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

TL;DR

This paper uses Reinforcement Learning to model human decision-making, providing insights into the emergence of moral principles, the existence of optimal ethical behavior, and how such policies can be learned through trial and error.

Contribution

It introduces a stylized computational model linking Reinforcement Learning with evolutionary ethics to analyze moral principles and their learnability.

Findings

Existence of an optimal policy under certain conditions

Optimal policies are learnable via trial and error

Framework can be extended to other areas of human behavior

Abstract

This paper adds to the efforts of evolutionary ethics to naturalize morality by providing specific insights derived from a computational ethics view. We propose a stylized model of human decision-making, which is based on Reinforcement Learning, one of the most successful paradigms in Artificial Intelligence. After the main concepts related to Reinforcement Learning have been presented, some particularly useful parallels are drawn that can illuminate evolutionary accounts of ethics. Specifically, we investigate the existence of an optimal policy (or, as we will refer to, objective ethical principles) given the conditions of an agent. In addition, we will show how this policy is learnable by means of trial and error, supporting our hypotheses on two well-known theorems in the context of Reinforcement Learning. We conclude by discussing how the proposed framework can be enlarged to study other potentially interesting areas of human behavior from a formalizable perspective.