Inference-Based Architecture for Decision-Making

This paper addresses the problem of decision paralysis, a significant challenge for decision-making models. It proposes a novel computational account based on hierarchical decision processes, separating intent and affordance selection. The use of forward and reverse Kullback-Leibler divergence for commitment modeling is a key innovation, offering a potential explanation for decision inertia and failure modes observed in autism research. The paper's focus on a general inference-based decision-making continuum is also noteworthy.

• •Proposes a computational model to explain decision paralysis.
• •Separates intent and affordance selection in decision-making.
• •Uses forward and reverse KL divergence for commitment modeling.
• •Simulations reproduce features of decision inertia and shutdown.
• •Treats autism as an extreme regime of a general decision-making continuum.