Katherine J. Strandburg

Prediction of emerging technologies based on analysis of the US patent citation network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.

Curiosity-Driven Research and University Technology Transfer

The debate about university technology transfer policy would benefit from increased attention to two parts of the technology transfer equation: the societal purpose of basic scientific research and the characteristics of scientific researchers.11This Chapter was prepared for the Colloquium on University Entrepreneurship and Technology Transfer hosted by the Karl Eller Center of the University of Arizona and sponsored by the Ewing Marion Kauffman Foundation. I am grateful to them for their support. I am also grateful to the participants in the Colloquium for helpful comments. Finally, I thank my research assistant, David Zelner, for assistance with this project. One purpose of curiosity-driven research is to provide a demand function that can serve as a proxy for the socially optimal (but unknowable) demand function for the unpredictable research that is necessary for long-term technological progress. Preserving the curiosity-driven research peer review “market” is thus important for that progress. This analysis highlights the importance of adequate funding for curiosity-driven research. A model of typical university scientists’ preferences can be used to assess how technology transfer policies may affect the social norms of the research community and the long-term viability of the curiosity-driven research endeavor. The analysis suggests that patenting will be an ineffective technology transfer mechanism unless researchers are precluded from using patenting to maintain control over follow-on research.

Social Norms, Self Control, and Privacy in the Online World

This chapter explores ways in which human limitations of rationality and susceptibility to temptation might affect the flow of personal information in the online environment. It relies on the concept of “willpower norms” to understand how the online environment might undermine the effectiveness of social norms that may have developed to regulate the flow of personal information in the offline world. Finally, the chapter discusses whether legal regulation of information privacy is an appropriate response to this issue and how such regulation should be formulated in light of tensions between concerns about self-control and paternalism.

The Inverse Problem of Evolving Networks — with Application to Social Nets

Many complex systems can be modeled by graphs [8]. The vertices of the graph represent objects of the system, and the edges of the graph the relationships between these objects. These relationships may be structural or functional, according to the modeler’s needs [1, 29, 7].

Estimating the Dynamics of Kernel-Based Evolving Networks

In this paper we present the application of a novel methodology to scientific citation and collaboration networks. This methodology is designed for understanding the governing dynamics of evolving networks and relies on an attachment kernel, a scalar function of node properties, that stochastically drives the addition and deletion of vertices and edges. We illustrate how the kernel function of a given network can be extracted from the history of the network and discuss other possible applications.

The Application of User Innovation and Knowledge Commons Governance to Mental Health Intervention

User innovation has been applied in many fields (von Hippel 2005), including those related to health care. This chapter describes what is, to our knowledge, the first application of user innovation to the mental health field. We apply user innovation and knowledge commons governance to a mental health problem of considerable importance: child traumatic stress. As we detail, user innovation provides a unique opportunity to develop and adapt interventions that meet the needs of children with traumatic stress and their families. Knowledge commons governance provides a way to share, vet, and improve these user innovations. This approach provides a solution to a critical problem related to the delivery of effective interventions in the mental health field, where the development of effective treatments often is impeded by the inflexibility of evidence-based treatments (Saxe and Acri 2016). First, we describe the problem of child traumatic stress and the imperative to provide effective treatments for children who suffer from it. Second, we detail the problem within the mental health field about adapting interventions so that they meet the needs of individuals with mental health problems and can be delivered in a variety of typical care settings. Third, we describe how we encourage user innovation and harness it in a knowledge commons by creating an intervention model for traumatized children that is flexible enough to address their needs in a variety of typical care settings and by providing infrastructure for sharing and vetting the innovations made by users in adapting the model to their particular circumstances. This intervention model is called Trauma Systems Therapy. It is currently disseminated in 14 states and has been adapted to work in a wide variety of service settings via the process of user

Knowledge Commons and the Road to Medical Commons

This book picks up where Governing Knowledge Commons, our 2014 collection of perspectives and case studies of knowledge commons governance, left off. Governing Knowledge Commons (GKC) laid down a research program that contributes to evidence-based policymaking about innovation and creative knowledge production, as well as the creation, preservation, and uses of existing and new knowledge. The cases presented in GKC are, in a word, diverse. They range from arts to sciences, from the professions to popular culture, from historical to contemporary. Governing Medical Knowledge Commons sustains that research program but with a specific, thematic focus. This book collects and presents a series of case studies of knowledge commons centered on recent and emerging experience in the life sciences, medical research, and medical practice.

