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Networks, Information and Choice

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Collective Decision Making

Part of the book series: Theory and Decision Library C ((TDLC,volume 43))

Abstract

We focus on information sharing in collaboration networks and discuss a feedback model for situational awareness, that combines exogenously given characteristics of nodes with their positioning within the network topology. Here, situational awareness is generally understood to mean “knowing what is going on”, implying the possession of knowledge and understanding to achieve a certain goal. Using this feedback model, we are able to identify the contribution of the network topology to the situational awareness of individual nodes and also to the network as a whole. Moreover, we present two stochastic variations to our model that reflect incentives and choices of the nodes regarding uploading information along links of the network. We finally also discuss a choice mechanism that guides nodes in deciding on the deletion and forming of links in a network. The results presented here may be useful to increase our understanding of the role of social, information and physical networks in complex operations.

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References

  • Barabási, A.-L. (2003). Linked: The new science of networks. Cambridge, MA: Plume Books.

    Google Scholar 

  • Berman, A., & Plemmons, R. J. (1979). Nonnegative matrices in the mathematical sciences. New York: Academic Press.

    Google Scholar 

  • Cares, J. (2005). Distributed networked operations. New York: Universe, Inc.

    Google Scholar 

  • Cares, J. (2006). Battle of the networks. Harvard Business Review, 84, 40–41.

    Google Scholar 

  • Darilek, R., Perry, W., Bracken, J., Gordon, J., & Nichiporuk, B. (2001). Measures of effectiveness for the information-age army. Santa Monica, CA: RAND Cooperation.

    Google Scholar 

  • Delver, R., & Monsuur, H. (2001). Stable sets and standards of behaviour. Social Choice and Welfare, 18, 555–570.

    Article  Google Scholar 

  • Deutsch, E., & Neumann, M. (1985). On the first and second order derivatives of the Perron vector. Linear Algebra and its Applications, 71, 57–76.

    Article  Google Scholar 

  • Dutta, B., & Jackson, M. O. (Eds.). (2003). Networks and groups, models of strategic formation. Heidelberg: Springer.

    Google Scholar 

  • Elton, J. H. (1987). An ergodic theorem for iterated maps. Ergodic Theory and Dynamical Systems, 7, 481–488.

    Article  Google Scholar 

  • Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64.

    Article  Google Scholar 

  • Gorsuch, R. L. (1983). Factor analysis. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Goyal, S. (2007). Connections. An introduction to the economics of networks. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Grant, T. J. (2006). Measuring the potetential benefits of NCW: 9/11 as case study. Proceedings of the 11th ICCRTS Conference. Washington, DC: US DoD C&C Research Program (CCRP).

    Google Scholar 

  • Herings, P. J.-J., van der Laan, G., & Talman, D. (2005). The positional power of nodes in digraphs. Social Choice and Welfare, 24(3), 439–454.

    Article  Google Scholar 

  • Jackson, M. O. (2009). Social and economic networks. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Knight, S., & Burn, J. (2005). Developing a framework for assessing information quality on the world wide web. Informing Science Journal, 8, 159–172.

    Google Scholar 

  • Ling, M. F., Moon, T., & Kruzins, E. (2005). Proposed network centric warfare metrics: From connectivity to the OODA cycle. Military Operations Research Society Journal, 10, 5–13.

    Google Scholar 

  • Miller, N. R. (1980). A new solution set for tournament and majority voting: Further graph-theoretical approaches to the theory of voting. American Journal of Political Science, 24, 68–96.

    Article  Google Scholar 

  • Monsuur, H. (1997). An intrinsic consistency threshold for reciprocal matrices. European Journal of Operational Research, 96(2), 387–391.

    Article  Google Scholar 

  • Monsuur, H., & Storcken, T. (2004). Centers in connected undirected graphs: An axiomatic approach. Operations Research, 52, 54–64.

    Article  Google Scholar 

  • Monsuur, H. (2007a). Assessing situation awareness in networks of cooperating entities: A mathematical approach. Military Operations Research, 12(3), 5–15.

    Google Scholar 

  • Monsuur, H. (2007b). Stable and emergent network topologies: A structural approach. European Journal of Operational Research, 183(1), 432–441.

    Article  Google Scholar 

  • Monsuur, H. (2008). Network-induces power base: The appreciation of contributing to the value of other nodes. In: H. L. Schneider & L. M. Huber (Eds.), Social networks: Developments, evaluation and influence (pp. 179–200). New York: Nova Science Publishers.

    Google Scholar 

  • National Research Council, Committee on Network Science for Future Army Applications. (2005). Network science. Washington, DC: The National Academies Press.

    Google Scholar 

  • Perry, W. L., & Moffat, J. (2004). Information sharing among military headquarters: The effects on decisionmaking. Santa Monica, CA: RAND Corporation.

    Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

    Google Scholar 

  • Slutzki, G., & Volij, O. (2005). Ranking participants in generalized tournaments. International Journal of Game Theory, 33, 255–270.

    Article  Google Scholar 

  • van Klaveren, P. D., Monsuur, H., Janssen, R. H. P., Schut, M. C., & Eiben, A. E. (2009). Exploring stable and emergent network topologies. Proceedings of the 21st Benelux Conference on Artificial Intelligence, to appear.

    Google Scholar 

  • Wang, Y., & Strong, D. M. (1996, Spring). What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33.

    Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

    Google Scholar 

  • Watts, D. J. (1999). Small worlds: The dynamics of networks between order and randomness. Princeton Studies in Complexity. Princeton, NJ: Princeton University Press.

    Google Scholar 

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Correspondence to René Janssen .

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Janssen, R., Monsuur, H. (2010). Networks, Information and Choice. In: Van Deemen, A., Rusinowska, A. (eds) Collective Decision Making. Theory and Decision Library C, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02865-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-02865-6_14

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