Deryc T Painter | Arizona State University (original) (raw)
Papers by Deryc T Painter
SSRN Electronic Journal
We characterize intergenerational income mobility at each college in the United States using data... more We characterize intergenerational income mobility at each college in the United States using data for over 30 million college students from 1999-2013. We document four results. First, access to colleges varies greatly by parent income. For example, children whose parents are in the top 1% of the income distribution are 77 times more likely to attend an Ivy League college than those whose parents are in the bottom income quintile. Second, children from low-and high-income families have similar earnings outcomes conditional on the college they attend, indicating that low-income students are not mismatched at selective colleges. Third, rates of upward mobilitythe fraction of students who come from families in the bottom income quintile and reach the top quintile-differ substantially across colleges because low-income access varies significantly across colleges with similar earnings outcomes. Rates of bottom-to-top quintile mobility are highest at certain mid-tier public universities, such as the City University of New York and California State colleges. Rates of upper-tail (bottom quintile to top 1%) mobility are highest at elite colleges, such as Ivy League universities. Fourth, the fraction of students from low-income families did not change substantially between 2000-2011 at elite private colleges, but fell sharply at colleges with the highest rates of bottom-to-top-quintile mobility. Although our descriptive analysis does not identify colleges' causal effects on students' outcomes, the publicly available statistics constructed here highlight colleges that deserve further study as potential engines of upward mobility.
Analysis of social networks has the potential to provide insight into a wide range of application... more Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Analysis of social networks has the potential to provide insight into a wide range of application... more Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.
As cities grow in size and density, why do some "ignite" into global engines of... more As cities grow in size and density, why do some "ignite" into global engines of innovation while others evolve instead into slums? To address this question we develop a view of urban development as an analogy to stellar evolution. Typical stellar gas clouds grow in mass, eventually reaching a temperature and density threshold at which they ignite to become stars. Yet some gas clouds, despite increasing density, fail to achieve a critical temperature and fade instead to objects known as degenerate stars-highly dense but without the vibrant emissions of normal stars. We speculate that cities, like igniting stars, require both density and a sufficient rate of interaction among constituents to become innovation engines. While this interaction rate is measured as temperature in stars, we take its urban analog to be the rate of human social interactions. To test this idea we decompose U.S. occupations into individual work activities, determine which of those activities are associated with face-to-face interactions, and then reaggregate the labor force of each U.S. metropolitan statistical area (MSA) into a metric of social interactiveness. We then calculate each MSA's density of social work activities and find that this measure is more highly correlated with an MSA's per capita patent production that simple population density. Thus, it is density of face-to-face interactions that appears more important for a city's rate of invention. We close by exploring whether this stellar analogy is useful for framing future research on cities, what questions it may help pose, and, more broadly, how physical, social, and natural scientists can all contribute to an interdisciplinary agenda to understand cities and their transitions more deeply.
This dissertation focuses on creating a pluralistic approach to understanding and measuring inter... more This dissertation focuses on creating a pluralistic approach to understanding and measuring interdisciplinarity at various scales to further the study of the evolution of knowledge and innovation. Interdisciplinarity is considered an important research component and is closely linked to higher rates of innovation. To create more innovative research, one must understand how interdisciplinarity operates. This dissertation begins by examining interdisciplinarity with a small scope, the research university. Here, the study uses metadata and co-authorship networks to examine how a change in university policies can increase interdisciplinarity. The New American University Initiative (NAUI) at Arizona State University (ASU) set forth the goal of making ASU a world hub for interdisciplinary research. Here, interdisciplinarity is produced from a deliberate, engineered, reorganization of the individuals within the university and their knowledge. Using a battery of social network analysis measurements, an algorithm was created to measure the changes in co-authorship networks from increased university support. The second case study expands the scope of interdisciplinarity from individual universities to a scientific discourse surrounding the Anthropocene. The idea began as the need for a new geological epoch. It underwent unsupervised interdisciplinary expansion due to climate change integrating itself into the core of the discourse becoming an anchor point for new disciplines to connect and join the discourse. The scope of interdisciplinarity increases again with the final case study about the field of evolutionary medicine. Evolutionary medicine is a case of engineered interdisciplinary integration between evolutionary biology and medicine. The primary goal of evolutionary medicine is to better understand ”why we get sick” using an evolutionary biology framework. This study explores large-scale interdisciplinarity through networks and metadata analyses indicating that evolutionary medicine successfully integrates the concepts of evolutionary biology into medicine.
