A Rational Distributed Process-level Account of Independence Judgment

TL;DR

This paper introduces a rational, distributed message-passing model called $ ext{D}^ ext{*}$ for human-like independence judgment, which efficiently detects dependencies and outperforms previous algorithms, with support from neuroscience.

Contribution

It presents the first process-level, distributed account of independence judgment that mimics brain features and outperforms existing AI algorithms in worst-case scenarios.

Findings

$ ext{D}^ ext{*}$ detects dependencies quickly when they exist.

$ ext{D}^ ext{*}$ outperforms previous algorithms in worst-case running time.

Supported by neuroscience research on neural implementations of Bayes nets.

Abstract

It is inconceivable how chaotic the world would look to humans, faced with innumerable decisions a day to be made under uncertainty, had they been lacking the capacity to distinguish the relevant from the irrelevant---a capacity which computationally amounts to handling probabilistic independence relations. The highly parallel and distributed computational machinery of the brain suggests that a satisfying process-level account of human independence judgment should also mimic these features. In this work, we present the first rational, distributed, message-passing, process-level account of independence judgment, called $\mathcal{D}^\ast$. Interestingly, $\mathcal{D}^\ast$ shows a curious, but normatively-justified tendency for quick detection of dependencies, whenever they hold. Furthermore, $\mathcal{D}^\ast$ outperforms all the previously proposed algorithms in the AI literature in terms of worst-case running time, and a salient aspect of it is supported by recent work in neuroscience investigating possible implementations of Bayes nets at the neural level. $\mathcal{D}^\ast$ nicely exemplifies how the pursuit of cognitive plausibility can lead to the discovery of state-of-the-art algorithms with appealing properties, and its simplicity makes $\mathcal{D}^\ast$ potentially a good candidate for pedagogical purposes.