Category

Attributes

Description

Processing tools and methods

Enablers/inhibitors

Facilitate methods to identify and monitor key enablers, inhibitors, measurements of interest, signals, signposts, and tipping points that contribute to or serve as a warning of a pending disruption.

 

Multiple perspectives—qualitative/human

Humans with varying backgrounds, of diverse cultures, ages, and expertise analyze data employing multiple tools and methods.

 

Outlier events/weak signal detection

Tools and methods for finding weak signals or extreme outliers in large data sets. Tools and processes to track and monitor changes and rates of change in linkages between data are essential.

 

Impact assessment processes

Employ methods to assess impact of potential disruptive technology and recommend potential methods to mitigate or capitalize on the disruption.

 

Threshold levels and escalation processes

Employ methods to set and modify warning signal threshold levels and escalate potentially high-impact signals or developments to other analytical perspectives or decision makers.

 

Forecast data object flexibility

Store data using object-oriented structures. The data objects being used to forecast can show flexibility in how they are stored. Data objects can be categorized in several ways, including but not limited to disruptive research, disruptive technologies, and disruptive events. Relationships and structures between these objects can be restructured and analyzed.

 

Visualization

Data should be visually represented intuitively and with interactive controls. System should support geospatial and temporal visualizations.

System attributes

Bias mitigation processes

Robust ongoing internal and external bias mitigation processes are in place.

 

Review and self-improvement

Processes in place to review and assess why prior disruptions were either accurately predicted or missed by the platform.

 

Persistence

Forecasts are ongoing and in real time.

 

Availability

System should be continuously accessible and globally available.

 

Openness

System should be open and accessible to all to contribute data, provide forecasts, analyze data, and foster community participation. The data, forecast, and signals generated from the system are publically available.

 

Scalability/flexibility (hardware and software)

System should scale to accommodate large numbers of users and large datasets utilizing standardized data and interchange formats.

 

Controlled vocabulary

Use standard vernacular for system benchmarks (watch, warning, signal, etc.), language and tagging.

 

Multiple native language support

Data should be gathered, processed, exchanged, translated, and disseminated in a broad range of languages.

 

Incentives

Reputation, knowledge, recognition, and other methods for incentivizing participation. Monetary incentives could be considered to get certain expert sources and research initiatives to contribute.

 

Ease of use (accessibility, communication tools, intuitive)

Make the site easily accessible. Navigation around the site should be intuitive and have communication tools to facilitate usability and community development.

Environmental considerations

Financial support

The system must be underpinned by long-term and substantial financial support to ensure that the platform can achieve its mission.

 

Data protection

Data must be protected from outages, malicious attack, or intentional manipulation. Robust back-up and recovery processes are essential.

 

Auditing and review processes

Put processes in place to regularly review platform strengths and weaknesses, biases, why disruptions were missed, and to audit changes to data, system, architecture, hardware, or software components.



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