The chapters (articles) presented in this book are meant to represent my thinking around the systemic structure of knowledge—as represented by theories, models, policy models, strategic plans, and so on (we will use these terms interchangeably). The most important idea here is that knowledge is more useful (for understanding, making effective decisions, communication, collaboration, planning, and reaching goals) when it is more systemic—more structured.
When I began in this field that idea of systemic structure existed; however, it was not very well developed. It was difficult or impossible to look at two theories and decide which one had the best structure (and so would be the most useful). My efforts were focused therefore on understanding the underlying structure—what is it that makes any (and all) theoretical knowledge useful. After 14 years of effort (and the writing of this book) I have reached a good stopping point. Rather than a set of loose axioms, the field now has Integrative Propositional Analysis (IPA); a clear and simple method for evaluating the structure of knowledge, along with a few other methods that are a bit more complex but also effective for advancing useful knowledge.
In the following section, I reflect briefly on some key articles, how they represent steps forward in my thinking. My main hope here is to provide some stepping stones for those who might want to understand and/or follow this path. And, from a broader perspective, support those who want to study how thinking evolves.
Wallis, S. E. (2006). A Study of Complex Adaptive Systems as Defined by Organizational Scholar-Practitioners. Fielding Graduate University, Santa Barbara.
The first step of the path began with a dissertation (a relevant portion of which is included with the present book). There, dimensional dialectics were used to understand how parts of a theory might co-define one another. This may be thought of as a kind of reflexive approach; but it is about how each part reflects on the others (how each defines or emerges from others), not how the reader might reflect upon each part.
The method of analysis was called reflexive dimensional analysis. At this stage, the term "robustness" was used to describe the theory as a whole. But it was not well defined. Concepts were labeled as dimensions or aspects. And, transformative change due to concatenated structures was described as emergence. This idea of emergence was to be dropped for many years, until picked up again in a very different form in the 2020 paper on levels and emergence.
Wallis, S. E. (2008). From reductive to robust: Seeking the core of complex adaptive systems theory. In A. Yang & Y. Shan (Eds.), Intelligent Complex Adaptive Systems (pp. 1-25). Hershey, PA: IGI Publishing.
A call for chapters on the topic of Complex Adaptive Systems theory led to the condensing of that dissertation into a single chapter. An anonymous reviewer said something like, "It's very nice but can you make the process simpler?" A great debt is owed to that reviewer because the comment started a process of re-thinking everything. A key realization emerged that the concepts and connections of a theory could be counted (very simple). And, because some sub-structures of theory support more understanding than others, those could also be counted (reasonably simple). Thus, the methodology took a big step forward because now it became possible to measure the level of structure for each theory with some level of rigor and objectivity—a formal measure of robustness.
In this chapter it became normal that arrows should represent only causal relationships. Also (although weakly described) that theories with more structure were more systemic and so more useful in application. In contrast, poorly structured theories were subject to more interpretation and would change more rapidly (though the changes would not be very useful). A large enough theory (or synthesis of theories) was likely to have a more systemic/interconnected core and a less systemic belt of disconnected concepts.
Wallis, S. E. (2008). Validation of theory: Exploring and reframing Popper's worlds. Integral Review, 4(2), 71-91.
A deepening exploration of knowledge led to this study of Popper's three worlds, with an eye toward understanding how theories may be tested (through a process of falsification) and so have their weaknesses identified and so could be improved. Those worlds helped separate the world of theoretical concepts from the world of data and the world of (we might say) human interest and emotion. All three worlds are important, of course, but more useful when we are able differentiate between them.
The ability to separate, to differentiate, of course is key to science. Another key is the ability to integrate, to synthesize.
Wallis, S. E. (2009). The complexity of complexity theory: An innovative analysis. Emergence: Complexity and Organization, 11(4), 26-38.
The ideas of falsification were tested in this next paper that used the new methods to explore Complexity Theory. This successful application to a new body of theory confirmed that the project was on the right track—that this suite of methods, techniques, and perspectives could be used with some level of objectivity and reliability to evaluate bodies of theory- to show where we stand and how to advance a body of theory.
Wallis, S. E. (2010). Toward a science of metatheory. Integral Review, 6(3 - Special Issue: "Emerging Perspectives of Metatheory and Theory"), 73-120.
Another part of this exploration in values asks what it meant to have a metatheory—a theory about theories—and how that might inform my further investigations. The results are summed up in this paper. Importantly, this was not a simple survey of perspectives. Instead, it was done with a focus on how make the use of metatheory to improve theory a more scientific endeavor. This would help scholars to avoid wallowing in speculative philosophy and make some progress in a world that desperately needs our help.
Wallis, S. E. (2010). The structure of theory and the structure of scientific revolutions: What constitutes an advance in theory? In S. E. Wallis (Ed.), Cybernetics and systems theory in management: Views, tools, and advancements (pp. 151-174). Hershey, PA: IGI Global.
The next big challenge was to show the evolution of theory. This was best done by evaluating a theory from physics. In what might be called a seminal work, this chapter on the structure of theory and the structure of scientific revolutions showed how the systemic structure of a theory changed and evolved over the centuries from relatively useless to useful knowledge. The ability to show progress is placed in contrast with the high rate of failure for social theories—and so hints at the opportunity to accelerate the development of the social sciences.
Here the methodology is approaching some level of maturity. The name of the methodology is now Propositional Analysis. The use of causal arrows and the focus on concatenated structures continues but some older terms are still in use such as robustness.
Wallis, S. E. (2013). How to choose between policy proposals: A simple tool based on systems thinking and complexity theory. Emergence: Complexity & Organization, 15(3), 94-120.
