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The main purpose of logic optimization lies in architecting highly integrated switching circuits. It is therefore a topic well covered in electrical engineering and logic design. However, it is hardly discussed in the social sciences, although several of its sub-fields have employed logic optimization algorithms since at least the mid-1980s. In particular, so-called configurational comparative methods (CCMs) have made extensive use of logic optimization techniques. Whereas numerous optimization algorithms have been proposed and implemented over the last decades in electrical engineering and logic design, these advancements were neither monitored nor imported by social scientists. One major example of the disconnection of CCMs to its parent discipline relates to multivalue optimization with multiple outputs. This study complements the new tool on Combinational Regularity Analysis (CORA) - a new branch of CCMs- by extending its use to multivalue coded multioutput functions. An extension of a 1-terms and 0-terms optimization algorithm is presented for modelling highly unspecified data characterized by large number of input factors and small to medium number of available cases, which is characteristic for most CCMs application studies with multivalue variables.

Re-Establishing a lost connection: Multivalue logic in causal data analysis in social science disciplines