Articles | Volume 16, issue 1
https://doi.org/10.5194/esd-16-189-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/esd-16-189-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jan 2025
Research article |
| 28 Jan 2025
| 28 Jan 2025
The Pareto effect in tipping social networks: from minority to majority
Jordan P. Everall
CORRESPONDING AUTHOR
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
Fabian Tschofenig
Department of Environmental Systems Sciences, University of Graz, Graz, Austria
Stanford Graduate School of Business, Stanford University, Stanford, CA, USA
Jonathan F. Donges
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
Ilona M. Otto
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
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Didier Swingedouw, Laura Jackson, Aixue Hu, Anastasia Romanou, Nicole C. Laureanti, Wilbert Weijer, Sina Loriani, Bette Otto-Bliesner, Ayako Abe-Ouchi, Lucas Almeida, Alessio Bellucci, Reyk Börner, Gokhan Danabasoglu, Donovan P. Dennis, Marion Devilliers, Sybren Drijfhout, Jonathan Donges, Friederike Fröb, Thomas L. Frölicher, Guillaume Gastineau, Heiko Goelzer, Chuncheng Guo, Urs Hofmann, Anna Höse, Colin Jones, Torben Koenigk, Ann Kristin Klose, Valerio Lembo, Jose Licon-Salaiz, Ken Mankoff, Virna Meccia, Irina Melnikova, Oliver Mehling, Laurie Menviel, Juliette Mignot, Jon I. Robson, Gavin A. Schmidt, Robin Smith, Yuchen Sun, Irene Trombini, Matteo Willeit, Richard Wood, Fanghua Wu, Lin Zhaohui, and Ricarda Winkelmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-1698, https://doi.org/10.5194/egusphere-2026-1698, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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This study presents a plan for climate model experiments to better understand how changes in freshwater in the North Atlantic affect major ocean currents. We designed coordinated simulations to test their response to warming, added freshwater, and possible recovery after weakening. Comparing results across models and past climate evidence helps improve confidence in projections and assess risks of large ocean circulation changes.
Bastian Ott, Jonathan F. Donges, and Johan Rockström
EGUsphere, https://doi.org/10.5194/egusphere-2026-79, https://doi.org/10.5194/egusphere-2026-79, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Climate adaptation is crucial but its impacts on the Earth system are not well understood. Our study finds climate adaptation contributes ~26 % of annual greenhouse gas emissions, ~74 % of human freshwater withdrawals, and even affects the ozone layer. While some of these impacts are driven by climate change, the majority is likely fuelled by economic, demographic, and technological developments. These findings highlight the need to balance adaptation with respect for Earth system resilience.
John M. Anderies, Max Bechthold, Jonathan F. Donges, Ingo Fetzer, Nico Wunderling, Wolfram Barfuss, and Johan Rockström
EGUsphere, https://doi.org/10.5194/egusphere-2025-6345, https://doi.org/10.5194/egusphere-2025-6345, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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This paper explores a new approach to understanding human-Earth system resilience. It introduces a measure that estimates how likely a system is to reach a safe and just state. Available knowledge about how the system works, its boundaries, and potential disruptions centrally constrain the measure. It could help decision-makers strike a balance between gaining knowledge, building capacity to act, and taking practical measures to improve resilience in many different types of systems.
E. Keith Smith, Carl Folke, Niklas Kitzmann, Manjana Milkoreit, Per Olsson, Ricarda Winkelmann, Anne-Sophie Crépin, Christina Eder, Niklas Harring, Jobst Heitzig, Alexia Katsanidou, Timothy M. Lenton, Franz Mauelshagen, Kelton Minor, Ilona M. Otto, Armon Rezai, Jürgen Scheffran, Isabelle Stadelmann-Steffen, Rick van der Ploeg, Nico Wunderling, and Jonathan F. Donges
EGUsphere, https://doi.org/10.5194/egusphere-2026-177, https://doi.org/10.5194/egusphere-2026-177, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Achieving climate and sustainability goals requires rapid, large-scale change. We introduce criticality – the likelihood a system is near a social tipping point – and critical agency – the capacity to shape those conditions. Our framework shows how coalitions and policies can trigger desired shifts and avoid harmful ones, linking complex systems theory with evidence to guide policymakers and practitioners.
Jannes Breier, Luana Schwarz, Hannah Prawitz, Werner von Bloh, Christoph Müller, Stephen Björn Wirth, Max Bechthold, Dieter Gerten, and Jonathan F. Donges
EGUsphere, https://doi.org/10.5194/egusphere-2025-4475, https://doi.org/10.5194/egusphere-2025-4475, 2025
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We present a new modelling framework that links global vegetation and agricultural modelling with human decision-making processes in an integrated simulation approach. This makes it possible to explore how farming practices and environmental changes influence each other over time. By combining climate, land use, and social dynamics in one system, the framework opens new ways to study food security, climate adaptation strategies, and long-term impacts.
Sina Loriani, Yevgeny Aksenov, David I. Armstrong McKay, Govindasamy Bala, Andreas Born, Cristiano Mazur Chiessi, Henk A. Dijkstra, Jonathan F. Donges, Sybren Drijfhout, Matthew H. England, Alexey V. Fedorov, Laura C. Jackson, Kai Kornhuber, Gabriele Messori, Francesco S. R. Pausata, Stefanie Rynders, Jean-Baptiste Sallée, Bablu Sinha, Steven C. Sherwood, Didier Swingedouw, and Thejna Tharammal
Earth Syst. Dynam., 16, 1611–1653, https://doi.org/10.5194/esd-16-1611-2025, https://doi.org/10.5194/esd-16-1611-2025, 2025
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In this work, we draw on palaeo-records, observations, and modelling studies to review tipping points in the ocean overturning circulations, monsoon systems, and global atmospheric circulations. We find indications for tipping in the ocean overturning circulations and the West African monsoon, with potentially severe impacts on the Earth system and humans. Tipping in the other considered systems is regarded as conceivable but is currently not sufficiently supported by evidence.
