Adoption and diffusion of knowledge:

1. Learning Across Product, Workgroup, and Geographic Boundaries (Erica R.H. Fuchs, Linda Argote and Dennis N. Epple, Carnegie Mellon University)

2. Clusters, Heritage and the Microfoundations of Spillovers - Lessons from Semi-Conductors (Steven Klepper and Francisco Veloso, Carnegie Mellon University)

3. Specific, General, and Target Sharing of Information Among Academic Researchers (Marcie C. Thursby, Jerry G. Thursby, National Bureau of Economic Research Inc.)

Measuring and tracking science and innovation:

4. Innovation Personnel and their Ecosystem: Career Choices and Trajectories of Scientists - Industry or Academia and Basic or Applied? (Rajshree Agarwal-Tronetti and Jay P. Kesan, University of Illinois Urbana-Champaign; Feniosky Pena-Mora, Columbia University)

5. Modeling Pharmaceutical Innovation Pipelines (Dissertation) ( Kenneth Flamm and Alexandra Stone, University of Texas at Austin)

6. From Grant to Commercialization: An Integrated Demonstration Database which Permits Tracing, Assessing, and Measuring the Impact of Scientific Funding. (Lee Fleming, Harvard University and Vetle I. Torvik, University of Illinois at Urbana-Champaign) (Joint with SRS)

7. Predictive Modeling of the Emergence and Development of Scientific Fields (David I Kaiser, David S. Jones, Vincent A. Lepinay, Massachusetts Institute of Technology)

Advancing understanding of entrepreneurship and innovation:

8. Firm IQ: A Universal, Uniform and Reliable Measure of R&D Effectiveness (Anne Marie Knott, Washington University in St. Louis)

9. Personal Credit, New Firm Formation and Entrepreneurial Firm Growth (Gordon M. Phillips, National Bureau of Economic Research, Inc.; Ethan Cohen-Cole, University of Maryland) (Joint with IOS)

10. Technology Disruptions in Industries: Assessing Their Frequency, Processes, and Impact (Kenneth L. Simons, Rensselaer Polytechnic Institute)

11. Innovation and Growth of Human Social Organizations from Cities to Corporations (Geoffrey B. West, Santa Fe Institute and Luis Bettencourt, Los Alamos National Laboratory)

New approaches to studying science and innovation:

12. Accelerating the Pace of Discovery by Changing the Peer Review Algorithm (Stefano Allesina, University of Chicago)

13. From Cycles to Spirals: Structural Analysis of Scientific Consensus Formation (Dissertation) (Peter S. Bearman and Uri Shwed, Columbia University)

14. Construct Utilization in the Behavioral Sciences (Kai R. Larsen and Jintae Lee, University of Colorado at Boulder; Eliot Rich, University at Albany)

15. New Methods to Enhance Our Understanding of the Diversity of Science (Andrew K. McCallum and Hanna M. Wallach, University of Massachusetts Amherst; Fiona Murray Massachusetts Institute of Technology)

16. Developing a Social-Cognitive, Multilevel, Empirically-Based Model of Public Engagement for the Shaping of Science and Innovation Policy (Lisa M. Pytlik Zillig and Alan J Tomkins, University of Nebraska-Lincoln; Peter Muhlberger, Texas Tech University)

Understanding the impact of structures and processes on science:

17. Management and Organizational Practices Across the US (Nicholas Bloom, Stanford University; Erik Brynjolfsson, Massachusetts Institute of Technology; John Van Reenen, London School of Economics) (joint with IOS, Econ, SRS and DRMS)

18. Toward a Theory of Innovation in Emerging Economies (Dan Breznitz, Georgia Institute of Technology) (Joint with IOS)

19. Innovation in Social Networks (Nicole Immorlica, Northwestern University; Rachel E. Kranton, Duke University) (Joint with Economics and CISE)

20. Bioethics Byplay?: The Performances of Bioethics in the Private and Public Sectors (Dissertation)

(Jason S. Robert and Jennifer E. Dyck Brian, Arizona State University.)

21. How Do Prizes Induce Innovation? Learning from the Google Lunar X-Prize (Dissertation) (Philip Shapira and Luciano Kay, Georgia Institute of Technology)

22. Scientific Knowledge Production for Solving Common Environmental Problems in a Developing Country (Dissertation) (David Winickoff and Javiera Barandiaran, University of California-Berkeley)

Implementing science policy:

23. Information Values in Translation: An Ethnography of Free and Open Source Software in Vietnam

(Dissertation) (Leah A Lievrouw and Nguyen Lilly, University of California Los Angeles)

24. Choosing a Portfolio of Technology Policies in an Uncertain World (Gregory F. Nemet, University of Wisconsin-Madison, Erin D. Baker, University of Massachusetts - Amherst)

25. The NIH Public Access Policy: Establishing a Basis for Assessing a Science Policy (John Willinsky, Stanford University)

26. Government Responses to Network Failures: The Case of the Manufacturing Extension Partnerships (Dissertation Joshua D. Whitford, Columbia University; Andrew Schrank, University of New Mexico)


1. Learning Across Product, Workgroup, and Geographic Boundaries

Researchers: Erica R.H.Fuchs, Linda Argote and Dennis N. Epple, Carnegie-Mellon University.

Abstract : Organizational learning is critical to the performance and long-run success of firms and nations. Organizations that are able to learn from their experience and transfer the knowledge they acquire throughout their establishments are more successful than their counterparts that are less adept at organizational learning. Yet organizations vary significantly in their ability to learn with some organizations demonstrating dramatic improvements and other organizations evidencing little or no learning. Leveraging extensive data collected over several years from a high technology, offshore product development and manufacturing facility of a U.S. firm, this project conducts three studies to shed light into learning across 1) product, 2) workgroup, and 3) geographic boundaries within the firm.

Intellectual Merit: The first study focuses on organizational learning in a multi-product production environment with high turnover. Ninety percent of U.S. output comes from multi-product facilities. Yet past studies of organizational learning have focused primarily on production sites with a single product or a product with minor variations. This first study investigates how product mix and employee turnover affect performance, whether turnover is more harmful for certain products and processes than others, and whether turnover of individuals with certain skills and experiences is more harmful than turnover of other individuals. The second study examines knowledge transfer across workgroups. The third study sheds insights into whether moving product development closer to manufacturing helps performance, which product development activities may be important to co-locate, and the impact on performance of moving more product development activities offshore. As more manufacturing has moved overseas, there have been concerns that knowledge work, such as R&D, will follow.

