Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


50th Annual Convention; Philadelphia, PA; 2024

Event Details

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Symposium #199
CE Offered: BACB
Recent Advancements in Behavioral Economic Applications
Sunday, May 26, 2024
8:00 AM–9:50 AM
Convention Center, 200 Level, 204 C
Area: EAB; Domain: Translational
Chair: Brandon Patrick Miller (University of Kansas)
Discussant: Derek D. Reed (Institutes for Behavior Resources, Inc.)
CE Instructor: Derek D. Reed, Ph.D.
Abstract: Behavioral economics provides a conceptually systematic approach to measuring reinforcer valuation that combines aspects of behavioral science and microeconomic principles. The relatively recent proliferation in the application of these frameworks comes both as a byproduct of the increased salience of their utility and via methodological advancements that permit extension to historically difficult-to-measure contexts (e.g., community health). Of notable use are tasks intended to measure patterns of discounting (i.e., change in perceived reinforcer value as a function of delayed or probabilistic contact) and operant demand (i.e., effort expenditure to defend baseline, or free-cost, access to a reinforcer as a function of systematically increasing cost requirements). As with any quantitative approach, methodological and interpretive considerations can dictate the conclusions draws from such analyses. This symposium describes four novel advancements in behavioral economic task applications, documenting development in modeling approaches, metric interpretations, and considerations when extending beyond the laboratory context. Our discussant Dr. Derek Reed will provide commentary on these approaches and the benefits of these advancements.
Instruction Level: Intermediate
Keyword(s): behavioral economics, discounting, operant demand
Target Audience: Attendees should have foundational knowledge in behavioral economics.
Learning Objectives: calculate behavioral economic metrics in resource-lean contexts; discuss advantages of "real" and hypothetical purchase task procedures; identify unit retention in discounting outputs

Estimating Reinforcer Efficacy From Demand Curves, Dose-Dependent Curves, and Choice: A Principled and Practical Approach

FEDERICO SANABRIA (Arizona State University)

Assessing the efficacy of reinforcers is as puzzling as it is important in behavior analysis and psychopharmacology. Rates of responding vary with schedule requirements, choice varies with reinforcer abundance, and contextual factors often play unknown roles on the impact of reinforcement on behavior. Maximization theory, supplemented with empirically supported assumptions, provides the basis for the estimation of reinforcer efficacy in individual subjects in laboratory settings. This approach identifies efficacy with the economic concept of utility; it assumes that subjects continually choose those activities with higher utility within the constraints imposed by the environment. From these assumptions, predictions may be drawn on how rate of reinforcement varies with price (response requirement), how rate of responding for drugs of abuse varies with dose, how discrete choices change with income (rate of choice presentation), among other important behavioral metrics. Data from these procedures may be used to estimate the utility—i.e., the efficacy—of reinforcers. This presentation emphasizes practical considerations for conducting the estimation of utility parameters such as unit utility and marginal rate of substitution.


Arc Elasticities of Operant Demand: A Potential Model-Independent Solution for Applied Researchers

(Basic Research)
MADISON GRAHAM (University of Kansas), Derek D. Reed (Institutes for Behavior Resources, Inc.), Brett Gelino (Johns Hopkins University School of Medicine), Justin Charles Strickland (Johns Hopkins University School of Medicine), Steven R Hursh (Institutes for Behavior Resources, Inc.)

Analyses of operant demand data have advanced precipitously over the past 5 years. These advances include refinement of quantitative models, newly proposed theoretical models, and advanced statistical modeling approaches. Concurrent with these advances has been an increase in behavioral economic extensions to applied questions in non-laboratory settings. Unfortunately, however, the advanced mathematical approaches to quantifying demand are often antithetical to applied pursuits where there are limited resources for software or inadequate training in quantitative modeling. Toward this end, some researchers have suggested the use of model-independent metrics of demand, such as observed markers along the demand curve (e.g., observed maximum output [Omax], price associated with observed maximum output [Pmax], breakpoints, observed intensity). While there is strong translational support for these observed markers, these fail to capture the most critical aspect of demand curve analyses: elasticity. We propose the use of arc elasticity to measure the percentage change in consumption as a function of the percentage change across specific price values. The presentation will provide specific data examples across numerous commodity types. We will present correlations between observed markers, model-derived variables, and clinical outputs with various approaches to arc elasticity. We will conclude with recommendations for applied researchers.

Behavioral Economic Modeling of Incentivized and Hypothetical Demand Procedures
(Basic Research)
JUSTIN CHARLES STRICKLAND (Johns Hopkins University School of Medicine), Brett Gelino (Johns Hopkins University School of Medicine)
Abstract: Hypothetical behavior is commonly used in the broader behavioral sciences to investigate behavioral repertoires of interest that are otherwise difficult or impossible to directly observe. Behavior analysts, understandably so for historic reasons, hold a skeptical view of using hypothetical arrangements in lieu of “real” behavior. This presentation will describe the behavioral economic modelling of data from incentivized and hypothetical purchase task procedures collected in a human laboratory study of reduced nicotine cigarette expectancies. Participants who smoke daily (N=21; 9 female) completed one practice and four experimental sessions in which expectancy (labelled “average” versus “very low” nicotine) and nicotine dose (0.80 mg versus 0.03 mg yield) were manipulated. Participants in acute withdrawal sampled experimental cigarettes followed by measurement of cigarette demand using an incentivized purchase task in which responses were randomly reinforced with purchased cigarettes and money and a hypothetical purchase task relying on verbal behavior manipulations. Analysis of incentivized and hypothetical outcomes showed a close correspondence for measures such as demand intensity (r = .56) and Pmax (r = .54). Practical considerations of modelling in these designs and methodological decisions regarding the ways in which hypothetical procedures differ from incentivized ones will be discussed.
Unit Retention and Metric Interpretation in the Delay Discounting Framework
(Basic Research)
BRETT GELINO (Johns Hopkins University School of Medicine), Justin Charles Strickland (Johns Hopkins University School of Medicine), Derek D. Reed (Institutes for Behavior Resources, Inc.), Matthew W. Johnson (Johns Hopkins University School of Medicine)
Abstract: Discounting analysis has offered countless insights into behavioral health outcomes and continues to draw transdisciplinary attention as a reliable descriptor of decision-making. This presentation highlights some analytical issues that may be underappreciated, namely that the delay discounting parameter (i.e., k) carries units which are not reported in most studies, leading potentially to errors in cross-study comparisons. This presentation describes the relation between delay discounting k and delay value (i.e., D) logically and via data manipulation. We reanalyzed three extant datasets across a variety of discounting assessment methods using systematically different regression delay values. Results support the notion that k retains the reciprocal of time as a unit (e.g., days-1 if days were used as units for D). Next, we examine the role of time perception in human discount responding, paying specific attention to the s psychophysical scalar of the hyperboloid-like descriptive models. Participants completed a series of tasks intended to gauge differences in subjective temporal experience. Results of this study show promise for important conceptual considerations that need be made when selecting an ideal descriptive model.



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