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Experimental economics is a subfield defined less by a single research question than by a persistent methodological debate: what can a controlled experiment tell us about economic behavior, and how should that experiment be designed? Since the 1960s, researchers have built competing schools around different answers to that question. The result is a pluralist landscape in which several methodological traditions coexist, each with its own standards of evidence, its own preferred type of experiment, and its own view of what makes a result credible.
Before the 1960s, most economists doubted that laboratory experiments could produce meaningful results about markets. The central objection was that paying subjects small sums in an artificial setting could not replicate the incentives that drive real economic decisions. Vernon Smith answered that objection with a methodological framework called Induced Value Theory. Smith argued that an experimenter could "induce" the same preferences that subjects would have in a natural market by satisfying three conditions: salience (a subject's payoff depends on their own decisions), monotonicity (subjects prefer more money to less), and dominance (the experiment's payoffs are large enough to outweigh any non-monetary motivations). When these conditions hold, the experimenter can treat the induced preferences as if they were the subject's true preferences, and the laboratory becomes a valid setting for testing economic theory.
Induced Value Theory was not a theory of behavior; it was a theory of experimental method. It provided the shared infrastructure that made later schools possible. Every subsequent school in experimental economics either built directly on Smith's conditions or defined itself by relaxing or challenging them. The theory's influence was so pervasive that for decades, the dominant standard for a well-designed experiment was simply whether it satisfied salience, monotonicity, and dominance.
By the 1970s, two schools had emerged that both accepted Induced Value Theory's basic logic but pushed it in very different directions. Behavioral Decision Theory took the individual decision-maker as its unit of analysis. Researchers in this tradition presented subjects with choices between gambles, intertemporal trade-offs, or risky prospects, using induced-value payoffs to measure how real choices deviated from the predictions of expected utility theory. The school's distinctive contribution was to treat those deviations not as noise but as systematic patterns worthy of their own models—Prospect Theory being the most famous example. Behavioral Decision Theory coexisted with the older rational-choice framework by narrowing its scope: instead of replacing expected utility theory, it aimed to describe when and why people depart from it.
Market Design Experiments, by contrast, took the institution—the rules of exchange—as its unit of analysis. Researchers in this school used induced-value payoffs to test how different auction formats, trading mechanisms, or market-clearing rules affected aggregate outcomes like efficiency, prices, and convergence. Where Behavioral Decision Theory asked whether individuals were rational, Market Design Experiments asked whether institutions could be designed to produce good outcomes even when individuals were not perfectly rational. The two schools shared a laboratory toolkit but answered different questions. Market Design Experiments eventually absorbed insights from Behavioral Decision Theory—for example, by incorporating bounded rationality into auction design—but the schools never merged, because their dependent variables (individual choice vs. market performance) remained distinct.
The 1990s brought a new wave of experimental work on strategic interaction. Behavioral Game Theory extended the logic of Induced Value Theory into games: ultimatum games, public goods games, trust games, and bargaining experiments. The school's central question was how real people play games when classical game theory assumes perfect rationality and common knowledge of that rationality. Behavioral Game Theory's distinctive contribution was to develop models—Quantal Response Equilibrium, learning models, and bounded-rationality refinements—that could explain the systematic deviations from Nash equilibrium that experiments consistently revealed.
At almost exactly the same time, the Social Preferences School began producing experiments that looked very similar but asked a different question. Instead of focusing on strategic reasoning, researchers in this tradition asked what preferences people bring into the laboratory. When a subject rejects an unfair offer in an ultimatum game, is that because they misjudged the strategic situation, or because they genuinely care about fairness? The Social Preferences School argued for the latter, developing models of inequity aversion, reciprocity, and conditional cooperation that treated social motives as part of the utility function rather than as a failure of strategic thinking.
The two schools remain intertwined. They use overlapping experimental designs and often publish in the same journals. But their assumptions conflict. Behavioral Game Theory tends to treat social preferences as one possible input into a strategic calculation; the Social Preferences School treats strategic reasoning as one possible channel through which social preferences are expressed. A researcher's choice between the two frameworks depends on whether the question is "how do people reason strategically?" or "what do people care about?"
Around 2000, a growing number of researchers began to argue that the laboratory's control came at too high a price. The Field Experiments School challenged the assumption that induced-value payoffs in an artificial setting could capture the richness of real economic environments. Field experiments moved the study of economic behavior out of the lab and into naturally occurring settings—schools, villages, labor markets, and online platforms—where subjects often did not know they were part of an experiment.
The Field Experiments School did not reject Induced Value Theory outright. Instead, it narrowed the scope of the theory's authority. Field experimenters argued that salience, monotonicity, and dominance were sufficient for internal validity but not for external validity: knowing that a treatment effect exists in the lab does not tell you whether it will replicate in the field. This critique changed lab practice by pushing experimenters to justify their settings more carefully and to combine lab and field methods within single research programs. Today, many researchers treat the lab-field distinction as a continuum rather than a binary, using field experiments to test the generalizability of lab findings and lab experiments to isolate mechanisms that field data cannot disentangle.
Neuroeconomics, which also emerged around 2000, pushed the search for mechanism in a different direction: inside the brain. Researchers in this school use fMRI, EEG, and other neural measurement tools to observe the neural processes that accompany economic decisions. The school's core claim is that behavioral data alone underdetermine the choice of model—two models that make identical behavioral predictions may imply very different neural processes, and neural data can discriminate between them.
Neuroeconomics is best understood as a transformation of Behavioral Decision Theory's research program. Both schools study individual decision-making under risk, uncertainty, and time delay. But where Behavioral Decision Theory treats the decision process as a black box whose outputs are choices, Neuroeconomics tries to open that box. This has created a living disagreement within the subfield. Critics argue that neural data add little beyond what behavioral experiments already reveal; proponents reply that neural evidence provides a stronger test of psychological mechanisms. The debate remains unresolved, and Neuroeconomics continues to coexist with Behavioral Decision Theory as a parallel approach rather than a replacement.
Today, no single school dominates experimental economics. Behavioral Decision Theory remains the standard framework for studying individual choice anomalies. Market Design Experiments continue to inform the design of real-world auctions and matching markets. Behavioral Game Theory and the Social Preferences School provide complementary lenses for strategic interaction. Field experiments have become a mainstream method across economics, not just within the subfield. Neuroeconomics is active but remains a minority approach, valued more for its theoretical ambitions than for its practical yield.
The leading schools agree on one fundamental point: experiments can produce credible evidence about economic behavior. They disagree about what kind of evidence is most credible. For Behavioral Decision Theory and Market Design Experiments, the gold standard is a tightly controlled laboratory setting with induced-value payoffs. For the Field Experiments School, the gold standard is a naturally occurring setting with real stakes. For Neuroeconomics, the gold standard includes neural data alongside behavioral data. These disagreements are not signs of weakness; they reflect the subfield's maturation into a pluralist discipline in which researchers choose their methods based on the question they want to answer.