We are surrounded by networks of smart agents that exchange information with each other through communication channels, as in the case of Internet of Things and autonomous systems. This project considers problems related to communication between these smart and strategic agents referred to as 'strategic communication', when the agents have misaligned or conflicting objectives. Extensions of known strategic communication models to realistic communication settings that involve channel noise and data compression remains an open challenge. The goal of this project is to develop mathematical models of strategic communication for such realistic scenarios. An immediate application of this research pertains to trustworthy and transparent machine learning (ML). ML is increasingly being used in systems that make decisions which affect people, including setting prices for items, scoring credit and job applications, filtering news and social media updates, recommending routes and places, and controlling smart homes and autonomous cars. As ML algorithms make these decisions, it is natural to ask for their transparency, which, on the other hand, makes them vulnerable to manipulation and algorithmic bias. Designing efficient and transparent ML algorithms that are robust to manipulation and bias is a difficult challenge. This project will address this challenge by explicitly taking such possibilities into account in the ML algorithm development through robust strategic communication models.
This research will address fundamental questions regarding strategic communication. This emerging research field requires revisiting key results in classical information theory and poses significant challenges, in terms of both analysis and optimization, requiring approaches and tools from multiple disciplines. The outcomes of the research are expected to constitute an essential step in understanding the interplay of game theory and economics with information theory, communications, and compression. Specific goals of this project are categorized into four groups. The first set of goals investigates optimal strategies in non-coded communication settings, with a particular focus on non-Gaussian sources and channels using tools from optimization in function spaces. The second set of goals concerns the transparency issues in ML. Building on the strategic communication models in non-coded scenarios, the project includes two research directions in this area: the quantification of the cost of transparency in various ML algorithms and the design of manipulation-aware ML algorithms. The third set of goals analyzes the role of compression in strategic communication scenarios, including the characterization and computation of fundamental limits, and the development of practical strategic data compression methods. The final set of goals explores networked strategic communication scenarios.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.