A Utilitarian Approach to Modeling Information Flows in Social Networks

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.

Authors:

• Usha Sridhar
• Sridhar Mandyam

Keywords:

• Borch's Theorem
• Economic value of information
• Information Exchange
• Pareto Optimal Solution
• Social Networks
• Utility Functions

References:

[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,
http://www.sims.berkeley.edu/~hal , 1999.