Category Archives: Complexity

The Right Thing and the Right Reason:

In today’s post, I am exploring the notion of “doing the right thing.”

We encounter this expectation everywhere in workplaces, personal relationships, and civic life. The phrase appears in mission statements, performance reviews, and everyday conversations. At first glance, it feels simple and reassuring. Of course we should do the right thing.

In regulated industries, this mantra becomes even more clearly pronounced. Every procedure, every record, and every audit echoes that expectation. It appears in training sessions, quality policies, and compliance frameworks.

I want to add an important layer: do the right thing for the right reason.

The distinction may seem subtle, yet it initiates a reflexive turn. It moves us from mechanical compliance to ethical responsibility.

A statement by itself carries no value. “Do the right thing” means nothing until someone makes it their own. The phrase appears to describe a fact, but it actually expresses a value judgment. Value enters only when a person acts from conviction, not from blind obligation. The second part, “for the right reason,” is where responsibility begins. It asks a crucial question about why I am doing this. That question transforms an empty slogan into a deliberate act grounded in personal values.

If I follow orders or check boxes without reflection, I might appear to do the right thing. But in truth, I have surrendered ownership. From the perspective of cybernetic constructivism, meaning is not handed down from the outside. It emerges within the observer. As Heinz von Foerster showed in his work on observing systems, we do not simply receive reality but construct it through our interactions and decisions.

When we speak of “the right thing,” the phrase suggests precision, as if a decision could fit reality without error. In practice, this rarely happens. Thought and reality belong to different domains. A decision formed in thought appears complete because ideas do not encounter resistance until they are acted out. The flaws surface only when they meet real conditions.

This is the illusion of completeness in the right thing, the comforting belief that something can be fully correct. It persists because thought gives us a sense of closure that reality cannot guarantee.

Here is where the phrase “for the right reason” matters. It does not make the decision perfect; it acknowledges that it never was. Adding this second part challenges the belief in absolute correctness and invites humility about what we can know. It says you cannot guarantee the outcome, but you can own the reasoning. That ownership gives the action its integrity. The emphasis shifts from claiming completeness to accepting responsibility. This matters because it prevents us from confusing the clarity of thought with the complexity of life.

I want to focus on this more with a question: When the time comes, can I do the right thing? This question seems simple, but it hides a deeper issue. What exactly is the right thing? We often talk as if the right thing exists “out there,” waiting for us to discover, a fixed fact like the boiling point of water. But this assumes that what appears complete in thought will remain complete in practice. That assumption is an illusion.

In many situations, the right thing is not given. It is what von Foerster calls an undecidable.

The Nature of the Undecidable:

Von Foerster introduced this term for questions that cannot be answered by logic, rules, or computation alone. An undecidable resists algorithmic resolution. Regulations provide structure and consistency, and they are essential. Yet they do not eliminate undecidables. They never will.

Undecidables exist because the variety of real-world situations far exceeds what any rulebook can anticipate. In cybernetics, variety means the number of possible states a system can take. The more possible situations, the greater the variety. And the world does not just throw edge cases at us. It quite often generates entirely new scenarios. Each innovation, each unique user context, and every unexpected failure mode creates conditions no standard procedure can fully capture.

No rulebook, whether corporate policy or government regulation, can provide ready-made answers to every question. Rules may reduce some complexity and provide crucial guidance, but they cannot close the gap between their finite scope and the indefinite creativity of reality. That gap is where undecidables live, and where human judgment becomes indispensable.

Von Foerster put it clearly:

“Only those questions that are in principle undecidable, we can decide.”

This is not a logical contradiction. It is an ethical imperative. The undecidable is not an error to fix or a loophole to close. It is an invitation to take responsibility. And responsibility cannot be delegated to systems or rules.

Many people resist this truth. We want the comfort of certainty. We prefer to believe the right thing exists as a fixed point, like a law of physics. If that were true, we would not bear the weight of decision. But ethics begins where algorithmic certainty ends. When we say “Just tell me the rule,” we try to trade agency for comfort. And in doing so, we risk betraying the very principles we claim to uphold.

The uncomfortable insight is this: the right thing has validity only as something we decide and own.

