# Modeling the Formation of Social Conventions from Embodied Real-Time Interactions

arXiv: https://arxiv.org/abs/1802.06108 · HTML: https://mlsanity.com/paper/1802.06108/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [Sequential memory improves sample and memory efficiency in Episodic Control](https://mlsanity.com/paper/2112.14734/) (2021)
- [The Coordination Gap: Multi-Agent Alternation Metrics for Temporal Fairness in Repeated Games](https://mlsanity.com/paper/2603.05789/) (2026)
- [Qualia and the Formal Structure of Meaning](https://mlsanity.com/paper/2405.01148/) (2024)
- [Temporal Fair Division in Multi-Agent Systems: From Precise Alternation Metrics to Scalable Coordination Proxies](https://mlsanity.com/paper/2605.14879/) (2026)

## Abstract (arXiv)

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent's reactive behaviors (pre-wired reflexes), along with an adaptive layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.