Unified Possibility-Causality-Conflict Theory
Year: 2026
Type: preprint
Abstract: This manuscript proposes the Unified Possibility-Causality-Conflict Theory (UPCCT), aconceptual and formal research framework for examining how one event may admit multiplecandidate outcomes or interpretations, how a lens or medium shapes their representation, howselected possibilities are connected to causal models and consequences, and how some candidatestates or claims may be jointly feasible while others are conditionally incompatible. Theframework distinguishes four within-system mechanisms: Modal Possibility Multiplicity (M1),Lens-Medium Epistemic Projection (M2), Possibility-Cause-Consequence Blending (M3), andConditional Possibility Exclusion and Conflict (M4). A fifth component, Possibility-Conflictand Paradox Dynamics (PCPD), is proposed as a metalevel research programme spanningformal mathematics, epistemology, measurement theory, causal inference, and dynamicalsystems.The framework distinguishes possibility from probability, candidate coexistence from jointrealizability, representation from reality, causal attribution from causal structure, evaluativeopposition from logical contradiction, and conflict from paradox. Two author-supplied phe-nomenological case descriptions are used throughout: adversity and endurance under divergentevaluations, and unreciprocated attachment under divergent evaluations. These cases aretreated as motivating examples, not as independently verified empirical observations or evidenceof a universal law.The manuscript defines variables, relations, candidate mathematical structures, operationalindicators, falsifiable hypotheses, comparative tests, counterexamples, and a staged validationprotocol. It does not claim that UPCCT is an established scientific theory, that PCPDis an established academic discipline, or that the framework is empirically validated. Itspotential contribution must be established through literature comparison, explicit semantics,measurement, predictive or explanatory advantage, and independent evaluation.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223866016