A Practical Question:

In the medical device industry, when I encounter an undecidable, my first question is always:

“How does this help or hurt the end user?”

That question brings the undecidable into focus. Regulations cannot cover every nuance. They can only guide. The decision remains mine. The responsibility cannot be outsourced.

Doing the right thing for the right reason is not about perfection. It is not about moral grandstanding. It is about intentionality, the choice to act from internal commitment rather than external command. It is the courage to decide when certainty is impossible and when existing protocols do not apply.

Von Foerster understood this deeply. When he spoke of undecidables, he was not describing a flaw in logic or a failure of system design. He was describing the essence of ethical life: that there are decisions no one can make for us. This insight formed the heart of his second-order cybernetics, which places the observer and their responsibility at the center of any system.

The Ladder We Must Throw Away:

Here I must acknowledge an irony. In adding the phrase “for the right reason,” I am still using the word “right.” By doing so, I risk introducing the very assumption I wanted to question: that rightness exists as something fixed and pre-given. This reflects a pattern throughout the article, where language itself hints at the various complexities we grapple with in an attempt to grasp or cope with the external world.

This is where Wittgenstein helps. In the Tractatus Logico-Philosophicus, he wrote that the propositions in his book were like a ladder. Once you have climbed it, you must throw it away. These propositions were tools, not eternal truths. They guide you to a vantage point, and then you move beyond them.

The phrase “do the right thing,” and even my expanded version, “do the right thing for the right reason,” works the same way. These are useful as orienting principles in regulated industries. They provide direction in moments of uncertainty. But if we cling to them as ultimate truths, we miss their purpose.

Like Wittgenstein’s ladder, their role is pragmatic and temporary. They guide us to a place where we can make responsible decisions. Once we understand that responsibility cannot be outsourced to a phrase or a rule, we can discard the ladder, not by abandoning the principle, but by letting go of the illusion that the phrase absolves us of thinking.

The deeper insight is this: the right thing does not exist as a given. It exists as something we must decide. And that decision, by its very nature, will always belong to us.

The next time you hear the phrase “do the right thing,” pause and ask:

What undecidable am I facing, and will I have the courage to decide it for the right reason, knowing that even the word “right” is only a ladder?

Final Words:

The tension between following rules and taking responsibility is not a flaw to fix. It is a fundamental condition of ethical life in complex systems. Von Foerster’s cybernetics teaches us that we cannot escape this tension by creating better rules or more comprehensive procedures. The variety of situations we face will always exceed the variety our systems can anticipate.

This does not diminish the value of regulations. They provide the backbone of responsible practice and create the conditions for ethical decisions. But they cannot substitute for judgment when the genuinely novel situation arises.

The courage to decide undecidables belongs to every professional who encounters the limits of the rulebook. When we recognize that meaning emerges within the observer, we are called to decide thoughtfully, with full awareness of our role in shaping the meaning of our actions.

This is neither comfortable nor easy. But it is the price of genuine ethical responsibility. The ladder remains useful until we no longer need it. The goal is to reach the place where we can make decisions worthy of the trust placed in us.

Always keep on learning…

If you enjoyed this post and find my work valuable, I would appreciate your support. You can explore more of my ideas in my latest book, Second Order Cybernetics, Essays for Silicon Valley, hard copy available at the Lulu Store.

A Good Enough Post:

In today’s post, I am exploring the notion that viability depends on our capacity for action, and that this capacity may not entirely rely on having a perfect grasp of “Truth.” This possibility, drawn from evolutionary theory, invites us to reconsider a deeply rooted assumption in human thought: that knowledge aims to reflect the world as it is. Perhaps organisms do not carry mirrors of an objective environment. Perhaps they generate workable patterns that allow action. If so, truth in the sense of full correspondence might be not only unnecessary for survival but impossible to achieve.

This shift from truth to adequacy might be more than a semantic difference. It suggests we could reconsider how perception, cognition, and action evolve under the pressure of complexity. Our nervous systems may not have emerged to catalog every detail of reality. They might have emerged to enable viable engagement. They filter, reduce, and transform. They make the unmanageable manageable. This economy of attention could be what allowed life to persist in an environment whose complexity always exceeds the capacity of any single organism.

