Matching Items (2)
Description
Social discounting underlies individual altruistic decision-making, and it is frequently measured as the amount of hypothetical money one is willing to forgo for another person as a function of social distance. In the classic social discounting task, individual participants are asked to imagine their friends along a continuum of social distance, that is then used to estimate participant’s social discounting rate. While an ever-growing proportion of social interactions takes place over social media, no research has yet characterized social discounting in that context. Moreover, no research has estimated social discounting rate using real persons’ social distance, instead of the hypothetical continuum described above. Using existing social media indicators of social distance, it is now possible to estimate social discounting rate based on real people, which may lead to more accurate social discounting measurements and may expand the discounting model to real-life situations. Specifically, using computer algorithms to estimate the social distance from social media data makes it possible to assess the utility of numeric social distance indicators and the most appropriate ways to represent them. The proposed study examined the extent to which a hyperbolic model for social discounting fits social distance information retrieved from Facebook pages; and assessed whether there were differences in discounting rate when real or hypothetical social distance is used; also to further investigate whether discounting rates based on real persons are in fact based on perceived social distance by the participant, or on the imaginary social distance scale (i.e., an experimental artifact.)
It was found that the social discounting model can be applied in the social media context, even when real Facebook friends’ profiles were used as substitutes of numeric social distance indicators. Additionally, people showed similar altruistic tendencies in both the numeric and profile social discounting tests on the Facebook environment. These findings were qualified, however, by a high rate of nonsystematic data for the profile group; a rate much higher than traditional numeric paradigm. This discrepancy suggested that the allocation rates between numeric and profile approaches need further investigation to determine the factors affecting individuals’ generosity as a function of social distance indicators.
It was found that the social discounting model can be applied in the social media context, even when real Facebook friends’ profiles were used as substitutes of numeric social distance indicators. Additionally, people showed similar altruistic tendencies in both the numeric and profile social discounting tests on the Facebook environment. These findings were qualified, however, by a high rate of nonsystematic data for the profile group; a rate much higher than traditional numeric paradigm. This discrepancy suggested that the allocation rates between numeric and profile approaches need further investigation to determine the factors affecting individuals’ generosity as a function of social distance indicators.
ContributorsJiang, Linle (Author) / Miller, Paul A. (Thesis advisor) / Robles-Sotelo, Elias (Committee member) / Silva, Yasin N. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Science is a formalized method for acquiring information about the world. In
recent years, the ability of science to do so has been scrutinized. Attempts to reproduce
findings in diverse fields demonstrate that many results are unreliable and do not
generalize across contexts. In response to these concerns, many proposals for reform have
emerged. Although promising, such reforms have not addressed all aspects of scientific
practice. In the social sciences, two such aspects are the diversity of study participants
and incentive structures. Most efforts to improve scientific practice focus on replicability,
but sidestep issues of generalizability. And while researchers have speculated about the
effects of incentive structures, there is little systematic study of these hypotheses. This
dissertation takes one step towards filling these gaps. Chapter 1 presents a cross-cultural
study of social discounting – the purportedly fundamental human tendency to sacrifice
more for socially-close individuals – conducted among three diverse populations (U.S.,
rural Indonesia, rural Bangladesh). This study finds no independent effect of social
distance on generosity among Indonesian and Bangladeshi participants, providing
evidence against the hypothesis that social discounting is universal. It also illustrates the
importance of studying diverse human populations for developing generalizable theories
of human nature. Chapter 2 presents a laboratory experiment with undergraduates to test
the effect of incentive structures on research accuracy, in an instantiation of the scientific
process where the key decision is how much data to collect before submitting one’s
findings. The results demonstrate that rewarding novel findings causes respondents to
make guesses with less information, thereby reducing their accuracy. Chapter 3 presents
an evolutionary agent-based model that tests the effect of competition for novel findings
on the sample size of studies that researchers conduct. This model demonstrates that
competition for novelty causes the cultural evolution of research with smaller sample
sizes and lower statistical power. However, increasing the startup costs to conducting
single studies can reduce the negative effects of competition, as can rewarding
publication of secondary findings. These combined chapters provide evidence that
aspects of current scientific practice may be detrimental to the reliability and
generalizability of research and point to potential solutions.
recent years, the ability of science to do so has been scrutinized. Attempts to reproduce
findings in diverse fields demonstrate that many results are unreliable and do not
generalize across contexts. In response to these concerns, many proposals for reform have
emerged. Although promising, such reforms have not addressed all aspects of scientific
practice. In the social sciences, two such aspects are the diversity of study participants
and incentive structures. Most efforts to improve scientific practice focus on replicability,
but sidestep issues of generalizability. And while researchers have speculated about the
effects of incentive structures, there is little systematic study of these hypotheses. This
dissertation takes one step towards filling these gaps. Chapter 1 presents a cross-cultural
study of social discounting – the purportedly fundamental human tendency to sacrifice
more for socially-close individuals – conducted among three diverse populations (U.S.,
rural Indonesia, rural Bangladesh). This study finds no independent effect of social
distance on generosity among Indonesian and Bangladeshi participants, providing
evidence against the hypothesis that social discounting is universal. It also illustrates the
importance of studying diverse human populations for developing generalizable theories
of human nature. Chapter 2 presents a laboratory experiment with undergraduates to test
the effect of incentive structures on research accuracy, in an instantiation of the scientific
process where the key decision is how much data to collect before submitting one’s
findings. The results demonstrate that rewarding novel findings causes respondents to
make guesses with less information, thereby reducing their accuracy. Chapter 3 presents
an evolutionary agent-based model that tests the effect of competition for novel findings
on the sample size of studies that researchers conduct. This model demonstrates that
competition for novelty causes the cultural evolution of research with smaller sample
sizes and lower statistical power. However, increasing the startup costs to conducting
single studies can reduce the negative effects of competition, as can rewarding
publication of secondary findings. These combined chapters provide evidence that
aspects of current scientific practice may be detrimental to the reliability and
generalizability of research and point to potential solutions.
ContributorsTiokhin, Leonid (Author) / Hruschka, Daniel J (Thesis advisor) / Morgan, Thomas JH (Thesis advisor) / Boyd, Robert (Committee member) / Frankenhuis, Willem E. (Committee member) / Bergstrom, Carl T. (Committee member) / Arizona State University (Publisher)
Created2018