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Understanding Networks and Defining Freedom

From capturing interactions and inferring the structure of data to determining the infringement of freedom

Published 2013

Gene regulatory networks are the source of many human diseases. How do we infer network structure from partial data? What is the network most likely to have produced
the little bit that we can see?

In November, the Association of Members of the Institute for Advanced Study (AMIAS) sponsored two lectures by Jennifer Chayes, Member (1994–95, 97) in the School of Mathematics, and Quentin Skinner, Member in the Schools of Historical Studies (1974–75) and Social Science (1976–79). All current and former Institute Members and Visitors are members of AMIAS, which includes some 6,000 scholars in more than fifty countries. To learn more about the organization, upcoming events, and opportunities to support the mission of the Institute, please visit Following are brief summaries of the lectures by Chayes and Skinner; full videos are available at and


Jennifer Chayes, Distinguished Scientist and Managing Director of Microsoft Research New England and New York City:

Everywhere we turn, networks can be used to describe relevant interactions. In the high-tech world, we see the internet, the world wide web, mobile phone networks, and online social networks. In economics, we are increasingly experiencing both the positive and negative effects of a globally networked economy. In epidemiology, we find disease spreading over ever-growing social networks, complicated by mutation of the disease agents. In problems of world health, distribution of limited resources, such as water resources, quickly becomes a problem of finding the optimal network for resource allocation. In biomedical research, we are beginning to understand the structure of gene-regulatory networks, with the prospect of using this knowledge to manage many human diseases.  

In her lecture, Chayes discussed models and techniques that cut across many disciplinary boundaries and gave a general perspective of some of the models that are being used to describe these networks, the network processes that are being studied, the algorithms that have been devised for the networks, and the methods that are being developed to indirectly infer network structure from measured data.


Quentin Skinner, Barber Beaumont Professor of the Humanities at Queen Mary, ­University of London:

The usual practice of defining the concept of individual freedom in negative terms as “absence of interference” is in need of qualification and perhaps abandonment. Because the concept of interference is such a complex one, there has been much dispute, even within the liberal tradition, about the conditions under which it may be legitimate to claim that freedom has been infringed.

In his lecture, Skinner considered these disputes, and then focused on those critics who have challenged the core liberal belief about absence of interference. Some doubt whether freedom is best defined as an absence at all, and instead attempt to connect the idea with specific patterns of moral behavior. Others agree that freedom is best understood in negative terms, but argue that it basically consists in the absence not of interference but of arbitrary power. In concluding his talk, Skinner drew some implications of this view for democratic government.

“A number of democratic states are practicing, without the consent or even knowledge of their citizens, systematic powers of surveillance. . . . If citizens begin to self-censor in the face of these powers, because, for example, of not knowing what use may be used of their emails and wanting to be sure of keeping out of trouble, they will have limited their own freedom of expression. They will have colluded in the undermining of their freedom.” 

Published in The Institute Letter Fall 2013