Networks Can Be Used to Analyze the Genomes of Humans and Model OrganismsSoon, sequencing one’s genome may become as commonplace as getting an X-ray. Consequently, personal genomes will increasingly serve as the lenses through which the public views biology. Addressing this, the focus of the Gerstein Lab is interpreting personal genomes, particularly in relation to disorders such as cancer. This endeavor has a number of related aspects. The Analysis of Diverse Networks |
 Understanding Networks of Neurons in the Primate BrainNetworks in the Brain Neurons are a primary cellular constituent of the brain. We each have about 1012 neurons, each making perhaps 5 x 104 connections to other neurons. Each connection (synapse) is like a small computer. How can such complex networks be understood in information processing terms? How might they be organized to compute different inferences? How much are they specified genetically? How much do they learn from the world? |
Social Learning and Word of Mouth NetworksA fundamental issue for economists is understanding the ways social learning, via word-of-mouth (WOM) between friends, affects demand; and hence, a firm’s pricing and advertising strategies. I have explored a couple of different questions in this area. Percolation Model of Demand |
 Networks, Institutional Transformation, and EmergenceSocial networks are fluid and interstitial social objects. When social networks link otherwise distinct social groups, they often fundamentally transform those groups and the societies they are a part of. My research focuses on how networks are implicated in the process by which new institutions emerge and old institutions change shape. |
 Network Optimization: Local Decisions Lead to Global ConsequencesSoon, many network optimization problems will involve each node in the network making decisions to achieve a global objective. Does the decision maker at a node need to know the structure of the whole network? Or is it sufficient to only know a small local neighborhood? The complexity of a network algorithm grows with the size of the local neighborhood. |
 Information, Interdependence, and Interaction: Where Does the Volatility Come From?The model analyzes the interactive behavior of agents who chose their individual action, a, based on their private preference, θ, and the average action of the other agents, A: a = (1-r)θ +rA We allow each agent to be uncertain about his own preference and the preferences (and hence action) of others. We seek to understand how equilibrium behavior is relative to the nature of the private information of the agents. We derive implications regarding how strongly the average choice can react to a shock in the average preferences. |
 Social Networks Can Be Used to Increase Human CooperationThe Conundrum of Cooperative Behavior Cooperation is central to the success of human societies and it is widespread, but social and biological scientists have long pondered how it can persist in the face of possible exploitation. One answer involves networked interactions and population structure. People often have control over whom they interact with, and interaction patterns change over time. This creates a new form of conditional action: people can change their network structure, not just their own cooperation behavior. |
 Networks Can Explain Who Profits and How Industries SurviveSocial relationships influence individuals’ and organizations’ access to information and resources. The patterns of relationships connecting actors therefore can help to explain both who profits and the evolution of industries. With various coauthors, I have explored a number of inter-related issues. Economic Geography |
Lecture VideosYINS Distinguished Lecturer Ron Breiger, "Community Detection: Beyond Community Structure" YINS Distinguished Lecturer Anna Nagurney, "Supply Chain Networks Against Time: From Food to Pharma" |