The Evolutionary Logic of Selective Attention

The earliest organisms had comparatively simple structures. Their survival depended on detecting a few vital differences: light and dark, motion and stillness, hunger and satiation. These differences were not representations of reality in its full richness. They were pragmatic distinctions, selected by evolution because they mattered for survival.

As ecosystems diversified, so did the organisms within them. Greater complexity in the environment favored organisms with richer internal structures. These structures allowed them to absorb more variety and generate more flexible responses. But this expansion had limits. No organism could ever match the full complexity of its environment. Every adaptation remained selective.

Yet evolution’s relationship with cognitive economy appears more nuanced than simple efficiency maximization. Many organisms maintain seemingly “wasteful” capacities (elaborate plumage, complex social behaviors, or redundant sensory systems) that prove crucial during rare but catastrophic events. This apparent contradiction might reveal something deeper. Evolution does not eliminate selectivity; it shapes what gets selected and how. The peacock’s tail represents a different kind of cognitive economy, one that trades metabolic efficiency for reproductive advantage. Even redundancy involves choices about what to duplicate and what to ignore.

Here we see why the word “better” seems always contextual. An organism appears better only in relation to its ecological niche and temporal horizon. There may be no universal scale of improvement. Adequacy appears always local, contingent on the demands of the situation, and provisional across time scales.

The Law of Requisite Variety and Regulatory Challenges

This principle finds a formal expression in W. Ross Ashby’s Law of Requisite Variety: only variety can absorb variety. A regulator must have as much variety in its responses as exists in the disturbances it faces. If the environment can vary in ten ways and the organism can respond in only five, some disturbances will remain unchecked, threatening viability.

Ashby’s law applies specifically to regulatory systems maintaining homeostasis, but its insights extend to cognitive systems facing similar challenges. Both must manage variety mismatches between their internal organization and environmental complexity. Yet matching variety does not mean copying the environment. No finite system can track every detail. Instead, regulation depends on attenuation and amplification. Organisms attenuate the vast variety of the environment into a reduced set of distinctions. They amplify the significance of certain cues to prioritize action.

This does not seem to be a flaw in design. It might be a condition of survival. The key point is this: attenuation may not be about discovering truth but about achieving functional adequacy within specific contexts and time frames. And here is a critical implication – what works today may fail tomorrow. Adequacy is dynamic because the variety we face today may not be the variety we face tomorrow. If we are not able to adapt to new disturbances, viability collapses. Our current struggle to integrate artificial intelligence into the workplace illustrates this point. Many organizational models were built on assumptions of human exclusivity in cognitive labor. Those assumptions worked for decades. Today, they are brittle because the environment has changed. Ashby’s law prevails.

The Shortcut Analogy: Logarithms and Cognitive Compression

To appreciate the elegance and risk of attenuation, consider a good enough historical analogy. Before the age of electronic calculators, navigation and astronomy depended on logarithmic tables. Multiplying large numbers was time-consuming and error-prone. Logarithms offered a remarkable shortcut: turn multiplication into addition. By converting numbers into their logarithmic values, sailors could compute distances and bearings quickly, reducing the cognitive load of calculation.

Crucially, these tables were extremely accurate within their domain of application. Lives depended on precise calculations, and navigators understood both the power and limitations of their tools. They built in multiple redundancies and cross-checks. This compression did not deliver the full detail of multiplication, but it delivered enough precision for safe passage across oceans when used with appropriate awareness of its boundaries.

Our minds seem to prefer operating in a linear way. Sequential thinking appears natural most likely because it proves cognitively economical. It reduces overwhelming complexity to manageable sequences we can follow. Like logarithmic tables, our conceptual frameworks trade completeness for efficiency. They allow us to act without drowning in detail. But there is an important difference. It is that logarithmic tables are mathematically precise within their defined limits. Human cognitive shortcuts however are bias-prone and culturally shaped, and they rarely come with warning labels. When we mistake our tools for the territory itself, the cost becomes invisible. Information is lost. Subtleties disappear. And when the environment changes, what once worked can become dangerous. This is the paradox: what enables us to cope also constrains what we can see. Our abstractions could be both our superpower and our vulnerability.

