109 citations · 302 across the 10 of their papers we have counts for
11 papers
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas +7
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate. This is particular…
Statistical discrimination in learning agents
Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao +9
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One pr…
Collaborating with Humans without Human Data
DJ Strouse, Kevin R. McKee, Matt Botvinick +2
Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning tech…
Neural Recursive Belief States in Multi-Agent Reinforcement Learning
Pol Moreno, Edward Hughes, Kevin R. McKee +2
In multi-agent reinforcement learning, the problem of learning to act is particularly difficult because the policies of co-players may be heavily conditioned on information only ob…
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Nenad Tomasev, Kevin R. McKee, Jackie Kay +1
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore queer concerns in privacy, censorship, language, online safety, health, and…
Open Problems in Cooperative AI
Allan Dafoe, Edward Hughes, Yoram Bachrach +5
Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such…
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Raphael Köster, Kevin R. McKee, Richard Everett +7
Game theoretic views of convention generally rest on notions of common knowledge and hyper-rational models of individual behavior. However, decades of work in behavioral economics…
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
Ian Gemp, Kevin R. McKee, Richard Everett +4
In multiagent systems, the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group, but also for each individual. Pri…
Social diversity and social preferences in mixed-motive reinforcement learning
Kevin R. McKee, Ian Gemp, Brian McWilliams +3
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinfor…
Relational inductive bias for physical construction in humans and machines
Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst +4
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of…
Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, Joel Z. Leibo, Matthew G. Phillips +9
Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas. Models based on behavioral economics are only able to ex…