The Accuracy of Mathematical Models of Justice Evaluations

Abstract

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.

Keywords:

• mathematical modeling
• measurement
• social psychology

Acknowledgments

This research was partly funded by a grant from the Swedish Research Council. Comments from an anonymous reviewer on a previous version of this manuscript were gratefully received.

Notes

Note. N = 57. If instead we estimate the model from the average observation for each A/C ratio (to compensate for the fact that the number of observations per A/C ratio varied between 1 and 3), all values reported in the table stay unchanged with exception for the max value of MREJ which changes from 1.95 to 1.97.

¹Each relative difference was meant to be used twice, but by mistake the relative difference 20% was used three times and −17% only once. This does not affect the point of the analysis, see note to Table 1.