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Research Roundup: Robots, Spectrum, and the Sharing Economy – How Do We Think About Innovation?

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This edition of Research Roundup highlights a study undertaken by the MIT Sloan Management Review and Boston Consulting Group, to identify the current state of artificial intelligence (AI) integration in the global economy. Researchers surveyed more than 3,000 executives, managers, and analysts and reviewed the organizations’ structures and best practices to construct a snapshot of current AI use across industries. They find that there exist “large gaps between today’s leaders — companies that already understand and have adopted AI — and laggards,” particularly in their approaches to data and resources (p. 1). The authors extrapolate from their findings several components necessary to successful integration of AI: a clear idea of how the technology will benefit the company, strategies to mitigate accompanying risks to security and access, a clear understanding of how they will train AI systems, including establishing reasoning patterns, inputting data, and earmarking financial resources for staff and maintenance, and, finally, leadership from above.

(Click through to the full post to see the abstract and link to this paper and 8 others on topics from cryptocurrency to regulating technological innovation)

 

Reshaping Business with Artificial Intelligence
Monopoly Without a Monopolist: An Economic Analysis of the Bitcoin Payment System
Internet of Things, Blockchain, and Shared Economy Applications
Should Robots Be Taxed?
The Evolution of U.S. Spectrum Values Over Time
Spectrum Efficiency at the Extensive and Intensive Edges
Search Engines and Data Retention: Implications for Privacy and Antitrust
Towards a Broad Understanding of Innovation and its Importance for Innovation Policy
Regulating Business Innovation as Policy Disruption

 

Descriptions of papers below are edited abstracts from authors

 

Reshaping Business With Artificial Intelligence
Sam Ransbotham, David Kiron, Philipp Gerbert, and Martin Reeves
Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now.

Monopoly Without a Monopolist: An Economic Analysis of the Bitcoin Payment System
Gar Huberman, Jacob Leshno, Ciamac Moalleni
Owned by nobody and controlled by an almost immutable protocol, the Bitcoin payment system is a platform with two main constituencies: users and profit-seeking miners who maintain the system’s infrastructure. The paper seeks to understand the economics of the system: How does the system raise revenue to pay for its infrastructure? How are usage fees determined? How much infrastructure is deployed? What are the implications of changing parameters in the protocol? A simplified economic model that captures the system’s properties answers these questions. Transaction fees and infrastructure level are determined in an equilibrium of a congestion queuing game derived from the system’s limited throughput. The system eliminates dead-weight loss from monopoly, but introduces other inefficiencies and requires congestion to raise revenue and fund infrastructure. We explore the future potential of such systems and provide design suggestions.

Internet of Things, Blockchain, and Shared Economy Applications
Steve Huckle, Rituparna Bhattacharya, Martin White, and Natalia Beloff
The focus of this research is understanding how blockchain can be exploited to create decentralized, shared economy applications that allow people to monetize, securely, their things to create more wealth. Shared economy applications such as Airbnb and Uber are well-known applications, but there are many other opportunities to share in the digital economy. While many types of shared economy scenarios are proliferating, few of them, so far, leverage the Internet of Things and blockchain as technologies to build distributed applications. This paper discusses how we might make use of the Internet of Things and blockchains to create secure shared economy distributed applications.

Should Robots Be Taxed?
Joao Guerreiro, Sergio T. Rebelo, and Pedro Teles
We use a model of automation to show that with the current U.S. tax system, a fall in automation costs could lead to a massive rise in income inequality. This inequality could be reduced by raising marginal income tax rates and taxing robots. But this solution involves a substantial efficiency loss for the reduced level of inequality. A Mirrleesian optimal income tax can reduce inequality at a smaller efficiency cost, but is difficult to implement. An alternative approach is to amend the current tax system to include a lump-sum rebate. In our model, with the rebate in place, it is optimal to tax robots only when there is partial automation.

