Dr. Manuel Sebastian Mariani

Senior Research Associate

 

Manuel is a Senior Research Associate at the University Research Priority Program on Social Networks since November 2017. He has a background in theoretical physics and received his Master degree in Physics at University La Sapienza, Roma, with a thesis on statistical-physics models of glasses.

Research

 
Manuel’s research aims to contribute to the solution of managerial and societally-relevant problems by combining methods from complexity science, machine learning, and agent-based modeling techniques. His research focuses on the ``micro-macro” connection between the heterogeneous drivers of the behavior of individual subjects (such as individuals, companies, and governments) and the emergence of collective behavior. In this light, Manuel’s research is computational because it leverages data mining and simulation techniques to connect individual and collective behavior; data-driven because the developed models and predictions are validated against empirical data.

Team

 

Mingwei Wang

 
Mingwei Wang

 

 

Publications

 
M. Cui, M. S. Mariani, M. Medo, Algorithmic bias amplification via temporal effects: The case of PageRank in evolving networks, Communications in Nonlinear Science and Numerical Simulation 104, 106029 (2022).

M. Medo, M. S. Mariani, L. Lü, The fragility of opinion formation in a complex world, Communications Physics, 4(1), 1-10 (2021).

J. Wang, S. Xu, M. S. Mariani, L. Lü, The local structure of citation networks uncovers expert-selected milestone papers, Journal of Informetrics 15: 101220 (2021).

S. Xu, L. Lü, M. S. Mariani, Modeling the dynamics of firms’ technological impact. Chinese Physics B 30, 120517 (2021).

M. Medo, M. S. Mariani, L. Lü, The simple regularities in the dynamics of online news impact, Journal of Computational Social Science 1–18 (2021).

M. S. Mariani, M. Palazzi, A. Solé-Ribalta, J. Borge-Holthoefer, C. J. Tessone, Absence of a resolution limit in in-block nestedness, Communications in Nonlinear Science and
Numerical Simulation 94, 105545 (2021).

M. S. Mariani, L. Lü, Network-based ranking in social systems: three challenges, Journal of Physics: Complexity 1, 011001 (2020).

S. Xu, M. S. Mariani, L. Lü, M. Medo, Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data, Journal of Informetrics 14:
101005 (2020).

S. Xu, Q. Zhang, L. Lü, M. S. Mariani, Recommending investors for new startups through diffusion on tripartite networks, Information Sciences 515, 103-115 (2020).

M. S. Mariani, M. Medo, F. Lafond, Early identification of important patents: Design and validation of citation network metrics, Technological Forecasting and Social Change 146: 644-654 (2019).

H. Liao, M.-K. Liu, M. S. Mariani, M. Zhou, X. Wu, Temporal similarity metrics for latent network reconstruction: The role of time-lag decay, Information Sciences 489: 182–192
(2019).

M. Medo, A. Zeng, Y.-C. Zhang, M. S. Mariani, Optimal timescale of community detection in growing networks, New Journal of Physics 21: 093066 (2019).

M. S. Mariani, Z.-M. Ren, J. Bascompte, C. J. Tessone, Nestedness in complex networks: observation, emergence, and implications, Physics Reports 813: 1–90 (2019).

S. Zhang, M. Medo, L. Lü, M. S. Mariani, The long-term impact of ranking algorithms in growing networks, Information Sciences 488, 257–271 (2019).

F. Zhou, L. Lü, M. S. Mariani, Fast influencers in complex networks, Communications in Nonlinear Science and Numerical Simulation 74, 69–83 (2019).

SNF

 

How individual-level choices drive collective consumer behavior in social networks

Many managerial decisions aim to influence collective consumer behavior, e.g., to stimulate the adoption of a new service or to promote sustainable consumption.

Collective consumer behavior results from the aggregation of many interconnected individual-level choices, whose dynamics determines the success or failure of new products and their creators. There exists a rich literature on both the drivers of individual-level choices and the collective dynamics of adoption processes, but only few studies integrating both perspectives. Specifically, the consumer behavior literature mainly focuses on the individual level by identifying the main drivers of consumer choices via experiments and surveys (e.g., Miller et al. 2011), but the obtained individual-level results are usually not integrated into dynamic models of innovation growth. On the other hand, the innovation growth literature mainly focuses on the collective level, by modeling the aggregate market dynamics of an innovation (Peres et al. 2010, Muller et al. 2019). The models are usually not calibrated on empirical individual-level behavior, which can results in misleading aggregate predictions of new product success. Studies integrating empirical individual-level choices and collective behavior are rare, yet much needed to improve marketing interventions aimed at influencing collective behavior.

This project is the first systematic effort to connect empirical individual-level choices with the collective success of new products and their creators in social systems. It will focus on three interrelated stages along a product’s lifecycle: the product’s creation (Work Package 1, WP1), its early adoption (WP2), and its collective adoption by a social network of individuals (WP3). The project has three main goals: (1) understand how individual-level creative strategies predict and drive the long-term collective success of product creators (WP1); (2) understand the role played by different groups of early adopters in the collective success of a new product (WP2, e.g., Mariani et al 2020); (3) understand how to integrate individual-level behavioral heterogeneity and social network structure to predict the collective success of a diffusion process as well as improve seeding policies (WP3). For each of the three goals, we will not only advance theoretical understanding, but also derive managerial implications for seeding and influencer marketing policies (e.g., Haenlein et al. 2020, Goldenberg et al 2021). These ambitious goals will be achieved via a rare combination of traditional methods in consumer behavior (such as discrete-choice models and experiments) with social network and machine learning methods.

References

 

– Goldenberg, J., Lanz, A., Shapira, D., & Stahl, F. (2021). Influencer Marketing. Impact at JMR. [https://www.ama.org/2022/02/16/the-research-behind-influencer-marketing/]

– Haenlein, M., Anadol, E., Farnsworth, T., Hugo, H., Hunichen, J., & Welte, D. (2020). Navigating the New Era of Influencer Marketing: How to be Successful on Instagram, TikTok, & Co. California Management Review, 63(1), 5-25. [https://journals.sagepub.com/doi/full/10.1177/0008125620958166]

– Mariani, M. S., Gimenez, Y., Brea, J., Minnoni, M., Algesheimer, R., & Tessone, C. J. (2020). The wisdom of the few: Predicting collective success from individual behavior. arXiv preprint arXiv:2001.04777. [https://arxiv.org/abs/2001.04777]

– Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How should consumers’ willingness to pay be measured? An empirical comparison of state-of-the-art approaches. Journal of marketing research, 48(1), 172-184. [https://journals.sagepub.com/doi/abs/10.1509/jmkr.48.1.172]

– Muller, E., & Peres, R. (2019). The effect of social networks structure on innovation performance: A review and directions for research. International Journal of Research in Marketing, 36(1), 3-19. [https://www.sciencedirect.com/science/article/pii/S0167811618300284]

– Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International journal of research in marketing, 27(2), 91-106. [https://www.sciencedirect.com/science/article/pii/S0167811610000236]