Artificial intelligence chatbots mimic human collective behaviour.

James K He, Felix P S Wallis, Andr��s Gvirtz, Steve Rathje
Author Information
  1. James K He: Artificial Societies Ltd., London, UK. ORCID
  2. Felix P S Wallis: University College London, London, UK.
  3. Andr��s Gvirtz: Artificial Societies Ltd., London, UK.
  4. Steve Rathje: New York University, New York, New York, USA.

Abstract

Artificial Intelligence (AI) chatbots, such as ChatGPT, have been shown to mimic individual human behaviour in a wide range of psychological and economic tasks. Do groups of AI chatbots also mimic collective behaviour? If so, artificial societies of AI chatbots may aid social scientific research by simulating human collectives. To investigate this theoretical possibility, we focus on whether AI chatbots natively mimic one commonly observed collective behaviour: homophily, people's tendency to form communities with similar others. In a large simulated online society of AI chatbots powered by large language models (N���=���33,299), we find that communities form over time around bots using a common language. In addition, among chatbots that predominantly use English (N���=���17,746), communities emerge around bots that post similar content. These initial empirical findings suggest that AI chatbots mimic homophily, a key aspect of human collective behaviour. Thus, in addition to simulating individual human behaviour, AI-powered artificial societies may advance social science research by allowing researchers to simulate nuanced aspects of collective behaviour.

