Evidence map›Paper›PMID 28360877›Full record

ArticleFrontiers in psychology2017

Social Discounting under Risk.

Jia Jin, Guanxiong Pei, Qingguo Ma

Abstract read
In one paragraph

Article in Frontiers in psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Social, psychological, and demographic characteristics of dehumanization toward immigrants.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jia JinBusiness School, Ningbo UniversityNingbo, China; Academy of Neuroeconomics and Neuromanagement, Ningbo UniversityNingbo, China.
Guanxiong PeiSchool of Management, Zhejiang UniversityHangzhou, China; Institute of Neural Management Sciences, Zhejiang University of TechnologyHangzhou, China.
Qingguo MaBusiness School, Ningbo UniversityNingbo, China; Academy of Neuroeconomics and Neuromanagement, Ningbo UniversityNingbo, China; Institute of Neural Management Sciences, Zhejiang University of TechnologyHangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a measure of how prosocial behavior depends on social distance, social discounting is defined as the decrease in generosity between the decision maker and the recipient as the social distance increases. While risk is a ubiquitous part of modern life, there is limited research on the relationship between risk and prosocial behavior. In the present experiment, we empirically test whether risk has an influence on social discounting. We use the choice titration procedure to examine this effect. Our data show that independent of risk, participants are less eager to forego money and exhibit more selfishness toward a specific person when the social distance increases; these findings are reflected in the hyperbolic model. Interestingly, risk influences the shape of the social discounting function, which is reflected in the notable different discount rates. Individuals who make decisions under risk yield a smaller discount rate than those who make decisions without risk, i.e., under risk subjects reduce less their generosity as a function of the social distance. Furthermore, this distinct type of generosity occurs typically among individuals with 10-distance recipients but not with the closest- and furthest-social-distance recipients.

Indexed as

generosityprosocial behaviorrisksocial discountingsocial distance

Identifiers

PMID28360877
PMCPMC5350097

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.