On intelligenCE And Networks - Synergistic research in Bayesian Statistics, Microeconomics and Computer Sciences - OCEAN

Until recently, most of the major advances in machine learning and decision making have focused on a centralised paradigm in which data are aggregated at a central location to train models and/or decide on actions. This paradigm faces serious flaws in many real-world cases. In particular, centralised learning risks exposing user privacy, makes inefficient use of communication resources, creates data processing bottlenecks, and may lead to concentration of economic and political power. It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralised networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges. OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification at the agent's level. OCEAN will study the interaction of learning with market constraints (scarcity, fairness), connecting adaptive microeconomics and market-aware machine learning. OCEAN builds on a decade of joint advances in stochastic optimisation, probabilistic machine learning, statistical inference, Bayesian assessment of uncertainty, computation, game theory, and information science, with PIs having complementary and internationally recognised skills in these domains. OCEAN will shed a new light on the value and handling data in a competitive, potentially antagonistic, multi-agent environment, and develop new theories and methods to address these pressing challenges. OCEAN will involve a fundamental departure from standard approaches and leads to major scientific interdisciplinary endeavours that will transform statistical learning in the long term while opening up exciting and novel areas of research.

Crucial to our work will be the development of algorithms to achieve our aims. We will develop both optimisations and sampling tools, and our approach will be rigorous requiring theoretical results to underpin our methods. We will make contributions to the emerging field in Machine Learning called Federated Learning, and develop methodologies which have strong privacy and statistical guarantees. However while federated learning deals with distributed learning, we wish to go considerably further to consider interacting, decision-making networked agents (not just inert collectors of data). To achieve this we will need to introduce economic endgame-theoretic ideas to understand concepts such as competition and social welfare. We will develop multi-armed bandit methods to deal with strategic experimentation and also consider online matching procedures within a dynamic exchange network.

The science behind OCEAN is a blend of new methods from numerical probability, Bayesian computational statistics, machine learning, distributed algorithms, multi-agent systems, and game theory, all deeply rooted in theoretical validation. Our vision to advance theory is critical to our proposal, as quantitative and rigorous statements about performance are essential to formulate meaningful trade-offs between computational, economic, and inferential goals.