The Evolution of U.S. Spectrum Values Over Time
Michelle P. Connolly, Nelson Sá, Azeem Zaman, Christopher Roark, and Akshaya Trivedi
Using data on all FCC auctions of spectrum related to cellular services from 1997 to 2015 we attempt to identify intrinsic spectrum values from winning auction bids. Our data set includes 17 auctions and close to 7,500 observations. Our results confirm previous theoretical and empirical findings for basic measures of demand such as population, population density, income levels, frequency levels, bandwidth, paired bands, and national licenses. Astonishingly, 49 percent of all cellular licenses since 1997 have been won by small bidders: 44 percent were won using small bidder credits, 14 percent were won in set-aside/closed licenses, and 9.5 percent were won in closed licenses using bidding credits. Increased spectral efficiency appears to be reducing spectrum scarcity as evidenced by its lowering of winning bids, while market level communications infrastructure has a significant positive impact on the demand for and price of spectrum. Additionally, auction results confirm that the relative value of higher frequency spectrum is increasing over time as new technologies develop.

Spectrum Efficiency at the Extensive and Intensive Edges
Benoit Pierre Freyens
Future 5G architectures will need access to additional spectrum either at the extensive edge (accessing hitherto unused spectrum or reframing assigned spectrum) or at the intensive edge (making more productive use of used spectrum). A full understanding and pursuit of spectrum efficiency dividends will be a deciding factor in the successful deployment of these future wireless networks. A stylized general equilibrium model illustrates the type of trade-offs involved with thinking about efficient spectrum assignments at the intensive edge and draws inference for the type of spectrum management reforms that would best accommodate the spectrum needs of the next generation of radio networks.

Search Engines and Data Retention: Implications for Privacy and Antitrust
Lesley Chiou and Catherine E. Tucker
This paper investigates whether larger quantities of historical data affect a firm’s ability to maintain market share in Internet search. We study whether the length of time that search engines retained their server logs affected the apparent accuracy of subsequent searches. Our analysis exploits changes in these policies prompted by the actions of policymakers. We find little empirical evidence that reducing the length of storage of past search engine searches affected the accuracy of search. Our results suggest that the possession of historical data confers less of an advantage in market share than is sometimes supposed. Our results also suggest that limits on data retention may impose fewer costs in instances where overly long data retention leads to privacy concerns such as an individual’s “right to be forgotten.”

Towards a Broad Understanding of Innovation and its Importance for Innovation Policy
Dirk Meissner, Wolfgang Polt, and Nicholas S. Vonortas
This paper considers the changes in the concept of innovation during recent decades and the degree to which such changes have been of significance to innovation policy. We observe that: (1) the notion of innovation in research, statistics, and policy is becoming increasingly broad; (2) while this broader notion is conceptually more adequate for understanding the complexity of innovation activity, it also makes it increasingly difficult to gain a clear, unambiguous picture of innovation activity; (3) policy concepts built upon this extended understanding of innovation are becoming more complex in terms of governance capacities, coordination capabilities, and evidence-based policy formulation. The broad perception of innovation will, in fact, require substantial innovations in political and administrative systems to apply.

Regulating Business Innovation as Policy Disruption: From the Model T to Airbnb
Eric Biber, Sarah E. Light, J. B. Ruhl, and James Salzman
Many scholars have invoked the term “disruptive innovation” when addressing the platform (sharing) economy, with sweeping claims about the dramatic changes this development promises for the law, regulation, and the economy. The challenges raised by the platform economy are surely important, but we argue that recent scholarship focusing on the immediacy and novelty of the platform economy has been ahistorical, and has therefore missed the bigger picture about how to regulate it. When business innovation upends a preexisting business model in a regulated industry, the result can be a disjunction between the structure of the regulatory system governing incumbent firms and the firms disrupting the industry: a policy disruption. Policy disruption can result from conscious choices by entrepreneurs to exploit legal loopholes or to challenge regulatory protections for incumbents. But it can just as easily result from gaps in a regulatory regime or fundamentally new business models that solve problems legal regimes have been designed to address.