Luana Schwarz, Jannes Breier, Hannah Prawitz, Max Bechthold, Werner von Bloh, Sara M. Constantino, Dieter Gerten, Jobst Heitzig, Ronja Hotz, Leander John, Christoph Müller, Johan Rockström, and Jonathan F. Donges
EGUsphere, https://doi.org/10.5194/egusphere-2025-4079, https://doi.org/10.5194/egusphere-2025-4079, 2025
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We present a novel global model that links farmer decisions with ecological processes to explore how agricultural systems co-evolve. Unlike previous tools, it captures feedbacks between society and nature at up-to planetary scale. We find that conservation practices can restore soil health and support stable harvests. Adoption spreads through learning and norms, showing how regeneration at the farm scale can ripple outward, contributing to global sustainability and Earth system resilience.
Colin Jones, Isaline Bossert, Donovan P. Dennis, Hazel Jeffery, Chris D. Jones, Torben Koenigk, Sina Loriani, Benjamin Sanderson, Roland Séférian, Klaus Wyser, Shuting Yang, Manabu Abe, Sebastian Bathiany, Pascale Braconnot, Victor Brovkin, Friedrich A. Burger, Patrica Cadule, Frederic S. Castruccio, Gokhan Danabasoglu, Andrea Dittus, Jonathan F. Donges, Friederike Fröb, Thomas Frölicher, Goran Georgievski, Chuncheng Guo, Aixue Hu, Peter Lawrence, Paul Lerner, José Licón-Saláiz, Bette Otto-Bliesner, Anastasia Romanou, Elena Shevliakova, Yona Silvy, Didier Swingedouw, Jerry Tjiputra, Jeremy Walton, Andy Wiltshire, Ricarda Winkelmann, Richard Wood, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2025-3604, https://doi.org/10.5194/egusphere-2025-3604, 2025
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We introduce a new Earth system model experiment protocol to help researchers understand how Earth might respond to positive, zero, and negative carbon emissions. This protocol enables different models to be compared following similar warming and cooling rates. Researchers use the models to explore how the Earth reacts to different climate futures, including the risk of tipping points being exceeded and whether changes can be reversed. The results will support improved long-term climate policy.
Max Bechthold, Wolfram Barfuss, André Butz, Jannes Breier, Sara M. Constantino, Jobst Heitzig, Luana Schwarz, Sanam N. Vardag, and Jonathan F. Donges
Earth Syst. Dynam., 16, 1365–1390, https://doi.org/10.5194/esd-16-1365-2025, https://doi.org/10.5194/esd-16-1365-2025, 2025
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Social norms are a major influence on human behaviour. In natural resource use models, norms are often included in a simplistic way leading to “black or white” sustainability outcomes. We find that a dynamic representation of norms, including social groups, determines more nuanced states of the environment in a stylised model of resource use while also defining the success of attempts to manage the system, suggesting the importance of representing both aspects well in coupled models.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
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The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
E. Keith Smith, Marc Wiedermann, Jonathan F. Donges, Jobst Heitzig, and Ricarda Winkelmann
Earth Syst. Dynam., 16, 545–564, https://doi.org/10.5194/esd-16-545-2025, https://doi.org/10.5194/esd-16-545-2025, 2025
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Social tipping dynamics have received recent attention as a potential mechanism for effective climate actions – yet how such tipping dynamics could unfold remains largely unquantified. We explore how social tipping processes can develop by enabling necessary conditions (exemplified by climate change concern) and increased perceptions of localized impacts (sea level rise). The likelihood of social tipping varies regionally, mostly along areas with the highest exposure to persistent risks.
Viktoria Spaiser, Sirkku Juhola, Sara M. Constantino, Weisi Guo, Tabitha Watson, Jana Sillmann, Alessandro Craparo, Ashleigh Basel, John T. Bruun, Krishna Krishnamurthy, Jürgen Scheffran, Patricia Pinho, Uche T. Okpara, Jonathan F. Donges, Avit Bhowmik, Taha Yasseri, Ricardo Safra de Campos, Graeme S. Cumming, Hugues Chenet, Florian Krampe, Jesse F. Abrams, James G. Dyke, Stefanie Rynders, Yevgeny Aksenov, and Bryan M. Spears
Earth Syst. Dynam., 15, 1179–1206, https://doi.org/10.5194/esd-15-1179-2024, https://doi.org/10.5194/esd-15-1179-2024, 2024
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In this paper, we identify potential negative social tipping points linked to Earth system destabilization and draw on related research to understand the drivers and likelihood of these negative social tipping dynamics, their potential effects on human societies and the Earth system, and the potential for cascading interactions and contribution to systemic risks.
Nico Wunderling, Anna S. von der Heydt, Yevgeny Aksenov, Stephen Barker, Robbin Bastiaansen, Victor Brovkin, Maura Brunetti, Victor Couplet, Thomas Kleinen, Caroline H. Lear, Johannes Lohmann, Rosa Maria Roman-Cuesta, Sacha Sinet, Didier Swingedouw, Ricarda Winkelmann, Pallavi Anand, Jonathan Barichivich, Sebastian Bathiany, Mara Baudena, John T. Bruun, Cristiano M. Chiessi, Helen K. Coxall, David Docquier, Jonathan F. Donges, Swinda K. J. Falkena, Ann Kristin Klose, David Obura, Juan Rocha, Stefanie Rynders, Norman Julius Steinert, and Matteo Willeit
Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, https://doi.org/10.5194/esd-15-41-2024, 2024
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This paper maps out the state-of-the-art literature on interactions between tipping elements relevant for current global warming pathways. We find indications that many of the interactions between tipping elements are destabilizing. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 °C or on shorter timescales if global warming surpasses 2.0 °C.