Governance of Biomedical Research Commons to Advance Clinical Translation: Lessons from the Mouse Model Community

The translation of laboratory and clinical research into interventions that improve individual and population health is an iterative process with systemic directionality from basic research through preclinical research, clinical research, clinical implementation, and population health outcomes research (National Center for Advancing Translational Sciences 2015). Engaged in translation are patients and patient advocacy organizations, researchers from public and private sectors and from multiple disciplinary backgrounds, clinical practitioners, as well as a myriad of ancillary support professionals, from business executives, accountants, and marketing/sales staff to venture capitalists and public and philanthropic fund administrators to health and safety regulators. New models of collaboration within this complex ecosystem are required to overcome waste and inefficiencies in current research and development (R&D)

Population Biobanks’ Governance: A Case Study of Knowledge Commons

The term “biobank” refers to a variety of research infrastructures that involve the collection, storage, and analysis of genetics samples that are linked to data regarding the health and lifestyle of the sample sources. Population biobanks are a particular type of biobank as they focus primarily on a population or a large subset of a population and permit research to explore the relationship between genes, environment, and lifestyle on large cohorts. Population biobanks are also an emerging knowledge commons: they are infrastructures made of pooled resources that researchers from all over the world can access for uses that are not predetermined by the biobank managers. In this chapter, I discuss how population biobanks are increasingly managed as commons, what role biobank governance experts have played in overcoming regulatory obstacles and in managing negative externalities, and areas of governance that need further refinement for population biobanks to be fully managed as commons. The case study traces the history of biobanking governance and discusses the process that has led to a convergence toward a commons approach. My analysis draws from the literature on biobank governance and on knowledge commons theory as well as my experience as a scholar who participated in the governance shift toward a commons approach to biobank governance. This case study provides important lessons to scholars of knowledge commons as it shows the importance of expertise in managing pooled resources as commons.

Challenges and Opportunities in Developing and Sharing Solutions by Patients and Caregivers: The Story of a Knowledge Commons for the Patient Innovation Project

The Patient Innovation project is an initiative that aims to create a knowledge commons for patients and nonprofessional caregivers to share and further develop their innovative solutions to medical care–related problems through an online platform, https://patient-innovation.com. Patients and nonprofessional caregivers are the largest, and most important, group of stakeholders in the health care value chain. After all, the system exists for their benefit. Traditionally, however, they have been perceived as passive recipients of medical care, merely buying and consuming the solutions and products that “medical producers” create and provide. This perspective has influenced the development of an entire health care ecosystem that reinforces the passive position of patients and caregivers. The assumption of passivity is highly flawed, as demonstrated by research aimed at studying innovation activity by “users” in health care and understanding the role of patients of chronic diseases (or their nonprofessional caregivers) in developing innovative solutions to help them cope with their health conditions (e.g., Oliveira et al. 2015; Oliveira and Canhão 2016). That collaborative and interdisciplinary research effort demonstrated that patients and their nonprofessional caregivers are major sources of health care product and service “user innovations” (e.g., Oliveira and von Hippel 2011; Oliveira et al. 2015; von Hippel 1988, 2005). Most of the studies of innovation activity by patients build on several decades of “user innovation” research. This research demonstrated that ordinary users, not only commercial entities and research laboratories, are an important source of innovation. In his seminal work in this area, Eric von Hippel, defined user innovators as firms and