Behavioral economic demand analyses that quantify the relationship between the consumption of a commodity and its price have proven useful in studying the reinforcing efficacy of many commodities, including drugs of abuse. An exponential equation proposed by Hursh and Silberberg (2008) has proven useful in quantifying the dissociable components of demand intensity and demand elasticity, but is limited as an analysis technique by the inability to correctly analyze consumption values of zero. Here, we examine an exponentiated version of this equation that retains all the beneficial features of the original Hursh and Silberberg equation, but can accommodate consumption values of zero and improves its fit to the data. In Experiment 1, we compared the modified equation to the unmodified equation under different treatments zero values in cigarette consumption data collected online from 272 participants. We found that the unmodified equation produces different results depending on how zeros are treated, while the exponentiated version incorporates zeros into the analysis, accounts for more variance, and is better able to estimate actual unconstrained consumption as reported by participants. In Experiment 2, we simulated 1000 datasets with demand parameters known a priori and compared the equation fits. Results indicate that the exponentiated equation was better able to replicate the true values from which the test data were simulated. In conclusion, an exponentiated version of the Hursh and Silberberg equation provides better fits to the data, is able to fit all consumption values including zero, and more accurately produces true parameter values.

The effects of EFT on delay discounting generalize to smokers; EFT also reduces laboratory-based cigarette self-administration. Potential mechanisms of EFT's effects are discussed as well as implications of EFT for clinical treatment of substance-use disorders.

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 one or more hypothetical or real commodities across a range of prices—have made data collection more practical and 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 purchasing 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 two general, empirically based assumptions: (1) global, price-dependent reduction in consumption, and (2) 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.