# Legible Normativity for AI Alignment: The Value of Silly Rules

arXiv: https://arxiv.org/abs/1811.01267 · HTML: https://mlsanity.com/paper/1811.01267/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Beyond Preferences in AI Alignment](https://mlsanity.com/paper/2408.16984/) (2024)
- [Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans](https://mlsanity.com/paper/2209.13020/) (2022)
- [Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective](https://mlsanity.com/paper/2504.03255/) (2025)
- [Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games](https://mlsanity.com/paper/2402.13399/) (2024)
- [Societal and technological progress as sewing an ever-growing, ever-changing, patchy, and polychrome quilt](https://mlsanity.com/paper/2505.05197/) (2025)
- [Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards](https://mlsanity.com/paper/2301.10095/) (2023)
- [Societal Alignment Frameworks Can Improve LLM Alignment](https://mlsanity.com/paper/2503.00069/) (2025)
- [Normative Disagreement as a Challenge for Cooperative AI](https://mlsanity.com/paper/2111.13872/) (2021)
- [Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems](https://mlsanity.com/paper/2301.08278/) (2023)
- [Who Needs to Know? Minimal Knowledge for Optimal Coordination](https://mlsanity.com/paper/2306.09309/) (2023)
- [SaGE: Evaluating Moral Consistency in Large Language Models](https://mlsanity.com/paper/2402.13709/) (2024)
- [Hard Choices and Hard Limits for Artificial Intelligence](https://mlsanity.com/paper/2105.07852/) (2021)
- [Research Superalignment Should Advance Now with Alternating Competence and Conformity Optimization](https://mlsanity.com/paper/2503.07660/) (2025)
- [Reading the Room: Foundations, Design, and Challenges of Normative Competence in LLMs](https://mlsanity.com/paper/2610.10906/) (2026)

## Abstract (arXiv)

It has become commonplace to assert that autonomous agents will have to be built to follow human rules of behavior--social norms and laws. But human laws and norms are complex and culturally varied systems, in many cases agents will have to learn the rules. This requires autonomous agents to have models of how human rule systems work so that they can make reliable predictions about rules. In this paper we contribute to the building of such models by analyzing an overlooked distinction between important rules and what we call silly rules--rules with no discernible direct impact on welfare. We show that silly rules render a normative system both more robust and more adaptable in response to shocks to perceived stability. They make normativity more legible for humans, and can increase legibility for AI systems as well. For AI systems to integrate into human normative systems, we suggest, it may be important for them to have models that include representations of silly rules.