Gaining Knowledge Expertise Through Social Networks

Christina Prell, University of Maryland - Sociology Department
As such, there is a subtle difference between the motivations described by Lin and those by Burt. Lin emphasizes a rational actor using knowledge of embedded resources (resources associated with other actors in the network) and Burt emphasizes rational actors pursuing a particular network position, or structure. Burt’s approach can be called a ‘brokerage’ network strategy, i.e. pursuing ties that would place a focal actor in a broker position; and Lin’s approach can be called an ‘embedded resource’ networking strategy, i.e. pursuing ties with actors who have control over resources that interest a focal actor. In this paper, I compare actors’ use of a ‘brokerage’ networking strategy versus a ‘embedded resource’ strategy as actors’ pursue the goal of increasing their own stock of knowledge . Which strategy yields the best pay-off, in the form of knowledge gains, and which strategy is the most costly? To explore this comparison, I simulate a great number of different networks, making use of Snijders’ Evaluation Function for guiding the rules of agents, and including a statistic to model ‘learning’ that is not originally found in his model. My preliminary findings suggest that pursuing brokerage and pursuing ties with knowledge experts each lead to very similar pay-offs: both strategies yield, on average, the same amount of ‘learning’ across the network. However, brokerage is a much less costly networking strategy. Finally, I compare both these strategies against a few other models (e.g. pursuing ties with others holding a similar knowledge level as oneself) for further discussion.
December, 4 2012 | 12:30 - | 329 Soc/Psych Building (McKinney Room)

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