AI has focused on a paradigm in which intelligence inheres in a single agent, and in which agents should be autonomous so that they can exhibit their intelligence independent of any tethering to human intelligence. Social aspects are entirely secondary in this paradigm. Thus, when AI systems are deployed in social contexts, the overall design of such systems is often naive—a centralized entity provides services to passive agents and reaps the rewards. Such a paradigm need not be the dominant paradigm for information technology. In a broader framing, agents are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems. Agents may supply data and other resources to the system, only if it is in their interest to do so. Critically, intelligent inheres as much in the overall system as it does in individual agents, be they humans or computers. This perspective is not unfamiliar to economics researchers, and thus a first goal in this line of work is to bring economics into contact with computer science and statistics. The long-term goal is two-fold—to provide a broader conceptual foundation for emerging real-world AI systems, and to upend received wisdom in the computational, economic, and inferential disciplines.
