The Mutual-and-World Model: A General Computational Framework for Adaptive Coordination Between Mutual Interaction and World-State Modeling

Computational systems increasingly depend on both interactions among multiple computational components and internal representations of the environments in which they operate. Mutual interaction enables components to exchange information, combine observations, compare hypotheses, resolve disagreements, and coordinate decisions. World models represent environmental states and dynamics, allowing systems to estimate current conditions, predict future states, and evaluate the consequences of alternative actions. Although these capabilities serve different functions, their effectiveness may depend on how they are coordinated. A world model can identify uncertainties that require additional interaction, while interaction-generated evidence can support the evaluation and revision of that model.

This paper proposes the Mutual-and-World Model (MWM), a general computational framework for adaptive coordination between mutual interaction and world-state modeling. The framework treats mutual interaction and world modeling as distinct but interdependent computational processes. It formalizes how a system can use world-state estimates, predictive uncertainty, and anticipated environmental consequences to determine whether interaction is necessary, which interaction procedure should be activated, and how much interaction is justified. It also formalizes how information generated through interaction can be evaluated and incorporated into a world model without assuming that agreement among components necessarily establishes environmental truth.

MWM is specified through a computational component representation, an interaction operator, a world-state model, an interaction activation policy, a world-model update operator, and a joint decision objective. A reference algorithm describes the coordination process, while conditional propositions identify circumstances under which adaptive coordination may improve expected utility, preserve evidence validity, and reduce unnecessary interaction. An experimental program is proposed to compare MWM with fixed interaction, interaction-independent world modeling, world-model-independent interaction, and established domain-specific methods.

The proposed framework is intended as an organizational abstraction for investigating the relationship between collaborative computation and environmental representation. It does not claim universal superiority over existing approaches. Its theoretical distinctiveness and practical value must be established through precise implementation, comparison with related architectures, and reproducible empirical evaluation.