The Self–Mutual–World–Meta–Future–Causality–Action–Information–Goal–Structure–Uncertainty–Resources–Memory–Evidence–Constraints Model: A Unified Framework for Adaptive Coordination in Computational Systems
Abstract
Computational systems increasingly operate in environments where effective behavior depends not only on internal processing and interaction among computational agents, but also on environmental representation, metacognitive control, future-state prediction, causal analysis, action selection, information management, goal representation, structural organization, uncertainty estimation, resource allocation, memory, evidence evaluation, and constraint satisfaction. Existing approaches address many of these functions through distinct research traditions, including world models, metareasoning, model-based planning, causal inference, multi-agent systems, information theory, memory-augmented computation, and constrained optimization. However, the relationships among these functions can create a complex coordination problem in which no single processing mechanism is necessarily optimal throughout an entire task.
This paper proposes the Self–Mutual–World–Meta–Future–Causality–Action–Information–Goal–Structure–Uncertainty–Resources–Memory–Evidence–Constraints Model (SMWMFC-AIGSURMEC), a conceptual framework comprising fifteen computational objects organized into interacting functional layers. The Self object represents internal computation; the Mutual object represents interaction among computational entities; the World object represents the state and dynamics of the environment; the Meta object monitors and regulates computational processes; the Future object represents predicted possible states; the Causality object represents hypothesized cause–effect relationships; the Action object represents executable interventions; the Information object represents information acquisition and transformation; the Goal object represents desired outcomes; the Structure object represents organizational relationships; the Uncertainty object represents incomplete knowledge and predictive uncertainty; the Resources object represents computational and operational budgets; the Memory object preserves information across processing stages; the Evidence object evaluates support for claims and decisions; and the Constraints object specifies admissible behavior and solutions.
The central proposal is that these objects should be coordinated through an adaptive controller that selects, configures, and terminates computational mechanisms according to task state, expected utility, uncertainty, evidence quality, causal relevance, predicted outcomes, and resource availability. The framework distinguishes the representation of an object from the mechanisms that operate on it, and it allows individual processing, cross-entity interaction, environmental simulation, metacognitive regulation, and action execution to be combined when appropriate. A formal system definition, an adaptive control objective, an operational algorithm, implementation principles, research hypotheses, and an empirical evaluation protocol are presented. The framework is a proposed theoretical and engineering design rather than an empirically validated system. Its contribution must be established through comparison with existing approaches and experiments testing whether coordinated selection among the fifteen objects improves performance, reliability, adaptability, and resource efficiency.
Related
- The Self-and-Mutual-and-Causality Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Causal Reasoning
- The Self-and-Mutual-and-Information Model: A General Computational Framework for Adaptive Coordination Between Autonomous Processing, Interactive Mechanisms, and Information Dynamics
- Quantum Entanglement and Superluminal Particles: Preservation of Causality under Relativistic Constraints
- THE QUALIFIED DETERMINISM PRINCIPLE: A Framework for Information-Conditioned Causality Across Scales
- Linear causality and stability constraints on relativistic second-order magnetohydrodynamics
BibTeX
@misc{zhang2026selfmutualworldmetafuturecausalityactioninformationgoalstructureuncertaintyresourcesmemoryevidenceconstraints,
title = {{The Self–Mutual–World–Meta–Future–Causality–Action–Information–Goal–Structure–Uncertainty–Resources–Memory–Evidence–Constraints Model: A Unified Framework for Adaptive Coordination in Computational Systems}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23433735},
url = {https://zenodo.org/records/23433735},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self–Mutual–World–Meta–Future–Causality–Action–Information–Goal–Structure–Uncertainty–Resources–Memory–Evidence–Constraints Model: A Unified Framework for Adaptive Coordination in Computational Systems
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23433735
UR - https://zenodo.org/records/23433735
AB - Computational systems increasingly operate in environments where effective behavior depends not only on internal processing and interaction among computational agents, but also on environmental representation, metacognitive control, future-state prediction, causal analysis, action selection, information management, goal representation, structural organization, uncertainty estimation, resource allocation, memory, evidence evaluation, and constraint satisfaction. Existing approaches address many of these functions through distinct research traditions, including world models, metareasoning, model-based planning, causal inference, multi-agent systems, information theory, memory-augmented computation, and constrained optimization. However, the relationships among these functions can create a complex coordination problem in which no single processing mechanism is necessarily optimal throughout an entire task. This paper proposes the Self–Mutual–World–Meta–Future–Causality–Action–Information–Goal–Structure–Uncertainty–Resources–Memory–Evidence–Constraints Model (SMWMFC-AIGSURMEC), a conceptual framework comprising fifteen computational objects organized into interacting functional layers. The Self object represents internal computation; the Mutual object represents interaction among computational entities; the World object represents the state and dynamics of the environment; the Meta object monitors and regulates computational processes; the Future object represents predicted possible states; the Causality object represents hypothesized cause–effect relationships; the Action object represents executable interventions; the Information object represents information acquisition and transformation; the Goal object represents desired outcomes; the Structure object represents organizational relationships; the Uncertainty object represents incomplete knowledge and predictive uncertainty; the Resources object represents computational and operational budgets; the Memory object preserves information across processing stages; the Evidence object evaluates support for claims and decisions; and the Constraints object specifies admissible behavior and solutions. The central proposal is that these objects should be coordinated through an adaptive controller that selects, configures, and terminates computational mechanisms according to task state, expected utility, uncertainty, evidence quality, causal relevance, predicted outcomes, and resource availability. The framework distinguishes the representation of an object from the mechanisms that operate on it, and it allows individual processing, cross-entity interaction, environmental simulation, metacognitive regulation, and action execution to be combined when appropriate. A formal system definition, an adaptive control objective, an operational algorithm, implementation principles, research hypotheses, and an empirical evaluation protocol are presented. The framework is a proposed theoretical and engineering design rather than an empirically validated system. Its contribution must be established through comparison with existing approaches and experiments testing whether coordinated selection among the fifteen objects improves performance, reliability, adaptability, and resource efficiency.
ER -