Advertisement

Sampled Fictitious Play on Networks

  • Alexander Nikolaev
  • Alexander SemenovEmail author
  • Eduardo L. Pasiliao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)

Abstract

We formulate and solve the problem of optimizing the structure of an information propagation network between multiple agents. In a given space of interests (e.g., information on certain targets), each agent is defined by a vector of their desirable information, called filter, and a vector of available information, called source. The agents seek to build a directed network that maximizes the value of the desirable source-information that reaches each agent having been filtered en route, less the expense that each agent incurs in filtering any information of no interest to them. We frame this optimization problem as a game of common interest, where the Nash equilibria can be attained as limit points of Sampled Fictitious Play (SFP), offering a method that turns out computationally effective in traversing the huge space of feasible networks on a given node set. Our key idea lies in the creative use of history in SFP, leading to the new History Value-Weighted SFP method. To our knowledge, this is the first successful application of FP for network structure optimization. The appeal of our work is supported by the outcomes of the computational experiments that compare the performance of several algorithms in two settings: centralized (full information) and decentralized (local information).

Keywords

Social networks Information diffusion Fictitious play 

Notes

Acknowledgements

Work of A. Semenov was funded in part by the AFRL European Office of Aerospace Research and Development (grant no. FA9550-17-1-0030). This material is based upon work supported by the AFRL Mathematical Modeling and Optimization Institute.

References

  1. 1.
    Adamic, L.A., Glance, N.: The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, pp. 36–43. ACM, New York (2005).  https://doi.org/10.1145/1134271.1134277
  2. 2.
    Brown, G.W.: Iterative solution of games by fictitious play (1951)Google Scholar
  3. 3.
    Dolinskaya, I.S., Epelman, M.A., Şişikoğlu Sir, E., Smith, R.L.: Parameter-free sampled fictitious play for solving deterministic dynamic programming problems. J. Optim. Theory Appl. 169(2), 631–655 (2016).  https://doi.org/10.1007/s10957-015-0798-5MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Epelman, M., Ghate, A., Smith, R.L.: Sampled fictitious play for approximate dynamic programming. Comput. Oper. Res. 38(12), 1705–1718 (2011).  https://doi.org/10.1016/j.cor.2011.01.023. http://www.sciencedirect.com/science/article/pii/S0305054811000451MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ghate, A., Cheng, S.F., Baumert, S., Reaume, D., Sharma, D., Smith, R.L.: Sampled fictitious play for multi-action stochastic dynamic programs. IIE Trans. 46(7), 742–756 (2014)CrossRefGoogle Scholar
  6. 6.
    Horn, R.A.: The hadamard product. In: Proceedings of Symposium in Applied Mathematics, vol. 40, pp. 87–169 (1990)Google Scholar
  7. 7.
    Lambert, T.J., Epelman, M.A., Smith, R.L.: A fictitious play approach to large-scale optimization. Oper. Res. 53(3), 477–489 (2005).  https://doi.org/10.1287/opre.1040.0178. http://dx.doi.org/10.1287/opre.1040.0178MathSciNetCrossRefGoogle Scholar
  8. 8.
    Monderer, D., Shapley, L.S.: Fictitious play property for games with identical interests. J. Econ. Theory 68(1), 258–265 (1996).  https://doi.org/10.1006/jeth.1996.0014. http://www.sciencedirect.com/science/article/pii/S0022053196900149MathSciNetCrossRefGoogle Scholar
  9. 9.
    Robinson, J.: An iterative method of solving a game. Ann. Math. 54, 296–301 (1951)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Nikolaev
    • 1
  • Alexander Semenov
    • 2
    Email author
  • Eduardo L. Pasiliao
    • 3
  1. 1.University at BuffaloBuffaloUSA
  2. 2.University of JyväskyläJyväskyläFinland
  3. 3.Air Force Research LaboratoryEglin AFBUSA

Personalised recommendations