Behavioral economic demand modeling chronology, complexities, and considerations: Much ado about zeros

Koffarnus, Mikhail N.; Kaplan, Brent A.; Franck, Christopher T.; Rzeszutek, Mark J.; Traxler, Haily K.

doi:10.1016/j.beproc.2022.104646

Cited by 5 publications

(14 citation statements)

References 34 publications

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“…The exponentiated model's parameters have the benefits of years of proven utility in correlating with addiction processes and clinical outcomes (Koffarnus & Kaplan, 2018;Martínez-Loredo et al, 2021). Because of this, further evaluations, comparisons, and relationships with other novel and wellestablished models of demand (see Koffarnus et al, 2022 for an overview) across species, drugs, and tasks are warranted. Because α has been predictive of actual treatment outcomes (González-Roz et al, 2020;Schwartz et al, 2021;Secades-Villa et al, 2016), future research is needed to determine how values derived from Rachlin's equation compare to real-life outcomes, relative to established This document is copyrighted by the American Psychological Association or one of its allied publishers.…”

Section: Practical Comparisonsmentioning

confidence: 99%

“…One limitation with these equations is that values of P max and O max cannot be solved for analytically because Q 0 shows up twice in the equation, so other attempts have been made to approximate it (Gilroy et al, 2019;Hursh & Roma, 2013). Other models of demand exist (e.g., Gilroy et al, 2021;Newman & Ferrario, 2020;Yu et al, 2014) and were recently discussed at length in Koffarnus et al (2022). Further discussion on them here is outside the scope of the current article.…”

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confidence: 99%

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Notes on demand: Conceptual and empirical benefits of applying Rachlin’s discounting equation to demand data.

Rzeszutek¹,

Franck²,

Traxler³

et al. 2023

Psychology of Addictive Behaviors

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Objective: Howard Rachlin wrote extensively on how value diminishes in a hyperbolic form, and he contributed to understanding choice processes between different commodities as a molar pattern of behavior. The field of behavioral economic demand has been dominated by exponential decay functions, indicating that decreases in consumption of a commodity are best fit by exponential functions. Because of the success of Rachlin’s equation at describing how hyperbolic decay affects the value of a commodity across various factors (e.g., delay, probability, social distance), we attempted to extend his equation to behavioral economic demand data for alcohol and opioids. Method: Rachlin’s discounting equation was applied to estimate consumption on alcohol purchase task data and nonhuman drug demand data. We compared results of his equation to the exponentiated demand equation using both a mixed-effects modeling approach and a two-stage approach. Results: Rachlin’s equation provided better fits to consumption data than the exponentiated equation for both mixed-effects and two-stage modeling. We also found that traditional demand metrics, such as Pmax, can be derived analytically when using Rachlin’s equation. Certain metrics derived from Rachlin’s equation appeared to be related to clinical covariates in ways similar to the exponentiated equation. Conclusions: Rachlin’s equation better described demand data than did the exponentiated equation, indicating that demand for a commodity may decrease hyperbolically rather than exponentially. Other benefits of his equation are that it does not have the same pitfalls as the current exponential equations and is relatively straightforward in its conceptualization when applied to demand data.

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Section: Practical Comparisonsmentioning

confidence: 99%

mentioning

confidence: 99%

Notes on demand: Conceptual and empirical benefits of applying Rachlin’s discounting equation to demand data.

Rzeszutek¹,

Franck²,

Traxler³

et al. 2023

Psychology of Addictive Behaviors

Self Cite

View full text Add to dashboard Cite

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“…Once data are readied for analysis, they are typically fit using a nonlinear model; demand metrics of interest can then be derived and assessed. The literature offers various models that may be better suited pending the characteristics of the unique data set (see Koffarnus et al, 2022). The complexities of model selection are beyond the scope of this paper.…”

Section: Best Practice For Cpt Developmentmentioning

confidence: 99%

Behavioral Economic Demand: How Simulated Behavioral Tasks Can Inform Health Policy

Reed

Gelino

Strickland

2022

Policy Insights from the Behavioral and Brain Sciences

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Consumers decide what to purchase, under conditions of constraint (e.g., commodity price). According to behavioral economic demand, commodity purchase task (CPT) can measure hypothetical decisions about purchases under varied simulated policy conditions (e.g., introduction of new cigarette taxes, happy hour drinking specials). These tasks permit rapid data collection without sacrificing methodological rigor or the validity of conclusions reached. The CPT allows researchers to simulate new policies, to determine their relative risks and benefits, thus offering an opportunity to optimize prior to rollout. Behavioral outcomes related to consumer purchases also make the CPT data readily translatable to policymakers, including constituent health behavior. This article provides a background on CPTs, a review of literature related to policy-aimed CPTs, and a start on best practices for other behavioral scientists interested in applying CPT to inform public policy efforts. It also serves as a primer for policymakers seeking to evaluate usage of this tool.

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“…While there are several equations used in demand research (see Koffarnus et al, 2022), we will only describe the exponentiated model of demand (Koffarnus et al, 2015), which is:

Q = Q_{0} * 10^{k (e^{- α Q_{0} X} - 1)}

where Q is the estimated consumption at some price, Q 0 is the estimated consumption when price is zero (intensity), k is a free parameter (typically the log span of the data +.5) used for model fitting, α is the relative change in consumption due to price of a commodity (sensitivity), and X is, typically, the price of the commodity. The exponentiated model is simply an exponentiation of the original exponential model (Hursh & Silberberg, 2008).…”

mentioning

confidence: 99%

“…While there are several equations used in demand research (see Koffarnus et al, 2022), we will only describe the exponentiated model of demand (Koffarnus et al, 2015), which is:…”

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confidence: 99%

Hyperbolic discounting and exponentiated demand: Modeling demand for cigarettes in three dimensions

Rzeszutek

Kaplan

Traxler

et al. 2022

J Exper Analysis Behavior

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Behavioral economics has been a fruitful area of research in substance use. Mathematical descriptions of how individuals temporally discount the value of a commodity have been correlated with substance use and mathematical descriptions of drug consumption decreasing as a function of price (i.e., demand) predict maladaptive substance use. While there is a logical assumption that temporal factors affect demand for a drug, little has been done to merge these models. Thus, the purpose of this study was to combine models of discounting and demand, extending Howard Rachlin's work and contributions to novel areas of study. Data from 85 participants from Amazon Mechanical Turk (MTurk) who completed a hypothetical cigarette purchase task that included price of and delay to cigarettes were analyzed. Multilevel modeling was used to determine descriptive accuracy of combined additive and multiplicative models of discounting and demand. Of the discounting models used in conjunction with the exponentiated demand equation, the Rachlin hyperboloid best described the delay dimension of consumption. The multiplicative version of the Rachlin equation applied to both delay and price outperformed other models tested. Therefore, existing models of discounting and demand can be extended to modeling consumption data from complex multidimensional experimental arrangements.

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Behavioral economic demand modeling chronology, complexities, and considerations: Much ado about zeros

Cited by 5 publications

References 34 publications

Notes on demand: Conceptual and empirical benefits of applying Rachlin’s discounting equation to demand data.

Notes on demand: Conceptual and empirical benefits of applying Rachlin’s discounting equation to demand data.

Behavioral Economic Demand: How Simulated Behavioral Tasks Can Inform Health Policy

Hyperbolic discounting and exponentiated demand: Modeling demand for cigarettes in three dimensions

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