The Science of Conceptual Systems: A Progress Report

Abstract

In this paper I provide a brief history of the emerging science of conceptual systems, explain some methodologies, their sources of data, and the understandings that they have generated. I also provide suggestions for extending the science-based research in a variety of directions. Essentially, I am opening a conversation that asks how this line of research might be extended to gain new insights—and eventually develop more useful and generally accepted methods for creating and evaluating theory. This effort will support our ability to generate theory that is more effective in practical application as well as accelerating the development of theory to support advances in other sciences.

Glossary [Adapted (and extended) from Wallis (2011, pp. 99–124)]

Aspect

See “Concept”

Atomistic logic

A kind of logical structure found within a proposition that is reductionist such as “A is valid” or “A is true.” Or, more concretely, “Apples are important.” These would score very low in a study of Integrative Complexity

Bibliometrics

Methods such as content analysis and citation analysis, typically used in library sciences, for the quantification of text from academic literature

Branching logic

A logical structure found within causal propositions including three or more concepts where a change in one concept causes change in two or more other concepts. For example, a Branching proposition might say that changes in A will cause changes in B and C. For a more concrete example, “More teamwork will lead to more cohesion, and more results, and more frustration

Case comparative study

Investigating and comparing two or more cases along multiple dimensions to draw insights and inferences. A useful start to building new theory (Eisenhardt and Graebner 2007)

Causal relationship (causality)

Where two or more concepts are related so that a change in one causes a change in one or more others. A causal relationship is often expressed as a proposition, hypothesis, or a diagram. A causal relationship such as, “More A causes more B.” may also be used as a general term in place of other more specific terms. Instead of saying “more” other indicators might be “better” or “less” (for example). Similarly, instead of “causes” more specific indicators might include such terms as “creates,” or “engenders.” In any case, the description must be specific to be valid. It is not useful to state (for example) that “A and B are interrelated” or “More A may cause more B” because the nature of the relationship is not causally defined. Logical structures often describe causal relationships (e.g. Linear, Branching, Concatenated)

Circular logic

A logic structure where one cause leads back to itself such as Changes in A cause changes in B cause changes in C cause changes in A. Circular logics may be seen as feedback loops which are held to be very useful in understanding systems. However, they can also be misleading if researchers rely on too few loops to adequately represent the system. Then they may appear as tautologies (e.g. more A causes more A)

Cognitive science

Interdisciplinary investigation of the human, social, and artificial processes around perception, information, reasoning, and decision making

Coherentist perspective

Coherence is focused on the relationships between beliefs. A statement is true/meaningful/useful based on its relationship with other statements (Šešelja and Straßer 2014; Sosa 2003). This is in contrast to a perspective of correspondence where a statement is held to be true based on its relationship to empirical data. The two perspectives are orthogonal and so most useful when used in combination

Complexity

A measure representing the number of concepts within a conceptual system. This may also be understood as the diversity of ideas within a document. For an abstract example, consider a conceptual system containing the propositions: A is true; More B causes more C; More B causes more D. In such a model, there are four concepts (A, B, C, D). Therefore, the Complexity of the conceptual system is C = 4

Concatenated logic

A logical structure found within a causal proposition including three or more concepts where changes in two or more concepts cause change in another concept. For an abstract example, a Concatenated proposition might state that changes in concept A and concept B will cause changes in concept C. In that example, C is the Concatenated concept, while A and B exist within a Concatenated relationship but are themselves not Concatenated. For a more concrete example, “More collaboration and more shared goals will result in more teamwork.” Here, “teamwork” is the Concatenated (and better understood) concept

Concept

The part of conceptual system that represents a concept, idea, or notion. The concept may be as concrete as in “apple” or as abstract as in “truth.” Concepts may be simple as in “numbers” or complex as in “left handed monkeys with undiagnosed trauma.” A concept is typically detectable, that is to say empirically measurable, but that is not an absolute standard. Concepts are part of propositions

Conceptual system

A set of interrelated concepts. Examples include theories, policy models, mental models, schemata, etc. Metaphorically, they serve as lenses to aid in understanding and effective engagement of the world (Wallis 2014b, d)

Correspondence perspective

Commonly associated with “normal” science or “Science One” (Müller and Tos 2012; Umpleby 2010) and the use of Toulminian logics, correspondence is focused on the relationship between a statement and some empirically verifiable existence

Dimensional analysis

The analysis of relationships by focusing on their dimensions and units of measure on a fundamental level (c.f. Gardner Jr 2004; Jacobson 2001)

