Learning to See: Interdisciplinary Theory and Adaptive Capacity in a Complex World

 Jeff McCarthy

Athabasca University

MAIS601: Making Sense of Theory

Instructor: Dr. Keith Williams

08.16.2026

Abstract

This paper asks what interdisciplinary theory and the study of theory can teach us about adaptive capacity in an increasingly complex world.  Using the COVID-19 pandemic and artificial intelligence as contemporary laboratories, I draw on theoretical perspectives from complexity, interdisciplinarity, ideology, leadership, organizational learning and memory, consciousness, and sensemaking.  Rather than considering these theories separately or searching for a single perspective that best explains adaptation, I place them in cross-theory conversation, examining where they reinforce, challenge, and complicate one another.  This reveals limits and connections that are less visible when theories are considered separately.

From this conversation, adaptive capacity emerges not as a single, heroic leadership competency or organizational characteristic, but as a continuing ability to see change, interpret uncertainty, question assumptions, learn and relearn from experience, protect and use important knowledge, and adapt when previous approaches no longer fit changing conditions.

I ultimately argue that cross-theory conversation is more than a way of studying adaptive capacity.  The willingness to move among perspectives, test explanations against one another, and reconsider how a problem is understood may itself be an adaptive practice.  In an increasingly complex world, our best preparation for disruption may therefore depend less on predicting what comes next than on becoming better at learning when our predictions fail.

Introduction: From Leadership to Adaptive Capacity

While refining my research question at the outset of my MAIS 602 graduate studies, I found myself seeking to better understand leadership during the COVID-19 pandemic.  My initial interests centred on adaptive leadership and decision-making under conditions of challenge, confusion, and uncertainty.  Throughout the pandemic, I observed remarkable examples of collaboration, innovation, and shared leadership across government departments, organizations, and communities.  I believed some of these practices had the potential to transform public service, public health, and the broader healthcare system.  Yet I also watched many of them disappear as the crisis passed.  What stayed with me was not simply how organizations responded during the pandemic, but how little attention seemed to be given to preserving what had been learned.  Valuable insights, relationships, and adaptive practices faded without sustained reflection, organizational learning, or memory.   Paradoxically, comparatively smaller emergencies such as wildfires, floods, ice storms, and hurricanes routinely conclude with an after-action report and documented lessons learned.  To date, no comparable exercise or analysis has occurred in New Brunswick following the far more consequential COVID-19 pandemic.

As MAIS 601 progressed, however, the way I saw the problem and my research question began to change.  Rather than asking how leadership creates adaptive capacity, I became interested in a broader question: what enables individuals, organizations, and communities to continue adapting as complexity increases?  At the same time, public attention has moved from the pandemic toward artificial intelligence, climate change, geopolitical instability, disruption of the international rules-based order, political polarization, and declining public trust.  These challenges are very different, but they have something important in common.  They cross disciplinary boundaries, interact with multiple networks and systems, and resist one-time fixes or purely technical solutions.  The more theoretical perspectives we explored, the less interested I became in finding the “right” leadership theory and the more interested I became in what became visible when the same problem was viewed differently.  My earlier questions about the pandemic and complexity had not disappeared; theory was changing the questions I was able to ask of them.

One of the important lessons I take from MAIS 601 is that theory shapes what we can see.  Looking at a problem through complexity theory draws our attention to relationships, interdependence, and emergence. Sensemaking makes us ask how people decide what is happening when circumstances are uncertain.  Critical theory deals with assumptions, ideology, and power.  None necessarily gives us a wrong picture, but neither does any one give us the whole picture.  Each brings something into clearer focus while perhaps leaving something else at the periphery.  Rather than asking which perspective offers the best explanation, I became more interested in what might become visible by moving between them. That, for me, became the value of interdisciplinary inquiry and changed my understanding of adaptive capacity.