With growing confidence in the methodology, its use was expanded and applied to policy models. For a method that had been developed for theories of the social sciences, and applied equally well to theories of physics, this was another step forward in broadening the scope of use.
Additionally, and importantly, this article introduces and compares five structures of causal logic (atomistic, linear, branching, circular and concatenated) and uses a case comparative study process to evaluate two policy proposals and show which one is more likely to be effective in practical application. This paper clarifies the predictive use of the methodology. That is to say, given two or more conceptual systems (be they theories or policies or plans) we can make a good prediction as to which one we should choose in order to inform our decisions and reach stated goals.
Wallis, S. E. (2014). Existing and emerging methods for integrating theories within and between disciplines. Organizational Transformation and Social Change, 11(1), 3-24.
With increasing confidence in the methodology, and having analyzed theories from multiple sciences, this paper was written to explore and demonstrate how existing approaches to integrating (synthesizing) theories between disciplines did not lead to progress—but rather to fragmentation and confusion. And, that using IPA could serve as a more rigorous tool to reverse that process to increase our collective capacity for integrating multiple perspectives and improving our collective knowledge. Here, a strong argument is also made against "parsimony"—the mistaken idea that the simpler theory is the better theory.
This conversation on interdisciplinary synthesis would reach a peak (at least a temporary one) with the 2020 publication, "The missing piece of the integrative studies puzzle" in the Interdisciplinary Science Reviews, 44(3-4).
Wallis, S. E., & Wright, B. (2015, March 4-6). Strategic Knowledge Mapping: The Co-creation of Useful Knowledge. Paper presented at the Association for Business Simulation and Experiential Learning (ABSEL) 42nd annual conference, Las Vegas, CA.
Another paper broadening the scope of use was presented (and won a "best paper" award) at the ABSEL (Association for Business Simulation and Experiential Learning) conference. This paper presented a "gamified" approach to the co-generation of useful knowledge. Briefly, by building the analytical process into the game process means that the more the game is played, the more that the resulting knowledge map will become a representation of useful knowledge. This push towards gamification it reaching new heights with an online version at: https://cauzality.com/.
This is the first paper to introduce the term Integrative Propositional Analysis (IPA)—which is used to this day—with the emphasis on integration found in the collaborative activity of the game participants. This paper also solidified the importance of looking at knowledge as maps. Something that has proved quite useful for strategic planning and implementation.
Wallis, S. E. (2016). The science of conceptual systems: A progress report. Foundations of Science, 21(4), 579-602.
Returning to the theme of studying theories, a good touchstone paper is in this progress report. Here, IPA is the method, causality is the best way to represent connections, Systemicity is used to represent "how systemic" a theory is, and complexity is used to represent the "simple complexity" of the number of concepts within a theory. This paper summarizes a few other papers to show the evolution of theories over time—from both the natural and social sciences. Importantly, this paper talk about using the IPA method to accelerate the development of the social sciences. Speculating that this kind of science multiplier could be used to double to usefulness/efficacy of our theories in a very few years.
Wallis, S. E. (2014). Abstraction and insight: Building better conceptual systems to support more effective social change. Foundations of Science, 19(4), 353-362.
Backing up in time, and twisting the dial on our focus, this paper broke new ground by exploring individual concepts (and their interrelationships) in a whole new way. Here, the argument is made that we can develop more effective theories by building them of concepts that are at or about the same level of abstraction.
Wallis, S. E. (2020). Orthogonality: Developing a structural/perspectival approach for improving theoretical models. Systems Research and Behavioral Science, 37(2), 345-359.
A more sophisticated version of that "abstraction approach" was finally developed in the paper on Orthgonality. From the orthogonal perspective, each concept of a theory must be orthogonal to all other concepts in the theory. That is to say represent something that is completely different from all other things represented by concepts in the theory. This is, perhaps, the most challenging of ideas presented yet well worth further exploration for developing better theories.
Because it has not been proven in multiple publications, orthogonality is considered somewhat experimental. Hence, its designation as an "IPAx" method.
Wallis, S. E. (2021). Understanding and improving the usefulness of conceptual systems: An Integrative Propositional Analysis-based perspective on levels of structure and emergence Systems Research and Behavioral Science, in press.
Another member of the IPAx family presents the challenging idea is that theories might have differing "levels." And, importantly, that those levels might represent levels of emergence. This new approach holds great promise for developing more sophisticated theories to more effectively address highly complex multilevel problems. It may also be seen as bringing closer together the world of theory and the world of our lived experience.
Wallis, S. E. (2020). Evaluating and improving theory using conceptual loops: A science of conceptual systems (SOCS) approach. Cybernetics and Human Knowing, 27(3).
The third member of the IPAx family starts with the idea that theories should contain feedback loops. While, generally, that idea has broad acceptance, few grasp the idea that the structure of those loops may be evaluated with objectivity and rigor. This paper provides breakthrough techniques for the evaluation of loops in theories.
The more useful theory will have more loops; and, a higher percentage of its concepts will be included in those loops. Importantly, it is also necessary to consider the "signs" (positive or negative) of those loops; an interesting balance is required.
Wallis, S. E. (2020). Commentary on Roth: Adding a conceptual systems perspective. Systems Research and Behavioral Science, 37(1), 178-181.
The final paper on this tour is the shortest. And, in some ways, the most humble. Yet, it serves to clarify an important perspective of "what it is all about." This paper shows how ideas of "conceptual systems" (theories, models, plans, etc.) within the world of concepts are related to "physical systems" (social systems, communication systems, environmental systems, etc.). And, in doing so, helps to clarify our collective understanding of the similarities and differences of these two worlds.
In doing so, and in concert with the other parts of this book, the stage is set for each reader to extend this exploration; to accelerate the evolution of useful knowledge for understanding and resolving the seemingly impossible problems of the world.