Broader Impact: These three studies provide empirical evidence about knowledge accumulation and transfer in an offshore environment, and thereby inform corporate decisions about off shoring as well as

U.S. industrial and manufacturing policy.



2. Clusters, Heritage and the Microfoundations of Spillovers - Lessons from Semi-Conductors

Researchers : Steven Klepper and Francisco Veloso, Carnegie-Mellon University.

Abstract: Knowledge industries tend to cluster geographically, which has long been thought to promote spillovers of knowledge through local employees of competing firms exchanging know-how and changing employers. Employees of incumbent firms also sometimes found their own spinoff firms, which can be a source of knowledge spillovers.

Intellectual Merit: The main purpose of the proposed work is to assess the strength of these alternative spillover mechanisms and their effect on firm performance in the context of the semiconductor industry, which famously clustered in Silicon Valley and secondarily in Boston, New York, and Los Angeles. Sorting out the importance of these alternative mechanisms is critical; if clusters promote knowledge exchange and employee mobility then clustering is socially beneficial whereas if spinoffs are the primary conduit for clustering then the benefits of clusters do not extend beyond spinoffs and their 'parents'. Using an annual buyers' guide, data are collected on the location and products of all semiconductor producers from the inception of the industry through 1987. Firm sales data are collected to identify the market leaders. The

pre-entry experience of each producer is traced using the annual buyers' guides to identify prior producers of other electronics producers and trade journals and web sources are used to identify spinoffs and their parents. The patents assigned to each semiconductor producer and the inventors of the patents are collected to trace the mobility of the inventors over time. An analysis is conducted of the influence of location and other factors on whether semiconductor producers entered producing semiconductor products at the technological frontier, and if not whether they later shifted to produce frontier products. The determinants of the mobility of inventors are analyzed, including whether mobility was greater for inventors in clusters. Various new semiconductor products were developed in the sample period, and analyses are conducted of the factors influencing whether firms adopted new products, including the role of location. All of these decisions are in turn related to the performance of firms, as reflected in their sales and longevity.

Broader Impact: Understanding whether clusters improve firm performance and the main mechanisms behind regional knowledge diffusion can yield important broader impacts for both strategy and policy. If direct knowledge sharing and inter-firm employee mobility are promoted by clustering and in turn contribute to clustering, then clustering has clear welfare benefits and would also benefit firms located in clusters. Alternatively, if spinoffs are the primary conduit for clustering, then external economies do not go beyond spinoffs and their 'parents' and clustering provides no social benefits per se nor any private benefits to firms located in clusters.



3. Specific, General, and Target Sharing of Information Among Academic Researchers

Researchers: Marie C. Thursby and Jerry G. Thursby, National Bureau of Economic Research Inc.

Abstract: Information sharing is essential to scientific progress. In principle, unconditional sharing of knowledge among academics is enforced by the priority-based scientific reward system in which the first person to discover a result gets credit for the discovery. There is, however, a tension between communal sharing and the competitive incentives for researchers during the research process itself. A scientist who shares results provides stepping stones for discovery by others who may not acknowledge the contribution. This tension, as well as commercial potential for academic work has led to concerns over misappropriation of scientific research and increased reluctance to share information ad materials.

Intellectual Merit: The project involves theory and a survey to support empirical research on what drives academic researchers to share information. The theory considers three contexts in which researchers share: one-on-one situations in which one researcher is asked by another to share specific data or materials; public sharing, such as conference participation where researchers present work that is neither published nor patented at the time of presentation; and target sharing in which they share certain types of information with trusted colleagues prior to public dissemination.

Preliminary results suggest that information sharing depends on the conditions in which the research process is embedded, and these conditions themselves depend upon dimensions of scientific policy (e.g., journal and federal funding agency policies). The models also provide hypotheses about the extent to which researchers share (and with whom) as a function of individual characteristics including type of research, age, and rank as well as other environmental factors. The theory provides the context for a survey of academics across a wide array of fields to include engineering, social sciences, biological and medical sciences, mathematics, physics and statistics.

Broader Impact: Combined with econometric analysis, the research provides a new framework for understanding of the ways in which open science operates or not across a broad spectrum of academia.



4. Innovation Personnel and their Ecosystem: Career Choices and Trajectories of Scientists - Industry or Academia and Basic or Applied?

Researchers: Rajshree Agarwal-Tronetti and Jay P. Kesan, University of Illinois Urbana-Champaign; Feniosky Pena-Mora, Columbia University

Abstract: The dynamism of the U.S. economy has been fueled with continued advances in basic and applied science, conducted within both academic and industry boundaries. Underlying these critical macro (institutional and national policy) level factors are individual decisions made by the scientists themselves, as they choose among careers options that relate to what they do: basic or applied science; and where they do it: academia or industry. An understanding of these micro level factors is necessary to formulate a0n individual career strategy, to strengthen the competitiveness of both firms and universities and to design sound economic policies

This proposal examines the micro factors related to scientific labor markets that influence institutional and policy issues. It is based on the premise that societal goals related to achieving an optimal level of basic and applied research can be best achieved by creating the appropriate incentive structure that provides for an optimal matching of scientists to careers in basic and/or applied science, within academic or industry settings. The research develops new theoretical models that build on matching, human capital and endogenous growth theory, and new empirical evidence using panel data on baccalaureate and doctoral graduates in science and engineering fields from Scientists and Engineers Statistical Data System (SESTAT) developed by the NSF.

Intellectual merit: The research provides a new framework to identify roles of preference, ability, job characteristics, and institutional settings in career choices and the career trajectory of scientists by combining a matching model and a human capital model. The interaction of individual and institutional characteristics is explicitly entered into a model and empirically tested to understand its impacts on individual choices and performances as well as on the entire market or economy. The research also offers new perspectives and a new micro-foundation for endogenous economic growth models by emphasizing interactions and synergies between basic and applied science. In particular, the framework in the research helps identify sources of economic growth that the linear model has failed to recognize.