SSRN Electronic Journal, 2021
For several decades interdisciplinary research has been pushed by funding agencies, science admin... more For several decades interdisciplinary research has been pushed by funding agencies, science administrators and generations of well-intentioned scientists. Interdisciplinary research is needed, so the argument goes, because the problems we face in medicine, environmental sciences, sociology or anthropology - the list can go on - are too complex to be mapped onto one traditional discipline. While the motivation for interdisciplinary research is clear, its actual success is less obvious. For one, we don’t quite know how to measure interdisciplinarity. While there have been many attempts at measuring interdisciplinarity, they typically refer to simple collaborations between field AAA and field BBB without taking into account the knowledge exchange at the heart of interdisciplinarity. Multiple studies find the need to distinguish interdisciplinary from multidisciplinary and transdisciplinary; where interdisciplinary combines disciplines in an integrative approach, multidisciplinary simpl...
Author Classifications of O*NET Individual Work Activities (IWA) used in "Innovations and Ec... more Author Classifications of O*NET Individual Work Activities (IWA) used in "Innovations and Economic Output Scale with Social Interactions in the Workforce"
The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commer... more The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commerce. Social distancing and stay-at-home orders leave businesses and cities wondering how future economic activity moves forward. The reduction in face-to-face interactions creates an impetus to understand how social interactivity influences economic efficiency and rates of innovation. Here, we create a measure of the degree to which a workforce engages in social interactions, analyzing its relationships to economic innovation and efficiency. We do this by decomposing U.S. occupations into individual work activities, determining which of those activities are associated with face-to-face interactions. We then re-aggregate the labor forces of U.S. metropolitan statistical areas (MSA) into a metric of urban social interactiveness. Using a novel measure of urbanized area, we then calculate each MSA’s density of social work activities. We find that our metric of urban socialness is positively c...
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Analysis of social networks has the potential to provide insight into a wide range of application... more Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.
Theory in Biosciences
To what extent do simultaneous innovations occur and are independently from each other? In this p... more To what extent do simultaneous innovations occur and are independently from each other? In this paper we use a novel persistent keyword framework to systematically identify innovations in a large corpus containing academic papers in evolutionary medicine between 2007 and 2011. We examine whether innovative papers occurring simultaneously are independent from each other by evaluating the citation and co-authorship information gathered from the corpus metadata. We find that 19 out of 22 simultaneous innovative papers do, in fact, occur independently from each other. In particular, co-authors of simultaneous innovative papers are no more geographically concentrated than the co-authors of similar non-innovative papers in the field. Our result suggests producing innovative work draws from a collective knowledge pool, rather than from knowledge circulating in distinct localized collaboration networks. Therefore, new ideas can appear at multiple locations and with geographically dispersed ...
ISIS, 2019
Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific ... more Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific publication trends. Even focusing on a particular scientific field, the rate of new publications far outpaces even the most studious historian's research capacity. Here, we summarize an approach to this problem using computational techniques of network analysis. As a complement to close analysis of particular documents, network analysis can give a large-scale perspective on the history of science, identifying relational patterns across a vast number of documents that might otherwise require an entire career to digest. To demonstrate the power of the approach, we apply network theory to a corpus of publications in evolutionary medicine. Six distinct networks, including those that focus on authors, keywords, and citations, quickly unearth a range of relevant historical information. We illustrate how interpretable historical conclusions can be drawn from a variety of quantitative metrics, including both established and powerful, novel methods for extracting global patterns or key elements and links of a network from the statistics of local quantities. Our aim is to provide an overview of network techniques for historians looking to add robust network analysis to their research repertoire.
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Analysis of social networks has potential to provide insight to wide range of applications. As da... more Analysis of social networks has potential to provide insight to wide range of applications. As datasets continue to grow, a key challenge is lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research in emergence of innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework, elucidating new insights into study of innovation patterns in publication networks.
