SoCS: Tipping Collective Action in Social Networks Tipping Collective Action on the Commons in Social Networks Tipping Collective Action in Social Networks Many of the challenges facing contemporary society are collective action problems, varying from emission reductions to reduce risks of climate change or vaccination for infectious diseases. Social scientists in various disciplines study collective action and there is a good understanding of the ability of small homogenous groups to cooperate in commons dilemmas. Many of contemporary problems such as climate change and pandemics cannot be solved by small scale communities alone. What is needed is a better understanding of collective action in large heterogeneous groups. Empirical research has shown that individuals have an increased likelihood to participate in collective action due to social influence. We propose to test whether we can increase contributions to collective action by using a social-computational system that provide the right messages to the right people such that cooperative behavior can cascade through a social network. The formulation of the messages and the targeting of seeds in networks will be tested by performing controlled decision-making experiments within artificially constructed and actual social networks. Participants can derive rewards by contributing to a series of public good experiments. Neighbors of contributors to the public good will receive messages to stimulate participation.Besides controlled experiments in artificial networks, we will perform experiments in actual social networks of students at Arizona State University to test the approach to actual collective action within the university community, such as increasing the rate of taking flu shots, voting turnout and energy saving in dorms. Furthermore, in cooperation with Ocean Conservancy we perform experiments using a mobile app to stimulate trash collection at beaches. Agent-based models are developed to explore the best ways to target social networks. Empirical data of the experiments will be used to develop heuristics for effective strategies to spread cooperation in social networks. We will also develop algorithms that detect those seeds in social networks that lead to the maximal spread of cooperative behavior. These algorithms will be tested during our experiments. Intellectual Merit: The major research tasks in this proposal include (a) hypothesis testing with controlled social science experiments on the web and with mobile apps, and (b) development of an algorithm to discover targets in social networks that increase the spread of cooperative behavior (c) development of agent-based models to improve the algorithms to discover seed targets. The target discovery algorithm will be used to target participants during web-based experiments. The social science experiments will enable us to test hypotheses on the effect of (a) targeting individuals in social networks with information, and (b) social influence of messages participants receive when neighbors contribute to the public good, on cooperation. The deliverables for the proposal include publications on new scientific insights on targeting individuals and groups in social networks to increase collective action, experimental open source software and protocols that will provided to the scientific community, anonymized data sets of experiments which will be made available to broader scientific community, and algorithms for target detection. Broader Impact: The proposed work will have a broad scientific, education and societal impact. The experimental work on collective action has been restricted to small group experiments. The webbased experiments enable researchers working on collective action problems, to perform experiments with large groups and test hypotheses that were not testable before. The research can lead to new theoretical frameworks for understanding collective action and diffusion. The software will be used in educational context enabling students to experience insights in the cutting edge research on collective action and derive a vivid experience how groups can solve collective action problems. Advances in our understanding of how we can transform collective action successes observed at the smaller scale, to larger scales can have a transformative impact. In particular, they can lead to applications using new social media tools to stimulate collective action in practical applications.
|Effective start/end date||7/1/12 → 6/30/17|
- National Science Foundation (NSF): $500,000.00
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