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 a new challenge to understand the mechanisms of LLMs' probabilistic reasoning.

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In Proceedings of the 47th Annual Meeting of the Cognitive Science Society, 2025.

@inproceedings{waldner:arxiv25, title={The Odyssey of the Fittest: Can Agents Survive and Still Be Good?}, author={Dylan Waldner and Risto Miikkulainen}, booktitle={Proceedings of the 47th Annual Meeting of the Cognitive Science Society}, month={ }, url="http://nn.cs.utexas.edu/?waldner:cogsci25", year={2025} }