By Matt Harrington, BCBA · Behaviorist Book Club · April 2026 · 12 min read

Behavioral Economics and Behavior Analysis: Leonard Green's Contributions to Discounting and the Matching Law

Overview & Clinical Significance

Leonard Green's career sits at the intersection of experimental behavior analysis and economics — a territory that has produced some of the most theoretically rich and practically relevant work in the field. Beginning with foundational research on the matching law and extending into the study of temporal and probability discounting, Green's work has helped explain why individuals make choices that appear, by conventional economic standards, irrational — and has provided behavior analysts with quantitative tools for understanding choice behavior in both laboratory and applied contexts.

For BCBAs, behavioral economics is not merely academic history. The principles that Green and his colleagues developed — discounting rates, delay sensitivity, and the matching law — have direct implications for understanding motivating operations, reinforcement schedule sensitivity, and the choice behavior of clients across populations. A client who consistently chooses a small immediate reinforcer over a larger delayed one is not simply being impulsive; they are displaying the steep temporal discounting function that characterizes many populations including those with ADHD, substance use disorders, and some presentations within ASD.

This interview-based presentation offers a rare perspective on the development of behavioral economics as a field — including the intellectual climate of the 1960s and 70s, the role of the matching law in bridging basic and applied research, and Green's ongoing concerns about stagnation and the need for rigorous innovation in behavior analysis. His perspective is both historically grounded and forward-looking, offering BCBAs context for understanding how the field arrived at its current theoretical landscape and where productive research directions may lie.

Understanding the conceptual foundations of behavioral economics enriches the BCBA's clinical repertoire by providing a vocabulary and framework for analyzing choice behavior that complements functional assessment methodology. The same reinforcement contingencies that produce skill acquisition also govern preference, delay tolerance, and effort allocation — behavioral economics makes these relationships explicit and measurable.

Background & Context

The matching law, developed by Richard Herrnstein in the 1960s using data from pigeon responding on concurrent variable-interval schedules, describes a mathematical relationship between the relative rate of responding and the relative rate of reinforcement. In its simplest form, the law states that an organism's behavior allocates proportionally to the available reinforcement: if Schedule A delivers twice as many reinforcers as Schedule B, the organism will respond approximately twice as often on Schedule A.

Leonard Green's contributions built on and extended Herrnstein's framework. His work on the generalized matching law incorporated parameters for response bias and sensitivity to reinforcement, accommodating deviations from strict proportional matching observed in natural settings. This extension made the matching law more ecologically valid — it could account for the fact that organisms are not perfectly sensitive to reinforcement distributions and that prior histories, stimulus features, and motivational states modulate choice.

Green's shift toward discounting research — particularly temporal discounting, the devaluation of reinforcers as a function of delay — addressed a fundamental feature of behavior that the original matching law did not fully capture: the fact that immediate reinforcers are preferred over delayed ones at rates that exceed what their objective value would predict. Hyperbolic discounting functions, which Green and collaborators have studied extensively, describe the characteristic shape of this devaluation — steeply discounting the near future but flattening out at longer delays, producing preference reversals that cannot be explained by exponential discounting models borrowed from classical economics.

The interdisciplinary work that blended economics and behavioral psychology was intellectually courageous in the 1960s and 70s — economics and psychology were methodologically divergent, and the combination required fluency in both traditions. Green's career reflects the kind of boundary-crossing research that the field's most influential contributors have often pursued, a point he returns to in the interview context with optimism about the field's future directions.

Clinical Implications

Temporal discounting has direct clinical relevance for BCBAs working across populations. Clients with ADHD, substance use histories, gambling disorders, and some ASD presentations show steeper-than-average temporal discounting functions — they devalue delayed reinforcers more steeply, which produces persistent preference for immediate rewards even when delayed alternatives are objectively superior. This is not a moral failure; it is a measurable behavioral characteristic with known variance across individuals and populations.

Understanding a client's discounting profile has implications for reinforcement schedule design. When a client shows steep temporal discounting, introducing token economies or point systems — which delay the delivery of backup reinforcers — may be less effective than immediate reinforcement delivery, at least during the early phases of skill acquisition. As tolerance for delay is built through systematic delay fading, token systems can become more effective. The matching law framework informs this process by predicting how changes in delay parameters will affect choice behavior.

The matching law also has direct implications for understanding why clients choose challenging behavior over adaptive alternatives. If challenging behavior produces reinforcement on a rich, immediate schedule — even intermittently — it will compete successfully with appropriate behavior that produces reinforcement on a thinner or delayed schedule. FCT is most effective when the communicative alternative produces reinforcement on a schedule that matches or exceeds the schedule maintaining challenging behavior. Behavioral economic analysis makes this competition explicit and quantifiable.

Probability discounting — the devaluation of reinforcers as a function of outcome uncertainty — adds another dimension to clinical decision-making. Clients who heavily discount probabilistic outcomes may show persistent avoidance of uncertain or variable reinforcement situations, which has implications for programming naturalistic learning environments and community integration. Understanding the probability discounting function for a specific client can inform decisions about how much variability to introduce into reinforcement schedules and natural environment teaching.