Maria Zeitz, Jan M. Haacker, Jonathan F. Donges, Torsten Albrecht, and Ricarda Winkelmann
Earth Syst. Dynam., 13, 1077–1096, https://doi.org/10.5194/esd-13-1077-2022, https://doi.org/10.5194/esd-13-1077-2022, 2022
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The stability of the Greenland Ice Sheet under global warming is crucial. Here, using PISM, we study how the interplay of feedbacks between the ice sheet, the atmosphere and solid Earth affects the long-term response of the Greenland Ice Sheet under constant warming. Our findings suggest four distinct dynamic regimes of the Greenland Ice Sheet on the route to destabilization under global warming – from recovery via quasi-periodic oscillations in ice volume to ice sheet collapse.
Jonathan F. Donges, Wolfgang Lucht, Sarah E. Cornell, Jobst Heitzig, Wolfram Barfuss, Steven J. Lade, and Maja Schlüter
Earth Syst. Dynam., 12, 1115–1137, https://doi.org/10.5194/esd-12-1115-2021, https://doi.org/10.5194/esd-12-1115-2021, 2021
Nico Wunderling, Jonathan F. Donges, Jürgen Kurths, and Ricarda Winkelmann
Earth Syst. Dynam., 12, 601–619, https://doi.org/10.5194/esd-12-601-2021, https://doi.org/10.5194/esd-12-601-2021, 2021
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In the Earth system, climate tipping elements exist that can undergo qualitative changes in response to environmental perturbations. If triggered, this would result in severe consequences for the biosphere and human societies. We quantify the risk of tipping cascades using a conceptual but fully dynamic network approach. We uncover that the risk of tipping cascades under global warming scenarios is enormous and find that the continental ice sheets are most likely to initiate these failures.
Cited articles
Airoldi, E. M. and Christakis, N. A.: Induction of social contagion for diverse outcomes in structured experiments in isolated villages, Science, 384, eadi5147, https://doi.org/10.1126/science.adi5147, 2024.
Albert, R. and Barabási, A.-L.: Statistical mechanics of complex networks, Rev. Mod. Phys., 74, 47–97, https://doi.org/10.1103/RevModPhys.74.47, 2002.
Amato, R., Lacasa, L., Díaz-Guilera, A., and Baronchelli, A.: The dynamics of norm change in the cultural evolution of language, P. Natl. Acad. Sci., 115, 8260–8265, https://doi.org/10.1073/pnas.1721059115, 2018.
Andersson, D., Bratsberg, S., Ringsmuth, A. K., and de Wijn, A. S.: Dynamics of collective action to conserve a large common-pool resource, Sci. Rep., 11, 9208, https://doi.org/10.1038/s41598-021-87109-x, 2021.
Andreoni, J., Nikiforakis, N., and Siegenthaler, S.: Predicting social tipping and norm change in controlled experiments, P. Natl. Acad. Sci. USA, 118, e2014893118, https://doi.org/10.1073/pnas.2014893118, 2021.
Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., Kao, A. B., Moran, R. E., Romanczuk, P., Rubenstein, D. I., Tombak, K. J., Bavel, J. J. V., and Weber, E. U.: Stewardship of global collective behavior, P. Natl. Acad. Sci. USA, 118, e2025764118, https://doi.org/10.1073/pnas.2025764118, 2021.
Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J.: Everyone's an influencer: quantifying influence on twitter, in: Proceedings of the fourth ACM international conference on Web search and data mining, 9 September 2011, New York, NY, USA, 65–74, https://doi.org/10.1145/1935826.1935845, 2011.
Barash, V., Cameron, C., and Macy, M.: Critical phenomena in complex contagions, Soc. Netw., 34, 451–461, https://doi.org/10.1016/j.socnet.2012.02.003, 2012.
Baronchelli, A., Felici, M., Loreto, V., Caglioti, E., and Steels, L.: Sharp transition towards shared vocabularies in multi-agent systems, J. Stat. Mech. Theory Exp., 2006, P06014, https://doi.org/10.1088/1742-5468/2006/06/P06014, 2006.
Bastos, M. T., Raimundo, R. L. G., and Travitzki, R.: Gatekeeping Twitter: message diffusion in political hashtags, Media Cult. Soc., 35, 260–270, https://doi.org/10.1177/0163443712467594, 2013.
Bellotti, E., Voros, A., Passah, M., Nongrum, Q. D., Nengnong, C. B., Khongwir, C., van Eijk, A., Kessler, A., Sarkar, R., Carlton, J. M., and Albert, S.: Social network and household exposure explain the use of malaria prevention measures in rural communities of Meghalaya, India, medRxiv [preprint], https://doi.org/10.1101/2023.04.23.23288997, 2023.
Bentley, R. A., Maddison, E. J., Ranner, P. H., Bissell, J., Caiado, C. C. S., Bhatanacharoen, P., Clark, T., Botha, M., Akinbami, F., Hollow, M., Michie, R., Huntley, B., Curtis, S. E., and Garnett, P.: Social tipping points and Earth systems dynamics, Front. Environ. Sci., 2, 35, https://doi.org/10.3389/fenvs.2014.00035, 2014.
Berger, J., Efferson, C., and Vogt, S.: Tipping pro-environmental norm diffusion at scale: opportunities and limitations, Behav. Public Policy, 7, 581–606, https://doi.org/10.1017/bpp.2021.36, 2021.
Bergquist, L. F. and Dinerstein, M.: Competition and Entry in Agricultural Markets: Experimental Evidence from Kenya, Am. Econ. Rev., 110, 3705–3747, https://doi.org/10.1257/aer.20171397, 2020.
Berner, R., Gross, T., Kuehn, C., Kurths, J., and Yanchuk, S.: Adaptive dynamical networks, Phys. Rep., 1031, 1–59, https://doi.org/10.1016/j.physrep.2023.08.001, 2023.
