Cognitive Adaptation: A Pragmatist Perspective

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Thus, a key question facing complexity researchers is which systems should form the core of the analyses, and how many levels of analysis are sufficient to provide a complete understanding of the system. The boundaries of social systems are also harder to define and control than in a classic CAS [ 21 , 34 ]. As we discovered in our efforts to develop simulation models of mental health patients, a patient may pass through multiple different practices, hospitals, and even districts over an episode of care, interacting with scores of individual agents, each operating in a different context.

Likewise, the boundaries of the implementation context proved hard to define. Despite beginning with a focus on the MHS as the key implementation context and the SLG as the key agents, it emerged through the course of the evaluation that the context of individual researchers e. Thus, system boundaries are often arbitrary, with implementation and evaluation researchers required to balance descriptive sufficiency with practicality. These issues lead us to a key consideration — in light of these debates in social complexity theory, how can complexity researchers make transparent and consistent decisions regarding research methodology.

Cognitive Adaptation: Insights from a Pragmatist Perspective » Brill Online

While social complexity theory offers a clear ontology, focusing on agent interactions and emergent system outcomes [ 34 ], it lacks a clear position on the epistemic contribution of studying CASs. We suggest that what is needed is a clear epistemology [ 4 ], and we suggest that pragmatism may provide the epistemological foundations required to structure the study of social complexity theory in healthcare.

We suggest that many healthcare workers would identify as pragmatists. The everyday use of the term pragmatism implies a focus on the practical and achievable, rather than the theoretical or ideal [ 41 ]. This idea of valuing the applied over the theoretical is mirrored in the philosophy of Pragmatism. Instead, pragmatists judge the value of knowledge and our ways of knowing by its context-dependent, extrinsic usefulness for addressing practical questions of daily life [ 43 ].

Perfect knowledge is not possible, nor required. For pragmatism, knowledge is only meaningful when coupled with action [ 38 ]. There are many similarities between the arguments of social complexity researchers and pragmatists. A key feature of pragmatism is the contextualization of knowledge [ 44 , 45 ].

Cognitive Adaptation: A Pragmatist Perspective

As contexts change, so too do the criteria of usefulness for knowledge. Similarly, social complexity theory calls for the matching of research approach to context and level of environmental complexity [ 4 , 9 ]. In complexity theory, these contexts could include different nested systems, and different time points [ 44 ]. Therefore, in order to maintain a coherent research agenda in a CAS, a unifying research question is required. In our project, the response to the challenge of working within this particular CAS manifested through the emergent formulation of two deeply pragmatic research questions: How can we the researchers help to improve strategic decision-making for mental health services?

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What can we learn of value through this process? This allowed us, as the context changed, to maintain the same focus for the project, but change and expand the evaluation focus from the experiences of the SLG to include, for instance, adaptations of the researchers to the changing stakeholder needs.

The same aims were addressed, but using different methods. The contextualization of knowledge does not reject the translation of knowledge between contexts. While pragmatism does hold that knowledge is not completely generalizable, it also argues that imported knowledge can play a role in shaping observation and perception and in suggesting possible solutions to the current problem [ 42 ]. For our project, this led us to re-define implementation success, not as a strict adherence to the project plan or the achievement of pre-determined outcomes i.

Indeed, what we learnt was that the simulation models themselves seemed not to be the main outcome of interest to the SLG; instead, it was the personal insights that members gained from the conceptual development discussions and our presentations of amalgamated patient data. Another key pillar of pragmatism is the active and social nature of inquiry. Dewey argued that the primary function of research is to solve societal problems [ 38 ]. Not only does pragmatism argue for a problem-solving approach to inquiry, but also to an action-based one.

All modes of experience, including research, are treated as interventions [ 42 ]. Research success within a pragmatic epistemology is measured by consequences, whether they be predicted or emergent. This aligns with the holistic system view of complexity theory, where outcomes are not pre-determined, but emergent [ 36 ]. Thus, complexity theory provides a way of operationalizing the study of emergent consequences, while pragmatism provides the impetus for change by measuring research quality with respect to its impact on social change.

