Justice Sensitivity and Cooperation Dynamics in Repeated Public Good Games

It is frequently observed that despite individual incentives to free ride, humans decide to cooperate with each other to increase social payoffs. In the current research, we address the effects of individual differences in justice sensitivity on cooperation. Using incentivized repeated public good games, we find that individual differences in justice sensitivity—the ease of perceiving, remembering, and reacting to injustice from the perspectives of an observer, beneficiary, or perpetrator, but not victim—substantially predicts cooperation in the absence of a punishment option. In contrast, when costly punishment is allowed for, cooperation becomes strategic as it also aims at avoiding subsequent punishment. If such a sanctioning mechanism is in place, justice sensitivity no longer predicts cooperation. The results regarding the degree of cooperation as reaction to initial non-cooperation of one’s counterparts highlight the role of justice-concerning personality traits for the sufficient provision of public goods, as sanctioning institutions are not always possible, effective, or suitable.     

Assessment of STR Typing Success Rate in Soft Tissues from Putrefied Bodies Based on a Quantitative Grading System for Putrefaction

To date, there is no systematic investigation of the association of short tandem repeat (STR) typing success rate in soft tissues with different signs of putrefaction. Herein, putrefaction was rated using a newly developed 19‐parameter system in soft tissues from a collective of 68 decaying bodies, and DNA yield was determined in 408 samples. DNA integrity was rated using a self‐devised pentaplex PCR generating an “integrity score” (S_i). STR typing success rate was then assessed for selected cases. DNA yield and S_i differed significantly between tissues with kidney on average exhibiting the highest S_i values. Statistical analysis revealed that nine parameters were significantly and positively correlated with S_i. The observed values for each of these nine parameters were summed up to generate a putrefaction score (S_p) for each sample. Our results show that STR typing success rate can be predicted based on S_p before expensive multiplex STR profiling is performed.