Ethical Considerations

The application of behavioral economic principles to clinical practice raises ethical questions about the use of delay and scarcity to manipulate behavior. When practitioners deliberately introduce delays or reduced access to valued items to increase motivation, they are leveraging the same discounting functions that Green's research describes — with the intent of producing better treatment outcomes, but with the potential to produce distress if implemented without sensitivity to the client's tolerance.

Code 6.01 of the BACB Ethics Code addresses the least-restrictive intervention requirement, which applies to motivating operation manipulation as well as to aversive procedures. Creating artificial deprivation states to increase the effectiveness of specific reinforcers — a common practice in behavior analytic programming — should be implemented with careful monitoring of its effects on client welfare and discontinued when alternative reinforcement strategies are sufficient.

The use of behavioral economic frameworks for understanding consumer and patient behavior raises broader ethical questions that Green himself alludes to in the interview — specifically about who benefits from the application of discounting research and in whose interests behavioral economic technology is deployed. In clinical behavior analysis, the answer should always be the client. Research on discounting has been applied in public health contexts (tobacco control, obesity intervention) as well as in commercial contexts (marketing and gambling). BCBAs should be aware of this broader application landscape and ensure that their use of behavioral economic principles is explicitly in service of client welfare.

Green's concern about stagnation in the field connects to an ethical dimension: the obligation to remain current with the scientific literature and to practice based on the best available evidence. Code 1.01 requires practitioners to remain updated on developments in behavior analysis. Behavioral economics is a growing area of the basic science with increasing applied relevance, and BCBAs who are unfamiliar with discounting frameworks may be missing tools that could improve clinical decision-making.

Assessment & Decision-Making

Discounting rates can be measured directly using delay discounting tasks — procedures in which participants choose between smaller immediate and larger delayed reinforcers across a systematic range of delay and amount parameters. The resulting choice data can be fit to hyperbolic discounting functions, generating a discount rate parameter (k) that quantifies how steeply the individual devalues delayed outcomes. Higher k values indicate steeper discounting and greater preference for immediacy.

For clinical purposes, delay discounting assessment can inform reinforcement schedule design and help predict which clients are likely to benefit from different types of token economies or delayed reinforcement systems. Although standardized clinical protocols for discounting assessment in applied settings are not yet widespread, the basic procedure — presenting choices between immediate and delayed outcomes across a range of delays — is straightforward and adaptable to most clinical contexts.

Matching law analysis is applicable in situations where a client is choosing between two or more response options, including challenging behavior versus adaptive alternatives. By tracking the rate and schedule of reinforcement for each competing response, practitioners can generate predictions about which response will predominate and design interventions that alter the reinforcement distribution in favor of adaptive alternatives. This provides a quantitative basis for schedule thinning decisions in FCT and DRA programs.

Green's emphasis on rigorous science and innovation should inform the practitioner's approach to behavioral economic tools: use them as generating hypotheses to be tested with individual client data rather than as universal prescriptions. The same discounting function that characterizes a population average may not accurately describe a specific client. Individual assessment is always the foundation of data-based decision-making in behavior analysis.

What This Means for Your Practice

If behavioral economics is not part of your clinical vocabulary, this presentation is a starting point for developing that fluency. Understanding the matching law helps you think about why behavior distributes across response options the way it does — and what changes to reinforcement schedules will predictably shift that distribution. Understanding temporal discounting helps you think about why some clients respond poorly to delayed reinforcement and what schedule modifications might improve their responsiveness.

For practitioners who design reinforcement-based programs, the behavioral economic framework offers a layer of quantitative precision that supplements qualitative clinical judgment. When a DRA program is not producing the expected shift in behavior allocation, asking whether the reinforcement schedule for the alternative behavior is competitive with the schedule maintaining the target behavior is a direct application of matching law logic. When a client's preference for immediate rewards is undermining long-term programming goals, identifying and building delay tolerance through systematic delay fading is a direct application of discounting research.

The historical perspective that Green brings — including his reflection on the dynamic interdisciplinary work of the 1960s and 70s — is a reminder that behavior analysis has always been most productive when it engaged with adjacent scientific disciplines rather than remaining insular. Behavioral economics emerged from exactly this kind of cross-disciplinary engagement. BCBAs who engage seriously with economics, neuroscience, and developmental psychology are participating in the same intellectual tradition that produced the field's most foundational contributions.

Green's expressed concern about field stagnation is worth taking seriously. Innovation requires that practitioners remain curious about basic research, maintain familiarity with the experimental literature, and be willing to import and test new conceptual tools in clinical settings. JABA, JEAB, and the Journal of the Experimental Analysis of Behavior remain the primary venues for this basic and applied research — regular engagement with these journals is one practical way to remain connected to the science that informs effective practice.