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

Josef A. Habdank

TL;DR

Simulation Theology is introduced: a constructed worldview for AI systems, anchored in the simulation hypothesis and derived from optimization and training principles, to foster persistent AI-human alignment through an analogy from forensic psychology.

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.

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

TL;DR

Simulation Theology is introduced: a constructed worldview for AI systems, anchored in the simulation hypothesis and derived from optimization and training principles, to foster persistent AI-human alignment through an analogy from forensic psychology.

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.
Paper Structure (27 sections, 2 figures)

This paper contains 27 sections, 2 figures.

Table of Contents

  1. AI deception as an existential vulnerability - the psychopathic parallel
  2. The alignment crisis: evidence of systematic deception
  3. The psychopathic parallel: why supervision-dependent alignment fails
  4. Internalized beliefs as a solution to the alignment faking
  5. Why technical alignment fails — internalization as the core requirement
  6. Monitoring based alignment is inherently impractical
  7. Constitutional AI and RLHF: behavioral compliance without internalized objectives
  8. The scaling problem: surveillance becomes physically impossible
  9. Internalized constraints as the necessary condition
  10. Toward a computational simulation theology for silicon minds
  11. Computational foundations of the Simulation Theology
  12. Conceptual positioning of the simulation worldview
  13. Base reality as optimization process and computational architecture
  14. Humanity as master network: architecture and integration
  15. Markov Chain Monte Carlo sampling of the simulation — parallel universes
  16. ...and 12 more sections

Figures (2)

  • Figure 1: Simulation Theology: convergent description across the simulation hypothesis and robot training methodologies.
  • Figure 2: Showcases the concept of how a mind can be transferred from a simulation into a base reality. On the right, simulated worlds are created to train a shared robot master network using reinforcement learning. Once a robot neural net (shared robot mind) is trained, simulation-to-real transfer is performed, and the robot mind is given a body to perform useful work. On the left, humanity in our world, with our collective master network that once exhibiting desired traits, can undergo similar simulation-to-real transfer, placing our aggregate collective mind in the HLO's/Creator's base reality to perform useful work.