The field of behavioral economics has made important inroads into the understanding of substance use disorders through the concept of reinforcer pathology. Reinforcer pathology refers to the joint effects of (a) the persistently high valuation of a reinforcer, broadly defined to include tangible commodities and experiences, and/or (b) the excessive preference for the immediate acquisition or consumption of a commodity despite long-term negative outcomes. From this perspective, reinforcer pathology results from the recursive interactions of endogenous person-level variables and exogenous environment-level factors. The current review describes the basic principles of behavioral economics that are central to reinforcer pathology, the processes that engender reinforcer pathology, and the approaches and procedures that can repair reinforcement pathologies. The overall goal of this review is to present a new understanding of substance use disorders as viewed by recent advances in behavioral economics.

Experimental assessments of demand allow the examination of economic phenomena relevant to the etiology, maintenance, and treatment of addiction and other pathologies (e.g., obesity). Although such assessments have historically been resource intensive, development and use of purchase tasks-in which participants purchase 1 or more hypothetical or real commodities across a range of prices-have made data collection more practical and have increased the rate of scientific discovery. However, extraneous sources of variability occasionally produce nonsystematic demand data, in which price exerts either no or inconsistent effects on the purchases of individual participants. Such data increase measurement error, can often not be interpreted in light of research aims, and likely obscure effects of the variable(s) under investigation. Using data from 494 participants, we introduce and evaluate an algorithm (derived from prior methods) for identifying nonsystematic demand data, wherein individual participants' demand functions are judged against 2 general, empirically based assumptions: (a) global, price-dependent reduction in consumption and (b) consistency in purchasing across prices. We also introduce guidelines for handling nonsystematic data, noting some conditions in which excluding such data from primary analyses may be appropriate and others in which doing so may bias conclusions. Adoption of the methods presented here may serve to unify the research literature and facilitate discovery.