*The Research Roundup is a semi-regular list of outside research we have found interesting and think is worth sharing. The views and conclusions of the papers’ authors do not necessarily reflect the opinions of anyone affiliated with TPI.*
The articles detailed in this month’s Research Roundup touch on the relationship between broadband infrastructure and economic growth, the increasing role of artificial intelligence in the global economy, and the need to balance convenience, security, and moral principles in Internet of Things (IoT) applications. In their piece titled Ethical Design in the Internet of Things, Gianmarco Baldini, Maarten Botterman, Ricardo Neisse, and Mariachiara Tallacchini discuss the reinforcing relationship between personal data and the performance of IoT devices and services. More data allows IoT devices to tailor the user experience more precisely which then reinforces its application – a smart thermostat that knows exactly when its owners are regularly out of the house, for example, may decrease the owners’ energy costs, but might also share its owners’ comings and goings with legitimate or illegitimate third parties. Even so, the authors call for more transparency in IoT technology, particularly in how personal data is collected and used, and for user input and choice in both processes. In doing so, they contribute to a conversation about convenience, efficiency, and privacy that is unlikely to abate, as more and more IoT devices reach mainstream markets. Click through for more detailed descriptions of this piece and other recent articles.
Descriptions of papers below are edited abstracts from authors
Internet of Things
Gianmarco Baldini, Maarten Botterman, Ricardo Neisse, and Mariachiara Tallacchini
Even though public awareness about privacy risks in the Internet is increasing, these risks are likely to become more relevant as the Internet of Things (IoT) evolves and collects and processes more and more data. This paper identifies the business drivers for exploring ways to monetize such data as well as the challenges posed for protection of user privacy. This paper also highlights the need for new approaches, which grant a more active role to the users of the IoT and which address other potential issues such as the Digital Divide or safety risks. A key facet in ethical IoT design is the transparency of the technology and services in how that technology handles data, as well as providing choice for the user. This paper presents a new approach for users’ interaction with the IoT, which is based on the concept of Ethical Design implemented through a policy-based framework. In the proposed framework, users are provided with wider controls over personal data or the IoT services by selecting specific sets of policies, which can be tailored according to users’ capabilities and to the contexts where they operate. The potential deployment of the framework in a typical IoT context is described with the identification of the main stakeholders and the processes that should be put in place.
Soroush Saghafian, Brian Tomlin, and Stephan Biller
The promised operational benefits of the Internet of Things (IoT) are predicated on the notion that better decisions will be enabled through a multitude of autonomous sensors (often deployed by different firms) providing real-time knowledge of the state of things. Due to sensor quality limitations, however, this knowledge will be imperfect. A sensor can improve its estimation quality by soliciting a state estimate from other sensors operating in its general environment. Target selection (choosing from which other sensors to solicit estimates) is challenging because sensors may not know the underlying inference models or qualities of sensors deployed by other firms. This lack of trust (or familiarity) in others’ inference models creates noise in the received estimate, but trust builds, and noise reduces over time the more a sensor targets any given sensor. This paper presents an initial and long run information sharing network for an arbitrary collection of sensors operating in an autoregressive environment. The state of the environment plays a key role in mediating quality and trust in target selection. Our findings shed light on the evolution of inter-firm sensor communication over time, and its implications for predicting and understanding the inter-firm connectedness and relationships that will arise as a result of the IoT.
Alexander J. A. M. van Deursen & Karen Mossberger
The “Internet‐of‐Things” (IoT) promises social benefits across a range of policy areas, such as energy, health, transportation, public safety, and environmental policy, but attention to the skills needed by individuals who use it will be an important issue for public policy, in order to ensure full exploitation of these technologies and to avoid unintended consequences. The authors argue that comparative benefits of IoT to people will vary based on differentiated skills and resources, enabling smaller groups of people to benefit, and disadvantaging others in new ways. This calls for renewed attention to digital skills and knowledge might at first seem paradoxical, given that many of these technologies operate autonomously and behind the scenes. The authors discuss evolving digital technologies and related skills, and explain, from a systems perspective, how the IoT differs from prior technologies, placing a premium on user knowledge and strategic skills. Finally, the authors join the issues of the digital divide and IoT skills to set an agenda for future IoT public policy and research.
