Essays on Utilizing Data Analytics and Dynamic Modeling to Inform Complex Science and Innovation Policies
Baghaei Lakeh, Arash
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In many ways, science represents a complex system which involves technical, social, and economic aspects. An analysis of such a system requires employing and combining different methodological perspectives and incorporation of different sources of data. In this dissertation, we use a variety of methods to analyze large sets of data in order to examine the effects of various domestic and institutional factors on scientific activities. First, we evaluate how the contributions of behavioral and social sciences to studies of health have evolved over time. We use data analytics to conduct a textual analysis of more than 200,000 publications on the topic of HIV/AIDS. We find that the focus of the scientific community within the context of the same problem varies as the societal context of the problem changes. Specifically, we uncover that the focus on the behavioral and social aspects of HIV/AIDS has increased over time and varies in different countries. Further, we show that this variation is related to the mortality level that the disease causes in each country. Second, we investigate how different sources of funding affect the science enterprise differently. We use data analytics to analyze more than 60,000 papers published on the subject of specific diseases globally and highlight the role of philanthropic money in these domains. We find that philanthropies tend to have a more practical approach in health studies as compared with public funders. We further show that they are also concerned with the economic, policy related, social, and behavioral aspects of the diseases. We uncover that philanthropies tend to mix and combine approaches and contents supported both by public and private sources of funding for science. We further show that in doing so, philanthropies tend to be closer to the position held by the public sector in the context of health studies. Finally, we find that studies funded by philanthropies tend to receive higher citations, and hence have higher impact, in comparison to those funded by the public sector. Third, we study the effect of different schemes of funding distribution on the career of scientists. In this study, we develop a system dynamics model for analyzing a scientist's career under different funding and competition contexts. We investigate the characteristics of optimal strategies and also the equilibrium points for the cases of scientists competing for financial resources. We show that a policy to fund the best can lead scientists to spend more time on writing proposals, in order to secure funding, rather than writing papers. We find that when everyone receives funding (or have the same chance of receiving funding) the overall optimal payoff of the scientists reaches its highest level and at this optimum, scientists spend all their time on writing papers rather than writing proposals. Our analysis suggests that more egalitarian distributions of funding results in higher overall research output by scientists. We also find that luck plays an important role in the success of scientists. We show that following the optimal strategies do not guarantee success. Due to the stochastic nature of funding decisions, some will eventually fail. The failure is not due to scientists' faulty decisions, but rather simply due to their lack of luck.
General Audience Abstract
Science helps us understand the world and enables us to improve how we interact with our environment. But science itself has also been the subject of inquiry by philosophers, sociologists, economists, historians, and scientists. The goal in the investigations of science has been to better understand how scientific advances occur, how to foster innovation, and how to improve the institutions that push science forward. This dissertation contributes to this area of research by asking and responding to several questions about the science enterprise. First, we study how communities of scientists in different parts of the world look at the seemingly same problem differently. We use a computational method to read through a large set of publications on the topic of HIV/AIDS (which includes more than 200,000 papers) and uncover the topics of these papers. We find that in the context of HIV/AIDS, contributions of behavioral and social scientists have increased over time. Moreover, we show that the share of these contributions in any counties’ total research output differs significantly. We further find that there is a significant relationship between one country’s rate of death, due to HIV/AIDS, and the share of behavioral and social studies in the overall research profile of that country on the topic of HIV/AIDS. Second, we investigate how different sources of research funding affect scientific activities differently. Specifically, we focus on the role of philanthropic money in science and its effect on the content and impact of research studies. In our analysis, we rely on computational techniques that distinguishes between different themes of research in the studies of a few diseases and also different statistical methods. We find that philanthropies tend to have a more practical approach to health studies as compared with public sources of funding. Meanwhile, we find that they are also concerned with the economic, policy related, social, and behavioral aspects of the diseases. Moreover, we show that philanthropies tend to mix and combine approaches and contents supported both by public and private sources of funding for science. We find that, in doing so, philanthropies tend to be closer to the position held by the public sector in the context of health studies. Finally, we show that studies funded by philanthropies tend to receive higher citations. This finding suggests that these studies have a higher impact in comparison to those funded by the public sector. Third, we study how different mechanisms for distributing research funding among scientists can affect their career and success. Many scientists should spend time on both writing papers and research grant proposals. In this work, we aim at understanding how a scientists should allocate her time between these two activities to maximize her career long number of papers. We develop a small mathematical model to capture the mechanisms related to the research career of a scientist in an academic setting. Then, for different schemes of funding distribution, we find the scientist’s time allocation that maximizes the number of papers she publishes over her career. We find that when funding is being allocated to the best scientists and best grant proposals, scientists’ best strategy is to spend more time on writing research grant proposals rather than papers. This decreases the total number of papers published by the scientists over their career. We also find that luck is important in determining the career success of scientists. Due to errors in evaluation of proposal qualities, a scientist may fail in her career regardless of whether she has followed the best strategy that she could.
- Doctoral Dissertations 
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