First published at: https://www.law.ox.ac.uk/business-law-blog/blog/2016/09/dynamic-regulation-investment-data-remedy-law’s-diminishing-capacity
Key words: Disruptive Innovation, Venture Capital, Venture Investments, Dynamic Regulation, Feedback Effects, Optimized Information for Regulation, Anticipatory Regulation, Big Data
In a series of recent papers – ‘Dynamic Regulation for Innovation’, ‘Regulation Tomorrow: What Happens When Technology Is Faster Than the Law?’, and ‘How to Regulate Disruptive Innovation – From Facts to Data’ – I evaluate the diminishing relationship between regulation and innovation. The so-called ‘pacing problem’ between innovation and regulation suggests that innovation driven by science and technology is accelerating, yet, simultaneously, federal and state agencies’ regulatory processes have slowed down.
Several factors contribute to this pacing problem. Some consensus exists that the legal and evidentiary burdens placed on regulatory authorities have increased substantially over time, precipitating the remarkable slowdown in rulemaking by regulatory agencies. The growing divergence between the time cycles of technological innovation and the time cycles of governments around the world contributes significantly to the pacing problem. Systemic factors such as the political and ideological structures in the rulemaking process, the political gridlock in a two-party system that impedes the passing of legislation, legislators’ disagreement on how outdated statutes should be updated, and the need for crises to precipitate legislative action also help to explain the pacing problem.
The existing regulatory infrastructure contributes significantly to this problem. In the existing regulatory framework, the regulatory challenges presented by disruptive innovation are largely associated with facts-based, ex-post, trial-and-error rule-making, with stable and presumptively optimal rules. In essence, some evidence exists that governments mostly regulate to react ex-post, after problems arose in the existing regulatory framework but rules are always assumed to be optimal even though optimality is unattainable in a rapidly innovating and increasingly complex world. Other regulatory challenges are presented by the slow speed of regulation, and ever-increasing unknown future contingencies in rulemaking. Because facts-based, ex-post, trial-and-error rule-making cannot anticipate regulatory issues created by innovation, rule-makers may not – or may much too late – realize what new regulatory demands apply to a given innovation. Rule-makers’ near exclusive reliance on stable and presumptively optimal rules created to attain permanent solutions for perceived regulatory issues ignores the ever-changing environment for rules driven by the exponential growth of technology, and the associated exponential growth of innovation. The timing of regulation in an environment of exponential innovation is a primary problem for regulators. Formal rulemaking in the existing regulatory infrastructure is almost always too time-consuming because the speed of product innovation often makes regulations pertaining to an innovative product obsolete before such regulations are finalized. Finally, the existing regulatory infrastructure, with stable and presumptively optimal rules, is largely incapable of addressing the ever-increasing unknown future contingencies associated with disruptive innovation. Given the pace of innovation, future contingencies in rulemaking are likely to increase substantially, making the dynamic anticipation of future contingencies more important for rulemaking.
Lawmaking and regulatory design need to become more dynamic and anticipatory in their efforts to address this pacing problem. Increased reliance on different sources of data surrounding new technologies can provide some signals or clues about what, when and, to a certain extent, how to regulate.
Here, I show that data relating to investment in new technology and innovation is of particular importance in this context. A plethora of investment data is readily available to make accurate predictions regarding what the next ‘big thing’ is likely to be. Such data can be used as an index or proxy for the necessity of regulation. Because start-up companies are the ones that usually challenge existing rules, laws and regulations, private data sources are widely available. The proliferation of the better hand-collected global databases on the market, such as CB Insights, PitchBook and Mattermark, can make an important contribution to a ‘data-driven’ regulatory approach.
Investment data can help to develop a list of technologies and issues that need to be the focus of regulatory attention. From such data, rule-makers can get a better – and earlier – sense of which technologies are developing and which technologies need regulatory attention. This might then allow regulators to be more pro-active and avoid wasting resources on technologies that are unlikely to make it to market. It would also allow regulators to more accurately define the scope of a technology by focusing on the type of firm that is attracting attention.
Wulf A. Kaal is an Associate Professor at the University of Saint Thomas School of Law (Minneapolis).