Networks of Strategic Agents: Social Norms, Incentives and Learning

Much of society is organized in networks: autonomous communication networks, social networks, economic networks. However, to enable the efficient and robust operation of networks several key challenges need to be overcome: the interacting agents (people, devices, software, companies, etc.) are strategic, heterogeneous and have incomplete information about the other agents. This dissertation develops systematic solutions to address these challenges. The first part of this dissertation studies how to incentivize self-interested agents to take socially optimal actions. In many service exchange networks, agents connect to other agents to request services (e.g. favors, goods, information etc.); however, since agents who provide service gain no (immediate) benefit but only incur costs, they have an incentive to withhold their service. This dissertation designs and analyzes incentives mechanisms that rely on various types of social reciprocation, including exchange of fiat money and rating systems. The analysis builds on the theory of repeated and stochastic games with imperfect monitoring, but requires significant innovations to address the unique characteristics and requirements of online communities and networks: the anonymity and heterogeneity of agents, informational constraints (for both agents and the network manager), real-time constraints, network topology constraints, etc. The second part of this dissertation studies how agents learn in networks. In many networks, agents need to learn how to cooperate with each other to achieve a common goal. This dissertation designs the first multi-agent learning algorithm that is able to achieve cooperation without requiring any explicit message exchange with other agents and to provide performance guarantees, including characterizing the speed of convergence. A final part of the dissertation aims to address the problem of adverse selection in networks. The goal is to design and analyze reputation-based social norms that aim to eliminate agents of low qualities from participating in networks and communities. For this, a system of reputation in which agents’ reputation is determined based on their productivity when working alone or with others. If the agents’ reputation at the time of their evaluation (determined by the social norm) is higher than a quality/productivity level (determined by the social norm) they can remain in the network; otherwise they are expelled. The dissertation designs and analyzes social norms that maximize the productivity of the society.