Time Cost and Demand: Implications for Public Policy

Schwartz, Lindsay P.; Hursh, Steven R.

doi:10.1007/s40614-022-00349-8

Cited by 6 publications

(5 citation statements)

References 52 publications

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“…Historic discounting work examines choice when delay is the primary limiting factor and is hypothesized to overlap with time-cost demand , conceptually and operationally. As found in discounting experiments (e.g., Jones & Rachlin, 2006;Killeen, 2015) and noted by Schwartz and Hursh (2022), a power transformation on time improves model fits of data (see Figures 2 and 3). Such an approach would have shifted the values for P max (via a shift in curve-predicted values across the range of assessed temporal prices) but would not have altered the broader conclusions of this analysis (insofar as said shift in prediction is consistent across conditions).…”

Section: Discussionsupporting

confidence: 63%

“…Behavioral-economic interpretations of complex choice offer insight that is otherwise difficult to discern (Critchfield & Reed, 2009) and may interest policy makers at varying scales Roma et al, 2017). Indeed, Schwartz and Hursh (2022) call for operant-demand analyses of time cost to inform prospective transportation policy. This study is among the first extension of cross-price demand modeling to examine the effects of time on choice.…”

Section: Discussionmentioning

confidence: 99%

“…4 Schwartz and Hursh (2022) suggest a power transformation (s) applied to the variable c to better account for delay as a cost and improve model approximation of observed choice. We discuss model outputs as derived from the standard iteration of Equation 1, but Figures 2 and 3 also portray these modified model fits.…”

Section: Competing Alternatives Demand Analysismentioning

confidence: 99%

“…A small body of work has examined time as a direct cost from an operant-demand perspective. Schwartz and Hursh (2022) suggest that time cost offers a meaningful entry point for assessing prospective transportationrelated public policy. One complication therein, however, is the additive effect of probability as a constraint in temporal cost assessments.…”

mentioning

confidence: 99%

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Using behavioral economics to optimize safer undergraduate late‐night transportation

Gelino,

Graham,

Strickland

et al. 2023

J of App Behav Analysis

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Many universities sponsor student‐oriented transit services that could reduce alcohol‐induced risks but only if services adequately anticipate and adapt to student needs. Human choice data offer an optimal foundation for planning and executing late‐night transit services. In this simulated choice experiment, respondents opted to either (a) wait an escalating delay for a free university‐sponsored “safe” option, (b) pay an escalating fee for an on‐demand rideshare service, or (c) pick a free, immediately available “unsafe” option (e.g., ride with an alcohol‐impaired driver). Behavioral‐economic nonlinear models of averaged‐choice data describe preference across arrangements. Best‐fit metrics indicate adequate sensitivity to contextual factors (i.e., wait time, preceding late‐night activity). At short delays, students preferred the free transit option. As delays extend beyond 30 min, most students preferred competing alternatives. These data depict a policy‐relevant delay threshold to better safeguard undergraduate student safety.

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Section: Discussionsupporting

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: Competing Alternatives Demand Analysismentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Using behavioral economics to optimize safer undergraduate late‐night transportation

Gelino,

Graham,

Strickland

et al. 2023

J of App Behav Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…In behavioral-economic demand analyses, the costs and benefits are typically operationalized in terms of effort (e.g., FR price) and reinforcers consumed, respectively. Price changes also yield concomitant changes in the delay to the reinforcer, however, so it is possible to view the costs in terms of the time between reinforcers (i.e., the interreinforcement interval) in addition to the effort per reinforcer (Schwartz and Hursh, 2022). Indeed, orderly demand functions have been reported with time rather than effort as a constraint on consumption (Bauman, 1991;Tsunematsu, 2001), and there is good reason to suspect that the time between successive social reinforcers would also yield orderly social demand functions.…”

Section: Discussionmentioning

confidence: 99%

Social familiarity and reinforcement value: a behavioral-economic analysis of demand for social interaction with cagemate and non-cagemate female rats

Schulingkamp,

Wan,

Hackenberg

2023

Front. Psychol.

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Rats were studied in social reinforcement procedures in which lever presses opened a door separating two adjacent spaces, permitting access to social interaction with a partner rat. The number of lever presses required for social interaction was systematically increased across blocks of sessions according to fixed-ratio schedules, generating demand functions at three different social reinforcement durations: 10 s, 30 s, and 60 s. The social partner rats were cagemates in one phase, and non-cagemates in a second phase. The rate at which social interactions were produced declined with the fixed-ratio price, and was well described by an exponential model that has been successfully employed with a range of social and non-social reinforcers. None of the main parameters of the model varied systematically with social interaction duration or with the social familiarity of the partner rat. On the whole, the results provide further evidence of the reinforcing value of social interaction, and its functional parallels with non-social reinforcers.

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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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Time Cost and Demand: Implications for Public Policy

Cited by 6 publications

References 52 publications

Using behavioral economics to optimize safer undergraduate late‐night transportation

Using behavioral economics to optimize safer undergraduate late‐night transportation

Social familiarity and reinforcement value: a behavioral-economic analysis of demand for social interaction with cagemate and non-cagemate female rats

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

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