Title

Social Network Analysis: A Means to Discovering the Tipping Point in Gaining Cooperation?

Document Type

Conference Proceeding

Publication Date

3-21-2014

Abstract

Today, one prominent feature of the development of more sustainable communities is the value of cooperation and collaboration among diverse industry, citizen, and stakeholder groups as they develop, alter, and implement common sustainability goals that work throughout a city, region or state. Rezaei and Kirley (2009) demonstrated non-relative payoffs can lead to cooperation which is in complete contrast to the proportional costs and benefits scenarios that have pervaded the literature on cooperation for decades. One element distinguishing this research is that we are not looking at altruism or retaliation, but rather focusing on whether or not the tipping point in the pay-off may be sufficient to obtain broad cooperation as is suggested by the Rezaei and Kirley findings using agent-based modeling. Since there are positive externalities with public goods, a tipping point makes the most sense. We hypothesize that there is a non-relative pay-off tipping point, at which the outcome becomes inevitable. We further hypothesize that with reverse data engineering, we can identify the tipping point. The primary issues this research answers are the identification of the tipping point and the factors that led the group to it. Specifically, this presentation uses social network analysis in identifying the tipping point and the determination if non-relative pay-offs lead to cooperation. The analysis uses digital traces of decision making that are collected in real time on a hypothetical model for building a sports stadium in Boise. The research presented advances our understanding of decision making on multiple scales, contributes to the literature on the value of asymmetric cost and benefits for gaining cooperation, and explores the use of social network analysis in terms of understanding the interactions between agents and a dynamically changing social network of agents. These are all areas where there has been little work to date.

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