Polyphonic Intelligence: Constraint-Based Emergence, Pluralistic Inference, and Non-Dominating Integration

TL;DR

This paper introduces the concept of polyphonic intelligence, emphasizing the coordination of multiple semi-independent inferential processes under shared constraints, challenging traditional convergence-based models of intelligence.

Contribution

It develops a formal variational framework for stable, pluralistic inference that maintains multiple explanatory trajectories without winner-takes-all selection.

Findings

Demonstrates simple computational systems implementing non-dominating inference

Clarifies differences from ensemble and mixture models

Discusses implications for neuroscience and AI

Abstract

Across neuroscience, artificial intelligence, and related fields, dominant models of intelligence typically privilege convergence: uncertainty is reduced, competing explanations are eliminated, and behaviour is governed by the optimisation of a single objective or policy. While this framing has proved powerful in many settings, it sits uneasily with biological and adaptive systems that maintain redundancy, ambiguity, and parallel explanatory processes over extended timescales. Here we propose an alternative perspective, termed polyphonic intelligence, in which coherent behaviour and meaning emerge from the coordination of multiple semi-independent inferential processes operating under shared constraints. Rather than resolving plurality through dominance or collapse, polyphonic systems sustain multiple explanatory trajectories and integrate them through soft alignment, compatibility relations, and bounded influence. We develop this perspective conceptually and formally, introducing a variational framework in which multiple coordinated approximations are maintained without winner-takes-all selection. This formulation makes explicit how plurality can remain stable, tractable, and productive, and clarifies how polyphonic inference differs from ensemble methods, mixture models, and Bayesian model averaging. Through proof-of-principle examples, we demonstrate that non-dominating, pluralistic inference can be implemented in simple computational systems without requiring centralised control or global convergence. We conclude by discussing implications for neuroscience, psychiatry, and artificial intelligence, and by arguing that intelligence may be more fruitfully understood as coordination without command rather than as the elimination of uncertainty.