Computational complexity refers to the representational aspect of problem-solving. This representational aspect is commonly understood to describe the regularities that organize the definition of the problem-situation, the qualities that designate its difficulty, either in terms of the process of arriving at a definition of the problem or in the process of arriving at a solution. It is for this reason, that it is commonly said that “representational design precedes computational efficacy.” The point of this maxim is to serve as a reminder that an efficient use of resources begins with the design of near decomposability, the specification of the properties belonging to a complex system or an environment that is subject to some form of transformation into a solution for a problem. Clearly stated: Less resources are necessary to solve a problem at a lower degree of difficulty than the resources necessary at a higher degree of difficulty. The question, then, is as follows: what happens when this central maxim of problem-solving cannot be applied? This dissertation explores such a class of problems by developing a language appropriate to them, the theorizing of what are referred to as “quasi-decomposable” problems. By “quasi-decomposable,” it is meant that the challenge of computational complexity is derived from the nature and character of discourse in which the demands of intelligent specification are necessarily deferred as an artifact of socially situated intelligibility. The size and scope of this class of problems is evaluated with respect to a critique of Herbert Simon’s theory of problem-solving, the derivation of the distinction between “skill-oriented” and “expertise-oriented” problem-solving, and its relevance to organizational settings in which attention is a scarce resource. In that respect, the study examines how computational complexity poses challenges for organizational leadership and the implementation of education and technology (artificial intelligence) at the scale in which a society requires both a specialized division of cognitive labor and the unity of cognitive labor regarding the realization of citizenship as an alignment of democratic reason and democratic values. In doing so, the study investigates the relationship between the modeling of efficiency and effectiveness (between behavioral economics and cognitive sociology) and concepts of expertise (the stratification of task performance, cultural performance, cognitive style). This allows for an extensive discussion of how socially situated intelligibility informs creativity and insight (adaptation and innovation by the means of intuition) and the attribution of inspiration and cognitive error. In this examination of the relationship between skill and computational complexity, expertise is shown to be an orientation to calculation in the course of heuristic search. The operation of this orientation is demonstrated through the progressive deepening of “quasi-decomposable” problems. This search strategy is revealed as a mechanism of social life by the necessity of communication posing a predicament for organizational design in which the depth of calculation for progressive deepening is distributed according to “the politics of expertise.”
","abstract_html":"<p>Computational complexity refers to the representational aspect of problem-solving. This representational aspect is commonly understood to describe the regularities that organize the definition of the problem-situation, the qualities that designate its difficulty, either in terms of the process of arriving at a definition of the problem or in the process of arriving at a solution. It is for this reason, that it is commonly said that “representational design precedes computational efficacy.” The point of this maxim is to serve as a reminder that an efficient use of resources begins with the design of near decomposability, the specification of the properties belonging to a complex system or an environment that is subject to some form of transformation into a solution for a problem. Clearly stated: Less resources are necessary to solve a problem at a lower degree of difficulty than the resources necessary at a higher degree of difficulty. The question, then, is as follows: what happens when this central maxim of problem-solving cannot be applied? This dissertation explores such a class of problems by developing a language appropriate to them, the theorizing of what are referred to as “quasi-decomposable” problems. By “quasi-decomposable,” it is meant that the challenge of computational complexity is derived from the nature and character of discourse in which the demands of intelligent specification are necessarily deferred as an artifact of socially situated intelligibility. The size and scope of this class of problems is evaluated with respect to a critique of Herbert Simon’s theory of problem-solving, the derivation of the distinction between “skill-oriented” and “expertise-oriented” problem-solving, and its relevance to organizational settings in which attention is a scarce resource. In that respect, the study examines how computational complexity poses challenges for organizational leadership and the implementation of education and technology (artificial intelligence) at the scale in which a society requires both a specialized division of cognitive labor and the unity of cognitive labor regarding the realization of citizenship as an alignment of democratic reason and democratic values. In doing so, the study investigates the relationship between the