Solving the Organizational Free Riding Problem with Social Networks

We describe how social networks can help reduce certain situations of free riding in organizations. The technique uses the homophily of an organization’s social network to select an advisory monitoring peer group likely to share knowledge and skills with each participant, and therefore most able to detect free riding in situations requiring those skills. We illustrate this application in the context of a new mechanism, decision insurance, which helps align decision makers’ risk preferences with those of their organization.

[1]  Bernardo A. Huberman,et al.  Email as spectroscopy: automated discovery of community structure within organizations , 2003 .

[2]  D. Kahneman,et al.  Timid choices and bold forecasts: a cognitive perspective on risk taking , 1993 .

[3]  Tad Hogg,et al.  Enhancing reputation mechanisms via online social networks , 2004, EC '04.

[4]  M. Olson,et al.  The Logic of Collective Action: Public Goods and the Theory of Groups , 1969 .

[5]  William Samuelson,et al.  Status quo bias in decision making , 1988 .

[6]  R. C. Merton,et al.  On Consumption-Indexed Public Pension Plans , 1982 .

[7]  Kenneth J. Arrow,et al.  Information Dynamics in the Networked World , 2003, Inf. Syst. Frontiers.

[8]  Fang Wu,et al.  Finding communities in linear time: a physics approach , 2003, ArXiv.

[9]  David Harlan Wood,et al.  Discovering Shared Interests Among People Using Graph Analysis , 1993 .

[10]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[11]  Bernardo A. Huberman,et al.  Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms , 2004, Manag. Sci..

[12]  Glyn A. Holton.  Defining Risk , 2004 .

[13]  Z. Shapira.  Risk Taking: A Managerial Perspective , 1995 .

[14]  J. March,et al.  Adaptation as Information Restriction: The Hot Stove Effect , 2001 .

[15]  Bernardo A. Huberman,et al.  E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations , 2005, Inf. Soc..

[16]  Sidney C. Sufrin,et al.  The Logic of Collective Action: Public Goods and the Theory of Groups. , 1966 .

[17]  E. Wenger,et al.  Communities and technologies , 2003 .

[18]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[19]  P. Slovic.  Perception of risk. , 1987, Science.

[20]  G. Hardin,et al.  The Tragedy of the Commons , 1968, Green Planet Blues.

[21]  Bernardo A. Huberman,et al.  Forecasting uncertain events with small groups , 2001, EC '01.

[22]  Tad Hogg,et al.  Communities of practice: Performance and evolution , 1994, Comput. Math. Organ. Theory.

[23]  Colin Camerer,et al.  Recent developments in modeling preferences: Uncertainty and ambiguity , 1992 .

[24]  Paul C. Tetlock,et al.  Information Markets: A New Way of Making Decisions , 2006 .

[25]  M. Olson,et al.  The Logic of Collective Action: Public Goods and the Theory of Groups. , 1973 .

[26]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[27]  Anthony F. Herbst,et al.  HEDGING BUSINESS CYCLE RISK WITH MACRO SWAPS AND OPTIONS , 1992 .

[28]  B. Rockenbach,et al.  The Competitive Advantage of Sanctioning Institutions , 2006, Science.

[29]  D. Prelec.  A Bayesian Truth Serum for Subjective Data , 2004, Science.

[30]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[31]  R. Shiller.  The New Financial Order: Risk in the 21st Century , 2003 .

[32]  Paul Slovic,et al.  The relative influence of probabilities and payoffs upon perceived risk of a gamble , 1967 .

[33]  J. March,et al.  Managerial perspectives on risk and risk taking , 1987 .

[34]  Chuck Lam,et al.  SNACK: incorporating social network information in automated collaborative filtering , 2004, EC '04.