This paper follows that approach. Using the COVID-19 pandemic and artificial intelligence as contemporary laboratories, it asks: what can interdisciplinary theory teach us about adaptive capacity in the face of an increasingly complex threat horizon?  I argue that adaptive capacity depends on more than leadership, expertise, or even learning alone.  It requires an ability to recognize change, interpret uncertainty, question established assumptions, learn collectively, retain what has been learned, and change course when existing ways of thinking no longer work.  No single theoretical perspective reveals all of these processes.  However, they suggest something more: the ability to move among different ways of understanding a problem may itself be part of what makes individuals, organizations, and communities more adaptive.

Complexity Changes Everything: Why Adaptability Has Become the Defining Challenge

Artificial intelligence, climate change, geopolitical instability, cyber threats, global pandemics, and political polarization are often discussed as separate societal challenges.  Each presents different risks, but they share an important characteristic: complexity. Complexity is more than another word for difficulty.  A problem can be complicated yet reasonably predictable and manageable with expertise, planning, and coordination.  Complex systems behave differently.  Newell (2001) describes them as systems composed of many interacting components and relationships, including feedback loops, that together produce patterns and behaviours that cannot be understood simply by examining each part in isolation.  This distinction matters because the problems I am interested in are not simply becoming harder; they are increasingly interconnected.

Newell’s (2001) account of complex systems also places limits on our ability to predict what happens next.  When multiple components interact through layers of relationships and feedback loops, understanding the individual parts does not necessarily allow us to anticipate the behaviour of the system as a whole.  That has an important consequence for adaptive capacity.  If complexity limits prediction, learning cannot end once a plan has been made or a decision taken.  We have to keep learning from what the system does in response.

The COVID-19 pandemic made this painfully clear.  What began as a public health emergency quickly affected healthcare, education, labour markets, supply chains, governance, politics, mobility, fundamental rights, and public trust.  Decisions made in one area created consequences in others, sometimes in ways that were impossible to anticipate.  Artificial intelligence presents a similar challenge.  We tend to describe AI as a technological development, but its effects already reach into work, education, ethics, governance, creativity, and our understanding of knowledge itself.  Neither COVID-19 nor AI can be understood very well by looking at only one part of the system in which it operates.

Taleb’s concept of the Black Swan pushes the problem of prediction even further.  His argument is not simply that unexpected events occur, but that rare and consequential events expose the limits of what we believe we can predict (Taleb, 2007).  This does not make planning or preparedness pointless.  It does suggest that prediction cannot be our only safeguard against disruption.  Newell and Taleb arrive at the problem differently, but both challenge the assumption that better knowledge will eventually give us control over uncertainty.  That shifts the question for me. Rather than asking only how we might better predict the next disruption, we should also ask how individuals, organizations, and communities can learn and adapt more effectively when our predictions fail.

This is where adaptive capacity becomes important.  If uncertainty cannot be eliminated, we need to become better at working within it.  That requires more than good decisions, technical expertise, or leaders with answers.  It requires recognizing when circumstances have changed, learning from what is happening, and reconsidering knowledge, structures, practices, and assumptions.  But before we can adapt to changing conditions, we have to understand what we are looking at.  If complex problems cannot be fully understood from a single vantage point, adaptability may begin by learning to see them from multiple vantage points.

Lens One: Interdisciplinary Theory and Learning to See

If complex problems cross disciplines as easily as they cross organizations, communities, and systems, then no single discipline is likely to explain them sufficiently.  COVID-19 is a good example.  A public health perspective was essential, but it could not by itself explain what was happening to education, labour markets, governance, politics, economics, individual behaviour, or public trust.  Each field could explain part of what was happening. None could explain the whole system.  This seems less like a failure of any particular discipline than a consequence of the problem's complexity.

Newell (2001) makes this connection explicit, arguing that “interdisciplinarity is necessitated by complexity” (p. 1).  Complex problems can look different depending on the angle from which they are examined, as different components and relationships come into view.  A disciplinary perspective can therefore provide a valid account of part of a problem without providing an adequate account of the problem as a whole.  Different theoretical perspectives need not compete to become the single best explanation.  They may be showing us different parts of the same complex problem.