Pragmatism and Cybernetic Constructivism

This brings us to the philosophical dimension of the topic. Pragmatism, particularly as articulated by William James and John Dewey, treats knowledge as a tool for action rather than a mirror of reality. A belief is “true” not because it corresponds to some ultimate fact but because it proves useful in guiding behavior within a specific context. Truth is redefined as what works, but this “working” must be understood across multiple time scales and contexts. Adequacy is not fixed. It requires constant revision as the environment shifts.

This is not a license for arbitrary belief or wishful thinking. Pragmatic truth remains constrained by consequences. A bridge designed on faulty engineering principles will collapse regardless of the designer’s confidence. A medical treatment based on wishful thinking will fail regardless of the practitioner’s intentions. The pragmatic test is whether our frameworks enable effective action in the world as it actually responds to our interventions. Reality provides feedback, even if we cannot access it directly.

Cybernetic constructivism shares this orientation. Heinz von Foerster reminds us that “the environment contains no information”. What we call information arises in the interaction between an organism and its surroundings. The world does not impose meaning; meaning is enacted. Maturana and Varela describe this as structural coupling. Organisms and environments co-determine each other through ongoing interactions.

Seen in this light, our nervous system does not passively record inputs but brings forth distinctions through its own organization, maintaining coherence in continuous interaction with its surroundings. Knowing becomes an adaptive dance rather than a passive recording. The goal is not to represent an independent world but to maintain viability within a world that is partially brought forth by the act of knowing. This does not mean stability is irrelevant. Reliable patterns of interaction matter. Some regularities can be engaged in ways that allow prediction and engineering. Scientific methodology succeeds not because it removes simplification but because it manages it systematically, using feedback processes such as replication and peer review to adjust and refine adequacy over time and in a social realm.

The Double-Edged Sword: Superpower and Kryptonite

The ability to compress complexity seems to have made life possible. Yet this same ability becomes dangerous when compression becomes rigidity. When abstractions are treated as final truths, systems lose their capacity for adaptation. Stafford Beer captured this danger when he observed that ignorance becomes “the lethal attenuator”. When we lose track of what our simplifications exclude, adequacy transforms into vulnerability.

Let’s look at some examples. The use of algorithms in hiring often reduces the complexity of human potential to a few simplified metrics, which can perpetuate bias. Climate models, although highly advanced, still miss certain feedback loops and critical tipping points. Social media recommendation engines compress human interests into engagement-focused categories, which can push users toward more extreme views by filtering out moderating influences. This is evident in the world nowadays.

Heinz von Foerster reminded us that although the map may not be the territory, the map is all we have. Our ways of making sense are always partial and limited, yet they are the only tools we can use to navigate complexity. Recognizing this helps us remain aware of our cognitive blind spots.

In each case, the problem is not the use of shortcuts but forgetting their limits combined with insufficient feedback. The map is never the territory. When we mistake our ways of making sense for reality itself, fragility follows. What helps us stay viable can also make us blind.

Ethical Implications: What Do We Choose to Ignore?

If we accept that knowledge is constructed for adequacy, not truth, then the question of responsibility becomes unavoidable. Every act of attenuation involves a choice about what to include and what to ignore. These choices shape not only individual survival but collective futures.

In social systems, ignoring complexity can marginalize voices that do not fit dominant abstractions. In technological systems, it can produce biases that perpetuate injustice. The ethic of constructivism is not to abandon simplification (without it, we could not act) but to cultivate awareness of its costs and remain open to revision.

At the individual level, deliberate exposure to dissenting views, reflective journaling on hidden assumptions, and iterative sensemaking can help maintain cognitive flexibility.

We can restate Ashby’s law by saying that viability requires variety. A society that suppresses diversity of thought and perspective reduces its internal variety and becomes brittle in the face of unforeseen challenges. To design for resilience, we must design for plurality.

Final Words:

Survival does not seem to require perfect knowledge. It has required workable distinctions, compressed into forms that enable timely action. This logic of adequacy explains why our minds favor shortcuts, why linear thinking feels natural, and why abstraction is indispensable. Yet it also warns us that what we simplify to live by can, in time, limit what we live for.

The challenge, or more precisely the necessity, might be to balance economy with humility. To remember that our conceptual logarithms, like the tables once used by navigators, are tools for a journey, not the journey itself. They serve us best when we keep them provisional, open to correction, and sensitive to the richness they cannot capture.

