What Is Game Theory? Research Areas, Funding, and Career Paths

Game theory is the branch of applied mathematics that studies strategic decision-making — situations where the outcome for each participant depends not only on their own choices but on the choices of others. It provides a formal framework for modeling how rational (or boundedly rational) agents interact when their interests may align, conflict, or do a mix of both, and it asks a small set of core questions: What outcome should we expect when self-interested agents interact? Is that outcome stable, in the sense that no one wants to unilaterally deviate from it? Can outcomes be redesigned — through rules, incentives, or institutions — to produce better results for everyone?

This guide answers “what is game theory” in real depth: its core concepts and methods, its major subfields, how it relates to neighboring disciplines — particularly mathematics and economics — and, because this page is published by CASRAI, a research-administration standards body, the funding landscape, research methods, and career pathways that matter to anyone conducting or administering game-theoretic research specifically.

What Is Game Theory? A Working Definition

Game theory formalizes strategic interaction as a “game”: a set of players, the strategies (possible actions) available to each, the information each player has when deciding, and a payoff for each player that depends on the combination of strategies everyone chooses. The defining feature that separates a game-theoretic problem from an ordinary decision-theory problem is interdependence: no player controls the outcome alone, and rational players must reason about what other rational players will do.

The field’s foundational solution concept is the Nash equilibrium, introduced by John Nash in 1950: a combination of strategies, one per player, such that no player can improve their own payoff by unilaterally switching strategies, given what everyone else is doing. A Nash equilibrium doesn’t guarantee the best possible outcome for the group — the Prisoner’s Dilemma, game theory’s best-known example, shows two rational players reaching a stable equilibrium that leaves both worse off than if they had cooperated. That gap between individually rational behavior and collectively efficient outcomes is one of the field’s recurring, practically important themes, and it is why game theory is used far beyond economics — in political science, biology, computer science, and public policy — anywhere multiple decision-makers’ interests interact.

Modern game theory traces its formal origin to John von Neumann and Oskar Morgenstern’s Theory of Games and Economic Behavior (1944), which established the mathematical framework connecting games to expected-utility decision theory. Nash’s equilibrium concept, along with subsequent refinements by Reinhard Selten, John Harsanyi, and others, extended the framework to sequential and incomplete-information settings; the 1994 Nobel Memorial Prize in Economic Sciences, shared by Nash, Selten, and Harsanyi, is generally treated as the field’s mainstream arrival within economics.

Major Subfields and Branches of Game Theory

Game theory is organized around several recognized subfields, each addressing a different structure of strategic interaction:

• Non-cooperative game theory — the dominant branch, analyzing situations where players act independently in their own self-interest, without binding agreements. Solution concepts include Nash equilibrium and its refinements (subgame-perfect equilibrium, Bayesian Nash equilibrium).
• Cooperative game theory — studies situations where players CAN form binding coalitions and must agree on how to divide the resulting joint payoff. Core concepts include the core (the set of stable coalition outcomes) and the Shapley value, a widely used method for fairly allocating value among coalition members.
• Games of complete vs. incomplete information — complete-information games assume every player knows the full structure of the game (payoffs, strategies available to everyone); incomplete-information (Bayesian) games, formalized by John Harsanyi, model settings where players have private information about their own type or payoffs — a framework central to auction theory and contract design.
• Repeated and evolutionary game theory — repeated games study how the same interaction played over many rounds can sustain cooperation that wouldn’t survive a one-shot game; evolutionary game theory, developed initially in biology (John Maynard Smith’s concept of the evolutionarily stable strategy), studies how strategies spread through a population under selection pressure rather than individual rational choice.
• Mechanism design — sometimes called “reverse game theory”: instead of analyzing outcomes under a fixed set of rules, mechanism design works backward from a desired outcome to design the rules (an auction format, a voting system, a matching algorithm) that will produce it when self-interested players respond strategically. Leonid Hurwicz, Eric Maskin, and Roger Myerson shared the 2007 Nobel Memorial Prize for founding this subfield.
• Algorithmic game theory — a newer subfield at the intersection with computer science, studying the computational complexity of finding equilibria, and the design of algorithms and online systems (auctions, ad exchanges, network routing) that perform well when participants act strategically.