Broader impacts: The research informs existing and prospective scientists embarking on alternative careers and seeking collaborations with others or acquiring their own complementary skills. It informs universities and firms of sources of synergies between basic and applied science, and helps them design an incentive scheme and an institutional arrangement for attracting and retaining innovative personnel, as well as extracting the benefits of science.



5. Modeling Pharmaceutical Innovation Pipelines

Researchers: Kenneth Flamm and Alexandra Stone, University of Texas at Austin.

Abstract: In the late 1990s, the U.S. government doubled investment in basic biomedical research to spur pharmaceutical innovation. The policy strategy was based on the assumption that more basic research would lead to greater technological opportunities for innovation. However, the anticipated results from increased R&D spending have not been realized. U.S. pharmaceutical companies received approval from the U.S. Food and Drug Administration (FDA) for fewer drug candidates in the early 2000s, after the increases in R&D spending, than in the early 1990s. Measures of returns to R&D spending in terms of the number of FDA approved drugs indicate stagnating productivity and likely declines in growth.

The apparent declines in the productivity of the pharmaceutical industry may reflect changes in the nature of technological opportunities and the R&D process. Productivity metrics that do not account for the methods used by firms integrate scientific and technological advances into innovation activities may be inaccurate.

Intellectual Merit: This dissertation research constructs a new data set that can be utilized to map the production of scientific advances through publicly funded research, and subsequent use in downstream innovation activities at the project level. The research develops and applies multiple metrics of innovative productivity, including the completion status of innovation activities and the time to complete an innovation activity to develop stylized facts about the use of scientific advances generated from publicly funded research by firms.

Broader Impact: This research advances the understanding of the role of scientific advances in pharmaceutical innovation by shedding more light on the innovation activities in which firms use externally generated knowledge inputs to improve productivity. The project-level models of productivity that are developed in this research may be used by policy makers to evaluate the impact of the scientific advances generated through publicly funded research on private-sector innovation activities, and should be applicable in other science intensive-industries, such as nanotechnology, energy technologies, and information technology.

This dissertation research analyzes opportunities to promote the diffusion of scientific advances generated through government funded research, and thereby stimulate innovation. Policy makers should be able to use the models and stylized facts generated by this research in order to identify ways in which research programs can be structured to more effectively stimulate commercial innovation. The results of this research are likely to be useful in designing intellectual property rights, organizing and funding public- private research consortia, and developing research priorities that align with industry needs.



6. From Grant to Commercialization: An Integrated Demonstration Database which Permits Tracing, Assessing, and Measuring the Impact of Scientific Funding

Researchers: Lee Fleming, Harvard University; Torvik I. Vetle, University of Illinois at Urbana-Champaign (Jointly funded with SRS).

Abstract: It has long been received wisdom that investment in science greatly facilitates the technological progress that ultimately improves economic productivity and living standards. Unfortunately, the systematic and quantitative evidence to support these arguments remains thin and expensive to produce. Name ambiguity makes this difficult: different scientists may share the same names, and conversely, the same scientist may have their name listed differently within or across publication and patent databases. As a consequence, the collaborative networks of scientists remain unidentified, and the gatekeepers between science and technology have not been systematically identified.

Intellectual Merit: This project develops a large-scale database that links Medline papers and U.S. patents, through identification of individuals who authored both papers and patents using state-of-the-art name disambiguation algorithms. These patent-paper-author links in turns enable identification of similar organizations and in some cases, science/technology fields and geography. The resulting database is used in three exemplary analyses that aim to: 1) study the impact of grants upon science and technical productivity, 2) identify the gatekeepers between science and technology and study how knowledge flows between these two realms, and 3) understand how collaborative, institutional, organizational, and regional factors influence these processes.

Broader Impact : The resulting database is publicly available. This both enables science policy scholars to develop systematic and convincing evidence for investment recommendations and enables person- centered research of the science-technology interface of numerous other kinds.



7. Predictive Modeling of the Emergence and Development of Scientific Fields

Researcher: David I Kaiser, David S. Jones, Vincent A. Lepinay, Massachusetts Institute of Technology.

Abstract: The explosion of scientific publishing in recent decades has created an embarrassment of riches. More information is available about more subjects in ways that are more accessible than ever before.

Searches on PubMed or Web of Science routinely return thousands of hits. The easy accessibility of gigantic numbers of publications creates substantial problems for researchers and granting agencies alike: scientists cannot possibly read everything published in their own fields, and policymakers have few clear bases upon which to form judgments of quality. How can reasonable decisions be made about which fields are likely to blossom into transformative research, if the problem of separating wheat from chaff grows exponentially with each passing year? Given the ever-increasing outpouring, it is more important than ever to develop some quantitative means of describing -- and, in turn, predicting -- the growth and development of scientific research. What factors might explain the changes over time of numbers of publications on a given topic, or numbers of researchers working in a given field? What interactions between researchers might account for a scientific community's growth and change? And how might various interventions -- policy initiatives, market forces, or other factors -- alter the dynamics of how scientific fields emerge and develop?

Intellectual Merit: The research develops new measures to model and predict processes of scientific research and innovation. The measures are based on mathematical models that capture various aspects of the research enterprise. These tools are sharpened with application to a suite of examples from the physical and biomedical sciences. These examples permit the investigation of different modes of supporting and steering the course of scientific research in six key areas: the growth and development of research publications on string theory, molecular electronics, and nanotechnology from the physical sciences; and research publications on H1N1 influenza, coronary angioplasty, and interventional cardiology from the biomedical sciences. These cases traverse a wide range of scales, from research topics within well-defined subfields to large, discipline-spanning endeavors.

Broader Impact: The dissemination of the results includes scientific publications, workshops with expert researchers, an informal bi-weekly seminar series for scholars across a wide range of disciplines - from history and sociology of science, to business and economics, to library science and computer science. In addition, new courses are offered at the undergraduate and graduate levels. Finally, the structure, materials, and resources for these courses are freely disseminated via the internet via MIT's OpenCourseWare initiative.



8. Firm IQ: A Universal, Uniform and Reliable Measure of R&D Effectiveness

Researchers: Anne Marie Knott, Washington University in St. Louis

Abstract: Despite the dramatic impact of R&D on firm and economic growth, there currently are no good measures of R&D effectiveness at the firm level. The most prevalent measure, patent counts, suffers from problems of universality (less than 50% of firms conducting R&D have any patents), uniformity (10% of U.S patents account for 81-85% of the economic value of all US patents) and reliability (patents are a poor predictor of firm market value).

