A Multicriteria Intelligence Aid Methodology Using MCDA, Artificial Intelligence, and Fuzzy Sets Theory

Table 1

MCDA versus MCIA.

	MCDA	MCIA
Characteristics of the problem	Decision-aid problems: complex, ill-defined, and human-oriented decision problems.	Intelligence support: complex, ill-defined, and human-oriented decision problems, uncertainty, need for anticipation, inference, and prediction.
Decision-maker	Involved in the process.	Competitor/threat; not involved in the process.
Alternatives/actions	Could be generated with techniques and tools for stimulation of creativity. The decision-maker generally participates in the action generation step.	Potential actions which need to be generated and anticipated from the competitor’s/threat’s available data on his present and past activities.
Criteria	Defined with top-down approach and bottom-up approaches.	Defined with top-down approach and bottom-up approaches and assumes that the competitor/threat objectives are well known.
Ratings	Crisp or imperfect.	Imperfect: imprecise, uncertain, ambiguous, and subjective.
Preferences	Elucidated and modeled by an interactive process with the decision-maker.	Need to be inferred from available data using artificial intelligence techniques.
Methodology objective	Screening, prioritizing, ranking, or selecting a set of decision-maker actions under independent, incommensurate, or conflicting criteria.	Generating and prioritizing a set of competitor/threat potential actions under independent, incommensurate, or conflicting criteria.