What question did this study set out to answer?

The aim is to determine whether six interrogative functions can address primitive uncertainty in nonlinguistic systems.

May 9, 2026Open Access

Testing Whether Six Interrogative Functions Form a Minimal Basis for Resolving Primitive State-of-Affairs Uncertainty in Nonlinguistic Multi-Agent Reinforcement Learning Systems

Key Points

The aim is to determine whether six interrogative functions can address primitive uncertainty in nonlinguistic systems.
Preregistered five hypotheses regarding necessity, redundancy, exhaustiveness, minimality, and substrate independence.
Evaluating functional channels: WHO, WHAT, WHERE, WHEN, HOW, and WHY.
Documenting protocol for future experimental collaboration.
No computational infrastructure available at this time to execute the design.
Preregistered hypotheses offer a framework for future testing and collaboration.
Establishes study priorities for further exploration in the field.

Abstract

This preregistration documents Protocol 8 of the Quantum Inquiry MARL Ethics Series. It tests whether six nonlinguistic functional channels corresponding to identity (WHO), content (WHAT), location or context (WHERE), time (WHEN), process (HOW), and causal or reward-ground structure (WHY) form a minimal basis for resolving primitive state-of-affairs uncertainty in nonlinguistic multi-agent reinforcement learning systems. The study operationalizes the empirical arm of a formal claim established in Tisler (2026, DOI: 10.5281/zenodo.20074811). Five hypotheses are preregistered: individual necessity (H1), nonredundancy and independence of ablation signatures (H2), joint exhaustiveness (H3), minimality (H4), and substrate independence (H5, exploratory). At time of deposit, computational infrastructure is not yet available to execute the design. The preregistration is deposited to establish priority and invite collaboration.

Testing Whether Six Interrogative Functions Form a Minimal Basis for Resolving Primitive State-of-Affairs Uncertainty in Nonlinguistic Multi-Agent Reinforcement Learning Systems

Key Points

Abstract

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