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This paper adds to the efforts of evolutionary ethics to naturalize moralityby providing specific insights derived from a computational ethics view. Wepropose a stylized model of human decision-making, which is based onReinforcement Learning, one of the most successful paradigms in ArtificialIntelligence. After the main concepts related to Reinforcement Learning havebeen presented, some particularly useful parallels are drawn that canilluminate evolutionary accounts of ethics. Specifically, we investigate theexistence of an optimal policy (or, as we will refer to, objective ethicalprinciples) given the conditions of an agent. In addition, we will show howthis policy is learnable by means of trial and error, supporting our hypotheseson two well-known theorems in the context of Reinforcement Learning. Weconclude by discussing how the proposed framework can be enlarged to studyother potentially interesting areas of human behavior from a formalizableperspective.

From computational ethics to morality: how decision-making algorithms  can help us understand the emergence of moral principles, the existence of an  optimal behaviour and our ability to discover it