Can we reformulate machine learning from the ground up with multiagent in mind? Modern machine learning primarily takes an optimization-first, single-agent approach, however, many of life’s intelligent systems are multiagent in nature across a range of scales and domains such as market economies, ant colonies, forest ecosystems, and decentralized energy grids.

Generative adversarial networks represent one of the most recent successful deviations from the dominant single-agent paradigm by formulating generative modeling as a two-player, zero-sum game. Similarly, a few recent methods formulating root node problems of machine learning and data science as games among interacting agents have gained recognition (PCA, NMF). Multiagent designs are typically distributed and decentralized which leads to robust and parallelizable learning algorithms.

We want to bring together a community of people that wants to revisit machine learning problems and reformulate them as solutions to games. How might this algorithmic bias affect the solutions that arise and could we define a blueprint for problems that are amenable to gamification? By exploring this direction, we may gain a fresh perspective on machine learning with distinct advantages to the current dominant optimization paradigm.