correlates, and evolutionary considerations, proposing WHMM-based simulations and neuroimaging studies as avenues to predict, detect, and potentially mitigate C-CB in humans and inform AI decision-making systems.

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 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.

Figures (2)

• Figure 1: Experiment example based on portugalwarondrugsusawarondrugsfailurewardrugs.
• Figure 2: Three possible worlds scenario