The usefulness of knowledge metric also creates a democratization of scientific endeavor. Scientific knowledge is treated not as a qualitatively different form of knowledge, but simply as a more formalized version of everyday human inquiry [ 48 ]. This idea of intuitive inquiry aligns with a theme, advanced by many scholars advocating for complexity theory in healthcare, that social actors already have an intuitive sense of complexity, which can be refined by the framework of complexity theory [ 4 , 9 ]. Social complexity theorists also argue for a natural fit between complexity approaches and participatory research, where participant and researcher frames of reference are treated as equally important to inquiry [ 20 ], failure is tolerated and expected [ 49 ], and innovation is allowed to emerge from any part of the system [ 9 ].

In our project, this led to a fundamental shift in the implementation evaluation from a focus purely on the participant experience, to one that included the experiences of the researchers. Our evaluation was focused on understanding the decision-making mental models of these individuals, and how they negotiated shared group processes and behaviors based on these individual models. As mentioned above, one way this manifested was as a change in engagement with members of the SLG. Both pragmatism and complexity theory also encourage a focus on the interactions of knowledge systems, and the study of how these intersections are negotiated [ 4 , 44 , 48 ].

The case study approach of the evaluation, supported by interviews and unstructured observation, allowed these themes to emerge, but there remains a challenge for creating more targeted research designs and methods capable of capturing, measuring, and interpreting these interactive and emergent processes. A key theme in the development of social complexity research is the call for mixed methods research [ 8 , 34 ].

As one of the key epistemologies for mixed method research, pragmatism offers a more structured approach to mixed methods research [ 42 ]. Pragmatism calls for choices of research questions and methods to be driven by the social purpose of the research, not the other way around [ 42 , 45 , 51 ]. Another of the risks identified by complexity theorists is the pre-emptive labelling of a system as complex [ 40 ]; a pragmatic approach does not require such a priori assumptions.

Rather, it allows for the flexible use of multiple methods to capture insights in a complex environment, which may later be interpreted using a range of frameworks.

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Therefore, our pluralism of evaluation methods i. Pragmatism also encourages reflection and experimentation, allowing for the evolution of interventions and evaluation in a similar fashion to a CAS [ 7 , 42 , 45 ]. Therefore, our shift in evaluation from the quantitative analysis of participant questionnaire responses to a grounded theory case study of research adaptation is not only consistent with complexity theory, but predicted by it, as a co-evolution of the researchers in context.

Thus, rather than rejecting the reductionist approach of classic complexity theory [ 20 ], pragmatism allows for the contribution of both quantitative and qualitative methods in addressing the research question. It also allows for different definitions of complexity theory. Complexity theory can be both an ontology for quantitative approaches and a metaphor for qualitative approaches.

Our case study illustrates how a pragmatic epistemology can support, and broaden, the application of complexity theory to healthcare implementation and evaluation. By starting from a pragmatic epistemology, we allowed our focus to be drawn to the most relevant ontology and methodologies for the study of this implementation. Complexity theory emerged as a relevant theory and ontology for the analysis; however, we do not hold that it is the only possible lens through which to evaluate the implementation.

A pragmatic frame encouraged us to embrace different types of inquiry and data collection methods, using questionnaire, interview, observation, and document analysis approaches. As the implementation progressed, we included new participants i. By doing so, we overcame one of the key challenges in social complexity theory — defining the CAS of interest.

In our evaluation, we pragmatically allowed implementation success to be defined by the collection of stakeholders, honoring the multitude of different expectations held by the research funding body, the academic community, and individual members of the SLG and research team.

We then began the data analysis with a critical incident approach to identify turning points in the system, which were investigated further with thematic analysis. It was only when the emerging themes resonated with a complexity theory interpretation of the project that we labeled our case study as a healthcare implementation CAS.

Herein, we described a too-familiar experience in health services implementation — a constantly changing implementation context— followed by a discussion of how complexity theory and pragmatism provide complementary approaches to the difficulties in evaluating such implementations. The commonalities between pragmatism and complexity theory are striking, and include a sensitivity to research context, a focus on applied research, and the valuing of different forms of knowledge.