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., and Fowler, J. H.: A 61-million-person experiment in social influence and political mobilization, Nature, 489, 295–298, https://doi.org/10.1038/nature11421, 2012.
Burns, T. R. and Flam, H.: The Shaping of Social Organization: Social Rule System Theory with Applications, Sage Publications, 456 pp., ISBN 978-0-8039-8027-3, 1987.
Castilla-Rho, J. C., Rojas, R., Andersen, M. S., Holley, C., and Mariethoz, G.: Social tipping points in global groundwater management, Nat. Hum. Behav., 1, 640–649, https://doi.org/10.1038/s41562-017-0181-7, 2017.
Centola, D.: The Spread of Behavior in an Online Social Network Experiment, Science, 329, 1194–1197, https://doi.org/10.1126/science.1185231, 2010.
Centola, D.: An Experimental Study of Homophily in the Adoption of Health Behavior, Science, 334, 1269–1272, https://doi.org/10.1126/science.1207055, 2011.
Centola, D. and Macy, M.: Complex Contagions and the Weakness of Long Ties, Am. J. Sociol., 113, 702–734, https://doi.org/10.1086/521848, 2007.
Centola, D., Becker, J., Brackbill, D., and Baronchelli, A.: Experimental evidence for tipping points in social convention, Science, 360, 1116–1119, https://doi.org/10.1126/science.aas8827, 2018.
Centola, D. M.: Homophily, networks, and critical mass: Solving the start-up problem in large group collective action, Ration. Soc., 25, 3–40, 2013.
Centola, D.: Change: How to Make Big Things Happen, Little Brown, 352 pp., ISBN 978-0-316-45733-0, 2021.
Centola, D. and Baronchelli, A.: The spontaneous emergence of conventions: An experimental study of cultural evolution, P. Natl. Acad. Sci., 112, 1989–1994, https://doi.org/10.1073/pnas.1418838112, 2015.
Chiang, Y.-S.: Birds of Moderately Different Feathers: Bandwagon Dynamics and the Threshold Heterogeneity of Network Neighbors, J. Math. Sociol., 31, 47–69, https://doi.org/10.1080/00222500601013536, 2007.
Christakis, N. A. and Fowler, J. H.: The spread of obesity in a large social network over 32 years, New Engl. J. Med., 357, 370–379, https://doi.org/10.1056/NEJMsa066082, 2007.
Christakis, N. A. and Fowler, J. H.: The Collective Dynamics of Smoking in a Large Social Network, New Engl. J. Med., 358, 2249–2258, https://doi.org/10.1056/NEJMsa0706154, 2008.
Constantino, S. M., Sparkman, G., Kraft-Todd, G. T., Bicchieri, C., Centola, D., Shell-Duncan, B., Vogt, S., and Weber, E. U.: Scaling Up Change: A Critical Review and Practical Guide to Harnessing Social Norms for Climate Action, Psychol. Sci. Publ. Int., 23, 50–97, https://doi.org/10.1177/15291006221105279, 2022.
Dakos, V. and Bascompte, J.: Critical slowing down as early warning for the onset of collapse in mutualistic communities, P. Natl. Acad. Sci. USA, 111, 17546–17551, https://doi.org/10.1073/pnas.1406326111, 2014.
David Tàbara, J., Frantzeskaki, N., Hölscher, K., Pedde, S., Kok, K., Lamperti, F., Christensen, J. H., Jäger, J., and Berry, P.: Positive tipping points in a rapidly warming world, Curr. Opin. Sust., 31, 120–129, https://doi.org/10.1016/j.cosust.2018.01.012, 2018.
De Domenico, M.: More is different in real-world multilayer networks, Nat. Phys., 19, 1247–1262, https://doi.org/10.1038/s41567-023-02132-1, 2023.
Dodds, P. S. and Watts, D. J.: Universal Behavior in a Generalized Model of Contagion, Phys. Rev. Lett., 92, 218701, https://doi.org/10.1103/PhysRevLett.92.218701, 2004.
Donges, J. F., Heitzig, J., Barfuss, W., Wiedermann, M., Kassel, J. A., Kittel, T., Kolb, J. J., Kolster, T., Müller-Hansen, F., Otto, I. M., Zimmerer, K. B., and Lucht, W.: Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World–Earth modeling framework, Earth Syst. Dynam., 11, 395–413, https://doi.org/10.5194/esd-11-395-2020, 2020.
Donges, J. F., Lochner, J. H., Kitzmann, N. H., Heitzig, J., Lehmann, S., Wiedermann, M., and Vollmer, J.: Dose–response functions and surrogate models for exploring social contagion in the Copenhagen Networks Study, Eur. Phys. J.-Spec. Top., 230, 3311–3334, https://doi.org/10.1140/epjs/s11734-021-00279-7, 2021.
Dunford, R., Su, Q., and Tamang, E.: The Pareto Principle, Plymouth Stud. Sci., 7, 140–148, https://doi.org/10.24382/swfr-wr17, 2014.
Efferson, C., Vogt, S., and Fehr, E.: The promise and the peril of using social influence to reverse harmful traditions, Nat. Hum. Behav., 4, 55–68, https://doi.org/10.1038/s41562-019-0768-2, 2020.
Ehret, S., Constantino, S. M., Weber, E. U., Efferson, C., and Vogt, S.: Group identities can undermine social tipping after intervention, Nat. Hum. Behav., 6, 1669–1679, https://doi.org/10.1038/s41562-022-01440-5, 2022.
Eker, S., Reese, G., and Obersteiner, M.: Modelling the drivers of a widespread shift to sustainable diets, Nat. Sustain., 2, 725–735, https://doi.org/10.1038/s41893-019-0331-1, 2019.
Everall, J.: Analysis Code and Data for Paper: The Pareto effect in tipping social networks: from minority to majority, Zenodo [code, data set], https://doi.org/10.5281/zenodo.14716597, 2025.