How Game Theory Relates to Neighboring Disciplines

Game theory is inherently interdisciplinary — it did not originate inside a single department, and it is taught and applied across several fields today:

• Mathematics. Game theory is formally a branch of applied mathematics, built on optimization, probability, and (for cooperative game theory) combinatorics and set theory. See CASRAI’s guide to mathematics for the broader mathematical foundations game theory draws on.
• Economics. Game theory is one of economic theory’s central tools, underlying modern industrial organization (oligopoly and competition), auction theory, contract theory, and much of microeconomic theory more broadly — most professional game theorists today hold positions in economics departments. See CASRAI’s guide to economics, and CASRAI’s guide to econometrics for how game-theoretic models are tested empirically.
• Political science. Formal/positive political theory applies game-theoretic models to voting, legislative bargaining, international conflict and deterrence, and coalition formation. Thomas Schelling’s game-theoretic work on conflict and strategy (Nobel Memorial Prize, 2005) sits directly at this boundary.
• Computer science. Algorithmic game theory and mechanism design underpin the design of online ad auctions, matching markets (e.g., school-choice and kidney-exchange algorithms), multi-agent AI systems, and network routing protocols.
• Biology. Evolutionary game theory is a standard tool in evolutionary biology for modeling the spread of behavioral strategies (cooperation, aggression, signaling) through a population under natural selection, independent of any assumption of conscious rational choice.
• Operations research. Game-theoretic models of competition and negotiation are a recognized tool within operations research and management science, alongside optimization and decision analysis.

What distinguishes game theory from all of these is its subject matter, not its home department: it studies the mathematical structure of strategic interaction itself, and that structure recurs identically whether the “players” are firms, nations, genes, or algorithms.

Who Funds Game Theory Research

Game theory research in the United States is funded primarily through federal science agencies, with the specific program depending on whether the work is framed as economic theory, applied mathematics, or computational/algorithmic:

• National Science Foundation (NSF) — Economics Program. Within the Division of Social and Economic Sciences (SES), part of the Directorate for Social, Behavioral and Economic Sciences (SBE), the Economics Program is a primary funder of theoretical game theory and mechanism design as core economic theory. SES’s Decision, Risk and Management Sciences program also regularly funds game-theoretic and decision-theoretic research relevant to this space.
• NSF — Directorate for Computer and Information Science and Engineering (CISE). The Algorithmic Foundations program, within the Division of Computing and Communication Foundations (CCF), is a significant funder of algorithmic game theory — the computational study of equilibria, auctions, and mechanism design — reflecting the field’s genuine split between economics and computer science.
• NSF — Division of Mathematical Sciences (DMS). As a branch of applied mathematics, game-theoretic work with a strong mathematical/analytical focus can also fall within DMS’s funding scope, alongside its home in SES or CCF depending on framing.
• Defense-related research offices. Because game theory formalizes strategic conflict and cooperation, it has long-standing applications in defense and security research; agencies including the Office of Naval Research (ONR) and the Air Force Office of Scientific Research (AFOSR) fund game-theoretic work on topics such as multi-agent systems and strategic security modeling as part of their broader mathematical and computational sciences portfolios. Research offices should confirm current program scope directly with each agency rather than assuming a fixed portfolio, since defense research priorities shift.
• Private foundations. Foundations that fund basic research in economic theory and theoretical computer science — for example, the Alfred P. Sloan Foundation and the Simons Foundation (including its Simons Institute for the Theory of Computing, which regularly supports algorithmic game theory) — periodically fund game theory research as part of broader economics or theoretical computer science initiatives, rather than through a dedicated “game theory” program.

As with any funding landscape, program scope, budgets, and priorities shift — research offices should confirm current guidelines directly on each funder’s own site before an application, rather than relying on a general overview like this one.

