Advertisement

Collective Intelligence and Collaboration: A Case Study in Airline Industry

  • Sónia A. C. TeixeiraEmail author
  • Pedro Campos
  • Renato Fernandes
  • Catarina Roseira
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 480)

Abstract

In order to improve their competitive performance, airline companies often adopt as a strategy to establish arrangement between two or more organizations agreeing to cooperate on a substantial level. This strategy is often known as airline alliances. A paradigm to analyze the collective intelligence behavior which emerges from a group, as a strategic alliance, is the flocking behavior. Inspired by the Cucker and Smale algorithm (C-S) we propose a new version of the flocking behavior algorithm applied to airline alliances. Our goal is to understand the link between strategic alliances and flocks. For this new approach, metrics were obtained for the parameters of C-S algorithm, namely position, velocity and influence, where the latter uses cooperative games. Besides, reinforcement learning mechanisms have been explored. Some relevant outputs for airline alliances as the permanence rate and the growth rate were computed for each of the five configurations in analysis.

Keywords

Collective intelligence Airline industry Flocking behavior 

References

  1. 1.
    Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems, proceed. In: NATO Advanced Workshop on Robots and Biological Systems (1989)Google Scholar
  2. 2.
    Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: a new scientific discipline. J. Intell. Manufact. 16(4–5), 439–452 (2005)CrossRefGoogle Scholar
  3. 3.
    Cucker, F., Smale, S.: Emergent behavior in flocks. IEEE Trans. Autom. Control 52(5), 852–862 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cucker, F., Smale, S.: On the mathematics of emergence. Jpn. J. Math. 2(1), 197–227 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Evans, N.: Collaborative strategy: an analysis of the changing world of international airline alliances. Tourism Manag. 22, 229–243 (2001)CrossRefGoogle Scholar
  6. 6.
    Fehr, E., Fischbacker, U.: Why social preferences matter: the impact of non-selfish motives on competition, cooperation, and incentives. Econ. J. 112(478), C1–C33 (2002)CrossRefGoogle Scholar
  7. 7.
    Fernandez de la Torre, P.E.: Airline alliances: the airline perspective DSpace@MIT. http://hdl.handle.net/1721.1/68159
  8. 8.
    Johnson, G., Whittington, R., Scholes, K., Angwin, D., Regnér, P.: Exploring Strategy, 10th edn edn. Pearson, Harlow (2014). ISBN 978-1-292-00255-2Google Scholar
  9. 9.
    Lévy, P.: Collective Intelligence Mankind’s Emerging World in Cyberspace. Challenges. Perseus Books, Cambridge (1997)Google Scholar
  10. 10.
    Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51(3), 401–420 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Papapetrou, P., Gionis, A., Mannila, H.: A shapley value approach for influence attribution. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 549–564. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23783-6_35 CrossRefGoogle Scholar
  12. 12.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. SIGGRAPH Comput. Graph. 21(4), 25–34 (1987). doi: 10.1145/37401.37406 CrossRefGoogle Scholar
  13. 13.
    Salminen, J.: Collective Intelligence in Humans: A Literature Review. ArXiv preprint arXiv:1204.3401, abs/1204.3, pp. 1–8 (2012). http://arxiv.org/abs/1204.3401
  14. 14.
    Shapley, L.S.: A value for n-person games. Ann. Math. Stud. 28, 307–317 (1953)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. Bradford Books, MIT Press, Cambridge (1998)Google Scholar
  16. 16.
    Vicsek, T., Czirok, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75(6), 1226–1229 (1995)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo. MIT Press, Cambridge (2015)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Sónia A. C. Teixeira
    • 1
    • 2
    Email author
  • Pedro Campos
    • 1
    • 2
  • Renato Fernandes
    • 1
    • 3
  • Catarina Roseira
    • 2
  1. 1.Faculty of EconomicsUniversity of PortoPortoPortugal
  2. 2.LIAAD - INESC TECPortoPortugal
  3. 3.CPES - INESC TECPortoPortugal

Personalised recommendations