This changed what I understood interdisciplinarity to mean.  Simply collecting several perspectives is not enough.  The harder work is to consider where those perspectives agree, where they conflict, what each leaves unexplained, and what becomes visible when their insights are brought together.  I began MAIS 601 inclined to ask which theory best explained the problem I was studying.  I now think that question is too limiting.  Complexity theory can reveal relationships and emergence; sensemaking can show us how people interpret what is happening; critical theory can force us to examine assumptions and power.  Their value is not that one eventually wins, but that putting them into conversation can produce a fuller understanding of a problem than any one perspective can provide.

That matters for adaptive capacity. If adaptation requires us to recognize when conditions have changed, then our ability to change how we understand those conditions matters too.  A familiar theoretical or disciplinary perspective may explain much of what we see while still obscuring something important. Moving among perspectives, testing them against one another, and reconsidering our understanding in response to what they show us may therefore be more than a way of studying adaptive capacity.  Interdisciplinary inquiry may itself be an adaptive practice.

Lens Two: Assumptions, Ideology and Limits

However, there is a problem with this argument.  Access to different perspectives does not guarantee that individuals or organizations will learn from them.  More information does not necessarily lead to better learning.  We interpret evidence through what we already know: our experiences, assumptions, values, identities, biases, and organizational cultures.  These frameworks are useful because they help us make sense of new situations.  They can also become limiting when they become so familiar that we stop noticing them.  If adaptive capacity depends partly on seeing a problem differently, we also have to ask what prevents us from doing so.

bell hooks offers one way into this problem through her understanding of theory as a liberatory practice.  Reflecting on her own experience, hooks (1991) writes, “I saw in theory then a location for healing” (p. 1).  Theory, for hooks, can help people critically understand their experiences and circumstances and imagine that things might be otherwise.  What matters for adaptive capacity is the willingness to reconsider what has come to seem natural or inevitable. Learning, from this perspective, is not simply the accumulation of more information.  It can require questioning the assumptions through which we interpret that information in the first place.  During the pandemic, for example, what constituted learning from a public health perspective could look very different from what was being learned politically.  Adaptive capacity requires not simply choosing between those interpretations but being willing to examine the assumptions through which each was constructed.

Zizek makes that task even more difficult.  Ideology, in his account, is not limited to explicit political beliefs or positions.  It also operates through assumptions about the world that can become so natural to us that we no longer recognize them as assumptions (Zizek, 2021). This helps explain how people and organizations can encounter new evidence without substantially changing their understanding of a problem.  Evidence that challenges an established view can instead be interpreted in ways that reinforce it.  Failure to adapt, then, may not result from a lack of information at all.  We may have the information in front of us and still make it conform to what we already believe.

Sheldrake presents a similar problem from a different angle.  In The Science Delusion, he questions whether some assumptions underlying scientific thinking have become so ingrained that they function as dogmas (TED, 2013).  His approach is to turn those assumptions back into questions.  We do not need to accept all of Sheldrake’s answers to see the usefulness of that approach.  What happens when something we have stopped questioning needs to become a question again?  Adaptive capacity may require us to do exactly that with knowledge, practices, and assumptions that once worked but may no longer address changing conditions.

The pandemic gives this problem practical relevance.  Experience with SARS, for example, was useful to organizations responding to COVID-19, but it could also become a limitation when familiarity with a more contained outbreak bred confidence that the new threat could be managed in much the same way.  The problem was not the experience itself.  The problem was allowing what had worked before to determine too quickly what we thought we were seeing.  Experience can prepare us for what comes next, but only if we remain willing to recognize the changing nature of complexity.   

hooks, Zizek, and Sheldrake therefore complicate the argument that interdisciplinary inquiry makes us more adaptive.  Seeing a problem from several perspectives is not enough if we remain unaware of the assumptions we bring to each one.  Adaptation requires a willingness to put our own understanding at risk: to consider that what we know, what has worked before, or what feels familiar may no longer be adequate.  But recognizing that possibility is only part of the problem.  Individuals and organizations still have to act on it, changing practices, relationships, priorities, frameworks and sometimes closely held assumptions.  This is where leadership returns to the argument, not as the ability to provide better answers, but as a way of helping people do the difficult work of learning and relearning. 

