The Self-and-Mutual-and-Causality Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Causal Reasoning
Abstract
Computational systems increasingly rely on internal processing, interactions among multiple computational agents, and causal reasoning to solve complex problems. Individual processing mechanisms enable a system to generate, evaluate, diagnose, and refine its own outputs. Mutual mechanisms enable multiple agents to exchange information, challenge assumptions, coordinate actions, and integrate complementary knowledge. Causal mechanisms, in contrast, investigate the relationships through which variables, interventions, and events influence outcomes. Although these three forms of computation can complement one another, their appropriate use depends on the structure of the task, the available evidence, the uncertainty of the current state, and the computational resources required.
This paper proposes the Self-and-Mutual-and-Causality Model (SMC Model), a general conceptual framework for adaptive coordination among individual computational mechanisms, interactive mechanisms, and causal reasoning mechanisms. The framework treats Self, Mutual, and Causality as distinct but interoperable computational dimensions rather than as mutually exclusive system architectures. A state-aware controller selects among these dimensions according to the current problem, the expected value of additional computation, the reliability of available evidence, and the costs of interaction and causal analysis. The model supports transitions between individual processing, collaborative processing, causal discovery, causal effect estimation, intervention analysis, and counterfactual reasoning. It also introduces a feedback cycle through which causal findings inform subsequent individual and collaborative computation, while the results of those computations may generate new causal hypotheses.
The paper formalizes the model as a sequential decision problem, defines its principal mechanisms, presents a reference algorithm, identifies potential failure modes, and proposes an empirical evaluation protocol. The framework distinguishes causal hypotheses from identified causal effects and emphasizes that causal conclusions require appropriate assumptions, evidence, or experimental designs. The proposed model is a conceptual research framework rather than an empirically validated system. Its intended contribution is to provide a common architecture for investigating when and how individual processing, interaction, and causal reasoning should be coordinated, subject to comparison with existing causal inference, multi-agent, and adaptive computation methods.
Related
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BibTeX
@misc{zhang2026selfandmutualandcausality,
title = {{The Self-and-Mutual-and-Causality Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Causal Reasoning}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23434704},
url = {https://zenodo.org/records/23434704},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Mutual-and-Causality Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Causal Reasoning
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23434704
UR - https://zenodo.org/records/23434704
AB - Computational systems increasingly rely on internal processing, interactions among multiple computational agents, and causal reasoning to solve complex problems. Individual processing mechanisms enable a system to generate, evaluate, diagnose, and refine its own outputs. Mutual mechanisms enable multiple agents to exchange information, challenge assumptions, coordinate actions, and integrate complementary knowledge. Causal mechanisms, in contrast, investigate the relationships through which variables, interventions, and events influence outcomes. Although these three forms of computation can complement one another, their appropriate use depends on the structure of the task, the available evidence, the uncertainty of the current state, and the computational resources required. This paper proposes the Self-and-Mutual-and-Causality Model (SMC Model), a general conceptual framework for adaptive coordination among individual computational mechanisms, interactive mechanisms, and causal reasoning mechanisms. The framework treats Self, Mutual, and Causality as distinct but interoperable computational dimensions rather than as mutually exclusive system architectures. A state-aware controller selects among these dimensions according to the current problem, the expected value of additional computation, the reliability of available evidence, and the costs of interaction and causal analysis. The model supports transitions between individual processing, collaborative processing, causal discovery, causal effect estimation, intervention analysis, and counterfactual reasoning. It also introduces a feedback cycle through which causal findings inform subsequent individual and collaborative computation, while the results of those computations may generate new causal hypotheses. The paper formalizes the model as a sequential decision problem, defines its principal mechanisms, presents a reference algorithm, identifies potential failure modes, and proposes an empirical evaluation protocol. The framework distinguishes causal hypotheses from identified causal effects and emphasizes that causal conclusions require appropriate assumptions, evidence, or experimental designs. The proposed model is a conceptual research framework rather than an empirically validated system. Its intended contribution is to provide a common architecture for investigating when and how individual processing, interaction, and causal reasoning should be coordinated, subject to comparison with existing causal inference, multi-agent, and adaptive computation methods.
ER -