In an experimental study of impulsive decision making in starlings (Bateson et al. Reference Bateson, Brilot, Gillespie, Monaghan and Nettle2014), birds showing greater telomere attrition (an integrative marker of a poor biological state) were found to favor sooner-smaller rewards (one pellet of food in 1 second) over larger-later ones (five pellets in x seconds). An interpretation of these results based on differential mortality risks would be as follows: Starlings in a poorer biological state have a greater probability of dying before collecting delayed rewards and should therefore privilege short-term benefits. This interpretation would be undermined, however, by the fact that dying during a choice experiment that did not exceed a few minutes is an extremely unlikely event, even for birds in poor states.
In the target article, the authors provide an explanation for the behavioral constellation of deprivation (BCD) that is mainly based on variation in extrinsic mortality. However, as in the starling example above, average differences in mortality are unlikely to account for socioeconomic-status- (SES-) specific discounting rates when rewards are delayed over short periods (e.g., weeks, months, or even a few years). This point can be illustrated in humans with the study by Ramos et al. (Reference Ramos, Victor, Seidl-de-Moura and Daly2013) cited by Pepper & Nettle (P&N), which reports that slum-dwelling youth discounted future rewards more than university students. In this study, the delay used in the questionnaire did not exceed 75 days. Thus, the estimated cumulative probability of dying during the following 75 days would have had to be very high to justify the preference of sooner but smaller rewards. Such a situation, though, is not expected to hold across the majority of populations where the BCD is observed.
Hence, a gap seems to emerge once one tries to explain present orientation with differences in mortality whenever decision making is affected during short timescales. One way to address such cases in line with the target article would be to examine other factors underlying variation in collection risks (e.g., individuals' social capital, population level of cooperation). However, a complementary approach that does not follow from P&N's framework would rely on factors independent of collection risk.
We see at least one corresponding source of time discounting that ought to be considered: the cost of waiting for a reward per se (i.e., the cost paid by an individual even when the benefits are guaranteed). But why should there be a cost of waiting in the absence of a collection risk? After all, in a population at a demographic equilibrium, x fitness units now are strictly equivalent to x fitness units later. Delaying a reward is costly, however, if this reward can be invested into an individual's capital to increase his or her future ability to exploit the environment. In such a case, delaying the reward entails an opportunity cost corresponding to the additional fitness units that would have been gained with the increased level of capital during the delay. This principle can be illustrated with a thought experiment: Imagine a farmer who participates in an economic study in which he is offered a choice between receiving $1,000 now or $2,000 in a month. Because this particular farmer does not own any expensive agricultural equipment, he is only able to sow half of his fields simultaneously. However, $1,000 now would allow him to buy new equipment and exploit his whole farm. This would yield him an expected $2,500 increase in revenue by the end of the month. Hence, our farmer should prefer the smaller-sooner reward, even though the collection risk in our example could be close to zero and the larger reward is only delayed by a month. Instead, the fact that his current level of capital is associated with a particularly high opportunity cost in productivity determines his choice. Conversely, imagine a farmer who already owns sophisticated agricultural machines taking part in the same study. For him, $1,000 is not enough to upgrade his equipment. Rather, he is currently trying to save $15,000 by the end of the month to buy some extra land. In this case, waiting a month for the larger reward more greatly reduces the amount of money he has to save.
Such effects of the current amount of capital are likely to be pervasive. Indeed, in addition to increased productivity, as in the above example, an individual's capital can also yield a reduction in mortality risk (e.g., by buying a house in a town's safest neighborhood) or protect against capital depreciation (e.g., by investing in fire insurance). Crucially, the effect of capital should also directly map SES differences in temporal discounting. Although a formal treatment is needed here, we expect that when people have almost no capital, even the smallest amount of resources are likely to drastically improve their productivity or reduce their mortality. Therefore, they should generally favor sooner rewards even during shorter timescales. The more capital one already has, however, the larger the amount of resources that will be required to significantly increase it further, and the less steeply that future rewards should be discounted.
As an illustration, compare the cost one might pay for living in a small apartment rather than a house to the cost of living on the streets. In the first case, it might be noisy neighbors, the lack of a garden, or the inability to host many relatives for dinner. In the second case, however, it includes physical degradation from being exposed to climatic hazards, lack of hygiene or assaults from others, the inability to collect welfare support, social and economic exclusion in general, and so on. Therefore, someone living on the streets is likely to prefer any basic accommodation now over an individual house in 6 months, whereas someone living in a small flat might be willing to wait 6 months for an even better house.
In conclusion, ultimately, the interactions between waiting costs per se and collection risk will determine individuals' temporal discounting. Hence, by adding this novel class of factors to P&N's framework, we can expect to deepen our understanding of the BCD.
