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This paper investigates the role of social learning in the diffusion of a new agricultural technology in Ghana. We use unique data on farmers ’ communication patterns to define each individual’s information neighborhood, the set of others from whom he might learn. Our empirical strategy is to test whether farmers adjust their inputs to align with those of their information neighbors who were surprisingly successful in previous periods. We present evidence that farmers adopt surprisingly successful information neighbors ’ practices, conditional on many potentially confounding factors including common growing conditions, credit arrangements, clan membership, and religion. The relationship of these input adjustments to experience further supports their interpretation as resulting from social learning. In ad-The authors have benefittedfromtheadviceofRichardAkresh,Federico Bandi, Alan Bester, Dirk Bergemann,

The world as we know it is changing. Economies world wide are undergoing a process of profound and continuing structural change, and the global village is becoming a reality driven by IT and communication technologies. With this in mind, many countries believe that they can benefit both economically and environmentally from better management of their spatial data assets by taking a perspective that starts at a local level and proceeds through state, national and regional levels to global level. This has resulted in the development of the Spatial Data Infrastructure (SDI) concept at these levels.

When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbors to guide their current decision making. This paper develops a general framework to study the relationship between the structure of information flows and the process of social learning. We show that in a connected society, local learning ensures that all agents obtain the same utility, in the long run. We develop conditions under which this utility is the maximal attainable, i.e. optimal actions are adopted. This analysis identifies a structural property of information structures -- local independence -- which greatly facilitates social learning. Our analysis also suggests that there exists a negative relationship between the degree of social integration and the likelihood of diversity. Simulations of the model generate spatial and temporal patterns of adoption that are consistent with empirical work. Key Words: Connected societies, conformism, social integ...

This article aims to reestablish the long-standing conjecture that conformity is high at the middle and low at either end of a status order. On a theoretical level, the article clarifies the basis for expecting such an inverted U-shaped curve, taking care to specify key scope conditions on the social-psychological orientations of the actors, the characteristics of the status structure, and the nature of the relevant actions. It also validates the conjecture in two settings that both meet such conditions and allow for the elimination of confounding effects: the Silicon Valley legal services market and the market for investment advice. These results inform our understanding of how an actor’s status interacts with her role incumbency to produce differential conformity in settings that meet the specified scope conditions.

We present a model of contagion that unifies and generalizes existing models of the spread of social influences and microorganismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g. a rumor or disease), variable magnitudes of exposure (dose sizes), and heterogeneity in the susceptibility of individuals. Through analysis and simulation, we examine in detail the case where individuals may recover from an infection and then immediately become susceptible again (analogous to the so-called SIS model). We identify three basic classes of contagion models which we call epidemic threshold, vanishing critical mass, and critical mass classes, where each class of models corresponds to different strategies for prevention or facilitation. We find that the conditions for a particular contagion model to belong to one of the these three classes depend only on memory length and the probabilities of being infected by one and two exposures, respectively. These parameters are in principle measurable for real contagious influences or entities, thus yielding empirical implications for our model. We also study the case where individuals attain permanent immunity once recovered, finding that epidemics inevitably die out but may be surprisingly persistent when individuals possess memory.

Abstract. We consider processes in which new technologies and forms of behavior are transmitted through social or geographic networks. Agents adopt behaviors based on a combination of their inherent payoff and their local popularity (the number of neighbors who have adopted them) subject to some random error. We characterize the long-run dynamics of such processes in terms of the geometry of the network, but without placing a priori restrictions on the network structure. When agents interact in sufficiently small, close-knit groups, the expected waiting time until almost everyone is playing the stochastically stable equilibrium is bounded above independently of the number of agents and independently of the initial state.

I investigate how different sources of information influence the diffusion of pharmaceutical innovations. In prescription-drug markets, both advertising and scientific information stemming from clinical trials can affect physicians’ prescription choices. Using novel indices of clinical-research output, I find that both marketing and scientific evidence directly influence the diffusion process in the antiulcer-drug market, with marketing having a more pronounced influence. I also find evidence that clinical outputs are important drivers of firms’ marketing efforts, affecting sales indirectly. Taken together, the direct and indirect effects of science on demand imply strong private incentives for clinical research. I conclude that product-market competition in the pharmaceutical industry is shaped by both advertising rivalries and scientific rivalries. Moreover, drug advertising may perform an important informative function.

We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends ’ actions are sometimes tempted to perform those actions. We are interested in the problem of studying the propagation of such “influence”, and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. We consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. Our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. Given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. We run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. The results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. To the best of our knowledge, this the first frequent pattern based approach to social network mining.

National Institute on Aging PO1-AG19783. In his classic 1957 study of hybrid corn diffusion, Griliches emphasized the importance of economic incentives and profitability in the adoption of new technology, and this focus has been continued in the economics literature. But there is a distinct literature with roots in sociology emphasizing the structure of organizations, informal networks, and “change agents. ” We return to a forty-year-old debate between Griliches and the sociologists by considering state-level factors associated with the adoption of a variety of technological innovations: hybrid corn and tractors in the first half of the 20 th century, computers, and the treatment of heart attacks during the last decade. First, we find that some states consistently adopted new effective technology, whether hybrid corn, tractors, or effective treatments for heart attacks such as Blockers. Second, the adoption of these new highly effective technologies was closely associated with measures of statelevel social capital (and education), but not per capita income, density, or (in the case of Blockers) with expenditures on heart attack patients. Economic models are useful in identifying why some regions are more likely to adopt early, but sociological barriers – perhaps related to a lack of social capital or informational networks – can potentially explain why other regions lag far behind.