Panel Leader: Jared Curhan (MIT)
Jared Curhan, Massachusetts Institute of Technology
The MIT AI Negotiation Competition
Inspired by Robert Axelrod’s (1984) famous Evolution of Cooperation tournament, the MIT AI Negotiation Competition enlisted negotiation experts, students, and faculty from over 50 countries to prompt large language models (LLMs) to engage in a wide variety of integrative and distributive negotiation tasks. Their challenge was to maximize value claiming (proportion of resources captured), value creation (joint benefit), and subjective value (counterpart satisfaction). A round-robin design involving thousands of negotiations yielded a rich and multifaceted dataset. Preliminary findings suggest that, as in Axelrod’s original tournaments, “nice” strategies play a surprisingly pivotal role in objective and subjective negotiation outcomes. This presentation will share key results, highlighting innovative prompts developed by the competition’s winners and examining broader theoretical implications for negotiation and AI.
Joel Leibo, Google DeepMind and Kings College
The Melting Pot Contest
Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by “solipsistic” approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a “substrate”) with a reference set of co-players (a “background population”), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity.
Johnathan Mell, University of Central Florida
The Automated Negotiating Agents Competition
Dr. Johnathan Mell will join the panel to discuss his five-year leadership of the Human Agent League at the Automated Negotiating Agents Competition (ANAC) from 2017 to 2021. During this period, Dr. Mell developed and maintained the IAGO platform, an online negotiation agent system that served as the competition platform and contributed to advancing the field of human agent negotiation. ANAC was a sponsored competition held at major conferences like IJCAI and AAMAS, attracting contributions from numerous academic and private industry groups. This international collaboration not only enhanced the state of the art in negotiation systems but also fostered new cognitive theories and practical insights into human-focused negotiating agents. Dr. Mell will discuss the significant advancements achieved during the competition, the complexities of sustaining a platform that supported cutting-edge research in artificial intelligence and cognitive science, and the potential future of IAGO 2.0 and subsequent generations of negotiating agent competitions.
Discussant
Robert Axelrod, University of Michigan