We consider a social learning model where agents learn about an underlying state of the world from individual observations as well as from exchanging information with each other. A principal (e.g. a firm or a government) interferes with the learning process in order to manipulate the beliefs of the agents. By utilizing the same forces that give rise to the ``wisdom of the crowd'' phenomenon, the principal can get the agents to take an action that is not necessarily optimal for them but is in the principal's best interest. We characterize which networks are susceptible to this kind of manipulation and derive conditions under which a social network is impervious and cannot be manipulated. In the process, we generalize some known centrality measures and describe how our model offers insights into designing networks that are resistant to manipulation.https://deepblue.lib.umich.edu/bitstream/2027.42/154046/1/reputation-v11Submit.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/154046/4/manipulation.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/154046/5/manipulation-final.pd