Without a good measure of effectiveness, it is difficult to characterize the impact of R&D investment, to understand the mechanisms through which R&D investments generate economic outcomes, or to generate tools guiding future investment. What firms, policy makers and academics need is a universal, uniform and reliable measure of R&D effectiveness. That measure should match constructs in economic theory (so academics can test them) and should provide guidance to firms and policy-makers about the choice of investment levels.

Intellectual Merit: Recent empirical methods have facilitated a new measure of R&D effectiveness using firm accounting data that is based in economic theory. This measure has the potential of predicting both firms' behavior and their market value. The measure is called IQ because it captures firms' technical problem solving capability in much the same way individual IQ captures analytical problem solving capability: those with higher IQ solve more problems per unit of input (dollars for firms, minutes for individuals) than those with lower IQ. Perhaps most importantly, firm IQ (unlike individual IQ) appears to be mutable over long periods of time. Accordingly, understanding the organizational structures and processes driving differences in IQ offers the potential for firms to improve their R&D effectiveness.

Work to date has estimated the IQ for publicly-traded US firms, characterized variation of IQ across industries, and identified high and low IQ firms within each industry. This pilot study represents the first stage in an effort to characterize the organizational configurations and processes driving IQ. The pilot comprises in-depth interviews with paired firms (one high IQ, one low IQ) in two industries. These interviews form the basis of rich case studies of the four firms, as well as identify candidate factors/organizational configurations to examine in a future quantitative study across the full spectrum of firms engaged in R&D.

Broader Impact: There are both immediate and long term benefits from this study. The immediate benefits of the IQ measure are 1) academics' use of the measure to resolve empirical anomalies in prior studies, 2) firms' use of the measure to compute their optimal R&D investment, and 3) policymakers' use of the measure to allocate funds based on firmer evidence. The longer term benefit from the full study (to be disseminated via conference presentations, journal articles and ultimately a book for practitioners) is advancing understanding about the effectiveness of R&D investments.



9. Personal Credit, New Firm Formation and Entrepreneurial Firm Growth

Researchers: Gordon M. Phillips, National Bureau of Economic Research, Inc.; Ethan Cohen-Cole, University of Maryland (Jointly funded with IOS).

Abstract: This project examines the relationship between individual personal credit, entrepreneurship and business formation. An important part of innovation policy targets small firms, but little is known about the links between individual finances and firm startups. The contribution of this research is to create a new dataset that combines data on individual credit reports with new firm startups. The research assesses the role of individual credit in influencing new firm start-up and longer term success.

Intellectual Merit: The contribution of the research is to advance understanding of the role of credit access and personal credit scores on individual decisions regarding personal and small business finance. This research provides new insights into the importance of personal credit and human capital in understanding:

a) individual decisions at the personal level such as delinquency, personal default and bankruptcy b) the formation, success and failure of small businesses that are partially funded by personal finance with personal guarantees, c) the link between human capital accumulation and the availability of credit from banks and other sources, and d) the effect of job churn on individual bankruptcy and new firm formation. This project also deepens understanding about the ways in which an important policy lever, Small Business Administration (SBA) loans, affects the subsequent growth of small entrepreneurial firms.

Broader Impact: The results of the research should inform innovation policies regarding access to credit by small firms and new firm formation. The research also creates an important new dataset for many researchers to study the links between access to credit and firm dynamics.



10. Technology Disruptions in Industries: Assessing Their Frequency, Processes, and Impact

Researchers: Kenneth L. Simons, Rensselaer Polytechnic Institute

Abstract: A central, yet little-researched, contributor to technology's advance and economic growth is "disruptive" technological change, which replaces older technologies and products with newer ones. Examples include electronic calculators replacing mechanical calculators, smaller hard disk drives replacing larger drives, or new generations of photolithographic aligners for semiconductor manufacture. Such technology disruption is considered a crucial influence on industries, has been shown to endanger businesses, and is central to the manner in which many new technologies contribute to societal welfare and economic growth.

Intellectual Merit: This research measures, for the first time, the frequency of disruptive technological change across a broad group of industries. It also assesses specific processes and impacts of disruption in a sample of industries. The research analyzes 47 narrowly defined, product-level industries, representative of various types of U.S. manufacturing, from approximately 1900 to 2010. Jointly analyzed are related industries that fulfilled similar purposes for buyers. Intensive data collection is necessary to develop longitudinal measures of, in part, business entry and exit, market share, and patented and non-patented technologies. Data are being collected with the aid of two teams of summer research assistants, painstakingly assembling evidence primarily from library and archival sources. The frequency of disruptive technological change is being assessed using alternative definitions of disruption, ranging from the replacement of incumbent producers by new entrants to the internal development of new technologies and products by leading incumbent producers. Specific theories of disruptive technological change are simultaneously being probed in many industries, through in-depth analysis of sources including historical, technological, and trade literatures.

Broader Impact: Knowing the frequency of disruptive technological change is essential to properly understand how technology is advancing the economy, and this frequency is being determined for the first time across many U.S. industries. Fruitful policy insights may result, since the research improves fundamental understanding of entrepreneurship, corporate policy, and technological change.

Understanding the interactions of entrepreneurs, businesses entering from other industries, and incumbent producers during technological disruptions is key to understanding how policies might enhance economic growth.



11. Innovation and Growth of Human Social Organizations from Cities to Corporations

Researchers: Geoffrey B. West, Santa Fe Institute and Luis Bettencourt, Los Alamos National Laboratory

Abstract: This research examines the social, organizational and infrastructural factors that promote innovation and lead to economic growth. It has three main goals. The first is to establish metrics of innovation, economic growth and size of social organizations across scales using datasets from urban systems and corporations around the world and across times scales. The second is to establish the quantitative connection between the dynamics of innovation and growth, through in-depth studies of cities and firms, with a specific study of the temporal evolution of Boeing's Commercial Aircraft Division. The third is to discover and model mathematically the micro-scale processes and network structures resulting in scaling of economic productivity and innovation, including those leading to economies of scale and learning in production and increasing returns in innovation, with a specific study of the component processes within Boeing Commercial Aircraft Division. The project draws on a variety of academic disciplines and features a close collaboration with industry. Concepts and hypotheses from the social sciences will be tested via analytical techniques from physics and statistical theory applied to large and comprehensive databases of cities and firms.

