L1.09: Is GEOINT an Art or Science?

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There are competing views on whether GEOINT is an art or a science. This controversy mirrors a long-standing debate in the intelligence community over whether intelligence analysis is based on subjective, intuitive judgment (an art), or systematic analytic methods (a science). One school is that intuition, experience, and subjective judgment dominate. Here, GEOINT is an art. Another school is that structured data and computers are most relevant. Here GEOINT is science-like. While this controversy is somewhat academic, it does have an impact on what automation can be appropriately applied and on the training and education of the worker. To help understand these points of view, I will define the terms using the Merriam-Webster Collegiate Dictionary, tenth edition, as:

  • Art - the conscious use of skill and creative imagination in the production of aesthetic objects.
  • Science - knowledge or a system of knowledge covering general truths or the operation of general laws, especially as obtained and tested through the scientific method.

GEOINT has the qualities of an art and science where there is no certain dividing line between the two. GEOINT's automated processes are not rich enough in underlying geographical knowledge to accurately reflect reality. This is to say, for something to be automatable, how it works must be described (modeled) and the facts (data) quantified. Since a model is a simplified view of reality, the model represents a limited set of rules which allows analysts to work out an answer if they have certain information. Quantifiability of the information is important because unquantifiable inputs (data) cannot be tested, and thus unquantifiable results can neither be duplicated nor contradicted. The table below categorizes the broad types of GEOINT problem-solving processes.

Table 1.2: Data Certainty versus Model Certainty
  Model Certainty:
Low
Model Certainty:
High
Data Certainty:
High
Model Building Puzzle Solving
Data Certainty:
Low
Mystery Solving Data Foraging

The upper right panel of the matrix identifies analysis type as Puzzle Solving (upper right quadrant) in which there is good knowledge of the data and models surrounding an output. This is, perhaps, the ideal. Analysts understand the problem and can take into account the data. The notion of fixed-in-advance standard procedures typically plays an important role in such geospatial analysis. However, many of the analytic tasks in geospatial intelligence fall outside this quadrant. Consider the Data Foraging (lower right) quadrant, in which there is agreement on models, but a lack of certain data. The notion of "foraging" for the data plays an important role in problem solving.

Model Building (upper left quadrant) is the opposite. In this analytic environment, there is seemingly sufficient data, but disagreement on the model. Analysis is characterized by analysts involved in a struggle for their model to prevail, and decisions emerge from that struggle. This kind of analysis is characterized by bargaining, accommodation, and consensus, as well as controversy. GEOINT as Mystery Solving (lower left quadrant) is the most open to debate. Under these conditions, science and technology tools have significantly less relevance. As a consequence, the analytic process is experience-based. In the end, this is the framing of complex questions, often called wicked problems. They can only be framed, not solved, and thus the logic of argument and analysis is important.

Is GEOINT an art or science? GEOINT's problems fall into any of the four quadrants ---- you, the individual doing the analysis, need to understand the problem and the nature of the problem solving process.