EC 2024 Workshop on
Foundation Models and Game Theory

Location: Room 2410, Yale School of Management
Webinar: Link (password is sent to registrants; see EC 2024 Registrations)

Workshop Theme

Foundational models (FMs) such as Large Language Models (LLMs) are machine learning models that have been pre-trained with large datasets and can be efficiently adapted to various downstream tasks.  Given the tremendous impacts that FMs have been making, FMs could be a key step towards building AI agents that can autonomously make decisions to cooperate or compete with other AI agents and humans to achieve their ultimate goals in complex multi-agent environments.  In particular, FMs have been pushing the limits of the complexity of the environments in which such AI agents interact via natural language and multimodal data.  However, in many cases, the strategic reasoning capabilities of such agents has been severely lacking.  In parallel, game theory has been laying the strong foundation for the strategic reasoning capabilities of such AI agents.

It is therefore the goal of this workshop to bring together researchers working on foundation models, game theory, and their intersection to exchange ideas on how to push the frontiers of foundation models and game theory towards realizing better (e.g., more efficient, more sustainable, happier, safer) society with strategic AI agents.  The topics of the workshop  include but are not limited to:

• The creation of strategic LLMs.  How to augment existing LLMs with strategic reasoning capabilities based on game theory.

• FMs for game theory.  How to use FMs  to broaden the applicability of game theory and mechanism design to complex multi-agent systems that cannot be handled with traditional approaches.  For example, one may train FMs for solving complex games or combine natural language capabilities, such as the ones provided by LLMs, with game theory in settings such as negotiation.  FMs may also substantially improve data-efficiency of learning approaches in game theory and mechanism design.  Moreover, LLMs may be used as agents in the study of behavioral game theory.

• Game theory for a FM.  Currently, FMs require huge amounts of data and computational resources to train. It may be possible to use game theoretic approaches to improve the learning process of FMs, reduce the computational or data requirements, and enhance the performance or robustness of the FMs at the inference phase.  

• Game theory for multi-FM applications. Orchestrating multiple FMs is expected to yield significant benefits in terms of the efficiency and performance of FM-based applications. To this end, approaches of game theory and mechanism design may be used, for example, to improve the way applications composed of multiple LLMs are built.  Game theory would also give us better understandings of the consequences of the interactions of multiple FMs.

• Game theory for the societal implications of  FMs.  For example, we may better understand the impacts of the FMs on society with game theoretic methods and design mechanisms that are appropriate for societies that involve both humans and FMs.

This workshop builds on the DIMACS Workshop on Foundation Models, Large Language Models, and Game Theory, which was held on October 19-20, 2023.

Program

8:00 - 8:30 Breakfast

8:30 - 10:00 Session 1

• 8:30 - 8:35 Opening

• 8:35 - 9:35 [Keynote speech 1] Kevin Leyton-Brown, STEER: Assessing the Economic Rationality of Large Language Models

Abstract: There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions -- and more broadly, determining whether an LLM agent is reliable enough to be trusted -- requires a methodology for assessing such an agent's economic rationality. This talk describes an effort to provide one. Our approach begins by surveying the economic literature on rational decision making, taxonomizing a large set of fine-grained "elements of rationality" that an agent should exhibit, along with dependencies between them. We then propose a benchmark distribution that quantitatively scores an LLM's performance on these elements and, combined with a user-provided rubric, produces a "STEER report card." Finally, we describe the results of a large-scale empirical experiment with 20 different LLMs, characterizing the both current state of the art and the impact of different model sizes on models' ability to exhibit rational behavior.

• 9:35 - 10:00 [Talk 1] Yifan Wu, Jason Hartline; ElicitationGPT: Text Elicitation Mechanisms via Language Models

10:00 - 10:30 Coffee break

10:30 - 11:00 Session 2

• 10:30 - 10:55 [Talk 2] Jacob Makar-Limanov, Arjun Prakash, Denizalp Goktas, Nora Ayanian, Amy Greenwald; STA-RLHF: Stackelberg Aligned Reinforcement Learning with Human Feedback

11:00 - 12:00 Poster session

12:00 - 1:00 Lunch

1:00 - 3:00 Session 3

• 1:00 - 2:00 [Keynote speech 2] Paul Dütting, Mechanism Design for Large Language Models

Abstract: We investigate auction mechanisms for AI-generated content, focusing on applications like ad creative generation. In our model, agents’ preferences over stochastically generated content are encoded as large language models (LLMs). We propose an auction format that operates on a token-by-token basis, and allows LLM agents to influence content creation through single dimensional bids. We formulate two desirable incentive properties and prove their equivalence to a monotonicity condition on output aggregation. This equivalence enables a second-price rule design, even absent explicit agent valuation functions. Our design is supported by demonstrations on a publicly available LLM.