Matthew Alan Taddy
We have seen in the past decade a sharp increase in the extent that companies use data to optimize their businesses. Variously called the ‘Big Data’ or ‘Data Science’ revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid rise of Artificial Intelligence (AI) that works by combining many ML algorithms together – each targeting a straightforward prediction task – to solve complex problems. The authors define a framework for thinking about the ingredients of this new ML-driven AI. Having an understanding of the pieces that make up these systems and how they fit together is important for those who will be building businesses around this technology. Those studying the economics of AI can use these definitions to remove ambiguity from the conversation on AI’s projected productivity impacts and data requirements. Finally, this framework should help clarify the role for AI in the practice of modern business analytics and economic measurement.
This research is the first to apply a decomposition framework to clarify the determinants of AI technology invention. Consisting of 13,567 AI technology patents for the 2000–2016 period, the worldwide dataset utilized in this paper includes patent publication data from the U.S., Japan, China, Europe, and the Patent Cooperation Treaty (PCT). We find that priority has shifted from biological and knowledge-based models to specific mathematical models and other AI technologies, particularly in the U.S. and Japan. A comparison of technology type and country shows that the characteristics of AI technology patent publication differ across companies and countries.
Jason Furman & Robert Seamans
The authors review the evidence that artificial intelligence (AI) is having a large effect on the economy. Across a variety of statistics—including robotics shipments, AI startups, and patent counts—there is evidence of a large increase in AI-related activity. They also review recent research in this area which suggests that AI and robotics have the potential to increase productivity growth but may have mixed effects on labor, particularly in the short run. In particular, some occupations and industries may do well while others experience labor market upheaval. The authors then consider current and potential policies around AI that may help to boost productivity growth while also mitigating any labor market downsides including evaluating the pros and cons of an AI specific regulator, expanded antitrust enforcement, and alternative strategies for dealing with the labor-market impacts of AI, including universal basic income and guaranteed employment.
George S. Ford
The author aims to quantify the relationship between higher broadband speeds (10 Mbps versus 25 Mbps) and growth rates in important economic outcomes in U.S. counties including jobs, personal income, and labor earnings. In doing so, he exposes the potential for severe selection bias in studies of broadband’s economic impact, which is addressed in this study using Coarsened Exact Matching. Once balanced, the data reveal no economic payoff from the 15 Mbps speed difference between the years 2013 and 2015 (when data is available). The author also revisits an early and widely-cited study on broadband’s effect on employment to evaluate the possible impacts of selection bias and conclude that the positive benefits of broadband reported in that particular study are likely spurious. The selection bias problem may infect other studies on the economic impacts of broadband Internet services.
The author presents the hypothesis that, after three decades of stability, there is now the prospect of significant change in the vertical and horizontal structure of the mobile market place. On the supply side, significant factors are, first, the availability of a new and very powerful form of mobile connectivity in the shape of 5G, and second, software defined networking, which allows a single network to provide a variety of heterogeneous services or ‘slices’. On the demand side, the digital transformation of the whole economy (and not just the communications sector) creates the need for diverse communications functions operating in a universe with a much wider set of digitally transformed services. The author argues that mobile operators will find themselves contesting customer relationships with firms or other organizations providing these services in an integrated fashion, and thus risk replacing their direct link with end users with becoming the wholesale supplier of an expanded but ‘commoditized’ communications product.
Joan Calzada Begoña, García-Mariñoso, Jordi Ribé, Rafael Rubio, and David Suárez
Next generation access networks will be critical for future economic growth and access to these infrastructures will have major consequences for territorial and social cohesion. This paper examines the economic and competition determinants that serve as incentives for operators to invest in fiber-to-the-home technology. The authors draw on a dataset that includes 6603 Spanish municipalities with access to broadband services to examine the incumbent provider’s (Telefónica) deployment of fiber in the period 2010–2013. They show that local loop unbundling competition had a strong positive impact on Telefónica’s fiber deployment, while bitstream competition had a negative effect. Moreover, the incumbent was more likely to invest in municipalities with a large presence of cable operators. The authors also consider how the municipalities’ sociodemographic characteristics affected the operator’s deployment decision. While market size and population density had a positive effect on investment, the level of unemployment and the percentage of elderly population had a negative impact.