Hyperbolic discounting of delayed social interaction

Abstract

The temporal discounting literature has demonstrated that the loss in value for delayed outcomes is most accurately modeled using a hyperbolic discounting equation. The hyperbolic-shaped function not only describes the shape of discounting for monetary outcomes, but also for other tangibles, such as alcohol, candy, CDs, erotica, cigarettes, cocaine, books, and DVDs. Furthermore, this hyperbolic shape has important theoretical implications. Despite the broad list of outcomes that are hyperbolically discounted, one class of outcomes has only recently received attention: social interactions. For the present study, we used standard binary-choice discounting procedures to explore the function that most accurately describes the change in value of delayed social interactions. The results from the present study suggest that, for 93 participants, the same equations (exponential, hyperbolic, and hyperboloid) used to describe the discounting of nonsocial commodities also describe the relation between delay and the value of social interaction. Similar to previous findings for nonsocial outcomes, the hyperbolic-shaped models provide a fit to the data superior to that of the exponential model.

Choice impulsivity: Definitions, measurement issues, and clinical implications.

“…Although money is the most commonly reported task stimulus, multiple CI tasks using nonmonetary commodities have shown good test–retest reliability and validity. The array of stimuli includes both primary natural reinforcers such as food (Estle et al, 2007; Rasmussen et al, 2010), sexual activity (Johnson & Bruner, 2013; Lawyer & Schoepflin, 2013; Lawyer et al, 2011; 2010), alcohol and other substances of abuse (Bickel et al, 2014; Odum, 2011), and conditioned, secondary reinforcers including music CDs, books, and DVDs (Charlton & Fantino, 2008), availability of social interactions (Charlton et al, 2013), and more abstract hypothetical concepts such as various types of health and environmental outcomes (Baker, Johnson, & Bickel, 2003; Hardisty & Weber, 2009; Miller & Chapman, 2001).…”

“…In general, although different discounting rates have been obtained across different commodities (Charlton & Fantino, 2008; Odum, 2011), the degree of discounting between hypothetical commodities is highly correlated (Odum, 2011), and the same temporal discounting equations have been found to work well with these nonmonetary commodities, with the hyperbolic function most commonly recommended (Charlton et al, 2013). For more abstract outcomes such as delayed health and environmental outcomes associated with behavioral choices, framing appears to be particularly important, with delayed gains typically discounted at higher rates than delayed losses (Baker, Johnson, & Bickel, 2003; Hardisty & Weber, 2009; Miller & Chapman, 2001; Mitchell & Wilson, 2010).…”

Discount models in intertemporal choice: an empirical analysis

“…With respect to the comparison of the fitting goodness of the different discount functions, most of previous studies were based on statistical tests (Kirby and Maraković, 1995; Kirby, 1997; McKerchar et al , 2010, 2013; Charlton et al , 2013). However, our manuscript has been based on a criterion of statistical information which has been recently incorporated into the study of intertemporal choice, viz the Akaike's information criterion.…”

“…With the aim to know the function which offers the best explanation to the individual choices, some researchers have compared the significance of different discount functions. For this purpose, some parametric techniques have been used, as the t -test (Kirby and Maraković, 1995) or the sign test (Kirby and Maraković, 1995; Kirby, 1997), and nonparametric tests, as the Wilcoxon matched-pairs signed-ranks test (Myerson and Green, 1995; Johnson and Bickel, 2002; Madden et al , 2003; McKerchar et al , 2010, 2013; Charlton et al , 2013), or the Kruskal–Wallis' test (Johnson et al , 2007). These comparisons have been made with different amounts, types of reward (real or hypothetical) and choice domains, as well as groups of people with different demographic and behavioral characteristics.…”

“…Ostaszewski et al (1998) and Ostaszewski and Karzel (2002), for example, were based on the t -test to analyze if the parameter s deviated from 1.0, in both small and large rewards. Other authors used linear contrasts (McKerchar et al , 2010, 2013; Charlton et al , 2013) whilst Green et al (1999) and Myerson and Green (1995) used nonparametric tests: the binominal test and the Wilcoxon matched-pairs signed-ranks, respectively. Myerson et al (2003), McKerchar et al (2010) and McKerchar et al (2013), for their part, were based on the Wilcoxon matched-pairs signed-ranks to compare the values of the parameter s among different amounts.…”