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42nd Annual Convention; Downtown Chicago, IL; 2016

Event Details

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Symposium #296a
CE Offered: BACB
Best Practice Recommendations for Behavioral Economic Demand Curve Analyses
Monday, May 30, 2016
3:00 PM–4:50 PM
Zurich C, Swissotel
CE Instructor: Derek D. Reed, Ph.D.
Chair: Derek D. Reed (The University of Kansas)
Discussant: Steven R. Hursh (Institutes for Behavior Resources, Inc.)
Abstract: 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.
Keyword(s): behavioral economics, demand curve, quantitative analysis
Construction, Interpretation, and Novel Application of Hypothetical Purchase Task Questionnaires
PETER G. ROMA (Institutes for Behavior Resources, Inc.), Brent Kaplan (The University of Kansas), Derek D. Reed (The University of Kansas), Steven R. Hursh (Institutes for Behavior Resources, Inc.)
Abstract: Hypothetical purchase task (HPT) questionnaires provide quantitative insights on behavioral, motivational, and decision-making processes, preferences, and outcomes at the individual, group, market, and population levels when measuring actual consumption is impossible, impractical, illegal, or unethical. However, the development of HPTs beyond the substance abuse field has been limited. To facilitate broader application of HPTs and provide empirical guidance for construction of novel tasks, we tested the effects of HPT price density (17, 9, or 5 prices) and purchase type (quantity purchased or probability of single purchase) on behavioral economic (BE) measures in 1,219 participants for six generic commodities related to food, household/utility, entertainment, and recreation. The Exponential Model of Demand provided excellent fits (mean R2=0.98). High density HPTs were most sensitive yet most resistant to distortion. BE value measures were lower in quantity vs. probability HPTs. Rank-ordering of commodity values agreed regardless of HPT structural manipulations. Expenditure curves were bimodal, but consistent with Exponential Model predictions. Researchers and practitioners should carefully consider the construction and interpretation of existing and novel HPTs, but these and other data reveal the generalizability of the HPT approach and should encourage novel applications to public health and safety, business, operational environments, and broader national policy.
A Comparison of Methods to Describe Economic Demand and Elasticity
LESLEIGH ANN CRADDOCK (New England Center for Children), Jason C. Bourret (New England Center for Children), Joshua Jackson (New England Center for Children), Allison Josephine Castile (New England Center for Children), Andrew Nuzzolilli (New England Center for Children)
Abstract: Economic demand describes the consumption of a commodity across increases in price. A typical demand curve takes a monotonically decreasing form. In other words, consumption decreases as price increases. In behavioral economic preparations, price and commodity may be considered synonymous with FR value and reinforcer, respectively. Two quantitative models have been frequently used in the behavior analytic literature to describe this relation (Hursh et al., 1988; Hursh & Silberburg, 2008). We directly compared fits of the two equations across multiple data sets. In addition, we investigate the utility of model-neutral, area-under-the-curve measures of demand elasticity.
Behavioral Economic Demand Curve Parameters Predict Response to Brief Alcohol Interventions
JAMES MURPHY (University of Memphis), Ashley Dennhardt (University of Memphis), Matthew Martens (University of Missouri), Jessica Skidmore (Scripps Whittier Diabetes Institute), Ali Yurasek (Brown University), James MacKillop (McMaster University), Meghan McDevitt-Murphy (University of Memphis)
Abstract: Identifying both predictors and mechanisms driving treatment response is necessary to improve alcohol treatment efficacy. The present study determine whether behavioral economic indices of alcohol reward value, measured before and immediately after a brief alcohol intervention, predict treatment response. Participants were 133 heavy drinking college students who were randomized to 1 of 3 conditions: brief motivational interview (BMI), brief computerized intervention (BCI), and assessment only. Baseline level of alcohol demand intensity (maximum consumption) significantly predicted drinks per week and alcohol problems at 1-month follow-up. BMI and BCI were associated with an immediate post-session reduction in alcohol demand (p < .001, n2p = .29) that persisted at the 1-month follow-up, with greater reductions in the BMI condition (p = .02, n2p = .06). Reductions in demand intensity and Omax (maximum expenditure) immediately post-session significantly predicted drinking reductions at 1-month follow up (p = .04, ∆ R2 = .02, and p = .01, ∆ R2 = .03, respectively). Behavioral economic reward value indices may function as risk factors for poor intervention response and as clinically relevant markers of change in heavy drinkers. The presentation will include a replication and extension of these results using a second clinical trial sample that included behavioral economic intervention elements.
Essential Aspects of "Essential Value" in Behavioral Economic Demand:  Recent Advances in Quantification
BRENT KAPLAN (The University of Kansas), Derek D. Reed (The University of Kansas), Peter G. Roma (Institutes for Behavior Resources, Inc.), Steven R. Hursh (Institutes for Behavior Resources, Inc.)
Abstract: Demand curve analyses are useful for quantifying the relation between consumption of a reinforcer, or commodity, and increasing constraints. One advantage of the exponential demand equation is that it yields a single parameter (α) to describe the rate of change in elasticity across the entire curve. However, because α is not independent of k (range of consumption in logarithmic units), Hursh (2014) recently proposed a new generalized essential value (EV) formula that corrects for this interaction. Also recently, researchers have proposed alternative methods to analyze data from demand curve studies, but the degree to which those equations adequately reflect EV is unknown. We examined data from demand curves across several previous studies to not only test the transitional validity of the new EV formula, but to also examine the degree to which new methods accurately describe the data. Results indicate that EV adequately corrects for differences in k among the majority of demand curves analyzed. In addition, we demonstrate comparative advantages and disadvantages of various methods of analyzing demand curves.


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