Fahimipour, A. K., Zeng, F., Homer, M., Traulsen, A., Levin, S. A., and Gross, T.: Sharp thresholds limit the benefit of defector avoidance in cooperation on networks, P. Natl. Acad. Sci. USA, 119, e2120120119, https://doi.org/10.1073/pnas.2120120119, 2022.
Farmer, J. D., Hepburn, C., Ives, M. C., Hale, T., Wetzer, T., Mealy, P., Rafaty, R., Srivastav, S., and Way, R.: Sensitive intervention points in the post-carbon transition, Science, 364, 132–134, https://doi.org/10.1126/science.aaw7287, 2019.
Feola, G.: Societal transformation in response to global environmental change: A review of emerging concepts, Ambio, 44, 376–390, https://doi.org/10.1007/s13280-014-0582-z, 2015.
Ferraz de Arruda, G., Petri, G., Rodriguez, P. M., and Moreno, Y.: Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs, Nat. Commun., 14, 1–15, https://doi.org/10.1038/s41467-023-37118-3, 2023.
Fink, C., Schmidt, A., Barash, V., Cameron, C., and Macy, M.: Complex contagions and the diffusion of popular Twitter hashtags in Nigeria, Soc. Netw. Anal. Min., 6, 1, https://doi.org/10.1007/s13278-015-0311-z, 2015.
Fink, C., Schmidt, A., Barash, V., Kelly, J., Cameron, C., and Macy, M.: Investigating the Observability of Complex Contagion in Empirical Social Networks, Proc. Int. AAAI Conf. Web Soc. Media, 10, 121–130, https://doi.org/10.1609/icwsm.v10i1.14751, 2016.
Fowler, J. H. and Christakis, N. A.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study, BMJ, 337, a2338, https://doi.org/10.1136/bmj.a2338, 2008.
Frei, L., Everall, J., and Ringsmuth, A. K.: Guided rewiring of social networks reduces polarization and accelerates collective action, arXiv [preprint], https://doi.org/10.48550/arXiv.2309.12141, 21 September 2023.
Galam, S. and Cheon, T.: Tipping Points in Opinion Dynamics: A Universal Formula in Five Dimensions, Front. Phys., 8, 566580, https://doi.org/10.3389/fphy.2020.566580, 2020.
Geier, F., Barfuss, W., Wiedermann, M., Kurths, J., and Donges, J. F.: The physics of governance networks: critical transitions in contagion dynamics on multilayer adaptive networks with application to the sustainable use of renewable resources, Eur. Phys. J.-Spec. Top., 228, 2357–2369, https://doi.org/10.1140/epjst/e2019-900120-4, 2019.
Gladwell, M.: The Tipping Point: How Little Things Can Make a Big Difference, Back Bay Books, 280 pp., ISBN 0-316-34662-4, 2001.
Granovetter, M.: Threshold Models of Collective Behavior, Am. J. Sociol., 83, 1420–1443, 1978.
Granovetter, M.: Economic Action and Social Structure: The Problem of Embeddedness, Am. J. Sociol., 91, 481–510, 1985.
Granovetter, M. S.: The Strength of Weak Ties, Am. J. Sociol., 78, 1360–1380, 1973.
Grodzins, M.: Metropolitan Segregation, Sci. Am., 197, 33–41, 1957.
Guilbeault, D. and Centola, D.: Topological measures for identifying and predicting the spread of complex contagions, Nat. Commun., 12, 4430, https://doi.org/10.1038/s41467-021-24704-6, 2021.
Guilbeault, D., Becker, J., and Centola, D.: Complex Contagions: A Decade in Review, in: Complex Spreading Phenomena in Social Systems: Influence and Contagion in Real-World Soc Networks, edited by: Lehmann, S. and Ahn, Y.-Y., Springer International Publishing, Cham, 3–25, https://doi.org/10.1007/978-3-319-77332-2_1, 2018.
Han, L., Lin, Z., Tang, M., Zhou, J., Zou, Y., and Guan, S.: Impact of contact preference on social contagions on complex networks, Phys. Rev. E, 101, 042308, https://doi.org/10.1103/PhysRevE.101.042308, 2020.
Hodas, N. O. and Lerman, K.: The Simple Rules of Social Contagion, Sci. Rep., 4, 4343, https://doi.org/10.1038/srep04343, 2014.
Hodbod, J., Milkoreit, M., Baggio, J., Mathias, J.-D., and Schoon, M.: Principles for a Case Study Approach to Social Tipping Points, in: Positive Tipping Points Towards Sustainability: Understanding the Conditions and Strategies for Fast Decarbonization in Regions, edited by: Tàbara, J. D., Flamos, A., Mangalagiu, D., and Michas, S., Springer International Publishing, Cham, 79–99, https://doi.org/10.1007/978-3-031-50762-5_5, 2024.
Holland, M. M., Bitz, C. M., and Tremblay, B.: Future abrupt reductions in the summer Arctic sea ice, Geophys. Res. Lett., 33, L23503, https://doi.org/10.1029/2006GL028024, 2006.
Holme, P.: Modern temporal network theory: a colloquium, Eur. Phys. J. B, 88, 234, https://doi.org/10.1140/epjb/e2015-60657-4, 2015.
Holme, P. and Rocha, J. C.: Networks of climate change: connecting causes and consequences, Appl. Netw. Sci., 8, 1–20, https://doi.org/10.1007/s41109-023-00536-9, 2023.
Iacopini, I., Petri, G., Baronchelli, A., and Barrat, A.: Group interactions modulate critical mass dynamics in social convention, Commun. Phys., 5, 64, https://doi.org/10.1038/s42005-022-00845-y, 2022.
Janas, M., Nikiforakis, N., and Siegenthaler, S.: Eliciting Thresholds for Interdependent Behavior, NBER Work. Pap., National Bureau of Economic Research, Inc., https://doi.org/10.3386/w32847, August 2024.
