Project

More than Mere Dead-Weight: The Variety of Ways that Regulators, Innovators, and Entrepreneurs Co-Create Disruptive Technological Innovation in Advanced Industrial Democracies

Goal: It has become cliché to note the speed of technological change and lament the inability of social and legal institutions to keep up. One phalanx of this narrative brandishes the word “disrupt” to storm the halls of stodgy industries and regulatory agencies intent on dismantling them. Yet despite this modern narrative of disruption, significant technological change is not the invention of the past year, decade or generation. Despite neoliberal and libertarian narratives which prompt disruptive entrepreneurs to use regulation as the foulest profanity to decry state inadequacy, regulators have adapted to technological change each time it arose. Although sometimes inadequate and never perfect, these adaptations invariably happened.

Failure is loud, success is quiet.  Regulatory failures like the Deepwater Horizon oil spill and 2008 Global Financial Crisis are loudly publicized. Much quieter are regulatory responses which are something other than failure like American recombinant DNA regulation following the 1975 Asilomar Conference. This mismatch reinforces a folk understanding of regulators as destined to fail.  Worse yet, loud proclamations of inept regulators’ inevitable failure often create failures when alternative rhetoric could avoid them.

We need to understand the range of regulatory responses not just the spectacular failures. Thus, this project asks how do regulators respond to disruptive technological innovations (DTIs) and why do particular regulatory regimes choose particular responses to particular disruptive innovations? 

To answer this question, this project develops a novel qualitative empirical method which combines deductive typological theory with logical Bayesianism to deductively develop and inductively refine a seven-model typology of regulatory response to DTI. This typology provides a conceptually complete and empirically validated map of the range of ways that regulators can respond to disruptive technological innovation.  This demonstration of variation should finally dispel pernicious narratives of inherently incompetent regulators by demonstrating that they can be more than merely dead-weight.

Date: 30 August 2015

Updates
0 new
6
Recommendations
0 new
0
Followers
0 new
3
Reads
1 new
79

Project log

Konrad Posch
added an update
I will be presenting a chapter from my dissertation project, entitled “Innovation Beyond the Imagination of the Market: How the State Drove an Economically Beneficial and Socially Responsible Innovation; The Adoption of Electronic Health Records in the US and EU” at the University of Texas Sixth Annual Graduate Conference in Public Law.
 
Konrad Posch
added an update
If you'll be in San Diego in April, come check out my presentation of the electronic health records (EHR) chapter from my dissertation which explores a case where the state drove an economically beneficial and socially desirable innovation beyond the imagination of market actors who held preferences contrary their economic interests.
I will also be chairing the panel and encourage you to check out the papers from the other presenters.
An abstract of the chapter and link to the conference panel may be found on my website at: https://konradposch.com/research/ under "Upcoming Conference Presentations."
 
Konrad Posch
added an update
I have been accepted into the 2019 NNCI Winter School on Responsible Innovation and Social Studies of Emerging Technologies. (https://www.nnci.net/winter-school)
At the Winter School, I will have the opportunity to interact with scholars interested in the governance of science and technology from across the natural, social, and biological sciences as well as learn ASU’s approach to responsible innovation, anticipatory governance and real-time technology assessment. With my background spanning natural and social sciences, it has never seemed odd to me that science and technology outcomes depend just as much on social characteristics as technical specifications.
As part of the Winter School, I will be presenting the GMO chapter in progress from my dissertation entitled : “Recombinant Pasts and CRISPR Futures: The Processes and Outcomes of Beneficially Constraining GMO Regulation in the US and Europe, 1975 to Present;” and abstract of which may be found on my website: https://konradposch.files.wordpress.com/2018/11/posch-2018-10-16-recombinant-pasts-and-crispr-futures-asu-nnci.pdf
If you will be attending the ASU NNCI Winter School and are interested in chatting with me about this project, my wider dissertation, or even swapping stories of modified cars, please reach out to me through ResearchGate or my website contact form.
 