Grounded theory

An approach to theory development that is non-positivist, yet is solidly based on the use of data—although the data are generally qualitative. Data, often from interviews, are coded and categorized. Then, relationships are identified between the categories to identify a useful theory (Charmaz 2006; Glaser and Strauss 1967)

Integral thinking

Understanding (or attempting to understand) the world from a transdiciplinary perspective where those many perspectives are interrelated

Integrative complexity (IC)

“Integrative complexity is a measure of the intellectual style used by individuals or groups in processing information problem solving, and decision making. Complexity looks at the structure of one’s thoughts, while ignoring the contents. It is scorable from almost any verbal materials: books, articles, fiction, letters, speeches and speech transcripts, video and audio tapes, and interviews.” http://www2.psych.ubc.ca/~psuedfeld/index2.html

Integrative propositional analysis (IPA)

Combined processes of qualitative and quantitative analysis involving rigorous hermeneutic deconstruction of propositions found in formal texts including the rigorous re-integration of propositions from those texts following a structured methodology. Also a process of meta-analysis for investigating conceptual systems to determine the Complexity of conceptual systems (diversity of concepts) and the Systemicity of the conceptual system (connectedness between concepts)

Linear logic

A logical structure found within a proposition describing simple causal relationship between two concepts. Such as, “More A causes more B.” Both A and B exist in Linear relationship to one another. Here, A is the causal concept and B is the resultant concept). Linear structures can be of any length (e.g. More A causes more B which causes more C which causes more D… which causes more Z). For a more concrete example, “Having more shared goals leads to more teamwork which, in turn, leads to more productivity.” Within an explanation, this may also be phrased as, “A is true because of B and B is true because of C… because of Z”

Logic model

A set of interrelated logic statements such as a theory or a Policy Model describing causal relationships between the elements of the model

Mental model

A representation within one’s mind about how the world works. Useful for understanding and engaging the world and for making predictions

Metapolicy

Generally, the study of policy—how the policy is created, applied, and evaluated. This may be rigorous as in the use of Integrative Propositional Analysis or fuzzier as in the use of historical narrative. May also be used to describe a policy on how to make policy

Metatheory

Primarily the study of theory, including the development of overarching combinations of theory, as well as the development and application of theorems for analyses that reveal underlying assumptions about theory and theorizing

Model

“A model is a simplified representation of a system at some particular point in time or space intended to promote understanding of the real system. As an abstraction of a system, it offers insight about one or more of the system’s aspects, such as its function, structure, properties, performance, behavior, or cost.” http://sebokwiki.org/wiki/Representing_Systems_with_Models

Narrative analysis

Qualitative approach for understanding how people make sense of their world based on spoken or written accounts of their experiences and how those experiences are interpreted

Parsimony

Generally, the understanding that a theory is better when it is smaller. Or, as small as possible while including ideas that are necessary or useful. Ockham’s Razor is a common example

Policy model

A cognitive or conceptual structure (like a theory) representing how a community or organization understands the world, thus enabling them to take specific actions to achieve their goals. As a sense-making structure, the policy model does not (strictly speaking) include goals or actions. Those are part of the broader policy

Proposition

“A proposition is a declarative sentence expressing a relationship among some terms.” (Van de Ven 2007, p. 117). For example, “More travel leads to more discovery.” (See Causal Relationship). Those terms are often understood to be “concepts”

Reflexive dimensional analysis (RDA)

A process for creating a unified conceptual system from multiple conceptual systems through a process of categorization, abstraction, dimensionalization, and the identification of causal connections

Robustness

An older term for “Systemicity”

Schema

Most commonly used in computer science and logics in referring to a formula and/or set of data used to represent a system

Scientometrics

Approaches of Bibliometrics applied to study the development of science within and between fields

Social/behavioral sciences

The purposeful study and advancement of understanding in all fields of human interaction including (but not limited to) psychology, sociology, policy, ethics, business, management, human development, organizational development, economics, and social anthropology

Systemicity

A ratio describing the interrelatedness between concepts of a conceptual system on a scale of zero to one. Systemicity is calculated by dividing the number of Concatenated concepts by the total number of concepts in a policy (see Integrative Propositional Analysis). Systemicity is a measure of how well integrated the propositions of a conceptual system are, the degree to which they are understood as existing in a systemic relationship, and the level of causality between the concepts. Systemicity is also related to the effectiveness of the conceptual system in practical application

Systems thinking

Understanding that the world is made of systems, this interdisciplinary way of thinking explores how those systems effect one another. Systems thinkers strive to obtain a deeper understanding by looking at the connections of many things, rather than seeking to understand things in isolation

Theory

An ordered set of assertions. Weick (1989, p. 517. Drawing on Southerland)