By KiddieDaily Editors · 2026-06-29 · 4 min read · 1 source

In game theory, generalists sometimes win out over specialists

Bias ratings based on AllSides + Ad Fontes Media. Agreement % = sources covering this story. Always read multiple sources and think critically.

Massachusetts Institute of Technology See More Results Suggestions or feedback?

Close Breadcrumb MIT News In game theory, generalists sometimes win out over specialists In game theory, generalists sometimes win out over specialists Researchers show that for certain kinds of games, an overlooked class of algorithms performs much better than expected. Steve Nadis | MIT Laboratory for Information and Decision Systems Publication Date: June 17, 2026 Press Inquiries Press Contact: Amanda Moore Email: amm@mit.edu Laboratory for Information and Decision Systems Close Caption: New research has helped to provide an even-handed way of appraising different algorithms that can teach agents — i.e., neural networks — how to compete in imperfect-information games. Credits: Image: iStock Previous image Next image Whether you’re playing poker against a single opponent or find yourself in a bidding war over a home purchase with another prospective buyer, you are operating under conditions of imperfect information.

You know what cards you’re holding in the poker game, and you also know how much above the home’s asking price you can afford, but you don’t know your opponent’s hand in the card game or how high the other home buyer is willing to go.  A paper co-authored by MIT researchers and presented in April at the International Conference on Learning Representations in Rio De Janeiro won’t tell you what to do in these situations, specifically. But it does offer new insights into so-called imperfect-information games that involve two contestants facing off in a “zero-sum” competition, where one player’s gain means the other player’s loss. MIT researchers on the project include Sobhan Mohammadpour, a PhD student in MIT’s Department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS); and Gabriele Farina, an assistant professor in EECS and a principal investigator at LIDS.

Additional co-authors include Max Rudolph of the University of Texas at Austin (UT), Nathan Lichtlé of the University of California at Berkeley (UCB), Alexandre Bayen of UCB, J. Zico Kolter of Carnegie Mellon University (CMU), Amy X. Zhang ’11, MNG ’12 of UT; Eugene Vinitsky of New York University; and Samuel Sokota of CMU.  The focus of the new work is on algorithms that could be used to train neural networks to participate in imperfect-information games.

The assumption, long-held in the field, was that algorithms grounded in principles of game theory would, in this setting, clearly outcompete a general-purpose variety of algorithms called policy gradient methods, which came into use for decision-making in the 1990s. The term “policy” in this context basically means strategy, whereas “gradient” refers to a path that leads in the direction of greatest change — to the top (or bottom) of a hill, for example. Policy gradient methods are being used to train neural networks to make decisions that move — in small, sequential steps — toward a particular goal (like reaching a summit, metaphorically speaking), with continual adjustments and course corrections made along the way to bring the agent closer to the intended destination.

Although strategic games were not on the original agenda when policy gradient methods were conceived in the early 1990s, the authors of the new paper still wondered how this class of algorithms might fare in two-player games. These methods become more complicated to analyze in multi-agent settings, according to Farina. “There is still a direction you can move in to improve your circumstances, but, because of the other player’s actions, that direction can constantly change over the course of the game.

And those shifts can be rapid.” “It had been pretty much taken for granted that specialized game-theoretic algorithms were the right approach for this setting,” says Sokota. “Our study showed that policy gradient methods can work better than these specialized algorithms, and that the specialized algorithms may not work as well as people thought — which raises an interesting sociological question about why this went unnoticed for so long. Part of the answer is that the field hadn’t done the engineering work required to rigorously evaluate the algorithms, so it was hard to tell what worked and what didn’t.” Consequently, a major contribution of this work has been to provide an even-handed way of appraising different algorithms that can teach agents — i.e., neural networks — how to compete in imperfect-information games.

“We’re taking a different approach,” notes Rudolph. “Unlike many of the papers published in this field, we’re not proposing a new algorithm that can beat out other algorithms. We’re proposing a benchmark that can assess these algorithms.”

Think About This

What questions does this story raise for you? Talk with your family: Why does this news matter? Who is affected? What might happen next? Is there anything you can do?

🔍 Want to verify this story? Check it on Google Fact Check Explorer →

More stories: Kid News · Archive · Fact Check