Research Methods and Tools in Game Theory

Game theory is a predominantly theoretical and mathematical field, but empirical and computational methods play a growing role:

• Formal mathematical modeling and proof. The core method: specifying a game’s players, strategies, information structure, and payoffs precisely, then proving properties of its equilibria (existence, uniqueness, efficiency) using tools from optimization, real analysis, and fixed-point theory.
• Experimental economics. Controlled laboratory experiments — often using classic games like the Prisoner’s Dilemma, ultimatum bargaining, public-goods games, and auctions — test whether real human behavior matches theoretical predictions, and have been central to the rise of behavioral game theory (relaxing the assumption of fully rational players).
• Computational methods. Algorithms for computing Nash equilibria and other solution concepts, particularly in games too large to solve by hand; this overlaps substantially with algorithmic game theory and computational complexity theory.
• Empirical and structural estimation. Econometric techniques for estimating the parameters of a game-theoretic model (e.g., firms’ cost structures in an oligopoly model) from real market data, connecting game theory to econometrics.
• Agent-based and evolutionary simulation. Simulating populations of interacting strategies over many rounds, used especially in evolutionary game theory and in settings where a closed-form analytical solution isn’t tractable.

Career and Training Pathways

Game theory is not typically its own standalone degree program; it is a specialization pursued within a broader home discipline:

• Undergraduate. Students typically encounter game theory as a course within an economics, mathematics, or (increasingly) computer science major, building on calculus, probability, and introductory microeconomics.
• Doctoral training. Most professional game theorists complete a PhD in economics (most common), applied mathematics, or computer science, specializing in game theory or microeconomic theory through advanced coursework and a dissertation. A handful of specialized joint programs in “economics and computation” or “decision sciences” also exist at some universities.
• Professional societies. The Game Theory Society is the field’s dedicated international professional society, organizing a World Congress every four years and publishing in the field’s core journals. The Econometric Society — covered in more depth in CASRAI’s guide to economics — has historically published a large share of foundational game theory scholarship in Econometrica. The Society for the Advancement of Economic Theory (SAET) is another international society focused on economic and game theory. Games and Economic Behavior is one of the field’s principal dedicated journals.
• Career destinations. PhD-trained game theorists work primarily in academic economics, mathematics, and computer science departments; in industry, demand has grown substantially for game-theoretic and mechanism-design expertise at technology companies designing auctions, marketplaces, and recommendation/ranking systems (sometimes under the title “market design” or “economics and computation”), as well as in antitrust economics consulting and, to a lesser extent, defense-related research roles.

Frequently Asked Questions

What is a Nash equilibrium?

A Nash equilibrium is a combination of strategies — one for each player in a game — such that no player can improve their own payoff by unilaterally switching to a different strategy, given the strategies everyone else is using. It is game theory’s central solution concept, introduced by John Nash in 1950.

Is game theory part of economics or mathematics?

Both, genuinely. Game theory originated and remains formally rooted in applied mathematics, but it has become one of the central analytical tools of modern economic theory, and most professional game theorists today are trained and employed within economics departments. It is also a substantial and growing subfield within computer science (algorithmic game theory) and appears in political science and biology as well.

What is the difference between cooperative and non-cooperative game theory?

Non-cooperative game theory studies players acting independently in their own self-interest without binding agreements — its central concept is the Nash equilibrium. Cooperative game theory studies situations where players can form binding coalitions and must agree on how to divide the resulting joint payoff, using concepts like the core and the Shapley value.

What is mechanism design?

Mechanism design, sometimes called “reverse game theory,” starts from a desired outcome and works backward to design the rules of a game — an auction format, a voting rule, a matching algorithm — that will produce that outcome when self-interested, strategic players respond to it. It underlies the design of major real-world auctions (including spectrum auctions) and matching systems.

What jobs use game theory?

Beyond academic positions in economics, mathematics, and computer science, game theory and mechanism design expertise is in demand at technology companies for auction, marketplace, and ranking-system design (often under the title “market design” or “economics and computation”), in antitrust and competition economics consulting, and in defense-related strategic analysis.

Related CASRAI Resources

This guide is part of CASRAI’s Branches of Science series, which maps the major academic and scientific disciplines along with the research-administration context — funding, methods, and career pathways — that a general encyclopedia entry typically omits. See the hub guide for the full index of published discipline pages organized by category. Closely related discipline guides include mathematics and economics, plus econometrics for the empirical methods used to test game-theoretic models against real-world data.