Jin, K. and Yu, U.: Reference to Global State and Social Contagion Dynamics, Front. Phys., 9, 684223, https://doi.org/10.3389/fphy.2021.684223, 2021.
Kaaronen, R. O. and Strelkovskii, N.: Cultural Evolution of Sustainable Behaviors: Pro-environmental Tipping Points in an Agent-Based Model, One Earth, 2, 85–97, https://doi.org/10.1016/j.oneear.2020.01.003, 2020.
Karimi, F. and Holme, P.: A Temporal Network Version of Watts's Cascade Model, in: Temporal Networks, edited by: Holme, P. and Saramäki, J., Springer, Berlin, Heidelberg, 315–329, https://doi.org/10.1007/978-3-642-36461-7_16, 2013.
Karsai, M., Kivelä, M., Pan, R. K., Kaski, K., Kertész, J., Barabási, A.-L., and Saramäki, J.: Small but slow world: How network topology and burstiness slow down spreading, Phys. Rev. E, 83, 025102, https://doi.org/10.1103/PhysRevE.83.025102, 2011.
Karsai, M., Iñiguez, G., Kaski, K., and Kertész, J.: Complex contagion process in spreading of online innovation, J. R. Soc. Interf., 11, 20140694, https://doi.org/10.1098/rsif.2014.0694, 2014.
Karsai, M., Iñiguez, G., Kikas, R., Kaski, K., and Kertész, J.: Local cascades induced global contagion: How heterogeneous thresholds, exogenous effects, and unconcerned behaviour govern online adoption spreading, Sci. Rep., 6, 27178, https://doi.org/10.1038/srep27178, 2016.
Kivelä, M., Pan, R. K., Kaski, K., Kertész, J., Saramäki, J., and Karsai, M.: Multiscale analysis of spreading in a large communication network, J. Stat. Mech., 2012, P03005, https://doi.org/10.1088/1742-5468/2012/03/P03005, 2012.
Korkmaz, G., Kuhlman, C. J., Ravi, S. S., and Vega-Redondo, F.: Spreading of social contagions without key players, World Wide Web, 21, 1187–1221, https://doi.org/10.1007/s11280-017-0500-y, 2018.
Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., and Donges, J. F.: Dynamics of tipping cascades on complex networks, Phys. Rev. E, 101, 042311, https://doi.org/10.1103/PhysRevE.101.042311, 2020.
Lacopini, L., Petri, G., Baronchelli, A., and Barrat, A.: Group interactions modulate critical mass dynamics in social convention, Commun. Phys., 5, 1–10, https://doi.org/10.1038/s42005-022-00845-y, 2022.
Lade, S. J., Bodin, Ö., Donges, J. F., Kautsky, E. E., Galafassi, D., Olsson, P., and Schlüter, M.: Modelling social-ecological transformations: an adaptive network proposal, arXiv [nlin, preprint], https://doi.org/10.48550/arXiv.1704.06135, 18 April 2017.
Lamperti, F., Dosi, G., Napoletano, M., Roventini, A., and Sapio, A.: Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model, Ecol. Econ., 150, 315–339, https://doi.org/10.1016/j.ecolecon.2018.03.023, 2018.
Lenton, T. M.: Tipping positive change, Philos T Roy Soc B, 375, 20190123, https://doi.org/10.1098/rstb.2019.0123, 2020.
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., and Schellnhuber, H. J.: Tipping elements in the Earth's climate system, P. Natl. Acad. Sci. USA, 105, 1786–1793, https://doi.org/10.1073/pnas.0705414105, 2008.
Leo, Y., Fleury, E., Alvarez-Hamelin, J. I., Sarraute, C., and Karsai, M.: Socioeconomic correlations and stratification in social-communication networks, J. Roy. Soc. Interface, 13, 20160598, https://doi.org/10.1098/rsif.2016.0598, 2016.
Leviston, Z. and Uren, H. V.: Overestimating one's “green” behavior: Better-than-average bias may function to reduce perceived personal threat from climate change, J. Soc. Issues, 76, 70–85, https://doi.org/10.1111/josi.12365, 2020.
Martin, R., Schlüter, M., and Blenckner, T.: The importance of transient social dynamics for restoring ecosystems beyond ecological tipping points, P. Natl. Acad. Sci. USA, 117, 2717–2722, https://doi.org/10.1073/pnas.1817154117, 2020.
Milkoreit, M.: Social tipping points everywhere? – Patterns and risks of overuse, Wires Clim. Change, 14, e813, https://doi.org/10.1002/wcc.813, 2023.
Milkoreit, M., Hodbod, J., Baggio, J., Benessaiah, K., Calderón-Contreras, R., Donges, J. F., Mathias, J.-D., Rocha, J. C., Schoon, M., and Werners, S. E.: Defining tipping points for social-ecological systems scholarship – an interdisciplinary literature review, Environ. Res. Lett., 13, 033005, https://doi.org/10.1088/1748-9326/aaaa75, 2018.
Min, B. and Miguel, M. S.: Threshold cascade dynamics on coevolving networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2305.13965, 23 May 2023.
Müller-Hansen, F., Schlüter, M., Mäs, M., Donges, J. F., Kolb, J. J., Thonicke, K., and Heitzig, J.: Towards representing human behavior and decision making in Earth system models – an overview of techniques and approaches, Earth Syst. Dynam., 8, 977–1007, https://doi.org/10.5194/esd-8-977-2017, 2017.
Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T., and Stern, P. C.: The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions, Nat. Energ., 6, 1011–1016, https://doi.org/10.1038/s41560-021-00900-y, 2021.
Nishioka, S. and Hasegawa, T.: Cascading Behavior of an Extended Watts Model on Networks, J. Phys. Soc. Jpn., 91, 124801, https://doi.org/10.7566/JPSJ.91.124801, 2022.