Drafts by Deryc T Painter
Theory in BioSciences
The origins of innovation in science are typically understood using historical narratives that te... more The origins of innovation in science are typically understood using historical narratives that tend to be focused on small sets of influential authors, an approach that is rigorous but limited in scope. Here, we develop a framework for rigorously identifying innovation across an entire scientific field through automated analysis of a corpus of over 6000 documents that includes every paper published in the field of evolutionary medicine. This comprehensive approach allows us to explore statistical properties of innovation, asking where innovative ideas tend to originate within a field's pre-existing conceptual framework. First, we develop a measure of innovation based on novelty and persistence, quantifying the collective acceptance of novel language and ideas. Second, we study the field's conceptual landscape through a bibliographic coupling network. We find that innovations are disproportionately more likely in the periphery of the bibliographic coupling network, suggesting that the relative freedom allowed by remaining unconnected with well-established lines of research could be beneficial to creating novel and lasting change. In this way, the emergence of collective computation in scientific disciplines may have robustness-adaptability tradeoffs that are similar to those found in other biosocial complex systems.
Thesis Chapters by Deryc T Painter
Computational Interdisciplinarity: A Study in the History of Science, 2019
This dissertation focuses on creating a pluralistic approach to understanding and measuring inter... more This dissertation focuses on creating a pluralistic approach to understanding and measuring interdisciplinarity at various scales to further the study of the evolution of knowledge and innovation. Interdisciplinarity is considered an important research component and is closely linked to higher rates of innovation. To create more innovative research, one must understand how interdisciplinarity operates.
This dissertation begins by examining interdisciplinarity with a small scope, the research university. Here, the study uses metadata and co-authorship networks to examine how a change in university policies can increase interdisciplinarity. The New American University Initiative (NAUI) at Arizona State University (ASU) set forth the goal of making ASU a world hub for interdisciplinary research. Here, interdisciplinarity is produced from a deliberate, engineered, reorganization of the individuals within the university and their knowledge. Using a battery of social network analysis measurements, an algorithm was created to measure the changes in co-authorship networks from increased university support.
The second case study expands the scope of interdisciplinarity from individual universities to a scientific discourse surrounding the Anthropocene. The idea began as the need for a new geological epoch. It underwent unsupervised interdisciplinary expansion due to climate change integrating itself into the core of the discourse becoming an anchor point for new disciplines to connect and join the discourse.
The scope of interdisciplinarity increases again with the final case study about the field of evolutionary medicine. Evolutionary medicine is a case of engineered interdisciplinary integration between evolutionary biology and medicine. The primary goal of evolutionary medicine is to better understand ”why we get sick” using an evolutionary biology framework. This study explores large-scale interdisciplinarity through networks and metadata analyses indicating that evolutionary medicine successfully integrates the concepts of evolutionary biology into medicine.
Books by Deryc T Painter
The Dynamics of Science: Computational Frontiers in History and Philosophy of Science, 2020
For several decades interdisciplinary research has been pushed by funding agencies, science admin... more For several decades interdisciplinary research has been pushed by funding agencies, science administrators and generations of well-intentioned scientists. Interdisciplinary research is needed, so the argument, because the problems we face in medicine, environmental sciences, sociology or anthropology – the list can go on – are too complex to be mapped onto one traditional discipline. While the motivation for interdisciplinary research is clear, its actual success is less obvious. For one, we don't quite know how to measure interdisciplinarity. While there have been many attempts at measuring interdisciplinarity, they typically refer to simple collaborations between field A and field B without taking into account the knowledge exchange at the heart of interdisciplinarity. We also have a difficult time distinguishing different degrees of interdisciplinarity. Do we mean actual collaborations between scholars from different disciplines or are we more interested in a combination of different conceptual and methodological approaches, perhaps even in one persons work? And how closely are those two layers linked? Does the successful application of different approaches require collaboration between scholars with different backgrounds? How can we tell whether any interdisciplinary approach is better and in what ways? Traditionally these questions are addressed in the context of individual case studies, such as with breakthrough discoveries. While those narratives provide detailed insights into some localized scientific cultures, we have no way of answering questions about interdisciplinarity at a larger scale. Yet understanding across individual cases is exactly the kind of information we need if we want to retool the scientific enterprise towards greater degrees of interdisciplinarity.