For implementation and evaluation, this fusion of approaches has significant implications:. A focus on researcher and stakeholder agency, in shaping the direction and outcomes of interventions. A re-definition of implementation success, not as a strict adherence to the project plan, or the achievement of pre-determined outcomes, but as the emergent outcomes of the project and lessons learned, as identified by all stakeholders. A flexibility in implementation and evaluation methods, encouraging the reflexive use of mixed methods to capture and adapt to the changing research context.

A rejection of the description-explanation divide, focusing instead on continual, collective learning, where case studies provide starting points, not theories, for future research. However, our recommendations are not without limitations. There are other epistemic options for complexity theory, including nested theories [ 34 ], an eclectic use of middle-range theories [ 37 ], or a pluralistic ontology of levels supported by emergence [ 26 ].

One of the more promising alternatives comes from Byrne et al. At face value, the arguments of complex realism seem not incommensurate with pragmatism [ 42 ]; however, we will leave a detailed comparison of these two approaches to future scholars. Alternatively, complexity theorists may entirely reject our suggestion of the need for an epistemology. Another limitation is posed by the theoretically agnostic position of pragmatism, as outlined above. It is highly likely that a pragmatic approach will not always support the application of complexity theory in healthcare implementation research.

While we believe this is a strength in the use of pragmatism in healthcare implementation, it may limit the uptake of pragmatism by researchers who specialize in complexity theory. The application of complexity theory to social science, including healthcare, is still in its infancy. So too is the formalization of pragmatism as a school of philosophy [ 43 ]. We present this article in that spirit and hope that our contribution sparks further discussion about the potential collaboration of pragmatism and complexity theory in informing implementation science and health services research.

Fuzzy boundaries: System boundaries are permeable and hard to define. Distributed control and self-organization: System patterns are not created by top-down control; instead, autonomous agents interact to create outcomes. Thus, organization in a CAS emerges naturally from local rules held by agents. Emergence: Interactions between agents create system outcomes that are not directly intended and are greater than the sum of the individual agent behaviors.

Unpredictability: The behavior of a CAS cannot be predicted due to its non-linearity, sensitivity to initial conditions, and historicism. Non-linearity: The magnitude of system input and agent interactions is not linearly related to the magnitude of changes in the system.

A Pragmatist Approach to Knowledge

A CAS can react suddenly to minor inputs or fail to change despite overwhelming external pressure. Sensitivity to initial conditions and historicism: Future agent actions are affected by past changes in the system, leading initial conditions to exert a strong influence on system behaviors. Non-equilibrium: CASs are characterized by continual change and do not reach equilibrium. Adaptation and co-evolution: Agents and systems evolve together, reacting to changes in the context to ensure optimal functioning and survival. Prioritize understanding over theoretical or methodological purity, encouraging the use of multiple methods.

All participants are advised at the beginning of participation in the study and in the consent form that they can discontinue participation at any time. Data already collected will still be used in the analysis unless the discontinuing participant specifically requests that it be removed. Additional research funding was provided by the Department of Psychiatry, Monash University, and the University of Calgary. The views, analyses, interpretations, and conclusions expressed in the article are those of the authors and not of the Australian Research Council, Monash University, or University of Calgary.

KL was responsible for the development of the ideas expressed, and drafting of the article. FM contributed expertise in using complexity theory in qualitative research. GM was responsible for the design of the simulation modeling intervention featured in the case study. All authors read and approved the final manuscript. All protocol amendments were submitted to, and approved by, the Human Research Ethics Committee of the partner Mental Health Service, using the appropriate ethics amendment forms.

Signed consent is obtained from all participants during their first in-person contact with the study. Any information gained in connection with this research project that can identify individuals will remain confidential. All information will be stored in password-protected files and folders on password-protected computers, that can only be accessed by the research staff. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Katrina M. Fiona McDermott, Email: ude. Graham N. Meadows, Email: ude. National Center for Biotechnology Information , U.

BMC Med. Published online Jun Meadows 1, 3, 4. Author information Article notes Copyright and License information Disclaimer. Corresponding author.