Intellectual merit: The research is based on a comprehensive, data-driven, quantitative study of innovation and discovery processes in social organizations that has the potential to generate transformative new insights on the general factors that affect the rate of innovation and growth. This project develops and tests theories of economic growth at the micro scale, relying on empirical data analysis, and in-depth case studies of a major corporation. The focus on the history and growth of Boeing and its processes of innovation, particularly on its technological and organizational breakthroughs, aging and scaling, and learning curves, is particularly unusual because of the access to detailed firm data.

Broader Impacts: Although this research is inherently risky, it offers the potential to develop a predictive theory of the growth of social organizations applicable across scales. As such, it could provide new insights into the conceptual basis for organizational theory, economics, social sciences and complex systems. This, in turn, would have a substantial impact on training, education and R&D programs in the public and private sector.



12. Accelerating the Pace of Discovery by Changing the Peer Review Algorithm

Researchers: Stefano Allesina, University of Chicago.

Abstract: Peer review is the main tool for scrutinizing scholarly publications, grant proposals and career advancements in science. However, the current peer review system is under severe strain, with consequences for the quality of science and the rapidity of dissemination of scientific results. Several studies have found that the current way of performing peer review can be inefficient, slow, and even biased. Almost every scientist has ideas on how to improve the system, but it is very difficult, if not impossible, to perform experiments to determine which measures are most effective. The project implements a simulation framework in which many ideas of how to improve the review process can be quantitatively tested.

Intellectual Merit: The framework is built using agent-based modeling. Scientists, manuscripts and journals are digital agents and a peer review system emerges from their interaction. Multiple experiments can be run: for example, one proof-of-concept application shows how changing the way peer review is performed can dramatically alter the pace at which science is disseminated.

The research develops a full-fledged and open-source simulation software that allows to study alternatives to the current system.

Broader Impacts: The proposed work is potentially transformative of the way science is carried out. This framework can be used to identify better and more efficient models for peer review, leading to profound changes on scientific publishing and funding. Also, if this exploratory research is successful, a new computational branch of sociology of science could emerge. Changing the way peer review is performed to favor faster and more efficient solutions could potentially have broad effects on the daily work of scientists, including more time for academic training and research, and less time spent revising and reformatting manuscripts and grant proposals. Favoring unbiased practices could enlarge the representation of minorities in science.



13. From Cycles to Spirals: Structural Analysis of Scientific Consensus Formation

Researchers: Peter S. Bearman and Uri Shwed, Columbia University

Abstract: The project develops a new strategy to explain and measure scientific consensus formation. It develops a quantitative measure of scientific consensus, based on an analysis of the structure of scientific citation networks. The measure is validated by exploiting changing consensus levels across time regarding several scientific propositions, such as "smoking causes cancer", "Human activity causes a climate change", etc. The analysis reveals a surprising dynamic of consensus formation in one case - the carcinogenicity of smoking - during the 1970s (before scientific consensus on the issue was consider a fact). The work is validated by qualitative, in depth interviews with the scientists who produced these high contestation levels through their work on developing a safer cigarette.

Intellectual Merit: The project advances the literature in three ways. First, the research develops a model for the process of consensus formation in science. Second, the project develops a new metric that measures consensus/contestation degree in scientific literatures. Finally, the research develops a new approach to account for temporality in scientific debates.

This project contributes to the history of science by explaining why claims such as "Coffee (does not) cause cancer" are accepted with no contestation, while the climate change claim met social contestation coupled with minor scientific contestation, and the carcinogenicity of smoking met social contestation coupled with surprising scientific contestation.

Broader Impact: This project offers a new property for comparative research into science policy, supplying a measure of scientific consensus levels that may be utilized over multiple dimensions, including time, space and social organization. The project offers a way of informing the public and policy makers on important scientific issues; currently, scientific claims are often discarded as "premature" or "inconclusive", while the experts promoting them may be considered to be promoting a political agenda. The proposed approach overrides such claims by evaluating consensus without expert interpretation, relying solely on the structure of scientific literature.



14. Construct Utilization in the Behavioral Sciences

Researchers: Kai R. Larsen, Jintae Lee, University of Colorado at Boulder; Eliot Rich, University at Albany, State University of New York.

Abstract: While it is natural for research disciplines to split into more specialized areas, the resulting divide complicates efforts to share the benefits of related research. Constructs are important underpinnings of a research method used in many behavioral and social sciences, and virtually identical constructs have been developed to support different theories, frequently under different names. As a result, useful connections are missed, constructs and theories are reinvented, and little knowledge exists about the construct origins and flow among disciplines.

Intellectual Merit: This project addresses the problem by investigating the following research question: is it possible to identify closely related constructs, including those in different disciplines and use this information to measure the extent to which existing constructs are utilized effectively? The question is examined in the domain of 'latent construct research using scales,' which is typified by the studies using questionnaire scales to assess latent constructs, thus spanning multiple disciplines in the behavioral and social sciences. For this domain, the notion of a Closely Related Construct (CRC), is formulated and operationalized using a method that integrates automated text analysis, citation analysis, and meta- analysis. Using CRC, a science metric termed Construct Utilization Ratio, is formulated that measures the extent to which closely related constructs are recognized between two units, where the unit may be chosen at different collective levels, such as between theories, journals, or research areas.

Broader Impact: The project examines how this metric can be used to quantitatively and qualitatively assess the extent to which CRCs for a given construct are recognized and utilized within and across different research areas. It can also reveal where opportunities and redundancies reside.



15. New Methods to Enhance Our Understanding of the Diversity of Science

Researchers : Andrew K. McCallum and Hanna M Wallach, University of Massachusetts Amherst; Fiona Murray, Massachusetts Institute of Technology.

Abstract : This project focuses on the development and implementation of new quantitative methods to provide a deeper understanding of science policy interventions. By building analytic tools that capture the diversity of science, this project moves beyond existing methods that typically analyze the rate of scientific innovation. This move is an important next step in the "science of science policy" agenda.