SSRN Electronic Journal
We characterize intergenerational income mobility at each college in the United States using data... more We characterize intergenerational income mobility at each college in the United States using data for over 30 million college students from 1999-2013. We document four results. First, access to colleges varies greatly by parent income. For example, children whose parents are in the top 1% of the income distribution are 77 times more likely to attend an Ivy League college than those whose parents are in the bottom income quintile. Second, children from low-and high-income families have similar earnings outcomes conditional on the college they attend, indicating that low-income students are not mismatched at selective colleges. Third, rates of upward mobilitythe fraction of students who come from families in the bottom income quintile and reach the top quintile-differ substantially across colleges because low-income access varies significantly across colleges with similar earnings outcomes. Rates of bottom-to-top quintile mobility are highest at certain mid-tier public universities, such as the City University of New York and California State colleges. Rates of upper-tail (bottom quintile to top 1%) mobility are highest at elite colleges, such as Ivy League universities. Fourth, the fraction of students from low-income families did not change substantially between 2000-2011 at elite private colleges, but fell sharply at colleges with the highest rates of bottom-to-top-quintile mobility. Although our descriptive analysis does not identify colleges' causal effects on students' outcomes, the publicly available statistics constructed here highlight colleges that deserve further study as potential engines of upward mobility.
Analysis of social networks has the potential to provide insight into a wide range of application... more Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Analysis of social networks has the potential to provide insight into a wide range of application... more Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.
As cities grow in size and density, why do some "ignite" into global engines of... more As cities grow in size and density, why do some "ignite" into global engines of innovation while others evolve instead into slums? To address this question we develop a view of urban development as an analogy to stellar evolution. Typical stellar gas clouds grow in mass, eventually reaching a temperature and density threshold at which they ignite to become stars. Yet some gas clouds, despite increasing density, fail to achieve a critical temperature and fade instead to objects known as degenerate stars-highly dense but without the vibrant emissions of normal stars. We speculate that cities, like igniting stars, require both density and a sufficient rate of interaction among constituents to become innovation engines. While this interaction rate is measured as temperature in stars, we take its urban analog to be the rate of human social interactions. To test this idea we decompose U.S. occupations into individual work activities, determine which of those activities are associated with face-to-face interactions, and then reaggregate the labor force of each U.S. metropolitan statistical area (MSA) into a metric of social interactiveness. We then calculate each MSA's density of social work activities and find that this measure is more highly correlated with an MSA's per capita patent production that simple population density. Thus, it is density of face-to-face interactions that appears more important for a city's rate of invention. We close by exploring whether this stellar analogy is useful for framing future research on cities, what questions it may help pose, and, more broadly, how physical, social, and natural scientists can all contribute to an interdisciplinary agenda to understand cities and their transitions more deeply.
This dissertation focuses on creating a pluralistic approach to understanding and measuring inter... more This dissertation focuses on creating a pluralistic approach to understanding and measuring interdisciplinarity at various scales to further the study of the evolution of knowledge and innovation. Interdisciplinarity is considered an important research component and is closely linked to higher rates of innovation. To create more innovative research, one must understand how interdisciplinarity operates. This dissertation begins by examining interdisciplinarity with a small scope, the research university. Here, the study uses metadata and co-authorship networks to examine how a change in university policies can increase interdisciplinarity. The New American University Initiative (NAUI) at Arizona State University (ASU) set forth the goal of making ASU a world hub for interdisciplinary research. Here, interdisciplinarity is produced from a deliberate, engineered, reorganization of the individuals within the university and their knowledge. Using a battery of social network analysis measurements, an algorithm was created to measure the changes in co-authorship networks from increased university support. The second case study expands the scope of interdisciplinarity from individual universities to a scientific discourse surrounding the Anthropocene. The idea began as the need for a new geological epoch. It underwent unsupervised interdisciplinary expansion due to climate change integrating itself into the core of the discourse becoming an anchor point for new disciplines to connect and join the discourse. The scope of interdisciplinarity increases again with the final case study about the field of evolutionary medicine. Evolutionary medicine is a case of engineered interdisciplinary integration between evolutionary biology and medicine. The primary goal of evolutionary medicine is to better understand ”why we get sick” using an evolutionary biology framework. This study explores large-scale interdisciplinarity through networks and metadata analyses indicating that evolutionary medicine successfully integrates the concepts of evolutionary biology into medicine.