Managing attenuation wisely is itself a complex adaptive challenge without simple solutions. It requires not just awareness of our limitations but active practices that surface hidden costs and maintain cognitive flexibility. It demands that we ask not whether our ways of making sense mirror reality, but whether they continue to support effective action in the conditions we now face, and whether we have ways to notice when they no longer do.

Engaging with complexity means getting better at being good enough, continuously. Our task is not to eliminate attenuation but to manage it wisely. And that begins with a question we often neglect. What do we choose to ignore, and how do we ensure that choice remains conscious, provisional, and responsive to feedback?

Always keep learning…

If you enjoyed this post and find my work valuable, I would appreciate your support. You can explore more of my ideas in my latest book, Second Order Cybernetics, Essays for Silicon Valley, hard copy available at the Lulu Store.

A Tale of a Thousand Models:

In today’s post, I am further exploring the notion of models and mental models. We often speak of mental models as though they are neat packages of knowledge stored somewhere in the mind. These models are typically treated as internal blueprints and as simplified representations of the world that help us navigate and make decisions. But what exactly do we mean when we call something a model? And are we always speaking about the same kind of thing?

The term model, in both technical and informal contexts, carries more ambiguity than we often acknowledge. In classical cybernetics, W. Ross Ashby gave the concept a central role. For Ashby, a model was a representation that could simulate the behavior of a system. A good regulator, he argued, must contain a model of the system it seeks to control. This model did not need to be a literal image or a complete mirror. It simply needed to have the right kind of functional correspondence with just enough structure to predict and act upon.

Ashby’s definition is rigorous and functional. The model need not share the same physical form or medium as the system it regulates. What matters is not material resemblance but structural correspondence across selected variables. The model must preserve the relations and transformations that enable viable regulation. Ashby called this ‘isomorphism’. This isomorphism does not demand total replication. It requires that the model preserve only those relations necessary for viable control. This is the basic premise of First Order Cybernetics.

This isomorphic correspondence is what makes the model useful for regulation. The regulator can manipulate the model, run it forward, test interventions, explore possibilities, and trust that the results will map back to the actual system. The model becomes a kind of structural analogue: a way of capturing pattern without requiring material similarity.

When we look deeper, something about this view of models can feel distant. It risks separating the observer from the observed, the knower from the known. It tends toward a view of knowledge that is separated from lived experience. What does it mean for an organism to contain a model of its world, if that organism is not a computer but a living, breathing being?

This is where the Thousand Brains Hypothesis (TBH) offers a helpful contrast. Jeff Hawkins, in developing this hypothesis, suggests that intelligence arises not from a single unified model of the world, but from many partial models working in parallel. Here, however, Hawkins seems to use ‘model’ in a markedly different sense than Ashby’s isomorphic structures. For Hawkins, a cortical column’s model is not a representation that stands apart from experience but a learned pattern of prediction embedded within sensorimotor engagement itself.

Each cortical column builds what Hawkins calls a model of objects in the world, but this model is constituted by the column’s capacity to predict sensory sequences as the body moves through space. The column does not store a picture of a coffee cup. Instead, it develops expectations about what sensations will follow from particular movements when encountering cup-like patterns. Some of these may be visual, some tactile, while others may be of a different sense altogether. The model is not a static thing, but a dynamic process. It is a way of being attuned to specific sensorimotor regularities.

While Hawkins retains the term “model,” his usage stretches its meaning. These patterns may not be models in the traditional sense at all. When we say a cortical column builds a model or learns expectations, we may still be trapped in representational thinking. The cortical column does not store information about objects. It maintains patterns of connectivity shaped by experience. These patterns do not represent the world per se. Instead, they enact a way of being responsive to it. A column’s knowledge of a coffee cup is not a stored description, but a readiness to engage with cup-like affordances. This is the key nuance I would like to offer.

This view of modeling resonates with Heidegger’s phenomenological understanding of being-in-the-world. Heidegger once noted that a hammer is not first known through its shape or composition, but through its use. It becomes present to us as ready-to-hand, as something we know by doing. Similarly, a cortical column knows an object by interacting with it, not by storing a detached image of it. As Heinz von Foerster once said, if you want to see, learn how to act.