Keywords

References

  1. Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O'Hare, G. M. P. (2017). Agent based modelling and simulation tools: A review of the state���of���art software. Computer Science Review, 24, 13���33. https://doi.org/10.1016/j.cosrev.2017.03.001
  2. Aher, G., Arriaga, R. I., & Kalai, A. T. (2022). Using large language models to simulate multiple humans. arXiv Preprint, arXiv220810264.
  3. Aiello, L. M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., & Menczer, F. (2012). Friendship prediction and homophily in social media. ACM Transactions on the Web (TWEB), 6(2), 1���33.
  4. Akata, E., Schulz, L., Coda���Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023). Laying repeated games with large language models. arXiv, arXiv230516867. https://doi.org/10.48550/arXiv.2305.16867
  5. Apicella, C., Norenzayan, A., & Henrich, J. (2020). Beyond WEIRD: A review of the last decade and a look ahead to the global laboratory of the future. Evolution and human behavior, 41(5), 319���329.
  6. Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence���based contagion from homophily���driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544���21549. https://doi.org/10.1073/pnas.0908800106
  7. Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3), 337���351. https://doi.org/10.1017/pan.2023.2
  8. Atari, M., Xue, M. J., Park, P. S., Blasi, D. E., & Henrich, J. (2024). Which Humans? https://doi.org/10.31234/osf.io/5b26t
  9. Baldassarri, D., & Abascal, M. (2017). Field experiments across the social sciences. Annual Review of Sociology, 43(1), 41���73. https://doi.org/10.1146/annurev���soc���073014���112445
  10. Barnes, M. L., Lynham, J., Kalberg, K., & Leung, P. (2016). Social networks and environmental outcomes. Proceedings of the National Academy of Sciences, 113(23), 6466���6471. https://doi.org/10.1073/pnas.1523245113
  11. Baumann, F., Lorenz���Spreen, P., Sokolov, I. M., & Starnini, M. (2020). Modeling Echo chambers and polarization dynamics in social networks. Physical Review Letters, 124(4), 48301. https://doi.org/10.1103/PhysRevLett.124.048301
  12. Bender, E. M., Gebru, T., McMillan���Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610���623). Association for Computing Machinery.
  13. Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT���3. Proceedings of the National Academy of Sciences of the United States of America, 120(6), e2218523120.
  14. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., & Amodei, D. (2020). Language models are few���shot learners. arXiv, arXiv200514165. https://doi.org/10.48550/arXiv.2005.14165
  15. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., & Fiedel, N. (2023). Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240), 1���113.
  16. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences of the United States of America, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118
  17. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID���19 social media infodemic. Scientific Reports, 10(1), 1���10. https://doi.org/10.1038/s41598���020���73510���5
  18. Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 66111.
  19. Conover, M., Ratkiewicz, J., Francisco, M., Gon��alves, B., Menczer, F., & Flammini, A. (2011). Political polarization on twitter. Proceedings of the International Aaai Conference on Web and Social Media, 5(1), 89���96.
  20. Crockett, M., & Messeri, L. (2023). Should large language models replace human participants? PsyArXiv. https://doi.org/10.31234/osf.io/4zdx9
  21. Cs��rdi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org
  22. Cs��rdi, G., Nepusz, T., Traag, V., Horv��t, S., Zanini, F., Noom, D., & M��ller, K. (2024). Igraph: Network analysis and visualization in Re (Version 2.0.3). https://CRAN.R���project.org/package=igraph
  23. D'Andrea, A., Ferri, F., & Grifoni, P. (2010). An overview of methods for virtual social networks analysis (pp. 3���25). Springer.
  24. Dasgupta, I., Lampinen, A. K., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2023). Language models show human���like content effects on reasoning tasks. arXiv, arXiv220707051. https://doi.org/10.48550/arXiv.2207.07051
  25. De Choudhury, M. (2011). Tie formation on twitter: Homophily and structure of egocentric networks. 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing (pp. 465���470). IEEE.
  26. Delgado, M. A., & Manteiga, W. G. (2001). Significance testing in nonparametric regression based on the bootstrap. The Annals of Statistics, 29(5), 1469���1507.
  27. Dillion, D., Tandon, N., Gu, Y., & Gray, K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences, 27, 597���600.
  28. Dow, M. M., & Cheverud, J. M. (1985). Comparison of distance matrices in studies of population structure and genetic microdifferentiation: Quadratic assignment. American Journal of Physical Anthropology, 68(3), 367���373.
  29. Epstein, Z., Hertzmann, A., & The investigators of human creativity. (2023). Art and the science of generative AI. Science, 380(6650), 1110���1111. https://doi.org/10.1126/science.adh4451
  30. Facts and Figures 2021: 2.9 billion people still offline. (2021). ITU Hub. https://www.itu.int/hub/2021/11/facts���and���figures���2021���2���9���billion���people���still���offline
  31. Faralli, S., Stilo, G., & Velardi, P. (2015). Large scale homophily analysis in twitter using a twixonomy. Twenty���Fourth International Joint Conference on Artificial Intelligence. IJCAI.
  32. Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force���directed placement. Software: Practice and Experience, 21(11), 1129���1164.
  33. Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., Jin, D., & Li, Y. (2023). S3: Social���network simulation system with large language model���empowered agents. arXiv, arXiv230714984. https://doi.org/10.48550/arXiv.2307.14984
  34. Geschke, D., Lorenz, J., & Holtz, P. (2019). The triple���filter bubble: Using agent���based modelling to test a meta���theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology, 58(1), 129���149. https://doi.org/10.1111/bjso.12286
  35. Ghaffarzadegan, N., Majumdar, A., Williams, R., & Hosseinichimeh, N. (2023). Generative agent���based modeling: Unveiling social system dynamics through coupling mechanistic models with generative artificial intelligence. arXiv, arXiv230911456. https://doi.org/10.48550/arXiv.2309.11456
  36. Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821���7826.
  37. Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108���1109. https://doi.org/10.1126/science.adi1778
  38. Gumel, A. B., Iboi, E. A., Ngonghala, C. N., & Elbasha, E. H. (2021). A primer on using mathematics to understand COVID���19 dynamics: Modeling, analysis and simulations. Infectious Disease Modelling, 6, 148���168. https://doi.org/10.1016/j.idm.2020.11.005
