Personalized Interventions to Improve Intergroup Relations and Promote Peace

ongoing

The recent increase in intergroup hostilities and the intertwining of local and global crises emphasizes the importance of finding ways to attenuate the destructive effects of intergroup conflicts. In recent decades, social psychologists have shown an increasing interest in offering interventions to reduce intergroup prejudice and promote intergroup reconciliation. Yet, in contrast to other scientific fields that strive towards more personalized approaches, most research on intergroup interventions applies the same interventions to all target populations. The proposed project aims to address this gap by introducing an innovative framework for personalized intergroup interventions. The proposed framework is composed of three main layers: a. personalization parameters, b. mechanisms connecting personalization parameters to concrete interventions, and c. the relevant interventions to be tested. The project includes four sequential parts. In part one (WP1) I will conduct six (one for each intervention) meta-analyses in which all available data of studies using these interventions will be gathered and moderations by personalization parameters will be tested. In WP2, online versions of all interventions will be created and tested in two large-scale online studies in two different contexts (Israeli-Palestinian and immigration in Europe) against relevant personalization parameters. Then, in WP3, I will attempt to pinpoint optimal compositions of different interventions, to maximize effects for distinct individuals. Finally, in WP4, I will conduct field interventions in two domains: education and social media, in which screening based on the parameters for personalization is plausible. The project will identify novel mechanisms by which different intergroup interventions differently influence different individuals. It will make important conceptual discoveries and apply them in order to shift intergroup relations using more accurate, theory-driven, personalized methods.