Intellectual Merit: Although understanding of institutional changes on the rate of inventive activity has improved markedly in recent years, effective science policy interventions must also be grounded in an understanding of their impact on diversity as well as productivity, construed both in terms of idea diversity

-- the array of different ideas derived from novel scientific insights -- and individual diversity -- the variety of people and organizations in social space engaged in scientific progress. To move forward with this crucial agenda requires a rich new set of tools. In developing such tools, this project extends prior work that focuses on "citation-counting," combining novel approaches from social and computer sciences to represent and analyze publication, patent and grant data in idea and social space. Specifically, the tools integrate two powerful methods: (a) statistical topic modeling and (b) social network analysis.

Broader Impact : These methods can also be extended to examine diversity across national, social and topic boundaries, thus providing quantitative tools to characterize issues of key significance in debates over national competitiveness. While these science policy questions could be addressed in a wide variety of settings, this project focuses on the varied data associated with the human genome and human genetics.



16. Developing a Social-Cognitive, Multilevel, Empirically-Based Model of Public Engagement for the Shaping of Science and Innovation Policy

Researchers: Lisa M. Pytlik Zillig and Alan J Tomkins, University of Nebraska-Lincoln; Peter Muhlberger, Texas Tech University.

Abstract: In the U.S. as elsewhere, public engagement activities are increasingly common, and sometimes even mandated, for informing science and innovation policy. However, social science has not kept up with public engagement practices: There is little guidance regarding which forms, features, and contexts of public engagements are 'effective' for what purposes and why.

Intellectual Merit: This project advances scientific understanding of and provides guidance for the design of successful public engagements in science and innovation policy. It accomplishes this via rigorous studies of the effects of varying public engagement features and purposes. The present research applies social- cognitive and learning theories to identify potential mechanisms and explanatory variables by which variations in public engagement activities result in different impacts. Studies are conducted in undergraduate biology courses in which students consider ethical, legal, and social issues relating to nanotechnology. In that context, this research (1) creates and tests measures of the potential explanatory variables and of outcomes relevant to varied definitions of 'effectiveness' of public engagements (e.g., for increasing the quality of input, participant knowledge gains, attitude changes); (2) experimentally investigates the impacts of varying two common dimensions of public engagement; and (3) develops a testable model of public engagement based on those results.

Broader Impacts: The outcomes of this research, including the creation of a testable model of public engagement and the provision of proven tools for continued study of public engagements, are designed to help researchers to further advance the scientific study of public engagement activities. In addition, the present research is designed to provide scientific confirmation that can be used by practitioners to guide their efforts in planning public engagement activities. Finally, by involving biology majors as both research participants and as research assistants, this research benefits science undergraduates, providing them opportunities to learn first-hand about social science research and enhancing their understanding of the value and roles of citizens’ contributions to science policy.



17. Management and Organizational Practices Across the US

Researchers: Nick Bloom, Stanford University. Erik Brynjolfsson Massachusetts Institute of Technology, John Van Reesen, London School of Economics (Jointly funded with IOS, Econ, SRS and DRMS).

Abstract: Some of the key determinants of productivity, economic growth, innovation, and income inequality are the management practices of firms. Unfortunately, in contrast to labor, capital and resource inputs, consistent and comprehensive data do not exist on management and organizational practices. This project collaborates with the Census Bureau to survey establishment about the nature and extent of these practices.

Intellectual Merit: This is the first large-scale cross-sector panel dataset that can be matched by the US research community to numerous existing datasets on productivity, innovation, employment, technology, energy and indicators of worker well-being (such as health) within the Census Research Data Centers.

Because of the importance of the topic, the Census Bureau believes this can be added as a repeated supplement to the Annual Survey of Manufacturing (ASM) in order to construct a panel of data on about 45,000 establishments. This survey is initially targeted at manufacturing because of the availability of a range of other manufacturing data and the relative ease of measuring productivity, but the plan is to rapidly extend this to other sectors, in particular retail and health care to build on our existing firm-level management surveys in these areas.

The project uses these data to address a number of important research, policy and managerial questions. For example:

- What is the relationship between management, organization, productivity and growth, and what policies can promote these practices to support the growth of US firms?

- What are the extent of regional, industrial and firm-size variations in management and organizational practices;

- What are the dynamics of organizational practices over time and firms?

- What types of management practices are associated with changes in inequality over time?

Broader Impacts: These questions are central to US policy-makers who seek to promote the success of US firms, and to make decisions based on a sound understanding of the economy. The data analysis should yield important academic and policy results.



18. Toward a Theory of Innovation in Emerging Economies

Researchers: Dan Breznitz, Georgia Institute of Technology (Jointly funded with IOS).

Abstract: Existing theories of innovative organizations have been based on the experience of firms in developed economies. Consequently, these theories take certain "minimal" institutional environments as given, and build their insights and explanations upon the framework of these assumptions. Some theorists go so far as to claim that without these pre-conditions, innovation is not possible. Thus the institutional characteristics of developed nations are described as necessary -- if not sufficient -- ingredients for innovation. In contrast, evidence from high technology firms in developing countries demonstrates not only that firms operating without such institutions can innovate, but also that innovative organizations can thrive in environments considered hostile to innovation and entrepreneurship.

Intellectual Merit: Despite their obvious innovative accomplishments, the institutional environments, uncertainty, and risks faced by these organizations differ greatly from those in the developed countries. This research project will develop a theory that explains the rise, behavior, and capability development of innovative organizations in a particular emerging economy: China. The aim is to build a theory useful for understanding and better predicting: i) the innovative output of high technology organizations given their set of firm-level capabilities and the institutional structure of their operating environment; ii) the types of capabilities these firms are likely, and unlikely, to develop; iii) the relationship of these capabilities to their innovative performance; and iv) given a set of capabilities and institutions, their likely development trajectory. The latter can be better used to aggregate from the firm level to the national or regional level, adding to a more accurate understanding of innovative and economic performance in emerging economies.

Broader Impacts: In addition to the intellectual contributions described above, the data and findings from this project should be of use to managers and policy makers seeking to ensure innovation and competitiveness in the context of rapidly globalizing supply chains.



19. Innovation in Social Networks

Researchers: Nicole Immorlica, Northwestern University; Rachel E. Kranton, Duke University (Jointly funded with Economics and CISE)

Abstract: The recent growth in large-scale networks and online communication has generated new ways in which networks can diffuse information. But a number of research questions remain unanswered, such as how networks affect innovation and how network structure shapes competition among ideas or technologies.