SSRN Electronic Journal, 2021
For several decades interdisciplinary research has been pushed by funding agencies, science admin... more For several decades interdisciplinary research has been pushed by funding agencies, science administrators and generations of well-intentioned scientists. Interdisciplinary research is needed, so the argument goes, because the problems we face in medicine, environmental sciences, sociology or anthropology - the list can go on - are too complex to be mapped onto one traditional discipline. While the motivation for interdisciplinary research is clear, its actual success is less obvious. For one, we don’t quite know how to measure interdisciplinarity. While there have been many attempts at measuring interdisciplinarity, they typically refer to simple collaborations between field AAA and field BBB without taking into account the knowledge exchange at the heart of interdisciplinarity. Multiple studies find the need to distinguish interdisciplinary from multidisciplinary and transdisciplinary; where interdisciplinary combines disciplines in an integrative approach, multidisciplinary simpl...
Author Classifications of O*NET Individual Work Activities (IWA) used in "Innovations and Ec... more Author Classifications of O*NET Individual Work Activities (IWA) used in "Innovations and Economic Output Scale with Social Interactions in the Workforce"
The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commer... more The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commerce. Social distancing and stay-at-home orders leave businesses and cities wondering how future economic activity moves forward. The reduction in face-to-face interactions creates an impetus to understand how social interactivity influences economic efficiency and rates of innovation. Here, we create a measure of the degree to which a workforce engages in social interactions, analyzing its relationships to economic innovation and efficiency. We do this by decomposing U.S. occupations into individual work activities, determining which of those activities are associated with face-to-face interactions. We then re-aggregate the labor forces of U.S. metropolitan statistical areas (MSA) into a metric of urban social interactiveness. Using a novel measure of urbanized area, we then calculate each MSA’s density of social work activities. We find that our metric of urban socialness is positively c...
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Analysis of social networks has the potential to provide insight into a wide range of application... more Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.
Theory in Biosciences
To what extent do simultaneous innovations occur and are independently from each other? In this p... more To what extent do simultaneous innovations occur and are independently from each other? In this paper we use a novel persistent keyword framework to systematically identify innovations in a large corpus containing academic papers in evolutionary medicine between 2007 and 2011. We examine whether innovative papers occurring simultaneously are independent from each other by evaluating the citation and co-authorship information gathered from the corpus metadata. We find that 19 out of 22 simultaneous innovative papers do, in fact, occur independently from each other. In particular, co-authors of simultaneous innovative papers are no more geographically concentrated than the co-authors of similar non-innovative papers in the field. Our result suggests producing innovative work draws from a collective knowledge pool, rather than from knowledge circulating in distinct localized collaboration networks. Therefore, new ideas can appear at multiple locations and with geographically dispersed ...
ISIS, 2019
Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific ... more Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific publication trends. Even focusing on a particular scientific field, the rate of new publications far outpaces even the most studious historian's research capacity. Here, we summarize an approach to this problem using computational techniques of network analysis. As a complement to close analysis of particular documents, network analysis can give a large-scale perspective on the history of science, identifying relational patterns across a vast number of documents that might otherwise require an entire career to digest. To demonstrate the power of the approach, we apply network theory to a corpus of publications in evolutionary medicine. Six distinct networks, including those that focus on authors, keywords, and citations, quickly unearth a range of relevant historical information. We illustrate how interpretable historical conclusions can be drawn from a variety of quantitative metrics, including both established and powerful, novel methods for extracting global patterns or key elements and links of a network from the statistics of local quantities. Our aim is to provide an overview of network techniques for historians looking to add robust network analysis to their research repertoire.
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Analysis of social networks has potential to provide insight to wide range of applications. As da... more Analysis of social networks has potential to provide insight to wide range of applications. As datasets continue to grow, a key challenge is lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research in emergence of innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework, elucidating new insights into study of innovation patterns in publication networks.
