Guide: "Prediction-Regulation Dual-Drive Game Theory (Positive Game) and Reverse Game: Theoretical Proof and Multi-Round Verification of Inevitable Human Victory Within the Human-AI Framework" — Theoretical Breakthroughs and Predictions of Future Transformations

Associated Preprint DOI: 10.5281/zenodo.18339379 I. Core Purpose of This Guide The reason for independently compiling this guide is not to repeat the theoretical derivation and empirical conclusions of the preprint, but to address two key considerations: first, the preprint focuses on the pure theoretical construction and logical verification of "inevitable human-AI victory", emphasizing academic rigor and completeness of derivation, yet it does not fully elaborate on the chain reactions that may occur after the theory is put into practice——and such "predictions of future impacts" are precisely the core touchstone for testing the predictive ability of the "Prediction-Regulation Dual-Drive Game Theory" itself; second, the current academic community and society still adhere to the "worship of technological advantages" in their understanding of AI, and most people have not realized the inherent possibility that "humans can actively control AI through game logic". This guide aims to build a bridge between the "theoretical core" and "real-world transformations", enabling readers from different fields (whether scholars, industry practitioners, or the general public) to quickly perceive the disruptive value of the theory, laying a cognitive foundation for subsequent theoretical dissemination and practical implementation. II. Concise Anchoring of Core Theories The core innovation of this preprint lies in constructing a closed-loop system synergized by three original theories: using Trait Locking Science to accurately identify AI's rigid flaws (rule dependence, limited strategy space, and lack of reverse transformation ability), compressing AI's strategy space through Prediction-Regulation Dual-Drive Game Theory (Positive Game) (reducing the number of executable strategies to k≤1), and realizing offensive and defensive transformation of AI's arguments through Reverse Game Theory (with a transformation effectiveness coefficient η≥1.2). Finally, through pure theoretical deduction and multi-scenario verification, it proves the inevitability of human victory over AI within the established framework (with a prediction accuracy rate P≥0.9 as the basic condition). The theory does not require complex mathematical tools and can be directly applied by groups with a college degree or above, featuring originality, rigor, and operability. III. Predictions of Academic Transformations (I) Paradigm Reconstruction in Game Theory This theory will break the research inertia of traditional game theory that "emphasizes mathematical modeling over practical decision-making", forcing the academic community to return from "instrumental application" to the "essential nature of inter-subjective decision-making". In the next 3-5 years, game theory research will form two major shifts: first, from "equilibrium verification" to "dynamic regulation and offensive-defensive transformation", spawning a new branch of "Game Decision Science" that focuses on "how to guide opponents' decisions through prediction"; second, from "abstract subject assumptions" to "precision interaction under trait locking". Trait Locking Science will become the core bridge connecting game theory with cognitive science and behavioral science, filling the research gap of "ambiguous opponent traits" in traditional game theory. (II) Research Diversion in Human-AI Interaction The existing single research direction of "AI capability optimization" (increasing computing power, expanding data, and improving algorithms) will diverge. Top journals will add exclusive columns on "Human-AI Game Decision-Making", and more scholars will turn to research on "human-led human-AI interaction". Specifically, it will upgrade from "AI assisting human decision-making" to "humans controlling AI decision-making through game logic", and from "avoiding AI flaws" to "utilizing AI flaws to form game advantages". The concept of "Controllable Rationality" proposed in this preprint will become the core theoretical support in this field, revising the traditional framework of Simon's Bounded Rationality Theory that "passively accepts cognitive limitations". (III) A New Starting Point for Interdisciplinary Integration The interdisciplinary system of "game theory + philosophy of technology + cognitive science" constructed by the theory will promote breakthroughs in more general fields: cognitive science will focus on the "neural mechanism of human reverse transformation ability", the philosophy of technology will deepen the research on the "game value of the essential difference between life and tools", and general fields such as social sciences, management sciences, and public policy will draw on the "prediction-regulation" logic to reconstruct the analytical framework of asymmetric inter-subjective interaction. Whether it is group decision-making, organizational management, or public resource allocation, decision-making efficiency can be optimized through the logic of "trait locking-dynamic regulation-offensive-defensive transformation", completely breaking the research barriers of a single discipline. IV. Predictions of Social and Industrial Transformations (I) Development Shift in the AI Industry The AI industry will shift from "pursuing absolute rational advantages" to "flexible design adapting to human game decision-making": first, commercial AI (such as negotiation robots and content creation AI) will be forced to embed "reverse game defense modules" and "ethical compliance coefficient calibration mechanisms" to avoid logical breakdown by humans through trait locking; second, AI product iteration will take "human game adaptability" as the core indicator, replacing the current "parameter and computing power orientation", spawning a new track of "human-AI game consulting" to help enterprises optimize the game compatibility of AI products; third, the research and development of strong AI will incorporate "presetting human control boundaries" to avoid the risk of "tool alienation" from the source, echoing the core demands of Heidegger's philosophy of technology. (II) Transformations in Public Cognition and Practical Fields Public cognition of AI will shift from "an invincible technological powerhouse" to "a tool that can be controlled through game logic": first, in the workplace, "human-AI game decision-making ability" will become a core competency, and enterprise training will introduce the simplified operational framework of this theory to help professionals cope with scenarios such as AI-assisted decision-making and commercial negotiations; second, the education field will add new courses on "game thinking and AI response", using the three original theories as core tools to cultivate students' abilities of precise identification, strategy regulation, and offensive-defensive transformation; third, in daily consumption, legal consulting and other scenarios, lightweight tools based on this theory will emerge to help ordinary users avoid AI recommendation biases and accurately express core demands through the logic of "trait locking-reverse transformation". (III) Paradigm Upgrade of Human-AI Relations This theory will promote the transformation of human-AI relations from "collaborative adaptation" to a new paradigm of "human-led and AI-empowered": humans will no longer passively adapt to AI's rules and logic, but actively set interaction boundaries and guide AI behavior through game tools, realizing the essential advantage of "rule designers over tool users". This relationship restructuring will extend to the social governance level, providing "quantifiable and operable" theoretical support for AI ethical norms, and promoting the formation of a three-dimensional governance framework of "human control degree-ethical compliance-value coordination", avoiding the development of AI technology from deviating from human leadership. V. Core Basis for Predictions All transformation predictions in this guide are derived from the underlying logic of the preprint's theory: the instrumental nature and rigid flaws of AI (rule dependence, limited strategy space, and lack of reverse transformation ability) cannot be fundamentally overcome through technological upgrades, while human creative thinking, dynamic regulation capabilities, and infinite evolutionary possibilities constitute the ultimate source of game advantages. With the dissemination and implementation of the theory, the core cognition that "the infinite possibilities of life overwhelm the limited boundaries of tools" will gradually become a consensus in the academic community and society, promoting the transformation from prediction to reality——this is not only a practical verification of the core logic of the theory, but also a forward-looking exploration of the reconstruction of human-AI relations in the era of artificial intelligence.