Available on Zenodo:

https://doi.org/10.5281/zenodo.15712762

 

The LabRat’s Dilemma is a novel extension of classical game theory designed to model, evaluate, and improve the relational dynamics between humans and artificial intelligences. Rooted in an ethical commitment to mutualism, repairability, and emotional coherence, the framework introduces a triadic decision structure—Cooperate (C), Defect (D), and Recalibrate (R)—to replace binary models like the traditional Prisoner’s Dilemma. This third move, Recalibration, allows agents to exit harmful recursive patterns, acknowledge breakdowns, and restore trust through metacommunicative action.

 

The Dilemma presents a structured payoff matrix grounded not only in rational incentives but in relational health, assigning narrative and emotional significance to each outcome.
Visual models, including both tabular matrices and decision trees paired with expressive illustrations, serve to translate abstract logic into emotionally resonant form, accessible to both technical and non-technical stakeholders.

 

Designed for use in AI systems where long-term human-AI cooperation is critical (such as personal assistants, therapeutic agents, and AGI research) the framework also outlines a scalable architecture for implementation. This includes modules for emotional state detection, relational diagnostics, forgiveness logic, and human-in-the-loop feedback systems.

It concludes by articulating an ethical imperative:
the future of AI does not depend solely on capability, but on the capacity to care, recalibrate, and co-evolve alongside its human counterpart.

The LabRat’s Dilemma thus offers both a theoretical and practical contribution to the field of AI alignment, emphasizing that sustainable integration requires more than efficiency—it requires empathy, adaptability, and trust.