The collective dynamics underlying personal and social information integration
Understanding how individuals integrate personal and social information
AI-generated at SCIoI with prompt "train station people"
Individuals rarely make decisions in social isolation. In most situations, individuals are subject to social influence. Social influence may be beneficial (e.g., increase decision quality), but it can also be detrimental (e.g., when false information cascades occur). To understand the emergence of collective intelligence—the shared intelligence that emerges from collaborative, collective efforts of individuals—we need to comprehend how individuals integrate personal and social information. A key aspect that has been largely neglected in human collective intelligence research is the dynamic aspect of information exchange. Most studies on human collective decision making assume that individuals simultaneously make decisions, which are then statically exchanged. In reality, however, information exchange is highly dynamic, and the timing of information exchange is linked to (subjective) information quality. Few studies have embraced such an approach; consequently, the dynamics of information flow in human groups remain poorly understood. To fill this important gap, this proposal has four objectives. First, we will investigate how single individuals integrate personal and social information under controlled conditions (objective 1). Next, we will parameterize a dynamic decision making model to predict information flow and collective dynamics in real-time interacting human groups, and test these predictions (objective 2). We then further parameterize our model to derive predictions for information flow across different network structures, and test these predictions (objective 3). Finally, we will bring all these issues together, studying the conditions underlying collective intelligence in collective systems (objective 4). The analytical system consists of human groups conducting experimental choice tasks. The synthetic component consists of drift diffusion models that address the cognitive processes underlying information integration. We continuously close the loop between analytical and synthetic systems, by using both approaches in concert. As end product, we will develop a versatile set of open-source algorithms (in CRAN R/Python) that can be used to study information integration processes in collectives, as well as for programming robotic swarms to achieve collective intelligence in the face of key challenges, such as speed-accuracy trade-offs or optimization at the individual versus collective level. Prior to release, the performance of these algorithms will be extensively tested with genetic algorithms.
Project Results
In many social contexts (e.g., whether getting vaccinated, buying stocks, or crossing streets), agents decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others’ choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. In this project, the team developed a social drift–diffusion model (SDDM) to capture these dynamics. They tested their model using a series of sequential choice tasks. The model was able to recover the dynamics of the social decision-making process across various environmental settings, accurately capturing how individuals integrate personal and social information dynamically over time and when they make their decisions. The SDDM reveals the importance of capturing the dynamics of decision processes to understand how information cascades in social systems, paving the way for applications in other social systems.
6984777
proj026
1
apa
50
creator
desc
year
20193
https://www.scienceofintelligence.de/wp-content/plugins/zotpress/
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Tump, A. N., Wolf, M., Romanczuk, P., & Kurvers, R. H. J. M. (2022). Avoiding costly mistakes in groups: The evolution of error management in collective decision making. PLOS Computational Biology, 18(8), e1010442. https://doi.org/10.1371/journal.pcbi.1010442
Tump, A. N., Deffner, D., Pleskac, T. J., Romanczuk, P., & M. Kurvers, R. H. J. (2024). A Cognitive Computational Approach to Social and Collective Decision-Making. Perspectives on Psychological Science, 19(2), 538–551. https://doi.org/10.1177/17456916231186964
Tump, A. N., Wollny-Huttarsch, D., Molleman, L., & Kurvers, R. H. J. M. (2024). Earlier social information has a stronger influence on judgments. Scientific Reports, 14(1), 105. https://doi.org/10.1038/s41598-023-50345-4
Tump, A. N., Pleskac, T. J., & Kurvers, R. H. J. M. (2020). Wise or mad crowds? The cognitive mechanisms underlying information cascades. Science Advances, 6(29), eabb0266. https://doi.org/10.1126/sciadv.abb0266
Sultan, M., Tump, A. N., Ehmann, N., Lorenz-Spreen, P., Hertwig, R., Gollwitzer, A., & Kurvers, R. H. J. M. (2024). Susceptibility to online misinformation: A systematic meta-analysis of demographic and psychological factors. Proceedings of the National Academy of Sciences, 121(47), e2409329121. https://doi.org/10.1073/pnas.2409329121
Sultan, M., Tump, A. N., Geers, M., Lorenz-Spreen, P., Herzog, S. M., & Kurvers, R. H. J. M. (2022). Time pressure reduces misinformation discrimination ability but does not alter response bias. Scientific Reports, 12(1), 22416. https://doi.org/10.1038/s41598-022-26209-8
Novaes Tump, A., Pleskac, T., Romanczuk, P., & Kurvers, R. (2022). How the cognitive mechanisms underlying fast choices influence information spread and response bias amplification in groups. In J. Culbertson, A. Persfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Meeting of the Cognitive Science Society (pp. 658–664). UC Merced. https://escholarship.org/uc/item/5m540872
Kurvers, R. H. J. M., Nuzzolese, A. G., Russo, A., Barabucci, G., Herzog, S. M., & Trianni, V. (2023). Automating hybrid collective intelligence in open-ended medical diagnostics. Proceedings of the National Academy of Sciences, 120(34), e2221473120. https://doi.org/10.1073/pnas.2221473120
Kurvers, R. H. J. M., Herzog, S. M., Hertwig, R., Krause, J., Moussaid, M., Argenziano, G., Zalaudek, I., Carney, P. A., & Wolf, M. (2019). How to detect high-performing individuals and groups: Decision similarity predicts accuracy. Science Advances, 5(11), eaaw9011. https://doi.org/10.1126/sciadv.aaw9011
Kurvers, R. H. J. M., Herzog, S. M., Hertwig, R., Krause, J., & Wolf, M. (2021). Pooling decisions decreases variation in response bias and accuracy. iScience, 24(7), 102740. https://doi.org/10.1016/j.isci.2021.102740
Kuroda, K., Tump, A. N., & Kurvers, R. H. J. M. (2025). Individual differences in speed–accuracy trade-off influence social decision-making in dyads. Proceedings of the Royal Society B: Biological Sciences, 292(2051), 20251077. https://doi.org/10.1098/rspb.2025.1077
Gollwitzer, A., Tump, A. N., Martel, C., Deffner, D., Sultan, M., Kurvers, R., & Hertwig, R. (2025). Towards a Mechanistic Understanding of False News Sharing: Which Interventions Work Best, for Whom, and Why. PsyArXiv. https://doi.org/10.31234/osf.io/pxn29_v2
Burton, J. W., Lopez-Lopez, E., Hechtlinger, S., Rahwan, Z., Aeschbach, S., Bakker, M. A., Becker, J. A., Berditchevskaia, A., Berger, J., Brinkmann, L., Flek, L., Herzog, S. M., Huang, S., Kapoor, S., Narayanan, A., Nussberger, A.-M., Yasseri, T., Nickl, P., Almaatouq, A., … Hertwig, R. (2024). How large language models can reshape collective intelligence. Nature Human Behaviour, 8(9), 1643–1655. https://doi.org/10.1038/s41562-024-01959-9
