Abstract
An information cascade is a circumstance where agents make decisions in a sequential fashion by following other agents. Bikhchandani et al., predict that once a cascade starts it continues, even if it is wrong, until agents receive an external input such as public information. In an information cascade, even if an agent has its own personal choice, it is always overridden by observation of previous agents’ actions. This could mean agents end up in a situation where they may act without valuing their own information. As information cascades can have serious social consequences, it is important to have a good understanding of what causes them. We present a detailed Bayesian model of the information gained by agents when observing the choices of other agents and their own private information. Compared to prior work, we remove the high impact of the first observed agent’s action by incorporating a prior probability distribution over the information of unobserved agents and investigate an alternative model of choice to that considered in prior work: weighted random choice. Our results show that, in contrast to Bikhchandani’s results, cascades will not necessarily occur and adding prior agents’ information will delay the effects of cascades.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
This cost is not used in our model.
- 2.
Luce refers to this as algebraic choice due to the common use of algebraic rather than probabilistic models in economics [18].
- 3.
\(C_0\) appears as a condition whenever a sequence of action variables does. Henceforth, we omit it for brevity.
- 4.
The implementation of our model in Python can be found at https://github.com/ashalya86/Information-cascade-models.
References
Anderson, L.R., Holt, C.A.: Information cascades in the laboratory. Am. Econ. Rev. 87, 847–862 (1996)
Banerjee, A.V.: A simple model of herd behavior. Q. J. Econ. 107(3), 797–817 (1992). https://doi.org/10.2307/2118364
Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100(5), 992–1026 (1992)
Bishop, C.M.: Pattern Recognition and Machine Learning, 1st edn. Springer-Verlag, New York (2006). https://link.springer.com/book/9780387310732
Blavatskyy, P.R.: Dual choice axiom and probabilistic choice. J. Risk Uncertain. 61(1), 25–41 (2020). https://doi.org/10.1007/s11166-020-09332-7
Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: Proceedings of the 23rd International Conference on the World Wide Web - WWW 2014, pp. 925–936. ACM Press (2014). https://doi.org/10.1145/2566486.2567997
De Vany, A., Lee, C.: Information cascades in multi-agent models. Technical report, University of California, Irvine, Department of Economics (1999). http://pages.stern.nyu.edu/~wgreene/entertainmentandmedia/Cascades.pdf
Duan, G.: Whinston: informational cascades and software adoption on the Internet: an empirical investigation. MIS Q. 33(1), 23 (2009). https://doi.org/10.2307/20650277
Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About A Highly Connected World. Cambridge University Press, UK (2010)
Huber, R.E., Klucharev, V., Rieskamp, J.: Neural correlates of informational cascades: brain mechanisms of social influence on belief updating. Soc. Cogn. Affect. Neurosci. 10(4), 589–597 (2015). https://doi.org/10.1093/scan/nsu090
Lee, Y.J., Hosanagar, K., Tan, Y.: Do I follow my friends or the crowd? Information cascades in online movie ratings. Manage. Sci. 61(9), 2241–2258 (2015). https://doi.org/10.1287/mnsc.2014.2082
Liu, C., Wang, W., Jiao, P., Chen, X., Sun, Y.: Cascade modeling with multihead self-attention. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020). https://doi.org/10.1109/IJCNN48605.2020.9207418
Liu, Q., Zhang, L.: Information cascades in online reading: an empirical investigation of panel data. Libr. Hi Tech 32(4), 687–705 (2014). https://doi.org/10.1108/LHT-06-2014-0052
Liu, Q., Zhang, X., Li, Y.: The influence of information cascades on online reading behaviors of free and paid e-books. Libr. Inf. Sci. Res. 42(1), 101001 (2020). https://doi.org/10.1016/j.lisr.2019.101001
Liu, Q., Zhang, X., Zhang, L., Zhao, Y.: The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation. Electron. Commer. Res. 19(3), 521–547 (2018). https://doi.org/10.1007/s10660-018-9312-0
Lohmann, S.: Collective action cascades: an informational rationale for the power in numbers. J. Econ. Surv. 14(5), 654–684 (2002)
Lu, Y., et al.: Exploring the collective human behavior in cascading systems: a comprehensive framework. Knowl. Inf. Syst. 62(12), 4599–4623 (2020). https://doi.org/10.1007/s10115-020-01506-8
Luce, R.D., Suppes, P.: Preference, utility, and subjective probability. In: Luce, Duncan, R., Bush, R.R., Galanter, E. (eds.) Handbook of Mathematical Psychology, vol. 3, p. 162. John Wiley & Sons (1965)
Mangold, W.G., Faulds, D.J.: Social media: the new hybrid element of the promotion mix. Bus. Horiz. 52(4), 357–365 (2009)
Peng, S., Yang, A., Cao, L., Yu, S., Xie, D.: Social influence modeling using information theory in mobile social networks. Inf. Sci. 379, 146–159 (2017). https://doi.org/10.1016/j.ins.2016.08.023
Pleskac, T.J.: Decision and choice: Luce’s choice axiom. In: International Encyclopedia of the Social & Behavioral Sciences, pp. 895–900. Elsevier (2015). https://doi.org/10.1016/B978-0-08-097086-8.43031-X
Wang, T., Wang, D.: Why Amazon’s ratings might mislead you: the story of herding effects. Big Data 2(4), 196–204 (2014). https://doi.org/10.1089/big.2014.0063
Watts, D.J.: A simple model of global cascades on random networks. In: Proceedings of the National Academy of Science, pp. 4766–5771 (2022)
Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. J. Consum. Res. 34(4), 441–458 (2007). https://doi.org/10.1086/518527
Acknowledgements
This work was supported by the Marsden Fund Council from New Zealand Government funding, managed by Royal Society Te Apārangi.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Srivathsan, S., Cranefield, S., Pitt, J. (2022). A Bayesian Model of Information Cascades. In: Theodorou, A., Nieves, J.C., De Vos, M. (eds) Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XIV. COINE 2021. Lecture Notes in Computer Science(), vol 13239. Springer, Cham. https://doi.org/10.1007/978-3-031-16617-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-16617-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16616-7
Online ISBN: 978-3-031-16617-4
eBook Packages: Computer ScienceComputer Science (R0)