@article{9a229af797dd4386838927c1711199c3,
title = "Biased perceptions explain collective action deadlocks and suggest new mechanisms to prompt cooperation",
abstract = "When individuals face collective action problems, their expectations about others' willingness to contribute affect their motivation to cooperate. Individuals, however, often misperceive the cooperation levels in a population. In the context of climate action, people underestimate the pro-climate positions of others. Designing incentives to enable cooperation and a sustainable future must thereby consider how social perception biases affect collective action. We propose a theoretical model and investigate the effect of social perception bias in non-linear public goods games. We show that different types of bias play a distinct role in cooperation dynamics. False uniqueness (underestimating own views) and false consensus (overestimating own views) both explain why communities get locked in suboptimal states. Such dynamics also impact the effectiveness of typical monetary incentives, such as fees. Our work contributes to understanding how targeting biases, e.g., by changing the information available to individuals, can comprise a fundamental mechanism to prompt collective action.",
keywords = "Decision Science, Psychology, Sociology",
author = "Santos, {Fernando P.} and Levin, {Simon A.} and Vasconcelos, {V{\'i}tor V.}",
note = "Funding Information: F.P.S. acknowledges support from the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Dynamic and Multi-scale Systems—Postdoctoral Fellowship Award. V.V.V. acknowledges funding from the Princeton Institute for International and Regional Studies, Rapid Switch Community. S.A.L. and V.V.V. acknowledge funding by the Army Research Office grant no. W911NF-18-1-0325 . S.A.L. acknowledges support from the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Dynamic and Multi-scale Systems—Collaborative Award, from the National Science Foundation grant no. CCF1917819 , and from C3.ai Inc. and Microsoft Corporation. The authors would like to thank members of the Behavioral Science for Policy Lab (Princeton University) and participants of the Theoretical Ecology Lab Tea (EEB, Princeton University) for discussions and very helpful suggestions. The authors thank Cathy H. Teng and Hugues Martin Dit Neuville for discussions and their careful pre-submission internal review. Funding Information: F.P.S. acknowledges support from the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Dynamic and Multi-scale Systems?Postdoctoral Fellowship Award. V.V.V. acknowledges funding from the Princeton Institute for International and Regional Studies, Rapid Switch Community. S.A.L. and V.V.V. acknowledge funding by the Army Research Office grant no. W911NF-18-1-0325. S.A.L. acknowledges support from the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Dynamic and Multi-scale Systems?Collaborative Award, from the National Science Foundation grant no. CCF1917819, and from C3.ai Inc. and Microsoft Corporation. The authors would like to thank members of the Behavioral Science for Policy Lab (Princeton University) and participants of the Theoretical Ecology Lab Tea (EEB, Princeton University) for discussions and very helpful suggestions. The authors thank Cathy H. Teng and Hugues Martin Dit Neuville for discussions and their careful pre-submission internal review. Conceptualization, F.P.S. S.A.L. and V.V.V.; Methodology, F.P.S. and V.V.V.; Software, F.P.S. and V.V.V.; Formal Analysis, F.P.S. and V.V.V.; Writing ? Original Draft, F.P.S. and V.V.V.; Writing ? Review & Editing, F.P.S. S.A.L. and V.V.V.; Supervision, S.A.L. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2021 The Authors",
year = "2021",
month = apr,
day = "23",
doi = "10.1016/j.isci.2021.102375",
language = "English (US)",
volume = "24",
journal = "iScience",
issn = "2589-0042",
publisher = "Elsevier Inc.",
number = "4",
}