modeling of efficiency and effectiveness (between behavioral economics and cognitive sociology) and concepts of expertise (the stratification of task performance, cultural performance, cognitive style). This allows for an extensive discussion of how socially situated intelligibility informs creativity and insight (adaptation and innovation by the means of intuition) and the attribution of inspiration and cognitive error. In this examination of the relationship between skill and computational complexity, expertise is shown to be an orientation to calculation in the course of heuristic search. The operation of this orientation is demonstrated through the progressive deepening of “quasi-decomposable” problems. This search strategy is revealed as a mechanism of social life by the necessity of communication posing a predicament for organizational design in which the depth of calculation for progressive deepening is distributed according to “the politics of expertise.”</p>","abstract_has_math":false,"creators":["Raphael, Michael W."],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Sociology","degree_department":null,"school":null,"contributors":[],"advisors":["Roslyn Bologh"],"committee_chairs":[],"committee_members":["Lynn Chancer","Thomas DeGloma","Michael E. Brown"],"year":2023,"date_issued":"2023-06-01T07:00:00Z","date_published":"2023-06-01T07:00:00Z","updated_at":"2026-07-24T02:00:26Z","subjects":["Cognitive Science","Communication Technology and New Media","Industrial and Organizational Psychology","Interpersonal and Small Group Communication","Organizational Communication","Philosophy of Language","Philosophy of Mind","Philosophy of Science","Political Theory","Politics and Social Change","Science and Technology Studies","Social and Cultural Anthropology","Social Control, Law, Crime, and Deviance","Social Psychology","Sociology of Culture","Theory and Philosophy","Theory, Knowledge and Science","authority","intelligibility","bounded rationality in behavioral economics and cognitive sociology","cognitive style","culture","expertise","communication and computational complexity"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/5353","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Roslyn Bologh"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lynn Chancer","Thomas DeGloma","Michael E. 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This representational aspect is commonly understood to describe the regularities that organize the definition of the problem-situation, the qualities that designate its difficulty, either in terms of the process of arriving at a definition of the problem or in the process of arriving at a solution. It is for this reason, that it is commonly said that “representational design precedes computational efficacy.” The point of this maxim is to serve as a reminder that an efficient use of resources begins with the design of near decomposability, the specification of the properties belonging to a complex system or an environment that is subject to some form of transformation into a solution for a problem. Clearly stated: Less resources are necessary to solve a problem at a lower degree of difficulty than the resources necessary at a higher degree of difficulty. The question, then, is as follows: what happens when this central maxim of problem-solving cannot be applied? This dissertation explores such a class of problems by developing a language appropriate to them, the theorizing of what are referred to as “quasi-decomposable” problems. By “quasi-decomposable,” it is meant that the challenge of computational complexity is derived from the nature and character of discourse in which the demands of intelligent specification are necessarily deferred as an artifact of socially situated intelligibility. The size and scope of this class of problems is evaluated with respect to a critique of Herbert Simon’s theory of problem-solving, the derivation of the distinction between “skill-oriented” and “expertise-oriented” problem-solving, and its relevance to organizational settings in which attention is a scarce resource. In that respect, the study examines how computational complexity poses challenges for organizational leadership and the implementation of education and technology (artificial intelligence) at the scale in which a society requires both a specialized division of cognitive labor and the unity of cognitive labor regarding the realization of citizenship as an alignment of democratic reason and democratic values. In doing so, the study investigates the relationship between the modeling of efficiency and effectiveness (between behavioral economics and cognitive sociology) and concepts of expertise (the stratification of task performance, cultural performance, cognitive style). This allows for an extensive discussion of how socially situated intelligibility informs creativity and insight (adaptation and innovation by the means of intuition) and the attribution of inspiration and cognitive error. In this examination of the relationship between skill and computational complexity, expertise is shown to be an orientation to calculation in the course of heuristic search. The operation of this orientation is demonstrated through the progressive deepening of “quasi-decomposable” problems. This search strategy is revealed as a mechanism of social life by the necessity of communication posing a predicament for organizational design in which the depth of calculation for progressive deepening is distributed according to “the politics of expertise.”