Lens Three: Leadership in Adaptive Systems

Leadership was the starting point for my interest in adaptive capacity, and it remains an important part of the explanation.  What has changed is where I position it.  I began by asking what kind of leader could help an organization navigate uncertainty.  I still think leadership matters, but I no longer think leadership alone explains adaptation.  Leaders operate within systems made up of people, relationships, routines, cultures, and experiences.  They can create conditions that help those systems adapt, but they cannot do the adapting for them.

Heifetz provides an important foundation for understanding why.  Adaptive leadership theory distinguishes between technical problems and adaptive challenges.  Technical problems can often be addressed through existing expertise, established procedures, and people who know what to do.  Adaptive challenges are different because existing expertise is no longer enough. They require people themselves to learn, reconsider assumptions and priorities, negotiate competing values, and develop new ways of working (Heifetz et al., 2009).  This changes what we expect from leadership.  When the problem is adaptive, the leader’s job is not to arrive with the correct answer.  It is to help create the conditions in which people can learn their way through a problem for which an adequate answer does not exist.

Complexity leadership theory expands this further.  Uhl-Bien et al. (2007) argue that learning, innovation, and adaptation can emerge through the interactions of people working within complex adaptive systems.  Leadership, from this perspective, is not located entirely in formal positions or individual leaders.  It can develop through relationships among people and ideas as they interact, disagree, exchange information, and respond to changing conditions. Just as importantly, complexity leadership theory does not make formal leadership or organizational structures unnecessary.  Direct leadership still matters, but so does enabling the informal relationships through which new ideas and adaptive responses can develop.  This helps explain some of what we experienced during the pandemic, when rapidly changing conditions created forms of collaboration and shared leadership across organizational boundaries that would have been difficult to create before.

Vaughan adds an important complication.  Heifetz and complexity leadership theory place considerable value on learning and adaptation, but Vaughan (1996) reminds us that organizational learning does not necessarily move in the right direction.  Her examination of NASA and the 1986 Challenger disaster shows how even a high-functioning organization can gradually normalize warning signs rather than learn from them.  NASA engineers had observed problems with the space shuttle’s O-ring seals on previous flights, yet those missions had not ended in disaster.  Over time, repeated experience with the irregularity contributed to its acceptance as an increasingly normal part of shuttle operations, a process Vaughan described as the normalization of deviance.  NASA did not lack information, expertise, or experience. Its culture and experience helped shape how the organization interpreted the risk before it.  Vaughan therefore complicates any easy relationship between experience, learning, and adaptation. Adaptive capacity cannot simply mean becoming better at learning from experience; it must also include the ability to question what experience has taught us.

Even when useful learning does occur, there is another problem: organizations have to remember it.  Walsh and Ungson (1991) describe organizational memory through processes of acquiring, retaining, and retrieving information from an organization’s past.  Importantly, that memory does not reside in one place.  It can be retained in individuals, organizational culture, routines and processes, structures, language and other conventions.  This brings me directly back to the pandemic.  An extraordinary amount was learned under pressure, but learning something during a crisis is not the same as ensuring that an organization can use that knowledge years later. People leave, temporary response structures stand down, relationships weaken, and practices developed during an emergency are abandoned in peacetime.  An after-action process is one way of interrupting that loss by deliberately asking what was learned and what should be retained.  Without some means of preserving and retrieving what was learned, the next crisis can force an organization into costly (time) processes.

Preserving knowledge presents another challenge.  In the same way someone gets to decide what is included in an after-action report, someone also gets to determine what becomes part of the official record.  In my earlier MAIS 602 work, Duranti’s (2018) analysis of archiving led me to think about after-action reports as potential “silent archives”: constructed records shaped not only by what is preserved, but also by what is left out.  This complicates Walsh and Ungson’s account of organizational memory.  An organization may successfully preserve a record of an event without preserving everyone’s experience of it, and those with the authority to create the official record may exercise power over what the organization later remembers and uses.  For adaptive capacity, then, remembering is not enough.  We also have to ask who constructed the memory, whose experience it represents, and what may have been left silent.

