A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective

Evelina Giacchi, Aurelio La Corte, Eleonora Di Pietro

2016

Abstract

All the processes taking place in a social network are characterised by dynamism, complexity and contextdependence. Processes involving knowledge have these features. The intrinsic characteristic of knowledge is represented by the value that it can generate in a network, due to its constant and continuous rate of growth. In a heterogeneous network not all the nodes have similar knowledge levels. Furthermore, not all the connections have the same importance. In order to consider knowledge as a resource and not as an obstacle, it is admittable that nodes can decide individually with whom transfer knowledge. Using a context-aware decision making perspective and considering each single node as a decision maker that has to decide in a particular context whether accept the transfer or not, it will be helpful to understand how and why certain mechanisms and behavioural patterns arise. In this paper, the proposed model considers the process of knowledge transfer as a decision making one, where each alternative, one of the nodes neighbor that wants to transfer knowledge, has an evaluation on the basis of two criteria, knowledge distance and confidence. Their values are dynamically updated at each time step on the basis of the quality of the knowledge transferred.

References

  1. Abowd, G., Dey, A., Brown, P., Davies, N., Smith, M., and Steggles, P. (1999). Towards a better understanding of context and context-awareness. In Gellersen, H.-W., editor, Handheld and Ubiquitous Computing, LNCS 1707, pages 304-307, Berlin Heidelberg. Springer.
  2. Barabási, A. and Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439):509-512.
  3. Barrat, A., Barthelemy, M., Pastor-Satorras, R., and Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11):3747-3752.
  4. Binswanger, H. P. (1980). Attitudes toward risk: Experimental measurement in rural india. American journal of agricultural economics, 62(3):395-407.
  5. Brown, J. S. and Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization science, 2(1):40-57.
  6. Bukowitz, W. R. and Williams, R. L.(2000). The knowledge management fieldbook. Financial Times/Prentice Hall Cioffi-Revilla, C. (2013). Introduction to Computational Social Science: Principles and Applications. Springer, Science & Business Media.
  7. Cowan, R. and Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of economic Dynamics and Control, 28(8):1557-1575.
  8. Cummings, J. N. (2004). Work groups, structural diversity, and knowledge sharing in a global organization. Management science, 50(3):352-364.
  9. Davenport, T. H. and Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Press.
  10. Di Stefano, A., Scatà, M., La Corte, A., Liò, P., Catania, E., Guardo, E., and Pagano, S. (2015). Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory. PloS one, 10(10):e0140646.
  11. Erdös, P. and Rényi, A. (1959). On random graphs I. Publ. Math. Debrecen, 6:290-297.
  12. Fedoroff, N. V. (2012). The global knowledge society. Science, 335:503.
  13. Giacchi, E., Di Stefano, A., La Corte, A., and Scatá, M. (2014). A dynamic context-aware multiple criteria decision making model in social networks. In Information Society (i-Society), 2014 International Conference on, pages 157-162. IEEE.
  14. Graham, I. D., Logan, J., Harrison, M. B., Straus, S. E., Tetroe, J., Caswell, W., and Robinson, N. (2006). Lost in knowledge translation: time for a map? Journal of continuing education in the health professions, 26(1):13-24.
  15. Guermah, H., Fissaa, T., Hafiddi, H., Nassar, M., and Kriouile, A. (2013). Context modeling and reasoning for building context aware services. In Computer Systems and Applications (AICCSA), 2013 ACS International Conference on, pages 1-7. IEEE.
  16. Gui, N., De Florio, V., Sun, H., and Blondia, C. (2011). Toward architecture-based context-aware deployment and adaptation. Journal of Systems and Software, 84(2):185-197.
  17. Guy, T. V., Karny, M., and Wolpert, D. (2015). Decision Making: Uncertainty, Imperfection, Deliberation and Scalability, volume 538. Springer.
  18. Hatak, I. R. and Roessl, D. (2015). Relational competencebased knowledge transfer within intrafamily succession an experimental study. Family Business Review, 28(1):10-25.
  19. Holt, C. A. and Laury, S. K. (2002). Risk aversion and incentive effects. American economic review, 92(5):1644-1655.
  20. Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr. Corbaz.
  21. Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, pages 263-291.
  22. La Corte, A., Scatá, M., and Giacchi, E. (2011). A bioinspired approach for risk analysis of ict systems. In Computational Science and Its Applications-ICCSA 2011, pages 652-666. Springer.
  23. Lambiotte, R. and Panzarasa, P. (2009). Communities, knowledge creation, and information diffusion. Journal of Informetrics, 3(3):180-190.
  24. Lazarsfeld, P. F., Merton, R. K., et al. (1954). Friendship as a social process: A substantive and methodological analysis. Freedom and control in modern society, 18(1):18-66.
  25. Luo, S., Du, Y., Liu, P., Xuan, Z., and Wang, Y. (2015). A study on coevolutionary dynamics of knowledge diffusion and social network structure. Expert Systems with Applications, 42(7):3619-3633.
  26. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization science,5(1):14-37
  27. Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin.
  28. Polanyi, M. (1967). The tacit dimension.
  29. Schilit, B. and Theimer, M. (1994). Disseminating active map information to mobile hosts. Network, IEEE, 8(5):22-32.
  30. Suwa, M., Scott, A. C., and Shortliffe, E. H. (1982). An approach to verifying completeness and consistency in a rule-based expert system. Ai Magazine, 3(4):16.
  31. Tasselli, S. (2015). Social networks and interprofessional knowledge transfer: The case of healthcare professionals. Organization Studies, page 0170840614556917.
  32. Wang, S. and Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2):115-131.
Download


Paper Citation


in Harvard Style

Giacchi E., La Corte A. and Di Pietro E. (2016). A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective . In Proceedings of the 1st International Conference on Complex Information Systems - Volume 1: COMPLEXIS, ISBN 978-989-758-181-6, pages 66-73. DOI: 10.5220/0005877300660073


in Bibtex Style

@conference{complexis16,
author={Evelina Giacchi and Aurelio La Corte and Eleonora Di Pietro},
title={A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective},
booktitle={Proceedings of the 1st International Conference on Complex Information Systems - Volume 1: COMPLEXIS,},
year={2016},
pages={66-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005877300660073},
isbn={978-989-758-181-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Complex Information Systems - Volume 1: COMPLEXIS,
TI - A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective
SN - 978-989-758-181-6
AU - Giacchi E.
AU - La Corte A.
AU - Di Pietro E.
PY - 2016
SP - 66
EP - 73
DO - 10.5220/0005877300660073