Forecasting economic result of business logic improvements using Game Theory for modeling user scenarios

A new approach to user behavior modeling based on Game Theory was proposed.  It was developed to consider initial intensity, a strategy applied, a profit gained, and resources utilized as inalienable attributes of users' behavior.  The approach covers various aspects of users' motivation and rational actions, not only a statistical image of a pool's summary.  Additionally, the given model is strongly connected to profit and loss parameters by operating with profit and utilized resources as parts of model inputs.  The proposed model can enable efficient modeling aimed to validate an economic result of existing interfaces and assume results of new ones.

  1. Kobsa A.  Generic user modeling systems.  User Modeling and User-Adapted Interaction. 11 (1–2), 49–63 (2001).
  2. Zhang M., Wang Y., Chai J.  Review of User Behavior Analysis Based on Big Data: Method and Application.  International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015). 99–103 (2015).
  3. Hassan M. T., Junejo K. N., Karim A.  Bayesian Inference for Web Surfer Behavior Prediction.  Lahore: Dept. of Computer Science, Lahore University of Management Sciences (2007).
  4. Borges J., Levene M.  Data Mining of User Navigation Patterns.  International Workshop on Web Usage Analysis and User Profiling. 92 –112 (1999).
  5. Cottam J. A., Blaha L. M.  Bias by default? A means for a priori interface measurement.  Cognitive Biases in Visualisations. 46–58 (2017).
  6. Wang G., Zhang X., Tang S., Zheng H., Zhao B. Y.  Unsupervised Clickstream Clustering for User Behavior Analysis.  Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 225–236 (2016).
  7. Petrovskiy M.  A data mining approach to learning probabilistic user behavior models from the database access log.  Proceedings of the First International Conference on Software and Data Technologies  – Volume 2: ICSOFT, 73–78 (2006).
  8. Beutel A.  User Behavior Modeling with Large-Scale Graph Analysis (Ph.D. paper).  Pittsburgh, PA: Computer Science Department, School of Computer Science, Carnegie Mellon University (2016).
  9. Wall E., Arcalgud A., Gupta K., Jo A.  A Markov Model of Users' Interactive Behavior in Scatterplots.  2019 IEEE Visualization Conference. 81–85 (2019).
  10. Kumbarovska V., Mitrievski P.  Behavioral-based modeling and analysis of Navigation Patterns across Information Networks.  Journal of Emerging Research and Solutions in ICT. 1 (2). 60–74 (2016).
  11. Canali D., Bilge L., Balzarotti D.  On the effectiveness of risk prediction based on users browsing behavior.  Proceedings of the 9th ACM symposium on Informatics, computer and communications security. 171–182 (2014).
  12. Cabafero L., Hervas R., Gonzйlez L, Fontecha J., Mondéjar T., Bravo J.  Characterization of mobile-device tasks by their associated cognitive load through EEG data processing.  Future Generation Computer Systems. 113, 380–390 (2020).
  13. Ellavarason E., Guest R., Deravi F.  Evaluation of the stability of swipe gesture authentication across usage scenarios of a mobile device.  Eurasip Journal on Information Security. 2020, Article number: 4 (2020).
  14. Sharma K., Giannakos M., Dillenbourg P.  Eye-tracking and artificial intelligence to enhance motivation and learning.  Smart Learning Environments. 7, Article number: 13 (2020).
  15. Sultan K, Ali H., Ahmad A., Zhang Z.  Call details record analysis: A spatiotemporal exploration toward mobile traffic classification and optimization.  Information. 10 (6), 192 (2019).
  16. Stylios I., Kokolakis S., Thanou O., Chatzis S.  Behavioral biometrics & continuous user authentication on mobile devices: A survey.  Information Fusion. 66, 76–99 (2021).
  17. Lee H., Upright C,, Eliuk S., Kobsa A.  Personalized visual recognition via wearables: A first step toward personal perception enhancement.  Personal Assistants: Emerging Computational Technologies. 95–112 (2018).
  18. Gerina F., Massa S. M., Moi F., Reforgiato Recupero D., Riboni D.  Recognition of cooking activities through air quality sensor data for supporting food journaling.  Human-Centric Computing and Information Sciences. 10, Article number: 27 (2020).
  19. Papoutsoglou M., Kapitsaki G., Angelis L.  Modeling the effect of the badges gamification mechanism on personality traits of Stack Overflow users.  Simulation Modelling Practice and Theory. 105, 102157 (2020).
  20. Smiderle R., Rigo S. J, Marques L. B,, Pesanha de Miranda Coelho J. A., Jaques P. A.  impact of gamification on students' learning, engagement, and behavior-based traits.  Smart Learning Environments. 7, Article number: 3 (2020).
  21. Bovermann K., Bastiaens T.  Towards a motivation design? Connecting gamification user types and online learning activities.  Research and Practise in Technology Enhanced Learning. 15, Article number: 1 (2020).
  22. Aizerman M. A., Braverman E. M., Rozonoer L. L.  Method of Potential Functions in the Theory of Learning Machines. Nauka, Moscow (1970), (in Russian).
  23. Labayen V., Magaiia E., Moratй D., Izal M.  Online classification of user activities using machine learning on network traffic.  Computer Networks. 181, 107557 (2020).
  24. Kobsa A.  What is explained by AI models?  Artificial intelligence. 174–189, (2018).
  25. Wassouf W. N., Alkhatib R., Salloum K.  Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study.  Journal of Big Data. 7, Article number: 29 (2020).
  26. Yu H., Sun L., Zhang F.  A Robust Bayesian Probabilistic Matrix Factorization Model for Collaborative Filtering Recommender Systems Based on User Anomaly Rating Behavior Detection.  KSII Transactions on Internet and Information Systems. 13 (9), 4684–4705 (2019).
  27. Vuong T., Saastamoinen M., Jacucci G., Ruotsalo T.  Understanding user behavior in naturalistic information search tasks.  Journal of the Association for Information Science and Technology. 70 (11), 1248–1261 (2019).
  28. Wu T., Zheng K., Wu C., Wang X.  User identification using real environmental human-computer interaction behavior.  KSII Transactions on Internet and Information Systems. 13 (6), 3055–3073 (2019).
  29. He S., Zheng X., Zeng D. D.  Modeling user behavior with competitive interactions.  Information and Management. 56 (4), 463 –475 (2019).
  30. Wang M., Wang G., Zhang Y., Li Z.  A high-reliability multi-faceted reputation evaluation mechanism for online service.  IEEE Transactions on Services Computing. 12 (6), 836–850 (2019).
Mathematical Modeling and Computing, Vol. 8, No. 3, pp. 560–572 (2021)