Nyborg, K., Anderies, J. M., Dannenberg, A., Lindahl, T., Schill, C., Schlüter, M., Adger, W. N., Arrow, K. J., Barrett, S., Carpenter, S., Chapin, F. S., Crépin, A.-S., Daily, G., Ehrlich, P., Folke, C., Jager, W., Kautsky, N., Levin, S. A., Madsen, O. J., Polasky, S., Scheffer, M., Walker, B., Weber, E. U., Wilen, J., Xepapadeas, A., and de Zeeuw, A.: Social norms as solutions, Science, 354, 42–43, https://doi.org/10.1126/science.aaf8317, 2016.
Ohtsuki, H., Hauert, C., Lieberman, E., and Nowak, M. A.: A simple rule for the evolution of cooperation on graphs and social networks, Nature, 441, 502–505, https://doi.org/10.1038/nature04605, 2006.
Okada, I., Okano, N., and Ishii, A.: Spatial opinion dynamics incorporating both positive and negative influence in small-world networks, Front. Phys., 10, 953184, https://doi.org/10.3389/fphy.2022.953184, 2022.
Oliveira, M., Neuhauser, L., and Karimi, F.: Stronger together? The homophily trap in networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2412.20158, 28 December 2024.
Otto, I. M., Wiedermann, M., Cremades, R., Donges, J. F., Auer, C., and Lucht, W.: Human agency in the Anthropocene, Ecol. Econ., 167, 106463, https://doi.org/10.1016/j.ecolecon.2019.106463, 2020a.
Otto, I. M., Donges, J. F., Cremades, R., Bhowmik, A., Hewitt, R. J., Lucht, W., Rockström, J., Allerberger, F., McCaffrey, M., Doe, S. S. P., Lenferna, A., Morán, N., Vuuren, D. P. van, and Schellnhuber, H. J.: Social tipping dynamics for stabilizing Earth's climate by 2050, P. Natl. Acad. Sci. USA, 117, 2354–2365, https://doi.org/10.1073/pnas.1900577117, 2020b.
Paluck, E. L. and Shepherd, H.: The salience of social referents: a field experiment on collective norms and harassment behavior in a school social network, J. Pers. Soc. Psychol., 103, 899–915, https://doi.org/10.1037/a0030015, 2012.
Paluck, E. L., Shepherd, H., and Aronow, P. M.: Changing climates of conflict: A social network experiment in 56 schools, P. Natl. Acad. Sci. USA, 113, 566–571, https://doi.org/10.1073/pnas.1514483113, 2016.
Pareto, V.: Manual of Political Economy, edited by: Page, A. N. and Schwier, A. S., A. M. Kelley, New York, 528 pp., ISBN 0-333-13545-8, 1971.
Peattie, K.: Green Consumption: Behavior and Norms, Annu. Rev. Env. Resour., 35, 195–228, https://doi.org/10.1146/annurev-environ-032609-094328, 2010.
Pieters, R., Bijmolt, T., van Raaij, F., and de Kruijk, M.: Consumers' Attributions of Proenvironmental Behavior, Motivation, and Ability to Self and Others, J. Public Policy Mark., 17, 215–225, 1998.
Proekt, A., Banavar, J. R., Maritan, A., and Pfaff, D. W.: Scale invariance in the dynamics of spontaneous behavior, P. Natl. Acad. Sci. USA, 109, 10564–10569, https://doi.org/10.1073/pnas.1206894109, 2012.
Reisinger, D., Tschofenig, F., Adam, R., Kogler, M. L., Füllsack, M., Veider, F., and Jäger, G.: Patterns of stability in complex contagions, J. Comput. Soc. Sci., 7, 1895–1911, https://doi.org/10.1007/s42001-024-00294-3, 2024.
Ritchie, P. D. L., Alkhayuon, H., Cox, P. M., and Wieczorek, S.: Rate-induced tipping in natural and human systems, Earth Syst. Dynam., 14, 669–683, https://doi.org/10.5194/esd-14-669-2023, 2023.
Robb, M.-A. D., John: Agency in Archaeology, Routledge, London, 288 pp., https://doi.org/10.4324/9781315866000, 2014.
Romero, D. M., Meeder, B., and Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter, in: WWW '11, New York, NY, USA, 695–704, https://doi.org/10.1145/1963405.1963503, 2011.
Sayama, H., Pestov, I., Schmidt, J., Bush, B. J., Wong, C., Yamanoi, J., and Gross, T.: Modeling complex systems with adaptive networks, Comput. Math. Appl., 65, 1645–1664, https://doi.org/10.1016/j.camwa.2012.12.005, 2013.
Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., van de Koppel, J., van de Leemput, I. A., Levin, S. A., van Nes, E. H., Pascual, M., and Vandermeer, J.: Anticipating Critical Transitions, Science, 338, 344–348, https://doi.org/10.1126/science.1225244, 2012.
Schelling, T. C.: Dynamic models of segregation, J. Math. Sociol., 1, 143–186, https://doi.org/10.1080/0022250X.1971.9989794, 1971.
Schleussner, C.-F., Donges, J. F., Engemann, D. A., and Levermann, A.: Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure, Sci. Rep., 6, 30790, https://doi.org/10.1038/srep30790, 2016.
Schunck, F., Wiedermann, M., Heitzig, J., and Donges, J. F.: A Dynamic Network Model of Societal Complexity and Resilience Inspired by Tainter's Theory of Collapse, Entropy, 26, 98, https://doi.org/10.3390/e26020098, 2024.
Schwarz, N. and Ernst, A.: Agent-based modeling of the diffusion of environmental innovations – An empirical approach, Technol. Forecast. Soc., 76, 497–511, https://doi.org/10.1016/j.techfore.2008.03.024, 2009.
