Proceedings of the Annual Meeting of the Cognitive Science Society

As AI models grow in power and generality, understanding how agents learn and make decisions in complex environments is critical to promoting ethical behavior. This study introduces the Odyssey, a lightweight, adaptive text-based adventure game, providing a scalable framework for exploring AI ethics and safety. The Odyssey examines the ethical implications of implementing biological drives—specifically, self-preservation—into three different agents: a Bayesian agent optimized with NEAT, a Bayesian agent optimized with stochastic variational inference, and a GPT-4o agent. The agents select actions at each scenario to survive, adapting to increasingly challenging scenarios. Post-simulation analysis evaluates the ethical scores of the agent's decisions, uncovering the trade-offs it navigates to survive. Specifically, analysis finds that when danger increases, agents ethical behavior becomes unpredictable. Surprisingly, the GPT-4o agent outperformed the Bayesian models in both survival and ethical consistency, challenging assumptions about traditional probabilistic methods and raising questions about the source of LLMs' probabilistic reasoning.