# Generating Indicators of Disruptive Innovation Using Big Data
## 2 Modelling Technology Progression
### Assessing Technology Progression  
The two studies conducted by the US National Research Council (NRC, 2009 [2]; NRC, 2010 [3]) did consider potential integrated and persistent approaches to technology assessment based on a mixture of automated and human analysis; this work never progressed. (The research lead and editor of NRC (2009) [2] and NRC (2010) [3] was contacted and confirmed that the work terminated in 2010.) [PERSON] et al. (2019) [16] suggests an outline for a unified model of a technology ecosystem, integrating the various approaches of the authors mentioned in Section 2.2. [PERSON] (2009) [5] offers the most complete macro model of technology anatomy, which is mapped out graphically in Figure 1. [PERSON] et al. (2019) [16] also suggested that measurement systems or indicators are necessary to validate, calibrate and assess the state of various elements of models of the technology ecosystem. These indicators could be like the source used in weather and climate modelling ([PERSON] 2010) [8] or in financial systems ([PERSON] 2017) [7]. Both modelling domains involve massive numbers of complex input variables and measurements, complex predictive models, and the need to particularly address calibrating for the level of uncertainty. The alignment of the model forecast with the measured state is continually assessed and the model output continually validated and re-calibrated. A key element of the progression of technology is the degree to which component technologies come together to solve a range of problems forming in effect a hybrid technology. This form of technology development is also potentially measurable. [PERSON] et al. (2019) [16] considered the rate of technology growth overall and in relation to other technologies, the geographic origin and spread of technology, the interlinkingto form new technologies (using the smartphone as an example as it is a hybrid of multiple technologies) and the application of a technology to a new field. Each of these topics can be found in the works of the authors noted above, and in some way measuring them would potentially provide useful indicators. Until recently there has been a significant dearth of approaches to automating the process of identifying new technologies of interest. While both NRC reports (NRC, 2009 [2]; NRC, 2010 [3]) discuss it in general terms as does [PERSON] (2005) [26] there does not seem to be a significant body of literature attempting it. One issue is that to identify technologies 'appearing over the horizon' via automated means is likely to require vast complexity, akin to language translation. Technologies in this stage also fit in the [PERSON] (2009) [5] model of path dependence in the high uncertainty stage. Specific areas are assessed by [PERSON] (2022) [11] which considers automated assessment of technology progression using Bitcoin as the specific example of potential disruption. [PERSON] (2022) [27] applies natural language processing and cognitive networks to investor day transcripts to assess progression in Insurance. Martin & Moodysson (2011) [28] and [PERSON] (2009) [29] also consider the issue of technology progression. [PERSON] (2014) [30] looks at London's 'Tech City' development and [PERSON] (2015) [31] considers spatial localization in relation to knowledge generation. The 2020 World Intellectual Property Organization (WIPO) report ranks countries by level of innovation, as well as to some degree sector biases, i.e., specific types of innovation of interest to specific countries or localities. More general approaches have also been developed recently. [PERSON] (2021) [32] provides a review of some of these. [PERSON] (2021) [33] proposes a machine learning approach using approaches that might be expected, Logistic Regression, Naives Bayes, Neural networks and ensemble analysis commonly used in meteorological models to assess sensitivity of results to input parameters. The suggestion is also that the approach should use both machine and collective intelligence.