Schwarz, N., Dressler, G., Frank, K., Jager, W., Janssen, M., Müller, B., Schlüter, M., Wijermans, N., and Groeneveld, J.: Formalising theories of human decision-making for agent-based modelling of social-ecological systems: practical lessons learned and ways forward, SESMO, 2, 16340–16340, https://doi.org/10.18174/sesmo.2020a16340, 2020.
Shalizi, C. R. and Thomas, A. C.: Homophily and Contagion Are Generically Confounded in Observational Social Network Studies, Sociol. Method Res., 40, 211–239, https://doi.org/10.1177/0049124111404820, 2011.
Smaldino, P.: Modeling Social Behavior: Mathematical and Agent-Based Models of Social Dynamics and Cultural Evolution, Princeton University Press, 360 pp., ISBN 978-0-691-22414-5, 2023.
Smith, K., Kitzmann, Ni., Milkoreit, M., Winkelmann, R., Crepin, A.-S., Eder, C., Harring, N., Heitzig, J., Katsanidou, A., Lenton, T., Mauelshagen, F., Kelton, M., Otto, I. M., Rezai, A., Scheffran, J., Stadelmann-Steffen, I., van der Ploeg, R., Wunderling, N., and Donges, J. F.: Criticality and critical agency in societal tipping processes towards sustainability, in preparation, 2025.
Statista: Number of vegetarians in Germany from 2016 to 2023, https://www.statista.com/statistics/651008/number-of-vegetarians-in-germany/ (last accessed: 8 January 2025), 2024.
Steffen, W., Rockström, J., Richardson, K., Lenton, T. M., Folke, C., Liverman, D., Summerhayes, C. P., Barnosky, A. D., Cornell, S. E., Crucifix, M., Donges, J. F., Fetzer, I., Lade, S. J., Scheffer, M., Winkelmann, R., and Schellnhuber, H. J.: Trajectories of the Earth System in the Anthropocene, P. Natl. Acad. Sci. USA, 115, 8252–8259, https://doi.org/10.1073/pnas.1810141115, 2018.
Strogatz, S. H.: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering, 2nd edn., CRC Press, Boca Raton, 532 pp., https://doi.org/10.1201/9780429492563, 2019.
Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., and Laurienti, P. J.: The Ubiquity of Small-World Networks, Brain Connect., 1, 367–375, https://doi.org/10.1089/brain.2011.0038, 2011.
Trutnevyte, E., Hirt, L. F., Bauer, N., Cherp, A., Hawkes, A., Edelenbosch, O. Y., Pedde, S., and van Vuuren, D. P.: Societal Transformations in Models for Energy and Climate Policy: The Ambitious Next Step, One Earth, 1, 423–433, https://doi.org/10.1016/j.oneear.2019.12.002, 2019.
Tschofenig, F., Reisinger, D., Jäger, G., Kogler, M. L., Adam, R., and Füllsack, M.: Stochastic modeling of cascade dynamics: A unified approach for simple and complex contagions across homogeneous and heterogeneous threshold distributions on networks, Phys. Rev. E, 109, 044307, https://doi.org/10.1103/PhysRevE.109.044307, 2024.
Watts, D. J.: A simple model of global cascades on random networks, P. Natl. Acad. Sci. USA, 99, 5766–5771, https://doi.org/10.1073/pnas.082090499, 2002.
Watts, D. J. and Dodds, P. S.: Influentials, Networks, and Public Opinion Formation, J. Consum. Res., 34, 441–458, https://doi.org/10.1086/518527, 2007.
Watts, D. J. and Strogatz, S. H.: Collective dynamics of “small-world” networks, Nature, 393, 440–442, https://doi.org/10.1038/30918, 1998.
Westley, F., Olsson, P., Folke, C., Homer-Dixon, T., Vredenburg, H., Loorbach, D., Thompson, J., Nilsson, M., Lambin, E., Sendzimir, J., Banerjee, B., Galaz, V., and van der Leeuw, S.: Tipping toward sustainability: emerging pathways of transformation, Ambio, 40, 762–780, https://doi.org/10.1007/s13280-011-0186-9, 2011.
Wiedermann, M., Smith, E. K., Heitzig, J., and Donges, J. F.: A network-based microfoundation of Granovetter's threshold model for social tipping, Sci. Rep., 10, 11202, https://doi.org/10.1038/s41598-020-67102-6, 2020.
Winkelmann, R., Donges, J. F., Smith, E. K., Milkoreit, M., Eder, C., Heitzig, J., Katsanidou, A., Wiedermann, M., Wunderling, N., and Lenton, T. M.: Social tipping processes towards climate action: A conceptual framework, Ecol. Econ., 192, 107242, https://doi.org/10.1016/j.ecolecon.2021.107242, 2022.
Xie, J., Sreenivasan, S., Korniss, G., Zhang, W., Lim, C., and Szymanski, B. K.: Social consensus through the influence of committed minorities, Phys. Rev. E, 84, 011130, https://doi.org/10.1103/PhysRevE.84.011130, 2011.
Zhang, Y., Lucas, M., and Battiston, F.: Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes, Nat. Commun., 14, 1605, https://doi.org/10.1038/s41467-023-37190-9, 2023.
Zhu, S.-S., Zhu, X.-Z., Wang, J.-Q., Zhang, Z.-P., and Wang, W.: Social contagions on multiplex networks with heterogeneous population, Phys. Stat. Mech. Its Appl., 516, 105–113, https://doi.org/10.1016/j.physa.2018.10.010, 2019.
Short summary
A social tipping process is a large change in a social group that can be started by few people. Does the 80/20 rule apply here? We see if this is the case for human social groups. We find that, if the social conditions allow, change occurs when around 25 % of people engage. While tipping can happen between 10 % and 43 %, most cases tip by 40 %. However, tipping is not guaranteed: when people are resistant, trusted friend groups and context-appropriate messaging help the process along.
A social tipping process is a large change in a social group that can be started by few people....
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