Have you ever met a human? We're not always the most rational bunch. More recent economic theory confronts that fact, taking into account the importance of psychology, societal influences and emotion in our decision-making.
So, are the theories that are predicated on homo economicus extinct? David C. Parkes, the George F. Colony Professor and Area Dean of Computer Science at Harvard John A. Paulson School of Engineering and Applied Sciences, doesn't think so. Humans may not always make rational decisions, but well-conceived algorithms do.
In a paper out today in the journal Science, Parkes and co-author Michael Wellman, of the University of Michigan, argue that rational models of economics can be applied to artificial intelligence (AI) and discuss the future of machina economicus.
At first glance, neoclassical economic theory and AI seem like strange bedfellows. Where and how do they overlap?
Parkes: The idea of rationality is a shared construct between AI and economics. When we frame questions in AI, we say: what are the objectives, what should be optimized and what do we know about the world we're in? The AI/economics interface has become quite fertile because there is a shared language of utility, probability, and reasoning about others.
Take, for example, the revelation principle in economics, which is a theory by which the design of economic institutions, such as markets, can be restricted to those where it is in the best interest of participants to truthfully reveal their utility functions. Today's Internet advertising systems, which are populated by artificial trading agents, are an operational version of this economic theory. Search engines are designing interfaces where advertisers reveal their budget constraints and goals, and these systems then provide the algorithms to fit those needs. You don't see many mechanisms like this in human societies but we may see them more and more in AI systems.
Where does current economic theory fall short in describing rational AI?
Machina economicus might better fit the typical economic theories of rational behavior, but we don't believe that the AI will be fully rational or have unbounded abilities to solve problems. At some point you hit the intractability limit—things we know cannot be solved optimally—and at that point, there will be questions about the right way to model deviations from truly rational behavior.
Poker is great example of a complicated reasoning problem: a lot of information is missing, you don't know the cards of the other players, you're uncertain about the card that will be dealt next and you're reasoning against another reasoning agent.
Recently, researchers developed an algorithm that effectively solves Heads Up Limit Texas Hold'em, applying game theory from economics. Researchers have developed an AI that has attained perfect rationality in this setting, and have done so using a number of general-purpose techniques. But this has come about after decades of research, and only for a restricted, two player version of poker.
But perfect rationality is not achievable in many complex real-world settings, and will almost surely remain so. In this light, machina economicus may need its own economic theories to usefully describe behavior and to use for the purpose of designing rules by which these agents interact.
Besides poker, what would a rational AI system do better than a human?
One of the more complicated things people do is buying and selling property. It's actually really hard to describe to your real estate agent what you're looking for. Your broker may have some ideas, but real feedback only comes when showing you properties.
