Analysis of Competing Hypotheses

The analysis of competing hypotheses (ACH) provides an unbiased methodology for evaluating multiple competing hypotheses for observed data. It was developed by Richards (Dick) J. Heuer, Jr., a 45-year veteran of the Central Intelligence Agency, in the 1970s for use by the Agency. ACH is used by analysts in various fields who make judgments that entail a high risk of error in reasoning. It helps an analyst overcome, or at least minimize, some of the cognitive limitations that make prescient intelligence analysis so difficult to achieve.

ACH was indeed a step forward in intelligence analysis methodology, but it was first described in relatively informal terms. Producing the best available information from uncertain data remains the goal of researchers, tool-builders, and analysts in industry, academia and government. Their domains include data mining, cognitive psychology and visualization, probability and statistics, etc. Abductive reasoning is an earlier concept with similarities to ACH.

Heuer outlines the ACH process in considerable depth in his book, Psychology of Intelligence Analysis. It consists of the following steps:

There are many benefits of doing an ACH matrix. It is auditable. It is widely believed to help overcome cognitive biases, though there is a lack of strong empirical evidence to support this belief. Since the ACH requires the analyst to construct a matrix, the evidence and hypotheses can be backtracked. This allows the decisionmaker or other analysts to see the sequence of rules and data that led to the conclusion.

The process to create an ACH is time consuming. The ACH matrix can be problematic when analyzing a complex project. It can be cumbersome for an analyst to manage a large database with multiple pieces of evidence.

Especially in intelligence, both governmental and business, analysts must always be aware that the opponent(s) is intelligent and may be generating information intended to deceive. Since deception often is the result of a cognitive trap, Elsaesser and Stech use state-based hierarchical plan recognition (see abductive reasoning) to generate causal explanations of observations. The resulting hypotheses are converted to a dynamic Bayesian network and value of information analysis is employed to isolate assumptions implicit in the evaluation of paths in, or conclusions of, particular hypotheses. As evidence in the form of observations of states or assumptions is observed, they can become the subject of separate validation. Should an assumption or necessary state be negated, hypotheses depending on it are rejected. This is a form of root cause analysis.

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