Konrad Posch
added an update
Come check out the methodological core of my dissertation which combines deductive typological theory with logical Bayesianism to develop a novel method for deductively deriving a robust typology from constitutive variables and the inductively refining that typology against empirical cases.
Abstract: Typologies and process tracing are two pillars of qualitative methodology used to answer big questions in interesting ways. Both are sometimes challenged by non-practioners as nothing more than intuition and conjecture wrapped up in seductive yet ill-defined terms. While the tone of these challenges may be based in methodological rivalry, the underlying critique highlights that the process and results of process tracing and typology generation can be opaque to non-practioners. In response to these challenges, rigorous process tracers have proposed three formalizations of process tracing based on set theory, directed acyclic graphs (DAGs), and logical Bayesianism while rigorous typological theorists have formally specified inductive and deductive methods to generate typologies. As with all formalizations, both the strengths and limitations of these methods were brought to light through the formalization.
The elementary method of process tracing is taught using the metaphor of Sherlock Holmes and uses colloquial names for four process tracing tests (straw in the wind, hoop, smoking gun, and doubly decisive). The set theoretic and DAG approaches both formalize this vivid imagery into graphical representations while the logical Bayesian approach formalizes the informativeness of the evidence into numerical representations of human sensory perception. Bayesian process tracing best disciplines the analytic process and provides clarity to the intermediate and final results in terms consistent with human perception and the real number mathematics which are a part of general rather than specialist education.
One of the key requirements of the Bayesian approach is that it requires a mutually exclusive and exhaustive (MEE) specification of the rival hypotheses in order to properly adjudicate between rival hypotheses because it places odds ratios only on pairs of hypotheses. While MEE hypotheses are universally good research practice, most methods and the other process tracing formalization do not require MEE hypotheses. While leading BayesPT scholars have pointed out that it is always possible to rephrase a given set of hypotheses into an MEE specification, most hypotheses are not initially specified as MEE. Although the respecification is logically straightforward, the extra work and complexity of the final set of hypotheses has led some critics to question the value MEE and BayesPT more broadly due to the "cost of entry." Thus, while MEE is not a logical limitation of BayesPT, it can be a practical one which we can alleviate with deductive typological theory.
When generating typological theories, scholars can either inductively generalize from empirical specifics or deductively combine the scores of generalized concepts to generate a set of types. Inductive typological theory runs the risk of missing logically possible combinations which have not (yet) empirically occurred. Deductive typological theory runs the risk of over specifying possible combinations which are empirically uninteresting or irrelevant. While the final typologies which are published generally involve both of these fundamental theory generating processes to some degree, the way in which they are used is often vague in final research reports.
In this paper, I demonstrate how the strengths of deductive typological theory can alleviate the limitations of logical Bayesian process tracing and vice versa. By definition, deductive typological theory completely maps a typological property space by constructing a mutually exclusive and exhaustive list of types based on combinations of constitutive variables. While Bayesian process tracing generally solves the requirement for exhaustive hypotheses by reasoned assumption, the process of deductive typological theory allows us to meet the requirement by design. Because Bayesian process tracing disciplines and clarifies how we are using case-specific knowledge and expert analysis, it allows us to transparently verify deductive typologies with inductive empirical knowledge. Together, deductive typological theorizing generates a set of mutually exclusive and exhaustive types which are then tested against empirical cases using logical Bayesianism to say how confident we can be that a particular case fits a given type. The results of this process can then be used inductively to refine the typology and applied iteratively to generate a final typology which balances analytic usefulness against empirical correspondence. Because the types developed by deductive typological theory need not be causal, I call this method Bayesian Type Verification (BayesTV) to distinguish it from the causal claims of Bayesian process tracing (BayesPT).
 
Konrad Posch
added an update
Presented a preliminary version of my regulatory response typology at the ECPR Standing Group on Regulation and Governance's Biennial conference in Lausanne, Switzerland. Thanks to Christian Ewert for helpful comments and chairing an excellent panel, Cristie Ford for and Lauren Fahy for excellent comments, and the REGGOV committee for hosting the premiere conference for Regulation and Governance.
Abstract:
It has become cliché to note the speed of technological change and lament the inability of social institutions to keep up. One phalanx of this narrative brandishes the word “disrupt” to storm the halls of stodgy industries and regulatory agencies intent on dismantling them. Yet despite this modern narrative of disruption, rapid and drastic technological change is not the invention of the past year, decade or generation. And despite the libertarian narratives which prompt disruptors to use regulation as the foulest profanity to decry state inadequacy, regulators do find ways to adapt to technological change each time it arises. Although never perfect and sometimes inadequate, these adaptations still happen.
Regulatory failures such as the Deepwater Horizon oil spill and the 2008 Global Financial Crisis are loudly publicized. Much quieter are the regulatory responses which are something other than failure. We need to understand the range of regulatory responses not just the spectacular failures. This paper develops a deductive typology of regulatory responses to disruptive technological innovation by specifying four variables which underlie both the folk economic theory behind the disruption narrative and the counter-narrative of beneficial constraints. Springing from Stigler’s “Theory of Economic Regulation” (1971), the folk economic theory views regulators as mere dead weight whose impact must be minimized to allow entrepreneurs to innovate. As developed in Streeck’s eponymous article (1997), beneficial constraints views regulators as constraining the focus of innovators so they create business models which ultimately benefit both firms and society. Both theories have empirical support, suggesting that they are both part of the larger story of regulation’s impact on innovation. To reconcile these two theories, this paper derives the four variables of relationship, access, impetus and desirable outcomes in order to generate a deductive typology with seven distinct models of regulatory response to disruptive technological innovation.
 
Konrad Posch
added a project goal
It has become cliché to note the speed of technological change and lament the inability of social and legal institutions to keep up. One phalanx of this narrative brandishes the word “disrupt” to storm the halls of stodgy industries and regulatory agencies intent on dismantling them. Yet despite this modern narrative of disruption, significant technological change is not the invention of the past year, decade or generation. Despite neoliberal and libertarian narratives which prompt disruptive entrepreneurs to use regulation as the foulest profanity to decry state inadequacy, regulators have adapted to technological change each time it arose. Although sometimes inadequate and never perfect, these adaptations invariably happened.
Failure is loud, success is quiet.  Regulatory failures like the Deepwater Horizon oil spill and 2008 Global Financial Crisis are loudly publicized. Much quieter are regulatory responses which are something other than failure like American recombinant DNA regulation following the 1975 Asilomar Conference. This mismatch reinforces a folk understanding of regulators as destined to fail.  Worse yet, loud proclamations of inept regulators’ inevitable failure often create failures when alternative rhetoric could avoid them.
We need to understand the range of regulatory responses not just the spectacular failures. Thus, this project asks how do regulators respond to disruptive technological innovations (DTIs) and why do particular regulatory regimes choose particular responses to particular disruptive innovations?
To answer this question, this project develops a novel qualitative empirical method which combines deductive typological theory with logical Bayesianism to deductively develop and inductively refine a seven-model typology of regulatory response to DTI. This typology provides a conceptually complete and empirically validated map of the range of ways that regulators can respond to disruptive technological innovation.  This demonstration of variation should finally dispel pernicious narratives of inherently incompetent regulators by demonstrating that they can be more than merely dead-weight.