Fusion of Game Theory and Big Data for AI Applications

  • Praveen ParuchuriEmail author
  • Sujit Gujar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11297)


With the increasing reach of the Internet, more and more people and their devices are coming online which has resulted in the fact that, a significant amount of our time and a significant number of tasks are getting performed online. As the world moves faster towards more automation and as concepts such as IoT catch up, a lot more (data generation) devices are getting added online without needing the involvement of human agents. The result of all this is that there will be lots (and lots) of information generated in a variety of contexts, in a variety of formats at a variety of rates. Big data analytics therefore becomes (and is already) a vital topic to gain insights or understand the trends encoded in the large datasets. For example, the worldwide Big Data market revenues for software and services are projected to increase from 42 Billion USD in 2018 to 103 Billion in 2027. However, in the real-world it may not be enough to just perform analysis, but many times there may be a need to operationalize the insights to obtain strategic advantages. Game theory being a mathematical tool to analyze strategic interactions between rational decision-makers, in this paper, we study the usage of Game Theory to obtain strategic advantages in different settings involving usage of large amounts of data. The goal is to provide an overview of the use of game theory in different applications that rely extensively on big data. In particular, we present case studies of four different Artificial Intelligence (AI) applications namely Information Markets, Security systems, Trading agents and Internet Advertising and present details for how game theory helps to tackle them. Each of these applications has been studied in detail in the game theory literature, and different algorithms and techniques have been developed to address the different challenges posed by them.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Machine Learning LabIIIT HyderabadHyderabadIndia

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