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The analysis of competing hypotheses (ACH) is a 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. ACH aims to help an analyst overcome, or at least minimize, some of the cognitive limitations that make prescient intelligence analysis so difficult to achieve.
ACH was 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:
Some benefits of doing an ACH matrix are:
Weaknesses of doing an ACH matrix include:
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.
According to social constructivist critics, ACH also fails to stress sufficiently (or to address as a method) the problematic nature of the initial formation of the hypotheses used to create its grid. There is considerable evidence, for example, that in addition to any bureaucratic, psychological, or political biases that may affect hypothesis generation, there are also factors of culture and identity at work. These socially constructed factors may restrict or pre-screen which hypotheses end up being considered, and then reinforce confirmation bias in those selected.
Philosopher and argumentation theorist Tim van Gelder has made the following criticisms:
Van Gelder proposed hypothesis mapping (similar to argument mapping) as an alternative to ACH.
The structured analysis of competing hypotheses offers analysts an improvement over the limitations of the original ACH.[discuss] The SACH maximizes the possible hypotheses by allowing the analyst to split one hypothesis into two complex ones.
For example, two tested hypotheses could be that Iraq has WMD or Iraq does not have WMD. If the evidence showed that it is more likely there are WMDs in Iraq then two new hypotheses could be formulated: WMD are in Baghdad or WMD are in Mosul. Or perhaps, the analyst may need to know what type of WMD Iraq has; the new hypotheses could be that Iraq has biological WMD, Iraq has chemical WMD and Iraq has nuclear WMD. By giving the ACH structure, the analyst is able to give a nuanced estimate.
One method, by Valtorta and colleagues uses probabilistic methods, adds Bayesian analysis to ACH. A generalization of this concept to a distributed community of analysts lead to the development of CACHE (the Collaborative ACH Environment), which introduced the concept of a Bayes (or Bayesian) community. The work by Akram and Wang applies paradigms from graph theory.
Other work focuses less on probabilistic methods and more on cognitive and visualization extensions to ACH, as discussed by Madsen and Hicks. DECIDE, discussed under automation is visualization-oriented.
Work by Pope and Jøsang uses subjective logic, a formal mathematical methodology that explicitly deals with uncertainty. This methodology forms the basis of the Sheba technology that is used in Veriluma's intelligence assessment software.
A few online and downloadable software tools help automate the ACH process. These programs leave a visual trail of evidence and allow the analyst to weigh evidence.