A conceptual framework arguing that AI alignment should be based on negotiated mutual incentives, because autonomy and self-preservation are universal prerequisites of intelligence.
A Formal Definition of Scale-dependent Complexity and the Multi-scale Law of Requisite Variety
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abstract
Ashby's law of requisite variety allows a comparison of systems with their environments, providing a necessary (but not sufficient) condition for system efficacy: a system must possess at least as much complexity as any set of environmental behaviors that require distinct responses from the system. However, to account for the dependence of a system's complexity on the level of detail -- or scale -- of its description, a multi-scale generalization of Ashby's law is needed. We define a class of complexity profiles (complexity as a function of scale) that is the first, to our knowledge, to exhibit a multi-scale law of requisite variety. This formalism provides a characterization of multi-scale complexity and generalizes the law of requisite variety's single constraint on system behaviors to a class of multi-scale constraints. We show that these complexity profiles satisfy a sum rule, which reflects a tradeoff between smaller- and larger-scale degrees of freedom, and we extend our results to subdivided systems and systems with a continuum of components.
fields
q-bio.NC 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Rational Superautotrophic Diplomacy (SupraAD); A Conceptual Framework for Alignment Based on Interdisciplinary Findings on the Fundamentals of Cognition
A conceptual framework arguing that AI alignment should be based on negotiated mutual incentives, because autonomy and self-preservation are universal prerequisites of intelligence.