In earlier reflections, I explored the limitations of treating mental models as internal representations. When we interact with a system or object, we are not retrieving stored pictures. Instead, we are drawing upon a history of lived engagement. Our orientation is not merely cognitive, but bodily and situated. The notion of a model here becomes something that reveals itself through action, not inspection. The Thousand Brains Hypothesis reinforces this idea by showing how perception and prediction are distributed. A single cortical column may only know part of an object in a specific sensory dimension, but through movement and integration with other columns, it participates in a kind of collective intelligence. There is no master map but only partial perspectives constantly updating and coordinating with one another. The columns are not comparing models. They are participating in a dynamic process of mutual constraint and coordination. This is what Maturana and Varela would recognize as structural coupling. Each column’s activity is shaped by its coupling with other columns, with the body, and with the environment. The result is a network of mutual specification rather than a collection of independent representations.

Intelligence, in this view, emerges not from the integration of discrete models but from the ongoing attunement of multiple sensorimotor streams. This attunement is guided not by accuracy but by viability. Viability is the organism’s capacity to maintain its structure and continue its pattern of living. It is often misunderstood that accuracy directly correlates with viability. The external world presents more complexity than any cognitive system can represent in full. The response, shaped by both constraint and energetic efficiency, is not to build exhaustive models but to maintain abstractions that are good enough. These are not symbolic summaries, but embodied dispositions formed through recurrent interaction.

This is not a flaw, but a feature of adaptive beings. Cognitive structures are not designed to capture the world exhaustively, but to filter it selectively. The principle of structural coupling rests on repetition. It rests on the organism’s ability to reinforce useful patterns over time. What endures are not accurate representations but habits of orientation that have proven viable. Cortical columns do not construct truthful depictions of the world. They cultivate ways of engaging that preserve continuity and coherence within the organism’s domain of living.

This stands in contrast to the classical view where the model is assumed to be singular, coherent, and representational. The model is not something we hold apart from the world, but something we become a part of through interaction with it*. This framing aligns with the constructivist view that organisms are informationally closed. An organism does not passively receive information from an objective world. It brings forth a world through its own structural coupling. What we call a model, then, is not a mirror of external reality but a structure of engagement, a dynamic fit between the organism and its environment.

The language of structure is important. Rather than thinking of models as things organisms have, we might think of them as patterns organisms are. A cortical column’s responsiveness to a coffee cup is not something it possesses but something it enacts. The pattern of connectivity is not a representation of the cup, but a way of being coupled to the cup’s affordances. Whether we call these models, structures of prediction, or patterns of skilled engagement, what unites them is that they are not static descriptions. They are emergent dispositions, formed through repeated interaction. Each term foregrounds a different aspect such as structure, process, or habit. However, they all point to intelligence as enacted rather than mirrored.

This is not to dismiss Ashby’s insight. His use of the term model was never about mirroring for its own sake. It was about enabling viable regulation and constructing just enough structure to explain and act. Perhaps it is more accurate to think of such models as habits of expectation. They are not representations but anticipations. They do not describe the world as it is but orient us toward what is likely to come. They are pragmatic, situated, and always in motion. Or perhaps the term model itself is too burdened. What we call a model may be better understood as a form of skilled attunement. It becomes a pattern of responsiveness that is cultivated through history, shaped by constraints, and sustained by viability. The cortical column does not model the coffee cup. It simply becomes responsive to it.

This reframing opens up deeper questions. If intelligence is not the construction of better representations but the cultivation of more viable engagements, what does this mean for artificial intelligence? Can machines learn to be responsive rather than simply predictive? Can they participate in the world, rather than map it?

The Thousand Brains Hypothesis, interpreted through the lens of structural coupling and lived engagement, suggests that intelligence emerges not from central models but from richly distributed interactions. It implies that robust intelligence does not require more accurate representations, but more diverse ways of being coupled to the world.

To model, in this deeper sense, is to engage. It is to live into a world that reveals itself not all at once, but gradually through action, adjustment, and care. Perhaps, the real power of what we call a model may not lie in what it represents, but in what it enables us to do. Or more accurately, in what it allows us to become.