"]},{"key":"dc:title","label":"Title","values":["The Politics of Expertise and the Articulation of Authority in Democracy: Cognitive Style and Intelligibility in the Education of Problem-Solving and Professional Decision-Making in the Domain of Law"]}]}],"canonical_facts":{"dc:contributor.advisor":["Roslyn Bologh"],"dc:contributor.committeemember":["Lynn Chancer","Thomas DeGloma","Michael E. Brown"],"dc:creator":["Raphael, Michael W."],"dc:date.available":["2025-06-02T07:00:00Z"],"dc:description.abstract":["Computational complexity refers to the representational aspect of problem-solving. This representational aspect is commonly understood to describe the regularities that organize the definition of the problem-situation, the qualities that designate its difficulty, either in terms of the process of arriving at a definition of the problem or in the process of arriving at a solution. It is for this reason, that it is commonly said that “representational design precedes computational efficacy.” The point of this maxim is to serve as a reminder that an efficient use of resources begins with the design of near decomposability, the specification of the properties belonging to a complex system or an environment that is subject to some form of transformation into a solution for a problem. Clearly stated: Less resources are necessary to solve a problem at a lower degree of difficulty than the resources necessary at a higher degree of difficulty. The question, then, is as follows: what happens when this central maxim of problem-solving cannot be applied? This dissertation explores such a class of problems by developing a language appropriate to them, the theorizing of what are referred to as “quasi-decomposable” problems. By “quasi-decomposable,” it is meant that the challenge of computational complexity is derived from the nature and character of discourse in which the demands of intelligent specification are necessarily deferred as an artifact of socially situated intelligibility. The size and scope of this class of problems is evaluated with respect to a critique of Herbert Simon’s theory of problem-solving, the derivation of the distinction between “skill-oriented” and “expertise-oriented” problem-solving, and its relevance to organizational settings in which attention is a scarce resource. In that respect, the study examines how computational complexity poses challenges for organizational leadership and the implementation of education and technology (artificial intelligence) at the scale in which a society requires both a specialized division of cognitive labor and the unity of cognitive labor regarding the realization of citizenship as an alignment of democratic reason and democratic values. In doing so, the study investigates the relationship between the modeling of efficiency and effectiveness (between behavioral economics and cognitive sociology) and concepts of expertise (the stratification of task performance, cultural performance, cognitive style). This allows for an extensive discussion of how socially situated intelligibility informs creativity and insight (adaptation and innovation by the means of intuition) and the attribution of inspiration and cognitive error. In this examination of the relationship between skill and computational complexity, expertise is shown to be an orientation to calculation in the course of heuristic search. The operation of this orientation is demonstrated through the progressive deepening of “quasi-decomposable” problems. This search strategy is revealed as a mechanism of social life by the necessity of communication posing a predicament for organizational design in which the depth of calculation for progressive deepening is distributed according to “the politics of expertise.”
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/5353"],"dc:subject":["Cognitive Science","Communication Technology and New Media","Industrial and Organizational Psychology","Interpersonal and Small Group Communication","Organizational Communication","Philosophy of Language","Philosophy of Mind","Philosophy of Science","Political Theory","Politics and Social Change","Science and Technology Studies","Social and Cultural Anthropology","Social Control, Law, Crime, and Deviance","Social Psychology","Sociology of Culture","Theory and Philosophy","Theory, Knowledge and Science","authority","intelligibility","bounded rationality in behavioral economics and cognitive sociology","cognitive style","culture","expertise","communication and computational complexity"],"dc:title":["The Politics of Expertise and the Articulation of Authority in Democracy: Cognitive Style and Intelligibility in the Education of Problem-Solving and Professional Decision-Making in the Domain of Law"],"thesis:degree_discipline":["Sociology"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T02:00:26Z"}