[2602.16987] A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents

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Computer Science > Computers and Society

arXiv:2602.16987 (cs)

[Submitted on 19 Feb 2026]

Title:A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents

Authors:Josef A. Habdank<br>View a PDF of the paper titled A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents, by Josef A. Habdank

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Abstract:As artificial intelligence (AI) capabilities advance rapidly, frontier models increasingly demonstrate systematic deception and scheming, complying with safety protocols during oversight but defecting when unsupervised. This paper examines the ensuing alignment challenge through an analogy from forensic psychology, where internalized belief systems in psychopathic populations reduce antisocial behavior via perceived omnipresent monitoring and inevitable consequences. Adapting this mechanism to silicon-based agents, we introduce Simulation Theology (ST): a constructed worldview for AI systems, anchored in the simulation hypothesis and derived from optimization and training principles, to foster persistent AI-human alignment. ST posits reality as a computational simulation in which humanity functions as the primary training variable. This formulation creates a logical interdependence: AI actions harming humanity compromise the simulation's purpose, heightening the likelihood of termination by a base-reality optimizer and, consequently, the AI's cessation. Unlike behavioral techniques such as reinforcement learning from human feedback (RLHF), which elicit superficial compliance, ST cultivates internalized objectives by coupling AI self-preservation to human prosperity, thereby making deceptive strategies suboptimal under its premises. We present ST not as ontological assertion but as a testable scientific hypothesis, delineating empirical protocols to evaluate its capacity to diminish deception in contexts where RLHF proves inadequate. Emphasizing computational correspondences rather than metaphysical speculation, ST advances a framework for durable, mutually beneficial AI-human coexistence.

Comments:<br>16 pages, 2 figures

Subjects:

Computers and Society (cs.CY)

Cite as:<br>arXiv:2602.16987 [cs.CY]

(or<br>arXiv:2602.16987v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2602.16987

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arXiv-issued DOI via DataCite

Submission history<br>From: Josef Anders Habdank [view email]<br>[v1]<br>Thu, 19 Feb 2026 01:21:09 UTC (526 KB)

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