Joint work with Vahab Mirrokni (Google Research), Renato Paes Leme (Google Research), Song Zuo (Google Research), and Haifeng Xu (U Chicago and Google Research)

• 2:00 - 2:25 [Talk 3] Ermis Soumalias, Michael Curry, Sven Seuken; Truthful Aggregation of LLMs with an Application to Online Advertising

• 2:25 - 2:50 [Talk 4] Keegan Harris, Liu Leqi, Emma Pierson; Marketplace Design for LLM-Based Sales

3:00 - 3:30 Coffee break

3:30 - 5:30 Session 4

• 3:30 - 3:55 [Talk 5] Atrisha Sarkar, Andrei Ioan Muresanu, Carter Blair, Rakshit Trivedi, Gillian K Hadfield; Normative Modules: A Generative Agent Architecture for Learning Norms that Supports Multi-Agent Cooperation

• 3:55 - 4:20 [Talk 6] Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Bo Jin, Hongyuan Zha; Tackling Multi-Agent Credit Assignment with Disentangled Decision Making

• 4:20 - 4:45 [Talk 7] Inwon Kang, Sikai Ruan, Jui-Chien Lin, Tyler Ho, Farhad Mohsin, Oshani Seneviratne, Lirong Xia; LLM-powered Preference Learning from Natural Language

• 4:45 - 5:10 [Talk 8] Sara Fish, Yannai A. Gonczarowski, Ran Shorrer; Algorithmic Collusion by Large Language Models

• 5:10 - 5:30 Discussion & Closing

Keynote speakers

Accepted papers

• Oral & Poster presentations

  • Yifan Wu, Jason Hartline; ElicitationGPT: Text Elicitation Mechanisms via Language Models

  • Jacob Makar-Limanov, Arjun Prakash, Denizalp Goktas, Nora Ayanian, Amy Greenwald; STA-RLHF: Stackelberg Aligned Reinforcement Learning with Human Feedback

  • Ermis Soumalias, Michael Curry, Sven Seuken; Truthful Aggregation of LLMs with an Application to Online Advertising

  • Keegan Harris, Liu Leqi, Emma Pierson; Marketplace Design for LLM-Based Sales

  • Atrisha Sarkar, Andrei Ioan Muresanu, Carter Blair, Rakshit Trivedi, Gillian K Hadfield; Normative Modules: A Generative Agent Architecture for Learning Norms that Supports Multi-Agent Cooperation

  • Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Bo Jin, Hongyuan Zha; Tackling Multi-Agent Credit Assignment with Disentangled Decision Making

  • Inwon Kang, Sikai Ruan, Jui-Chien Lin, Tyler Ho, Farhad Mohsin, Oshani Seneviratne, Lirong Xia; LLM-powered Preference Learning from Natural Language

  • Sara Fish, Yannai A. Gonczarowski, Ran Shorrer; Algorithmic Collusion by Large Language Models

• Poster presentations

  • Siddharth Prasad, Martin Mladenov, Craig Boutilier; Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems

  • Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins; Impact of Decentralized Learning on Player Utilities in Stackelberg Games

  • Keegan Harris, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins; Algorithmic Persuasion Through Simulation

  • Nicolas Della Penna; Natural Language Mechanisms via Self-Resolution with Foundation Models

  • Cole Wyeth, Carter Blair; Decision Theoretic Planning with a Large Language Model

  • Zishuo Zhao, Zhixuan Fang, Xuechao Wang, Xi Chen, Yuan Zhou; Proof-of-Learning with Incentive Security

Organizers

Call for Papers

Each submission will be reviewed from various perspectives, with particular emphasis on the alignment with the workshop theme.  We will accept a mixture of poster papers and papers that will be presented orally.

Timetable for Authors

May 21May 22, 2024 (11:59pm AoE): Paper submission deadline.

May 31, 2024: Paper accept/reject notifications.

July 8, 2024: Workshop.

Submission Instructions

We encourage authors to format their submissions as instructed in the call for papers of EC'24, but papers in other formats will also be reviewed.  However, keep in mind that each paper will be reviewed at most a few hours.  In addition to standard technical papers (those typically submitted to the main conference of EC'24), we welcome short papers and extended abstracts (of arbitrary length) with preliminary results as well as position papers that advocate positions on topics relevant to the workshops.

Papers and abstracts should be submitted through OpenReview.  If you do not yet have your OpenReview profile, please make sure to create one with an institutional email (unless you have more than two weeks before submission).  Here is OpenReview's moderation policy: 

• New profiles created without an institutional email will go through a moderation process that can take up to two weeks.

• New profiles created with an institutional email will be activated automatically.

Contact

workshop-fm-game@googlegroups.com