  39. Harispe, S., Ranwez, S., Janaqi, S., & Montmain, J. (2015). Semantic similarity from natural language and ontology analysis. Synthesis Lectures on Human Language Technologies, 8(1), 1���254.
  40. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2���3), 61���83.
  41. Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying twitter topic���networks using social network analysis. Social Media+ Society, 3(1), 2056305117691545.
  42. Homans, G. C. (1974). Social behavior: Its elementary forms (Revised ed., xi, p. 386). Harcourt Brace Jovanovich.
  43. Jung, J., Bramson, A., & Crano, W. D. (2017). An agent���based model of indirect minority influence on social change and diversity. Social Influence, 13(1), 18���38. https://doi.org/10.1080/15534510.2017.1415961
  44. Kang, J. H., & Lerman, K. (2012). Using lists to measure homophily on twitter. AAAI Workshop on Intelligent Techniques for Web Personalization and Recommendation (Vol. 18). AAAI Press.
  45. Knoke, D., & Yang, S. (2019). Social network analysis. SAGE publications.
  46. Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez���Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060���1062. https://doi.org/10.1126/science.aaz8170
  47. Lewis, K., Gonzalez, M., & Kaufman, J. (2011). Social selection and peer influence in an online social network. Proceedings of the National Academy of Sciences of the United States of America, 109(1), 68���72. https://doi.org/10.1073/pnas.1109739109
  48. Li, N., Gao, C., Li, Y., & Liao, Q. (2023). Large language model���empowered agents for simulating macroeconomic activities. arXiv, arXiv231010436. https://doi.org/10.48550/arXiv.2310.10436
  49. Li, Y., Zhang, Y., & Sun, L. (2023). MetaAgents: Simulating interactions of human behaviors for LLM���based task���oriented coordination via collaborative generative agents. arXiv, arXiv231006500. https://doi.org/10.48550/arXiv.2310.06500
  50. Lotito, Q. F., Zanella, D., & Casari, P. (2021). Realistic aspects of simulation models for fake news epidemics over social networks. Future Internet, 13(3), 76. https://doi.org/10.3390/fi13030076
  51. McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv Preprint, arXiv180203426.
  52. McPherson, M., Smith���Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415���444. https://doi.org/10.1146/annurev.soc.27.1.415
  53. Mosleh, M., Martel, C., Eckles, D., & Rand, D. G. (2021). Shared partisanship dramatically increases social tie formation in a twitter field experiment. Proceedings of the National Academy of Sciences of the United States of America, 118(7), e2022761118. https://doi.org/10.1073/pnas.2022761118
  54. Newman, M. E. (2003). Mixing patterns in networks. Physical Review E, 67(2), 26126.
  55. Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23), 8577���8582.
  56. Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (pp. 1���22). Association for Computing Machinery. https://doi.org/10.1145/3586183.3606763
  57. Pastor���Galindo, J., Nespoli, P., & Ruip��rez���Valiente, J. A. (2023). Generative agent���based social networks for disinformation: research opportunities and open challenges. arXiv, arXiv231007545. https://doi.org/10.48550/arXiv.2310.07545
  58. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
  59. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text���to���text transformer. Journal of Machine Learning Research, 21(140), 1���67.
  60. Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large���scale networks. Physical Review E, 76(3), 36106.
  61. Rao, J. N., & Scott, A. J. (1984). On chi���squared tests for multiway contingency tables with cell proportions estimated from survey data. The Annals of Statistics, 12(1), 46���60.
  62. Rathje, S., He, J. K., Roozenbeek, J., van Bavel, J. J., & van der Linden, S. (2022). Social media behavior is associated with vaccine hesitancy. PNAS Nexus, 1(4), pgac207.
  63. Rathje, S., van Bavel, J. J., & van der Linden, S. (2021). Out���group animosity drives engagement on social media. Proceedings of the National Academy of Sciences of the United States of America, 118(26), e2024292118. https://doi.org/10.1073/pnas.2024292118
  64. Ryan, L., & D'Angelo, A. (2018). Changing times: Migrants' social network analysis and the challenges of longitudinal research. Social Networks, 53, 148���158.
  65. Sentence Transformers. (n.d.). All���MiniLM���L6���v2 [computer software]. Hugging Face. https://huggingface.co/sentence���transformers/all���MiniLM���L6���v2
  66. Silva, P. C. L., Batista, P. V. C., Lima, H. S., Alves, M. A., Guimar��es, F. G., & Silva, R. C. P. (2020). COVID���ABS: An agent���based model of COVID���19 epidemic to simulate health and economic effects of social distancing interventions. Chaos, Solitons & Fractals, 139, 110088. https://doi.org/10.1016/j.chaos.2020.110088
  67. Simpson, W. (2001). QAP: The quadratic assignment procedure. In North American STATA users' group meeting (Vol. 1, pp. 1���17). STATA Press.
  68. Sunstein, C. R. (2019). Conformity. New York University Press. https://doi.org/10.18574/nyu/9781479896585.001.0001
  69. Titzmann, P. F. (2014). Immigrant adolescents' adaptation to a new context: Ethnic friendship homophily and its predictors. Child Development Perspectives, 8(2), 107���112.
  70. Titzmann, P. F., & Silbereisen, R. K. (2009). Friendship homophily among ethnic German immigrants: A longitudinal comparison between recent and more experienced immigrant adolescents. Journal of Family Psychology, 23(3), 301���310.
  71. Valente, T. W., & Pitts, S. R. (2017). An appraisal of social network theory and analysis as applied to public health: Challenges and opportunities. Annual Review of Public Health, 38, 103���118.
  72. Veselovsky, V., Ribeiro, M. H., & West, R. (2023). Artificial artificial artificial intelligence: Crowd workers widely use large language models for text production tasks. arXiv, arXiv230607899. https://doi.org/10.48550/arXiv.2306.07899
  73. Wahlstr��m, M., & T��rnberg, A. (2021). Social media mechanisms for right���wing political violence in the 21st century: Discursive opportunities, group dynamics, and Co���ordination. Terrorism and Political Violence, 33(4), 766���787. https://doi.org/10.1080/09546553.2019.1586676
  74. Waller, I., & Anderson, A. (2021). Quantifying social organization and political polarization in online platforms. Nature, 600(7888), 264���268. https://doi.org/10.1038/s4158602104167x
  75. Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112, 220���233. https://doi.org/10.1016/j.trc.2020.01.027
  76. Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A. K., Goel, S., & Hendrycks, D. (2023). Representation engineering: A top���down approach to ai transparency. arXiv preprint, arXiv231001405.

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