Towards understanding of the social hysteresis: an agent-based approach

Grant no.: 2019/35/B/HS6/02530

Funding agency: Narodowe Centrum Nauki (NCN, National Science Center Poland)

Funding scheme: OPUS

Funding period: 60 months (5 years) from 2020-10-01 till 2025-09-30

Budget: 693 600 PLN

Title in Polish: Zrozumieć histerezę społeczną: podejście oparte na modelowaniu agentowym

Research team:

Principal Investigator (Kierownik):

• dr hab. Katarzyna Weron (Sznajd-Weron)

Investigators (Wykonawcy):

• Barbara Kamińska
• Dr Arkadiusz Jędrzejewski
• Maciej Doniec (scholarship)
• Arkadiusz Lipiecki (scholarship)

Collaborators (Współpracownicy):

• Dr inż. Bartłomiej Nowak 
• Jakub Pawłowski
• Bartosz Stoń M.Sc. student
• Ph.D. / M.Sc. / B.Sc. student

Aims and scope:

Social hysteresis (SH) and tipping points (TP) are common features of complex social systems. For example, empirical studies suggest that public opinion exhibits both phenomena, which means that public opinion remains seemingly resistant to change (hysteresis) and then sudden, abrupt shift of opinion can be observed at the tipping point. It has been also suggested that SH may explain several important issues in sociology of health, such as recent epidemic of mental illness among undergraduate students or the persistence of vaccine compliance problem.  Although it may seem that SH and TP are just fancy buzzwords, empirical social studies have confirmed that they are not just abstract ideas.

Results of these findings inspired us to pose a question about the origin of SH and TP: how and why they appear in social systems and what factors influence them? One of possible explanations goes in line with Bourdieu’s concept, due to which SH is a consequence of interrelations between habitus (a property of actors e.g. individuals, groups or institutions) and field (social space). In addition to this concept there are two other, more analytical  approaches, that could be used to describe SH and TP. The first one is related to the mathematical theory of nonlinear dynamics, more specifically to the catastrophe theory. It describes certain aggregate variables by differential equations and therefore does not allow to answer the question why the system behaves in a certain way. The second one comes from the statistical physics of phase transitions. It builds a microscopic model that consists of many mutually interacting individuals and thus SH, as well as TP, appears as an emergent, macroscopic property of the system. Such a bottom-up approach, known in the social sciences as agent-based modeling (ABM), is aimed at understanding why the system behaves in a certain way and  what factors can modify this behavior. For example, it allows to study the role of the social network, the role of different channels of social influence, or the role of individual agents’ traits in shaping SH. Hence, it is possible to answer the question whether to reduce SH it is better to influence people through social media or mass media. Therefore, within this project we will use ABM to gain a deeper, qualitative and quantitative understanding of SH and TP. The research methodology will include computer simulations of agent-based models, as well as - where possible - analytical methods of statistical physics and non-linear dynamics.

Tasks:

Evaluating the impact of environmental conditions on the social hysteresis and tipping points Determining which channel of social influence (direct contacts, social media, mass media, etc.) has the highest impact on the social hysteresis and tipping points Evaluating the role of individuals' traits in shaping the social hysteresis Development, analysis and validation of agent-based models for empirically motivated problems

Expected impact:

From the point of view of basic research it will mainly contribute to: (1) social dynamics by providing a comprehensive understanding of two fundamental phenomena, i.e., social hysteresis and tipping points, (2) social psychology by providing explanations of what factors at the microscopic level, such as individual traits or types of social response can lead to the emergence of social hysteresis, observed at the macroscopic level, (3) social agent-based modeling by showing how this type of approach can be conducted rigorously and related to the mathematical theory of nonlinear dynamics, as well as to the theory of phase transitions. From the utilitarian point of view, it should allow for providing policy recommendations concerning the reduction of social hysteresis.

