Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31100
A Utilitarian Approach to Modeling Information Flows in Social Networks

Authors: Usha Sridhar, Sridhar Mandyam


We propose a multi-agent based utilitarian approach to model and understand information flows in social networks that lead to Pareto optimal informational exchanges. We model the individual expected utility function of the agents to reflect the net value of information received. We show how this model, adapted from a theorem by Karl Borch dealing with an actuarial Risk Exchange concept in the Insurance industry, can be used for social network analysis. We develop a utilitarian framework that allows us to interpret Pareto optimal exchanges of value as potential information flows, while achieving a maximization of a sum of expected utilities of information of the group of agents. We examine some interesting conditions on the utility function under which the flows are optimal. We illustrate the promise of this new approach to attach economic value to information in networks with a synthetic example.

Keywords: Social Networks, Utility Functions, pareto optimal solution, information exchange, Borch's Theorem, Economic value of information

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1190


[1] Acemoglu, D., Ozdaglar, A, Ali:Spread of Misinformation in Social Networks, 2009.
[2] Acemoglu.D, Dahleh, M.A, Lobel. I, and. Ozdaglar,A., "Bayesian learning in social networks", The Review of Economic Studies, 2011.
[3] Anagnostopoulos, Kumar, A.R., Mahdian, M. : "Influence and correlation in social networks", In Proc. of the 14th ACM Int. Conf. on Knowledge Discovery and Data Mining (KDD), 2008.
[4] Borch, K.," Equilibrium in a Reinsurance Market", Econometmca, 30, 1962, pp 424-444,.
[5] Buhlmann, H.," The general economic premium principle", Astm Bulletm 11, 1980, pp 52-60.
[6] Chade, H and Edward E. Schlee, E.E, "Another Look at the Radner- Stiglitz Nonconcavity in the Value of Information", Journal of Economic Theory, 107, 2002, pp 421-452.
[7] DeGroot, Morris H., `Reaching a Consensus', Journal of the American Statistical Association 69(345):, 1974, pp 118-121.
[8] DeMarzo, M., Vayanos, D., Zwiebel, J.,: "Persuasion Bias, Social Influence, and Uni-Dimensional Opinions", Quarterly Journal of Economics 118, 2003, pp 909-968 .
[9] Freixas and Kihlstrom, R. Risk aversion and information demand, in ÔÇÿÔÇÿBayesian Models of Economic Theory-- (M. Boyer and R. Kihlstrom, Eds.), Elsevier, Amsterdam, 1984, pp. 93-104.
[10] Gerber, H.U. and Gerard,P.," Utility Functions: From Risk Theory to Finance", North American Actuarial Journal, Volume 2, Number 3, 2007.
[11] Goyal, S.: "Learning In Networks", Handbook of Social Economics, 2010.
[12] Jackson, M.O. Social and Economic Networks, Princeton University Press, 2008.
[13] Kihlstrom, R., " A Bayesian model of demand for information about product quality", Int. Econ. Rev. 15 , 1974, pp 99-118.
[14] Lawrence D.L. "The Economic Value of Information", Springer- Verlag, New York, 1999.
[15] Moscarini, G and Smith, L. The law of large demand for information, Econometrica,70, 2001
[16] Varian, H . "Economics and Search", SIGIR, Aug 1999, , 1999.