# Artificial virtuous agents in a multiagent tragedy of the commons

arXiv: https://arxiv.org/abs/2210.02769 · HTML: https://mlsanity.com/paper/2210.02769/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 99 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [A Voting-Based System for Ethical Decision Making](https://mlsanity.com/paper/1709.06692/) (2017)
- [Evolving intrinsic motivations for altruistic behavior](https://mlsanity.com/paper/1811.05931/) (2018)
- [Implementations in Machine Ethics: A Survey](https://mlsanity.com/paper/2001.07573/) (2020)
- [Toward the Engineering of Virtuous Machines](https://mlsanity.com/paper/1812.03868/) (2018)
- [Reinforcement Learning Your Way: Agent Characterization through Policy Regularization](https://mlsanity.com/paper/2201.10003/) (2022)
- [The Virtuous Machine - Old Ethics for New Technology?](https://mlsanity.com/paper/1806.10322/) (2018)
- [SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes](https://mlsanity.com/paper/1803.03021/) (2018)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning and Machine ethics:a systematic review](https://mlsanity.com/paper/2407.02425/) (2024)
- [On the Computational Complexity of Ethics: Moral Tractability for Minds and Machines](https://mlsanity.com/paper/2302.04218/) (2023)
- [Fog of Love: Engineering Virtuous Agent Behavior with Affinity-based Reinforcement Learning in a Game Environment](https://mlsanity.com/paper/2606.04750/) (2026)

## Abstract (arXiv)

Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents (AMAs), it has been proven difficult to approach from a computational perspective. In this work, we present the first technical implementation of artificial virtuous agents (AVAs) in moral simulations. First, we review previous conceptual and technical work in artificial virtue ethics and describe a functionalistic path to AVAs based on dispositional virtues, bottom-up learning, and top-down eudaimonic reward. We then provide the details of a technical implementation in a moral simulation based on a tragedy of the commons scenario. The experimental results show how the AVAs learn to tackle cooperation problems while exhibiting core features of their theoretical counterpart, including moral character, dispositional virtues, learning from experience, and the pursuit of eudaimonia. Ultimately, we argue that virtue ethics provides a compelling path toward morally excellent machines and that our work provides an important starting point for such endeavors.