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Communicating sentiment and outlook reverses inaction against collective risks.

Zhen WangMarko JusupHao GuoLei ShiSunčana GečekMadhur AnandMatjaz PercChris T BauchJürgen KurthsStefano BoccalettiHans Joachim Schellnhuber
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Collective risks permeate society, triggering social dilemmas in which working toward a common goal is impeded by selfish interests. One such dilemma is mitigating runaway climate change. To study the social aspects of climate-change mitigation, we organized an experimental game and asked volunteer groups of three different sizes to invest toward a common mitigation goal. If investments reached a preset target, volunteers would avoid all consequences and convert their remaining capital into monetary payouts. In the opposite case, however, volunteers would lose all their capital with 50% probability. The dilemma was, therefore, whether to invest one's own capital or wait for others to step in. We find that communicating sentiment and outlook helps to resolve the dilemma by a fundamental shift in investment patterns. Groups in which communication is allowed invest persistently and hardly ever give up, even when their current investment deficits are substantial. The improved investment patterns are robust to group size, although larger groups are harder to coordinate, as evidenced by their overall lower success frequencies. A clustering algorithm reveals three behavioral types and shows that communication reduces the abundance of the free-riding type. Climate-change mitigation, however, is achieved mainly by cooperator and altruist types stepping up and increasing contributions as the failure looms. Meanwhile, contributions from free riders remain flat throughout the game. This reveals that the mechanisms behind avoiding collective risks depend on an interaction between behavioral type, communication, and timing.
Keyphrases
  • climate change
  • human health
  • healthcare
  • mental health
  • machine learning
  • risk assessment
  • traumatic brain injury
  • deep learning
  • single cell
  • virtual reality
  • rna seq