Persistently Forecasting disruptive technological innovation

The National Academies has a 136 page document that examines the better ways to forecast disruptive technological innovation

This is one of the main objectives of Nextbigfuture. Nextbigfuture is trying to act as an Office of Technological Assessment and evaluate what is the best ways to use existing technologies and to forecast disruptive technological innovation potential and opportunities.

Persistent Forecasting of Disruptive Technologies

Technological innovations are key causal agents of surprise and disruption. In the recent past, the United States military has encountered unexpected challenges in the battlefield due in part to the adversary’s incorporation of technologies not traditionally associated with weaponry. Recognizing the need to broaden the scope of current technology forecasting efforts, the Office of the Director, Defense Research and Engineering (DDR&E) and the Defense Intelligence Agency (DIA) tasked the Committee for Forecasting Future Disruptive Technologies with providing guidance and insight on how to build a persistent forecasting system to predict, analyze, and reduce the impact of the most dramatically disruptive technologies. The first of two reports, this volume analyzes existing forecasting methods and processes. It then outlines the necessary characteristics of a comprehensive forecasting system that integrates data from diverse sources to identify potentially game-changing technological innovations and facilitates informed decision making by policymakers.

The committee’s goal was to help the reader understand current forecasting methodologies, the nature of disruptive technologies and the characteristics of a persistent forecasting system for disruptive technology. Persistent Forecasting of Disruptive Technologies is a useful text for the Department of Defense, Homeland Security, the Intelligence community and other defense agencies across the nation.

This is the first of two reports on disruptive technology forecasting. Its goal is to help the reader understand current forecasting methodologies, the nature of disruptive technologies, and the characteristics of a persistent forecasting system for disruptive technology. In the second report, the committee plans to summarize the results of a workshop which will assemble leading experts on forecasting, system architecture, and visualization, and ask them to envision a system that meets the sponsor requirements while incorporating the desired attributes listed in this report.

The value of technology forecasting lies not in its ability to accurately predict the future but rather in its potential to minimize surprises. It does this by various means:
• Defining and looking for key enablers and inhibitors of new disruptive technologies,
• Assessing the impact of potential disruption,
• Postulating potential alternative futures, and
• Supporting decision making by increasing the lead time for awareness

The Office of the Director of Defense Research and Engineering (DDR&E) and the Defense Intelligence Agency (DIA) Defense Warning Office (DWO) asked the National Research Council (NRC) to set up a committee on forecasting future disruptive technologies to provide guidance on and insight into the development of a system that could forecast disruptive technology.

Several pioneering systems already exist that attempt to forecast technology trends, including TechCast, Delta Scan, and X2.

Analysis of these systems offers important insights into the creation of persistent forecasts:
• TechCast (1998). Voluntary self-selecting of people who examine technology advances on an ad hoc basis. The system’s strengths include persistence, quantification of forecasts, and ease of use.
• Delta Scan (2005). Part of the United Kingdom’s Horizon Scanning Centre, organized with the goal of becoming a persistent system.
• X2 (2007). Persistent system with a novel architecture, qualitative assessment, and integration of multiple forecasting techniques.


• Openness. An open approach allows the use of crowd resources to identify potentially disruptive technologies and to help understand their possible impact. Online repositories such as Wikipedia and have shown the power of public-sourced, high-quality content.

• Persistence. In today’s environment, planning cycles are highly dynamic, and cycle times can be measured in days instead of years. For this reason it is important to have a forecasting system that monitors, tracks, and reformulates predictions based on new inputs and collected data.

• Transparency. The contributors and users of the system need to trust that the system operators will not exploit personal or other contributed information for purposes other than those intended. The system should publish and adhere to policies on how it uses, stores, and tracks information.
• Structural flexibility. This should be sufficient to respond to complexity, uncertainty, and changes in technology and methodology.
• Easy access. The system should be easy to use and broadly available to all users.
• Proactive bias mitigation. The main kinds of bias are cultural, linguistic, regional, generational, and experiential. A forecasting system should therefore be implemented to encourage the participation of individuals from a wide variety of cultural, geographic, and linguistic backgrounds to ensure a balance of viewpoints. In many fields, technology is innovated by young researchers, technologists, and entrepreneurs. Unfortunately, this demographic is overlooked by the many forecasters who seek out seasoned and established experts. It is important that an open system include input from the generation most likely to be the source of disruptive technologies and be most affected by them.
• Incentives to participate.
• Reliable data construction and maintenance.
• Tools to detect anomalies and sift for weak signals. A weak signal is an early warning of change that typically becomes stronger when combined with other signals.
• Strong visualization tools and a graphical user interface.
• Controlled vocabulary. The vocabulary of a forecast should include an agreed-upon set of terms that are easy for both operators and users to understand.

Benchmarking and a process for building a persistent forecasting system are discussed.

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