The goal of the research is to examine how research communities and links between them affect innovation. It develops a theory of how connections and absences of connections among individuals influence the process of innovation, with particular attention paid to the role of "boundary agents" who straddle different communities.

Intellectual Merit: The project is poised at the frontier of computer science and economics. Computer scientists have long studied how information is created and processed. Economists have long studied incentives for innovation. This project introduces a new paradigm. It studies innovation in networks of people and firms, where information flows through network links. Developing this paradigm requires a deep understanding of the graph-theoretic properties of the network and the incentives of the participants. The combined tools of the principal investigators represent a significant methodological advance not only for computer science and economics, but the sciences more generally.

Broader Impact: The project increases participation of women and under-represented groups in science. A graduate course trains students in state-of-the-art tools in network science from all relevant fields including economics, social sciences, mathematics, and computer science, while undergraduate courses incorporate social network examples. In addition, the research advances understanding about the participation of underrepresented groups in social and economic networks. Finally, the scientific aims of the project develop knowledge that can inform policy makers in designing incentives for innovation, discovery, and diversity.



20. Bioethics Byplay?: The Performances of Bioethics in the Private and Public Sectors

Researchers : Jason S. Robert and Jennifer E. Dyck Brian, Arizona State University.

Abstract: This dissertation compares bioethics committees in the private and public sectors of the United States to better understand the similarities and differences between their recommendations and impact on scientific practice. Although individuals may assume private sector ethics boards are less effective or strident than public sector ethics committees, there is little empirical evaluation of this assumption. This project studies the missions, personnel, and activities of two private sector ethics committees, SmithKline Beecham's Ethics and Public Policy Board and Advanced Cell Technology's Ethics Advisory Board, and of two government ethics committees, the National Bioethics Advisory Commission and the President's Council on Bioethics. The goal is to compare experiences and tensions across public and private sector bioethics committees (in terms of mandate, membership, legitimacy, and advice), and the impact (if any) bioethics committees can have on their particular intended audiences.

Investigating how ethics committees function in corporate laboratories and government agencies (and the cross-over of ethicists between the two sectors) will contribute to a broader understanding of how these boards work in practice. Through literature review, qualitative analysis of semi-structured interviews, and theory-building, this project will contribute empirically grounded policy guidance about the constitution and mandate of future bioethics committees. Moreover, an analysis of ethics boards' contributions in both the public and private sectors will provide knowledge about their strengths and weaknesses, thereby potentially aiding organizations that wish to create more effective ethics advisory committees. In turn, more effective ethics boards may enhance the societal benefits derived from science and technology research and development.



21. Research in Science of Science and Innovation Policy: How Do Prizes Induce Innovation? Learning from the Google Lunar X-Prize

Researchers: Philip Shapira and Luciano Kay, Georgia Institute of Technology.

Abstract: This project investigates technology prizes and the means by which they induce innovation. Specifically, the project examines three aerospace prize competitions: (1) the Ansari X-Prize (rewarding "the first non-government organization to launch a reusable manned spacecraft into space twice within two weeks") (2) the Northrop Grumman Lunar Lander Challenge (NGLLC) (for "building and flying a rocket- powered vehicle that simulates the flight of a vehicle on the Moon"), and (3) the ongoing $30 million Google Lunar X-Prize (GLXP), which requires participants to land a robot on the surface of the Moon. This project studies three main aspects of those competitions: (1) how prize entrants respond to incentives, (2) how they organize R&D activities, and (3) how technology advancement takes place in this context.

Intellectual Merit: The project combines a two-stage research design, multiple data sources, and different data gathering methods, including questionnaires, interviews and direct observation. The first stage tests a model of prizes that links incentives with entrant characteristics, R&D organization, and technological outcomes. This model is tested and revised by studying the Ansari X-Prize and NGLLC competitions. The second stage applies the revised model to the ongoing GLXP and prize entrants, which has exceptional significance due to its real-time data access and possibility to explore real-time perceptions of prize entrants in a competitive context characterized by technological uncertainty.

Broader Impact : The project contributes to innovation theory. In addition, it draws significant implications for policies to advance technology, promote competitiveness, or achieve other societal goals by using prizes. Moreover, insights gained from this research can be also applied across fields like business, technology management, and engineering systems design. More broadly, this project contributes a better understanding on the potential of technology policies in times of economic crisis and the incentives that may promote engagement of different social groups, including underrepresented groups and students, for training within a competitive environment.



22. Scientific Knowledge Production for Solving Common Environmental Problems in a Developing Country

Researchers : David Winickoff and Javiera Barandiaran, University of California-Berkley.

Abstract: This study will examine the development and character of environmental research groups in Chile to demonstrate how specific notions of “basic” and “applied” science have been constituted, and how these definitions shape the formation of research groups and the use of science policies that promote sustainable development. The study will involve collection, compilation, and analysis of historical data on the distribution of research funding from grant proposal applications to public agencies, and contemporary data on the composition, structure and actions of research groups.

Intellectual Merit: This dissertation will inform theoretical debates on the private or public character of knowledge, with implications for why government funds science and research development. In addition, the results will extend both theories of organizational behavior and expertise creation to scientific groups in developing countries. In sum, the analysis will improve our understanding of the role of universities in the social and economic development.

Broader Impact : This study will contribute to the scientific production in developing countries, advancing theories of science-based development. Developing countries are characterized by low public standing of science, traditionally low funding, underdeveloped research infrastructure and a small and fractured scientific community. The results will have implications for research and innovation policies in developing countries.



23. Information Values in Translation: An Ethnography of Free and Open Source Software in Vietnam

Researchers: Leah A. Lievrouw and Nguyen Lilly, University of California-Los Angeles.

Abstract: This study examines how free and open source software (FOSS) change as they are designed and implemented in different countries. Recent studies describe how FOSS has emerged as part of a long history of 'democratizing' technologies. These studies show how FOSS has been designed as the technological extension of particular political values and thus represent a very specific configuration of American libertarian culture and politics. As FOSS is implemented in countries without this political cultural history, however, we can anticipate these technologies to change and even clash with existing values. In turn, this study examines how these values change as FOSS migrates into new countries.

