Zoom Talk: Anita Keshmirian (LMU) and Borut Trpin (MCMP)

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Anita Keshmirian (LMU): Many Heads Are More Utilitarian Than One

Moral judgments have a very prominent social nature, and ineveryday life, they are continually shaped by discussions with others. Psychological investigations of these judgments, however, have rarely addressed the impact ofsocial interactions. To examine the role of social interaction on moral judgmentswithin small groups, we had groups of 4 to 5 participants judge moral dilemmasfirst individually and privately, then collectively and interactively, and finally individually a second time. We employed both real-life and sacrificial moral dilemmas in which the character’s action or inaction violated a moral principle to benefit the greatest number of people. Participants decided if these utilitarian decisions were morally acceptable or not. In Experiment 1, we found that collective judgments in face-to-face interactions were more utilitarian than the statistical aggregate of their members compared to both first and second individual judgments. This observation supported the hypothesis that deliberation and consensus within a group transiently reduce the emotional burden of norm violation. In Experiment 2, we tested this hypothesis more directly: measuring participants’ state anxiety in addition to their moral judgments before, during, and after online interactions, we found again that collectives were more utilitarian than those of individuals and that state anxiety level was reduced during and after social interaction. The utilitarian boost in collective moral judgments is probably due tothe reduction of stress in the social setting.

Borut Trpin (MCMP): A computational exploration of probabilistic learning rules in unreliable circumstances

Douven (2020) recently presented a simulation study based on agent-based optimization which shows that non-Bayesian learning rules will typically prevail over Bayesian updating. Following on this idea, we changed the sources of information in the simulations (i.e., diagnostic tests used by doctors in an ICU) to not necessarily be fully reliable. Our results show that in this case the context affect which learning rule will be selected by agent-based optimization. Hence, non-Bayesian learning rules may have their place, but so does standard Bayesian updating which prevails as the optimal rule in specific contexts. The implications for epistemology and for psychology of reasoning will also be briefly discussed.