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  • 1.
    Ehn, Micael
    Stockholms universitet, Centrum för evolutionär kulturforskning.
    Temporal discounting leads to social stratification2012In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 36, no 4, p. 245-255Article in journal (Refereed)
    Abstract [en]

    Social stratification is present in all modern societies. Do income differences simply reflect inherited differences in individual abilities and resources? If not, why does not everyone choose strategies that lead to high income? This article shows that the psychological phenomenon known as temporal discounting will lead to differences in educational attainment and social stratification in any society where the demand for workers with a higher level of education is higher than for those with a lower level. The model is used to predict income differences between people with and without college education in seven developed countries, based only on official statistics of the cost and length of college education. The model explains 93% of the variance, strongly suggesting that temporal discounting is a major factor behind income differences.

  • 2.
    Eriksson, Kimmo
    Mälardalen University, School of Education, Culture and Communication.
    The Accuracy of Mathematical Models of Justice Evaluations2012In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 36, no 2, p. 125-135Article in journal (Refereed)
    Abstract [en]

    Jasso (1978) proposed a universal law of justice evaluations describing a logarithmic relationship between the perceived injustice of a reward and the ratio between this reward and the just reward. In applications this model is treated as if it were exact, whereas analogous models in psychophysics have empirically established degrees of uncertainty. In this article I make the first assessment of the magnitude of error in the logarithmic model of justice evaluations, using published data and a novel experiment. For the standard application of the model, where just rewards are inferred from justice evaluations, I find that the inherent inaccuracy leads to errors of about 15% on average. I also compared the logarithmic model to 2 nonlogarithmic models. Almost 20% of my respondents made justice evaluations that were more consistent with one of the latter models, suggesting that no single model is really universal.

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