These theories change where I locate adaptive capacity.  Heifetz moves leadership away from providing answers and toward cultures of learning.  Uhl-Bien and her colleagues show how learning and adaptation develops through relationships that no individual leader controls.  Vaughan warns that experience can reinforce poor assumptions rather than correct them.  Walsh and Ungson show why useful learning has to remain available to the organization, while Duranti makes us ask what was preserved and what was left out. Adaptive capacity therefore cannot rest with an individual leader.  It depends on whether the larger system can learn, question what it has learned, preserve what matters, and retrieve that knowledge before its needed. 

Lens Four: Artificial Intelligence and the Future of Adaptive Capacity

If complexity makes adaptive capacity increasingly important, artificial intelligence may be the stress test for everything previously argued.  AI is not simply another technology to which we must adjust.  It is changing how information is produced and accessed, how people work, how knowledge is created, and how decisions are made.  More importantly, we are trying to understand, govern, and adapt to AI while the technology itself is still changing.  The challenge is therefore not simply whether we can learn to use new tools, but whether individuals, organizations, and communities can keep learning as the tools and their effects continue to change around us.  In that sense, AI presents some of the pandemic's adaptive challenges without the prospect of flattening the curve. 

Lanier offers a useful challenge to how we think about this problem.  He questions the tendency to talk about artificial intelligence as though the technology itself is independently driving what happens next.  As he puts it, “This idea of surpassing human ability is silly because it’s made of human abilities” (Lanier, 2024).  AI is something humans are building, funding, deploying, and choosing how to use.  The data on which it depends, the purposes for which it is developed, and the decisions about where it should or should not be used remain connected to human choices and values.  Technological change therefore does not remove human responsibility for deciding how to respond.  Learning what AI can do is only part of the challenge.  We still must decide what we want it to do and where its use makes sense.

Putting Lanier into conversation with the earlier theories changes our view of the problem again.  Complexity theory warns that the effects of AI will interact with social, economic, political, and organizational systems in ways we cannot predict.  Heifetz helps explain why many of the resulting questions are adaptive rather than technical: decisions about employment, education, privacy, creativity, accountability, and human judgment involve competing values, not simply problems greater expertise can solve.  Zizek adds another layer by prompting us to question the assumptions embedded in our enthusiasm for efficiency, innovation, and technological progress.  Together, these perspectives move us from asking simply what AI can do to asking what its use will change, whose values will shape those changes, and what those choices reveal about us.

Noë and Weick add another dimension by challenging the assumption that more information necessarily produces greater understanding.  Noë’s account of consciousness rejects the idea that knowing can be reduced to processing inside the brain; understanding is connected to our active engagement with the world (Noë, 2009).  Weick approaches the problem at the organizational level, where sensemaking becomes crucial when circumstances are uncertain and people must interpret incomplete or ambiguous information before deciding how to act (Weick, 1995).  AI may give us faster access to more information than ever before, but neither perspective gives us reason to assume that greater access will make us better at understanding what it means.  In emergency management, there is an edict that first reports are always wrong for a reason.  AI can contribute to sensemaking, but as Noë and Weick challenge us, it cannot be mistaken for the human work of relating and making sense.

This distinction matters because adaptive capacity is not the same thing as processing capacity.  AI can identify patterns, synthesize information, generate possibilities, and extend what humans are able to do. None of that guarantees that an individual, organization, or community will respond wisely to changing conditions.  The theories explored here suggest that adaptation also requires questioning assumptions, interpreting uncertainty, negotiating competing values, learning with others, remembering what has been learned, and recognizing when previous solutions no longer work.  AI may become extraordinarily capable at helping us perform some of these tasks, but that only makes the others more important.  If AI is the stress test, then the test is ultimately not whether machines can continue to learn.  It is whether the human systems creating and using them can do the same.