Theory in BioSciences
The origins of innovation in science are typically understood using historical narratives that te... more The origins of innovation in science are typically understood using historical narratives that tend to be focused on small sets of influential authors, an approach that is rigorous but limited in scope. Here, we develop a framework for rigorously identifying innovation across an entire scientific field through automated analysis of a corpus of over 6000 documents that includes every paper published in the field of evolutionary medicine. This comprehensive approach allows us to explore statistical properties of innovation, asking where innovative ideas tend to originate within a field's pre-existing conceptual framework. First, we develop a measure of innovation based on novelty and persistence, quantifying the collective acceptance of novel language and ideas. Second, we study the field's conceptual landscape through a bibliographic coupling network. We find that innovations are disproportionately more likely in the periphery of the bibliographic coupling network, suggesting that the relative freedom allowed by remaining unconnected with well-established lines of research could be beneficial to creating novel and lasting change. In this way, the emergence of collective computation in scientific disciplines may have robustness-adaptability tradeoffs that are similar to those found in other biosocial complex systems.
Computational Interdisciplinarity: A Study in the History of Science, 2019
This dissertation focuses on creating a pluralistic approach to understanding and measuring inter... more This dissertation focuses on creating a pluralistic approach to understanding and measuring interdisciplinarity at various scales to further the study of the evolution of knowledge and innovation. Interdisciplinarity is considered an important research component and is closely linked to higher rates of innovation. To create more innovative research, one must understand how interdisciplinarity operates.
This dissertation begins by examining interdisciplinarity with a small scope, the research university. Here, the study uses metadata and co-authorship networks to examine how a change in university policies can increase interdisciplinarity. The New American University Initiative (NAUI) at Arizona State University (ASU) set forth the goal of making ASU a world hub for interdisciplinary research. Here, interdisciplinarity is produced from a deliberate, engineered, reorganization of the individuals within the university and their knowledge. Using a battery of social network analysis measurements, an algorithm was created to measure the changes in co-authorship networks from increased university support.
The second case study expands the scope of interdisciplinarity from individual universities to a scientific discourse surrounding the Anthropocene. The idea began as the need for a new geological epoch. It underwent unsupervised interdisciplinary expansion due to climate change integrating itself into the core of the discourse becoming an anchor point for new disciplines to connect and join the discourse.
The scope of interdisciplinarity increases again with the final case study about the field of evolutionary medicine. Evolutionary medicine is a case of engineered interdisciplinary integration between evolutionary biology and medicine. The primary goal of evolutionary medicine is to better understand ”why we get sick” using an evolutionary biology framework. This study explores large-scale interdisciplinarity through networks and metadata analyses indicating that evolutionary medicine successfully integrates the concepts of evolutionary biology into medicine.
The Dynamics of Science: Computational Frontiers in History and Philosophy of Science, 2020
For several decades interdisciplinary research has been pushed by funding agencies, science admin... more For several decades interdisciplinary research has been pushed by funding agencies, science administrators and generations of well-intentioned scientists. Interdisciplinary research is needed, so the argument, because the problems we face in medicine, environmental sciences, sociology or anthropology – the list can go on – are too complex to be mapped onto one traditional discipline. While the motivation for interdisciplinary research is clear, its actual success is less obvious. For one, we don't quite know how to measure interdisciplinarity. While there have been many attempts at measuring interdisciplinarity, they typically refer to simple collaborations between field A and field B without taking into account the knowledge exchange at the heart of interdisciplinarity. We also have a difficult time distinguishing different degrees of interdisciplinarity. Do we mean actual collaborations between scholars from different disciplines or are we more interested in a combination of different conceptual and methodological approaches, perhaps even in one persons work? And how closely are those two layers linked? Does the successful application of different approaches require collaboration between scholars with different backgrounds? How can we tell whether any interdisciplinary approach is better and in what ways? Traditionally these questions are addressed in the context of individual case studies, such as with breakthrough discoveries. While those narratives provide detailed insights into some localized scientific cultures, we have no way of answering questions about interdisciplinarity at a larger scale. Yet understanding across individual cases is exactly the kind of information we need if we want to retool the scientific enterprise towards greater degrees of interdisciplinarity.