Publications:

Peer-reviewed articles in JCR-listed journals

2025 (3+), 2024 (1), 2022 (6), 2021 (2)

1. Kamińska, B., Sznajd-Weron, K. (2025) Impact of cognitive dissonance on social hysteresis: Insights from the expressed and private opinions model, Expert Systems with Applications 273, 126851 (doi:10.1016/j.eswa.2025.126851)

2. Doniec, M., Mullick, P., Sen, P., Sznajd-Weron, K. (2025) Modeling biases in binary decision-making within the generalized nonlinear q-voter model, Chaos 35, 043133 (doi:10.1016/j.eswa.2025.126851)

3. Jędrzejewski, A., Kowalska-Pyzalska, A., Pawłowski, J., Sznajd-Weron, K. (2025) Everyone is different, but does it matter? The role of heterogeneity in empirically grounded agent-based models of alternative fuel vehicles diffusion., Operations Research & Decisions 35 (doi:10.1016/j.eswa.2025.126851)

4. Sznajd-Weron, K., Jędrzejewski, A., Kamińska, B. (2024) Toward understanding of the social hysteresis: Insights from agent-based modeling, Perspectives on Psychological Science 19, 511 (doi:10.1177/17456916231195361)

5. Lipiecki, A., Sznajd-Weron, K. (2022) Polarization in the three-state q-voter model with anticonformity and bounded confidence, Chaos, Solitons and Fractals 165, 112809 (doi: 10.1016/j.chaos.2022.112809)

6. Doniec, M., Lipiecki, A., Sznajd-Weron, K. (2022) Consensus, polarization and hysteresis in the three-state noisy q-voter model with bounded confidence, Entropy 24(7), 983 (doi: 10.3390/e24070983)

7. Jędrzejewski, A., Sznajd-Weron, K. (2022) Pair approximation for the q-voter models with quenched disorder on networks, Physical Review E 105(6-1), 064306 (doi: 10.1103/PhysRevE.105.064306)

8. Nowak, B., Grabisch, M., Sznajd-Weron, K. (2022) Threshold model with anticonformity under random sequential updating, Physical Review E 105(5), 054314 (doi: 10.1103/PhysRevE.105.054314)

9. Nowak, B., Sznajd-Weron, K. (2022) Switching from a continuous to a discontinuous phase transition under quenched disorder, Physical Review E 106(1), 014125 (doi: 10.1103/PhysRevE.106.014125)

10. Weron, T., Sznajd-Weron, K. (2022) On reaching the consensus by disagreeing, Journal of Computational Science 61, 101667 (doi: 10.1016/j.jocs.2022.101667)

11. Abramiuk-Szurlej, A., Lipiecki, A., Pawłowski, J., Sznajd-Weron, K. (2021) Discontinuous phase transitions in the q-voter model with generalized anticonformity on random graphs, Scientific Reports 11(1), 17719 (doi: 10.1038/s41598-021-97155-0)

12. Nowak, B., Stoń, B., Sznajd-Weron, K. (2021) Discontinuous phase transitions in the multi-state noisy q-voter model: quenched vs. annealed disorder, Scientific Reports 11(1), 6098 (doi: 10.1038/s41598-021-85361-9)

Peer-reviewed articles in non JCR-listed journals

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Book chapters

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Conference papers/posters

2022 (2), 2021 (2)

1. Jarema, M., Sznajd-Weron, K. (2022) Private and public opinions in a model based on the total dissonance function: A simulation study. In: D. Groen et al. (eds.), Computational Science – ICCS 2022, Lecture Notes in Computer Science 13351, Springer (doi: 10.1007/978-3-031-08754-7_20)

2. Jędrzejewski, A., Sznajd-Weron, K., Pawłowski, J., Kowalska-Pyzalska, A. (2022) Purchasing decisions on alternative fuel vehicles within an agent-based model. In: D. Groen et al. (eds.), Computational Science – ICCS 2022, Lecture Notes in Computer Science 13351, Springer (doi: 10.1007/978-3-031-08754-7_74)

3. Gołȩbiowska, M., Sznajd-Weron, K. (2021) The evolution of political views within the model with two binary opinions. In: M. Paszynski et al. (eds.), Computational Science – ICCS 2021, Lecture Notes in Computer Science 12744, Springer (doi: 10.1007/978-3-030-77967-2_25)

4. Weron, T., Sznajd-Weron, K. (2021) How to reach consensus? Better disagree with your neighbor. In: M. Paszynski et al. (eds.), Computational Science – ICCS 2021, Lecture Notes in Computer Science 12744, Springer (doi: 10.1007/978-3-030-77967-2_26)

Forthcoming publications, submitted papers and work in progress 

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