Game theoretical inference of human behavior in social networks

Pagan, Nicolò; Dörfler, Florian

doi:10.1038/s41467-019-13148-8

Cited by 19 publications

(15 citation statements)

References 48 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, except for the fitness model 16 in which users are connected with probability proportional to the individuals’ fitness attributes, the large multidisciplinary interest in the study of network formation has so far privileged topological and socio-economic aspects observed in offline social networks (or in online social networks which mimic them, e.g., Facebook) and neglected the effect of the UGC. For example, Stochastic Actor-Oriented Models 17 , in sociology, and strategic network formation models 18 , in economics, assume actors decide their ties according to a utilitarian principle based on sociological elements, such as reciprocity or network closure 19 , or topological measures, e.g., in-degree or closeness centrality 20 , or a combination of them 21 . These models typically lead to networks characterized by bilateral social connections and high transitivity.…”

Section: Introductionmentioning

confidence: 99%

A meritocratic network formation model for the rise of social media influencers

Pagan

Mei

et al. 2021

Nat Commun

Self Cite

View full text Add to dashboard Cite

Many of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.

show abstract

Section: Introductionmentioning

confidence: 99%

A meritocratic network formation model for the rise of social media influencers

Pagan

Mei

et al. 2021

Nat Commun

Self Cite

View full text Add to dashboard Cite

show abstract

“…Just imagine, however computer science can be separated from mathematics when doing computing. How computer science disproves mathematics when the program flow requires logic (Graham, Knuth, Patashnik, & Liu, 1989), especially when optimization must play a role or the intelligence is implanted to overcome human behaviour (Delle Monache, 2016;Gillen, Freeman, & Tootell, 2017;Nasution, 2018g;Pagan & Dörfler, 2019;Hosni, Li, & Ahmad, 2020). Not only is the study of program complexity at the core of computer science (McCabe, 1976), but the efficient use of energy is the accompaniment of program implementation (Mayer & Dänekas, 2013;Karanikolas & Liaramantzas, 2017).…”

Section: Resultsmentioning

confidence: 99%

The birth of a science

Nasution

2020

Hist. sci. technol.

View full text Add to dashboard Cite

Physics, biology, chemistry, for example, do not already only study programs in many universities but are at the forefront of the development of science, and that is science itself. The development of the basic foundation becomes the mother of science, such as mathematics, as a suspicion of the birth of new sciences. The interests related to the life and welfare of humans always invites the possibility of the birth of science in concepts, but mathematics confirms it. In certain cases, as an application, or it gives birth to answers in technology form, which is distorted according to the social demands of society. Every science has its methodology, which is based on the logic that helps to reason. A natural model is an approach to generating methods, as well as the principles that develop in artificial intelligence based on mathematics. This principle not only changes the way of perceiving data, but it also establishes new definitions of data, and changes statistics, optimization, and other fields. Each science also has implications for other sciences but also has applications that are also related to other sciences. Every science supports technology to improve social welfare. Historically, century by century, many new scientific fields were born, and it has evidence from the publication of scientific works to books as scientific standards. Then, the teaching curricula supported its initial dissemination and its development through research with evidence of scientific publications either through papers in proceedings of scientific meetings or articles in the journals. A study program about science in the related faculty is mandatory, and it is as the spearhead in further scientific and technological development. So everything starts and ends at one point, namely the birth of new science, as the birth of data science. This paper describes the birth of science by involving historical traces. The reflection of scientific development is starting from the foundation to become a scientific field, namely data science. It also illustrates the roadmap of a scientific indirectly.

show abstract

“…Bertrand-Nash competition (Narahari et al, 2009), computational social science, e.g. social networks formation (Pagan & Dörfler, 2019), biology, e.g. swarms of birds (Molloy et al, 2018), automatic control, e.g.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Behaviour Estimation in Parametric Games

Maddux¹,

Pagan²,

Belgioioso³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

A central question in multi-agent strategic games deals with learning the underlying utilities driving the agents' behaviour. Motivated by the increasing availability of large data-sets, we develop an unifying data-driven technique to estimate agents' utility functions from their observed behaviour, irrespective of whether the observations correspond to (Nash) equilibrium configurations or to action profile trajectories. Under standard assumptions on the parametrization of the utilities, the proposed inference method is computationally efficient and finds all the parameters that rationalize the observed behaviour best. We numerically validate our theoretical findings on the market share estimation problem under advertising competition, using historical data from the Coca-Cola Company and Pepsi Inc. duopoly.

show abstract

Game theoretical inference of human behavior in social networks

Cited by 19 publications

References 48 publications

A meritocratic network formation model for the rise of social media influencers

A meritocratic network formation model for the rise of social media influencers

The birth of a science

Data-Driven Behaviour Estimation in Parametric Games

Contact Info

Product

Resources

About