Interdisciplinary Theory: What I Learned to See

What, then, did I learn by looking at adaptive capacity through these different theoretical perspectives?  More than anything, I learned that theories become more useful when they are put into conversation with one another.  Each exposes something another leaves incomplete: learning can go wrong, useful knowledge can disappear, preserved knowledge can exclude, and even multiple perspectives remain shaped by the assumptions we bring to them. Adaptive capacity became visible not through any one theory, but through the relationships and tensions between them.

I also learned that adaptation begins before action is taken.  Individuals, organizations, and communities must first recognize that something has changed and that their existing explanations, experiences, knowledge, or culture may no longer suffice.  Complexity means one vantage point may reveal only part of a problem, while our own assumptions may constrain what we are able to see.  Adaptive capacity therefore begins not with certainty, but with enough humility and discipline to question what we think we know.

Learning is no more straightforward.  Experience and adaptation cannot be treated as the same thing: organizations can learn, but they can also normalize the wrong things. Even useful learning can disappear if it is not repeatedly retained and retrieved, while what is preserved may itself be incomplete.  These perspectives suggest a sequence I did not see when I began with leadership: experience has to be interpreted, learning has to be questioned, and what remains useful has to be preserved and available for the next Black Swan.

The theories also changed how I understand leadership.  Adaptive challenges cannot simply be handed to an expert or heroic leader for an answer; the system itself has to learn its way through the challenge.  Complexity leadership theory takes the argument further by showing how adaptation can develop through relationships and interactions that no individual leader controls. Leadership’s contribution to adaptive capacity may therefore be less about directing adaptation than creating conditions in which people can learn, relearn, challenge one another, experiment, and develop responses together.  Adaptive capacity is collective as much as it is individual.

The most demanding lesson may be that adaptation can require us to change ourselves, not simply our response to a problem. Knowledge, experience, practices, and beliefs that once served us may become part of what prevents us from seeing a problem differently.  Adaptation is uncomfortable by necessity.  It requires accepting that some of what made us successful in the past may eventually need to change.  This level of positionality and reflectivity may be the most strengthening aspect of adaptive capacity.

Conclusion

This paper began by asking what interdisciplinary theory can teach us about adaptive capacity in an increasingly complex world.  I began with leadership and the question of how leaders and organizations respond effectively to uncertainty.  I now see adaptive capacity more broadly, as a continuing ability to recognize change, interpret uncertainty, question assumptions, learn and remember, and evoke change.  No single theory brought me to that understanding.  It came through the relationships, tensions, and gaps between them.

That may be the most important change in my thinking through MAIS 601.  I leave the course less interested in finding the theory that best explains a problem and more interested in what happens when different theories are allowed to challenge one another.  Heifetz becomes more useful when Vaughan makes learning messier.  Newell becomes more useful when Zizek reminds me that multiple perspectives do not untie us from our assumptions.   Noë and Weick complicate the promise of artificial intelligence by separating access to information from the human work of understanding it.  Cross-theory conversation does not eliminate uncertainty or produce the “right” answer.  It makes it harder to mistake one explanation for the whole problem.

This has also pushed my inquiry toward a new question.  If adaptive capacity can be strengthened rather than simply observed after a crisis, how might we develop it intentionally? That question connects this paper to my work in MAIS 644, where I began considering adult education not simply as preparation for employment or individual development, but as part of the social infrastructure through which communities continue learning.  I now see a stronger connection between the two inquiries.  If complexity requires people and institutions to keep reconsidering what they know, then lifelong learning may be one way for communities to build the capacity to do so.

The application of theory does not offer a panacea to conquer complexity every time, nor does interdisciplinarity allow us to predict what comes next.  What they can give us are better ways to examine a problem, test our assumptions, and recognize what one way of seeing may have missed.  Humanity has always lived with uncertainty, disruption, and change.  What is different now is the speed, scale, and interconnectedness of the challenges we create and encounter.  Our response cannot depend entirely on improving prediction.  Our best preparation for an uncertain future may lie less in predicting what comes next than in strengthening our capacity to learn and adapt to whatever does

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