Intellectual Merit: Internationally, FOSS is increasingly deployed in direct association with government policy intervention. Government involvement with FOSS has led advocates to decry such activities as antithetical to the very spirit of FOSS itself. Given this conflict in values, this study asks: (1) How does government policy shape FOSS' design and implementation in Vietnam?; (2) How do the values of openness, freedom, and democracy change as FOSS moves into new countries?; and (3) What other actors and organizations are involved in FOSS' design and implementation and what kind of say do they have in these processes? This dissertation improvement grant will fund fieldwork for one year of two free and open source organizations in Hanoi, Vietnam, one for-profit and one non-profit. This fieldwork will consist of participant observation of software development work in both these organizations and semi-structured interviews with their employees as well as with members of the wider FOSS community.

Broader Impacts : The proposed research will contribute to a broader understanding of the meanings attached to a technology as it travels to new countries and contexts. Given the particularities of the Vietnam context, the proposed research will also contribute to a broader understanding of how cultural values can cause technologies to change as well as what happens with a technology clashes with existing values.



24. Choosing a Portfolio of Technology Policies in an Uncertain World

Researchers: Gregory F. Nemet, University of Wisconsin-Madison; Erin D. Baker, University of Massachusetts-Amherst

Abstract: Addressing climate change without damaging the economy will require substantial improvements to energy technologies. These improvements depend on investments in, and the production of, new knowledge -- both in the laboratory and in commercial use. Because knowledge, due to its special nature, is notoriously difficult for private firms to control and profit from exclusively, it is argued that government support is required to assure that opportunities are not squandered. The literature is clear that the presence of multiple market failures and multiple technical options means that good government policy needs to have a portfolio of policies addressing a portfolio of technologies. However, there are many possible diversified portfolios. This research applies science to science and innovation policy in order to estimate the consequences of combinations of technology policy instruments on the climate and on the economy.

Intellectual Merit: This project aims to provide a framework for designing a portfolio of technology policies to address climate change. The researchers model the effects of combinations of policy instruments on a portfolio of technologies, when both the outcomes of the technology policies and the effects of climate change are uncertain. The project evaluates combinations of three policy instruments: government funded R&D; subsidies for demand; and carbon prices. It focuses on two important technologies: solar PV and Carbon Capture and Storage (CCS), while developing a framework amenable to the consideration of a larger set of technologies. Some of the key questions the project addresses are:

- What factors are most important in choosing the best mix of R&D and subsidies? How does the mix change with increasing uncertainty in climate damages?

- What drives the optimal mix of a two-technology portfolio?

- To what extent do R&D and subsidies affect the optimal level of emissions abatement?

- How large is the hedging value that subsidies provide?

The researchers collect and use expert elicitations to estimate the probability of R&D investment producing technical improvements in CCS. Simultaneously and iteratively, they develop a bottom-up cost model to estimate the cost reductions expected from both economies of scale and learning-by-doing as deployment of CCS technology expands. They implement this model -- together with an earlier model of solar PV -- into an Integrated Assessment Model, allowing for the optimization of portfolios of policy instruments designed to reduce the cost of climate change mitigation. This configuration allows cost benefit optimization of policy choices under varying probability distributions over damages and technical outcomes.

Broader Impact: Improved models that help improve the allocation of public funds could have an important fiscal impact. In addition, the research will inform the development of classes and train students in public policy model building.



25. The NIH Public Access Policy: Establishing a Basis for Assessing a Science Policy

Researchers: John Willinsky, Stanford University

Abstract: In April, 2009, the National Institutes of Health (NIH) Public Access Policy began to require that all research publications resulting from NIH funding be made publicly accessible within a year of publication through deposit on PubMed Central. The NIH Public Access Policy is part of a larger move toward open access in scholarly publishing. It would appear to have a yet-to-be-assessed potential to increase the public value of federally funded knowledge production, with at least potential implications for personal health decisions, evidence-based medicine and policy-making, and citizens' participation in policy deliberations, as well as playing a more active role in health-care decisions affecting them and their families. The research develops and validates a reliable set of methods and instruments, as well as some initial baseline measures, for accurately assessing the value of the government's public access policy among likely beneficiaries of this public access, namely, health-care practitioners and administrators, patient families, and patient and patient advocacy agency staff.

The project has three goals: 1) develop survey items, interview protocols, and environmental scans that make sense to the constituent groups; 2) produce knowledge of value to these groups as well as researchers and policy makers; and 3) provide some baseline measures of current research awareness and access.

Intellectual Merit:

The project lays the groundwork for a scientific analysis of levels of public engagement with publicly accessible research. The results provide a deeper understanding of whether Open Access policies have the intended impact in four key areas: research discovery, comprehension, evaluation, and utilization.

Broader Impact:

As public access to research literature increases, there is a need to develop a scientific framework for analyzing public levels of engagement with this knowledge. This project develops important protocols, instruments and training procedures as well as establishes initial baseline measurements to inform future research on the topic.



26. Government Responses to Network Failures: The Case of the Manufacturing Extension Partnerships


Researchers: Joshua D. Whitford, Columbia University; Andrew Schrank, University of New Mexico.

Abstract: While inter-firm networks provide an increasingly important alternative to arm's length transactions in knowledge-intensive industries, they are notoriously difficult to build and maintain. Various hypotheses have been advanced for the sources of such collaboration, suggesting that they derive from cultural, organizational and institutional factors. This research attempts to quantify the relative importance of the factors contributing to the success of collaboration projects by developing and analyzing survey and interview-based indicators of public inputs to network production among small and mid-sized firms -- with a particular focus on their provision through the Manufacturing Extension Partnerships sponsored by the Federal Government.

Intellectual Merit: This research contributes to the literature about the rationale for government intervention to foster innovation in the United States. Government intervention is typically justified as a response to market failure, and represents an effort to make markets more competitive. However, a growing body of literature argues that innovation tends to occur in "collaborative spaces" that are sheltered from market competition. The focus of the research is to examine whether there is a mismatch between the process of innovation and the rationale for innovation policy. The research examines the relative importance of public inputs to network production among small and medium-sized firms in a study of the Manufacturing Extension Partnerships (MEPs) sponsored by the National Institute of Standards and Technology.

Broader Impact : The research produces a publicly available data set that can be used to examine the relationship between government inputs and network production. The results inform a variety of different disciplines, such as economics, sociology and political science. The study also engages and trains graduate students in the research processes.