FIRST FORECASTING SYSTEM: INTELLIGENCE CYCLE OPTION

The name Intelligence Cycle Option was given to the system design that uses an option similar to the classic approach used by the intelligence community: hypothesize, task, collect, and analyze. This system starts with a “big question,” which initiates creative hypothesis generation fed by passive data collection (from movies, media, and online databases, for example) and active data gathering (e.g., crowdsourcing, games). The inputs then flow through hypothesis evaluation and testing by experts or participants in the science and technology, financial, and sociopolitical arenas or through mechanisms such as gaming and crowdsourcing. The raw output from these processes is shaped into a narrative1 for stakeholders and then fed back into the hypothesis engine. See Figure 2-1 for the option design from the workshop.

The system design was organized around four functions:

  1. The input of a “big question,”

  2. Signal identification and hypothesis generation,

  3. Hypothesis evaluation and testing, and

  4. The authoring of potential future narratives.

The Input of a Question

The forecasting process is initiated with a big-picture question posed by the stakeholder for a particular audience (i.e., Congress, the White House) and communicated to the system director/manager (see Table 2-1 for a detailed description of the different roles). Generating the big-question-based raw hypotheses requires an understanding of the forces that shape the perspective of the customer, and this is a key responsibility of the director/ manager, who is responsible for all interaction with the stakeholders. Throughout the development of the forecast, the stakeholder will receive feedback from the data, collection, hypothesis-generation, and hypothesis-evaluation processes and be able to assign parameters to ensure that the final narrative addresses the original purpose.

Another approach to generating the big question is to leverage outside experts or the crowd to suggest a list of big questions that stakeholders should consider. This list could be reduced through discussions with stakeholders to identify one or two big question(s) that should be addressed using this process. This approach is useful for finding questions that would not normally be generated by people inside a system, and it is a valuable way to avoid closed ignorance.2

Processes

The raw hypothesis based on the stakeholders’ big question is fed into an interconnected enterprise of passive and active data gathering, analysis, and hypothesis generation. The forecasting system’s hypothesis managers add a rough story and idea to the question and send the hypothesis to the passive and active analysis functions. Information is passively collected using software-centric textual and multimedia data mining and statistical analysis to identify applications, technologies, or ideas that have garnered increased interest, that cross subject areas, or cross regional boundaries. Inputs might include themes or ideas from U.S. and foreign movies and literature, electronic discussions of technologies and applications, cultural media, and volume and pricing of key technologies on eBay. Financial input might come from venture capital information sources or from internal agency sources of the government (e.g., the Federal Reserve, the Department of Energy’s Energy Information Administration, the Bureau of Labor Statistics).

Social and political input might come from U.S. and foreign government organizations, academic institu-

1

In this report, a “narrative” is defined as an account of events providing a context within which a prediction takes on broader significance.

2

“Closed ignorance” is defined as follows: Information is available, but stakeholders are unwilling or unable to consider that some outcomes are unknown. A form of closed ignorance occurs when individuals or groups with purposeful goals or objectives find that their goals and objectives are contrary to the need to identify disruptions.



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