In an experimental study of impulsive decision making in starlings (Bateson et al. Reference Bateson, Brilot, Gillespie, Monaghan and Nettle2014), birds showing greater telomere attrition (an integrative marker of a poor biological state) were found to favor sooner-smaller rewards (one pellet of food in 1 second) over larger-later ones (five pellets in x seconds). An interpretation of these results based on differential mortality risks would be as follows: Starlings in a poorer biological state have a greater probability of dying before collecting delayed rewards and should therefore privilege short-term benefits. This interpretation would be undermined, however, by the fact that dying during a choice experiment that did not exceed a few minutes is an extremely unlikely event, even for birds in poor states.
In the target article, the authors provide an explanation for the behavioral constellation of deprivation (BCD) that is mainly based on variation in extrinsic mortality. However, as in the starling example above, average differences in mortality are unlikely to account for socioeconomic-status- (SES-) specific discounting rates when rewards are delayed over short periods (e.g., weeks, months, or even a few years). This point can be illustrated in humans with the study by Ramos et al. (Reference Ramos, Victor, Seidl-de-Moura and Daly2013) cited by Pepper & Nettle (P&N), which reports that slum-dwelling youth discounted future rewards more than university students. In this study, the delay used in the questionnaire did not exceed 75 days. Thus, the estimated cumulative probability of dying during the following 75 days would have had to be very high to justify the preference of sooner but smaller rewards. Such a situation, though, is not expected to hold across the majority of populations where the BCD is observed.
Hence, a gap seems to emerge once one tries to explain present orientation with differences in mortality whenever decision making is affected during short timescales. One way to address such cases in line with the target article would be to examine other factors underlying variation in collection risks (e.g., individuals' social capital, population level of cooperation). However, a complementary approach that does not follow from P&N's framework would rely on factors independent of collection risk.
We see at least one corresponding source of time discounting that ought to be considered: the cost of waiting for a reward per se (i.e., the cost paid by an individual even when the benefits are guaranteed). But why should there be a cost of waiting in the absence of a collection risk? After all, in a population at a demographic equilibrium, x fitness units now are strictly equivalent to x fitness units later. Delaying a reward is costly, however, if this reward can be invested into an individual's capital to increase his or her future ability to exploit the environment. In such a case, delaying the reward entails an opportunity cost corresponding to the additional fitness units that would have been gained with the increased level of capital during the delay. This principle can be illustrated with a thought experiment: Imagine a farmer who participates in an economic study in which he is offered a choice between receiving $1,000 now or $2,000 in a month. Because this particular farmer does not own any expensive agricultural equipment, he is only able to sow half of his fields simultaneously. However, $1,000 now would allow him to buy new equipment and exploit his whole farm. This would yield him an expected $2,500 increase in revenue by the end of the month. Hence, our farmer should prefer the smaller-sooner reward, even though the collection risk in our example could be close to zero and the larger reward is only delayed by a month. Instead, the fact that his current level of capital is associated with a particularly high opportunity cost in productivity determines his choice. Conversely, imagine a farmer who already owns sophisticated agricultural machines taking part in the same study. For him, $1,000 is not enough to upgrade his equipment. Rather, he is currently trying to save $15,000 by the end of the month to buy some extra land. In this case, waiting a month for the larger reward more greatly reduces the amount of money he has to save.
Such effects of the current amount of capital are likely to be pervasive. Indeed, in addition to increased productivity, as in the above example, an individual's capital can also yield a reduction in mortality risk (e.g., by buying a house in a town's safest neighborhood) or protect against capital depreciation (e.g., by investing in fire insurance). Crucially, the effect of capital should also directly map SES differences in temporal discounting. Although a formal treatment is needed here, we expect that when people have almost no capital, even the smallest amount of resources are likely to drastically improve their productivity or reduce their mortality. Therefore, they should generally favor sooner rewards even during shorter timescales. The more capital one already has, however, the larger the amount of resources that will be required to significantly increase it further, and the less steeply that future rewards should be discounted.
As an illustration, compare the cost one might pay for living in a small apartment rather than a house to the cost of living on the streets. In the first case, it might be noisy neighbors, the lack of a garden, or the inability to host many relatives for dinner. In the second case, however, it includes physical degradation from being exposed to climatic hazards, lack of hygiene or assaults from others, the inability to collect welfare support, social and economic exclusion in general, and so on. Therefore, someone living on the streets is likely to prefer any basic accommodation now over an individual house in 6 months, whereas someone living in a small flat might be willing to wait 6 months for an even better house.
In conclusion, ultimately, the interactions between waiting costs per se and collection risk will determine individuals' temporal discounting. Hence, by adding this novel class of factors to P&N's framework, we can expect to deepen our understanding of the BCD.