Final Words:

This shift from models as internal representations to models as patterns of skilled engagement challenges deeply held assumptions about knowledge, cognition, and intelligence. It is not merely a technical redefinition. It is a philosophical turning. If cognition is not about mirroring the world but about maintaining a viable relation to it, then intelligence becomes a matter of fitting rather than mapping. It is not about what we store, but about how we respond. Even this post is not free of modeling. It draws distinctions, frames structures, and builds conceptual pathways. But it does so with an orientation toward viability, not toward finality. The second order reflexive nature of this inquiry (modeling the limits of models) underscores the point. Intelligence is not found in having the final answer, but in remaining open to reframing, recoupling, and reengaging as the world shifts around us.

This reframing also casts new light on the ambitions of artificial intelligence. If intelligence is not the construction of better representations but the cultivation of more viable engagements, then it becomes clear that AI systems, as currently conceived, may be fundamentally limited. The limitation is not merely technical. It is existential. Intelligence, in this deeper sense, emerges from embodied interaction, historical coupling, and recursive responsiveness to a world that matters. Machines that manipulate symbols or detect statistical regularities may approximate aspects of intelligent behavior, but they remain ungrounded in the affective, bodily, and experiential dynamics that make living cognition what it is. Responsiveness is not a product of prediction alone. It emerges from vulnerability, concern, and the need to maintain coherence amid complexity.

Without changes in their environment shaping how they persist, machines may simulate participation, but they do not truly engage. They act without inhabiting. They process without perspective. Perhaps this is one of the main reasons artificial intelligence may fall short of achieving sentience. It relies on static internal representations and lacks the embodied, experiential living necessary for understanding, concern, or care. Without lived coupling, there may be behavior, but not presence. There may be processing, but not perspective.

While navigating complexity, my hope is that this reframing offers both humility and hope. Humility, because it reminds us that our understanding is always partial and situated. Hope, because it suggests that intelligence is not a fixed capacity, but a living process which is co-created, and transformed through our engagements with the world and with each other in a social realm. I will finish with an excellent quote from Di Paolo, Rhohde and De Jaegher:

Organisms do not passively receive information from their environments, which they then translate into internal representations. Natural cognitive systems are simply not in the business of accessing their world in order to build accurate pictures of it. They participate in the generation of meaning through their bodies and action often engaging in transformational and not merely informational interactions; they enact a world.

Always keep learning…

* Hat tip to Heinz von Foerster’s wonderful quote. Am I apart from the universe or am I a part of the universe?

On Probability…

In today’s post, I am exploring the nature of probability. Is probability an intrinsic feature of events that evolves over time, or is it something else entirely? My view is that probability is best understood as a measure of an observer’s uncertainty that can change as new information becomes available, rather than as a property that events themselves possess.

Probability is not an intrinsic property of events that evolves over time. It is a measure of an observer’s uncertainty that changes as the observer gains new information.

This insight becomes clear when we consider what happens before and after an event of interest occurs. You might assign a 35% probability that your favorite team will win their championship match in 2025 based on their team, coaching staff, recent performance, and other factors. When your team does indeed win the championship in 2025, you no longer speak of a 35% chance afterward. You know they won, so your uncertainty about whether your team would capture the 2025 title is gone. The event itself has not changed. What has changed is simply your information about it.

This example reveals something fascinating. The event does not have a probability that flows through time. Your favorite team winning the 2025 championship does not possess an inherent “35% chance property” that somehow transforms into a “100% chance property” when they claim victory. Rather, probability expresses your epistemic state. It expresses what you know and do not know about the event. As your knowledge updates, so does the probability you assign.

Before the season, the probability of 35% captured your uncertainty given incomplete information about how this specific championship race would unfold. After they win, your uncertainty about whether your team won the 2025 championship disappears because you have complete information about this particular outcome. The players were competing and making decisions throughout the season, but your knowledge of the final result was incomplete and then became complete. Probability tracks this change in knowledge, not a change in the event itself.

