Workshop Information
Tuesday 14 December - NeurIPS 2021

Key Dates

  • Paper Submission Deadline: 25 September 2021

  • Final Decisions by: 23 October 2021

  • Workshop: Tuesday 14 December 2021

Mentorship Program

The Cooperative AI mentorship program will pair a junior researcher who plans to submit a paper to the workshop with a senior researcher with expertise that could benefit the paper. The senior researcher will provide feedback on the research method and paper. The suggested format is for the mentor and mentee to exchange information via email and meet to discuss the work via conference call.


Sign-up link for mentees: https://forms.gle/1vB2paJfRojCfv6s9

Sign-up link for mentors: https://forms.gle/NgiiCLMRKt1yHYwD7


We will match mentors and mentees on a rolling basis, so please sign up ASAP. Any junior researcher (< 3 years of PhD) is welcome to apply, but we will prioritize matching those from underrepresented groups if there are not enough mentors. Once we match a mentor and mentee, we will reach out to connect them via the contact details provided via the sign-up form.

Aims and Focus

The human ability to cooperate in a wide range of contexts is a key ingredient in the success of our species. Problems of cooperation—in which agents seek ways to jointly improve their welfare—are ubiquitous and important. They can be found at every scale, from the daily routines of highway driving, communicating in shared language and work collaborations, to the global challenges of climate change, pandemic preparedness and international trade.

With AI agents playing an ever greater role in our lives, we must endow them with similar abilities. In particular they must understand the behaviors of others, find common ground by which to communicate with them, make credible commitments, and establish institutions which promote cooperative behavior. By construction, the goal of Cooperative AI is interdisciplinary in nature. Therefore, our workshop will bring together scholars from diverse backgrounds including reinforcement learning (and inverse RL), multi-agent systems, human-AI interaction, game theory, mechanism design, social choice, fairness, cognitive science, language learning, and interpretability. Our workshop will include a panel discussion with experts spanning these diverse communities.

This year we will organize the workshop along two axes. First, we will discuss how to incentivize cooperation in AI systems, developing algorithms that can act effectively in general-sum settings, and which encourage others to cooperate. Such systems are crucial for preventing disastrous outcomes (e.g. in traffic), and for achieving joint gains in interaction with other agents, human or machine (e.g. in bargaining problems). In the long run such systems may also provide improved incentive design mechanisms to help humans avoid unfavorable equilibria in real world settings.

The second focus is on how to implement effective coordination, given that cooperation is already incentivized. Even in situations where everyone agrees to cooperate, it is still very difficult to establish and perpetuate the common conventions, language and division of labour necessary to carry out a cooperative act. For example, we may examine zero-shot coordination, in which AI agents need to coordinate with novel partners at test time. This setting is highly relevant to human-AI coordination, and provides a stepping stone for the community towards full Cooperative AI.

Call for Papers

Please submit paper submissions through OpenReview via this link.

We invite high-quality paper submissions of new work in the following topics:

  • Agent cooperation

  • Multi-agent communication

  • Team formation, trust, and reputation

  • Negotiation and bargaining

  • Resolving commitment problems

  • Agent societies, organizations, and institutions

  • Equilibrium computation

  • Markets, mechanism design, and economic cooperation

  • Multi-agent learning

  • Multi-agent and Human-AI coordination (including zero-shot)

  • Human cooperation, theory of mind, peer modeling, and social cognition.


Accepted papers will be presented during joint virtual poster sessions and be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. Submissions should be anonymous, up to eight pages excluding references, acknowledgements, and supplementary material, and should follow NeurIPS format. Submissions must not have been previously accepted to the main NeurIPS 2021 conference. Submissions should also not have been previously accepted at other ML or AI conferences (ICML, ICLR, AAAI, AAMAS, IJCAI). The review process will be double-blind.