Association for Behavior Analysis International

The subdiscipline of behavioral science known as "operant behavioral economics" (hereafter termed simply "behavioral economics") integrates concepts from microeconomic theory and behavior analysis. Behavioral economics provides scientists, researchers, practitioners, and policy makers with unique insights into motivation and reinforcer efficacy. Of particular noteworthiness is the influence of behavioral economics in the domains of addiction, behavioral pharmacology, and empirical public policy. Central to behavioral economics' success is its unique demand curve analysis that quantifies the degree to which an organism/agency defends its baseline rate of consumption of a target commodity (i.e., its blisspoint). Recent advances in data collection for demand curve studies, as well as the quantitative modeling and analysis of subsequent data, have advanced both the theoretical interpretations and practical applications of behavioral economic principles. This symposium highlights these recent advances in both data collection for and quantitative analyses of demand curves. Contributors will provide data-based recommendations for best practices in this line of research.

Advanced; Researchers with an interest in behavioral economics or quantitative analysis.

In the application of behavioral economic principles to issues of societal importance, a critical unit of analysis is unit elasticity—termed Pmax—which represents the price value at which inelastic demand shifts to elastic. The Pmax price point may thereby serve as a target price for excise taxation, price regulation boundaries, or understanding how consumers value commodities against real-world market prices. Quantifying Pmax is dependent on quantification of demand to generate demand curve parameters. Recent advances in demand analysis have yielded competing models of demand, but the extent to which these models influence Pmax has not yet been explored—this is particularly alarming given the proliferation of Pmax applications to inform federally funded projects tasked with informing regulatory policy. This study extracted existing data from seminal articles containing demand curves for substances of abuse. The exponential and exponentiated demand models were used to generate best-fit parameter values, which were input into Hursh's (2014) exact Pmax equation, as well as slope-based Pmax values independent of a theoretical model of unit elasiticty. We identified alarming discrepancies in Pmax across the exponential and exponentiated models, suggesting the field must address modeling issues before proceeding with applying these analyses to inform policy-level decisions.

The alcohol purchase task has been considered a gold standard tool for measuring demand for alcohol. Although different alcohol purchase tasks have been used in many experimental studies, there is no research on the procedural differences between the different versions of alcohol purchase tasks that have been used to measure demand. The present study sought to examine the extent to which market price anchor placement in purchase task price sequences influence demand. We recruited 298 participants from Amazon Mechanical Turk to complete an alcohol purchase task with four different possible price sequences. Price sequences represented standard, left, right, or center placement of market values. We stratified participants across the four different price sequences. Nonsystematic data were removed (trend: 8.7%; bounce: 3.0%; reversals from zero: 3.6%); we analyzed remaining data using exponential and exponentiated demand equations. Exponential analyses indicate no significant differences in demand between responses in any of the four price sequences; whereas exponentiated analyses indicate a significant difference between groups. This research is valuable in considering how purchase tasks are used to inform public policy and used to guide creation of clinical scales for alcohol demand. Implications for best practice will be discussed.

Behavioral economic demand offers a multi-dimensional evaluation of drug reinforcement and abuse liability. However, generating demand curves with self-administration is time consuming and costly. Human researchers sometimes use hypothetical drug purchasing tasks as quick, cost-efficient alternatives, in which a participant self-reports how many units of a described drug he/she would purchase at a range of prices. Two limitations of typical purchasing tasks are: 1) commodities are described rather than experienced, and 2) inability to control for expectancy (placebo effect). We developed the Double-Blind Drug Purchasing Task, a hybrid task in which we administered drugs and placebos in separate double-blind sessions. Participants then made hypothetical purchase decisions at the conclusion of each session in reference to the drug/placebo administered. We administered this task to 56 users of either cocaine, methamphetamine, or alcohol. Drug purchasing decreased reliably as an orderly function of price. Drug was reliably purchased more than placebo. Elasticity tended to be negatively correlated with clinically relevant variables: money spent on drug and use frequency. Ongoing research is comparing the novel task against operant laboratory demand methods in tobacco users purchasing/earning cigarettes. The DBPT may serve as an efficient method for determining drug demand under pharmacologically rigorous, placebo-controlled conditions.