Fig. 2: The seven socioeconomic motives and their hypothetical effects on the third party’s utility gain to intervene.
a, Five classes of computationally well-defined socioeconomic motives that expand into seven motive terms in utility calculation. Parameters α and β control disadvantageous (self < other) and advantageous (self > other) inequality aversion, respectively. This illustration of disadvantageous SCI between self and transgressor but advantageous SCI between self and victim may not apply to post-intervention inequality, where the direction of SCI might be reversed. The SCI type only depends on whether self > other or self < other, regardless of the other being transgressor or victim. Parameter γ controls victim-centered disadvantageous (victim < transgressor) inequality aversion. Parameter κ controls the direction and strength of the RP motive (victim > transgressor after intervention). Parameter ω controls EC (maximizing others’ total payoff). Parameters ηno and ηyes respectively control inaction and action ID (attenuated perception of inequality under higher intervention cost). b, Heatmaps illustrating how each motive’s strength influences ΔU (utility of choosing yes − utility of choosing no) in the third-party intervention decision. Each motive is shown by a pair of panels with the small and large parameters controlling the motive’s magnitude differently. For simplicity, when the effect of a single parameter is examined, all other parameters are set to zero. The exceptions are ηno and ηyes, for which parameter γ is set to 1, because their utility terms are multiplied by γ. Each heatmap has four submaps: divided horizontally by scenario (punishment left, helping right) and vertically by impact ratio (1.5 bottom, 3.0 top). The x axis denotes inequality severity (near equality left to extreme inequality right), and the y axis denotes intervention cost (low bottom to high top). Color code, ΔU: reddish for stronger preference to choose yes, bluish for stronger preference to choose no. For illustration purposes, the ΔU were scaled separately for each column and separately for positive and negative values. Each motive shows a distinct influence on ΔU and would thus lead to distinguishable effects on third-party intervention decision behaviors. Credit: a, head icon, X. Mai.