Your favorite team winning the 2025 championship is a singular, unrepeatable event. This singularity principle applies to every event, whether it is the outcome of a coin toss or whether you miss a train. Even when we consider the 2026 championship, that represents a completely separate event requiring its own probability assessment. You might again assign some probability to your team winning in 2026, but this concerns a different season with different players, different opponents, and different circumstances. The fact that your team won in 2025 provides information that might influence your assessment of their 2026 chances, but each championship stands as a distinct event with its own associated uncertainty.

Different philosophical schools interpret probability in various ways. Frequentists focus on long-run patterns, while others emphasize physical propensities in systems. I adopt the Bayesian perspective here, which treats probability as quantifying an observer’s degree of belief about uncertain outcomes. This framework excels at handling partial information and belief updating as new evidence arrives.

The Bayesian approach formalizes how rational observers should revise their beliefs. You start with a prior probability based on available information. When new evidence arrives, Bayes’ theorem shows how to calculate an updated posterior probability, which then serves as the prior for the next update. Certainty represents probability at its extremes (belief of 1 or 0), but most real-world knowledge involves intermediate probabilities reflecting justified but incomplete information.

Let us return to the championship example with this framework in mind. Your initial 35% probability assignment reflects partial knowledge about the 2025 season that remains open to revision. When your favorite team wins the championship, your belief updates to certainty: probability 1. This transition represents a shift in your epistemic state, not a change in some objective property of the championship outcome. The probability assigned to the event changes only because your information changes.

Your team winning the 2025 championship might influence how you assess their chances for future seasons, but each championship represents a separate event. The 2026 championship is not the same event as the 2025 championship because it involves different circumstances, different player development, different opponents, and different strategic decisions that create their own uncertainty. Your experience from the 2025 season provides information for assessing future championship races, but the probability you assign to the 2026 contest addresses a distinct event with its own epistemic challenges.

Once an event’s outcome becomes known, assigning forward-looking probabilities to that specific completed event loses predictive meaning. However, probabilities retain important roles in other contexts. We use explanatory probabilities to reason about hidden causes of observed effects, and counterfactual probabilities to explore alternative scenarios for learning and decision-making. These applications all involve managing uncertainty about things we do not fully know.

Some philosophers argue for objective chances embedded in physical reality, claiming that the world itself has genuine probabilistic features. Even these can be understood through a Bayesian lens as rational betting odds conditioned on our best current knowledge about physical laws and initial conditions. From this epistemic perspective, probability fundamentally reflects our relationship to knowledge and uncertainty, not immutable features of external events.

Understanding probability as observer-dependent rather than event-dependent has practical implications. It explains why different people can reasonably assign different probabilities to the same event because they possess different information. It clarifies why probabilities can seem to “change” as we learn more: our knowledge evolves while events themselves follow deterministic or genuinely random processes. Most importantly, it positions probability as a dynamic tool for rational reasoning under uncertainty rather than a mysterious property that events carry through time.

Finally, it is important to recognize that while our beliefs may remain probabilistic, our decisions in the real world must ultimately resolve into binary choices. We decide to carry an umbrella or not, to take the highway or not, to treat a patient or not. Practical action demands that we collapse our probabilistic beliefs into definitive commitments. This reinforces that probability serves as a bridge between uncertainty and action, not as a property that events carry through time.

Final Words:

This epistemic view of probability transforms how we think about uncertainty and prediction. Rather than searching for probabilities “out there” in the world, we recognize them as tools for managing our own knowledge and ignorance.

As Pierre Simon Laplace eloquently put it: “Probability theory is nothing but common sense reduced to calculation.”

Once we embrace probability as a measure of what we know rather than what events are, we can use it more effectively as the rational tool it was always meant to be.

Always keep learning…

The Arbitrariness of Objectivism:

The readers of my blog might be aware that I appreciate the nuances of cybernetic constructivism. Cybernetic constructivism rejects the idea that we have access to an objective reality. It does not deny that there is an external reality independent of an observer. However, we do not have direct access to it. Additionally, the external world is more complex than us. As part of staying viable, we construct a version of reality that is unique to our interpretative framework. This takes place in a social realm, and error corrections happen because the construction occurs in the social realm.

Heinz von Foerster, the Socrates of Cybernetics, formulated two imperatives that provide insight into this framework. The first is the ethical imperative that states “act so as to increase the number of choices.” The second is the aesthetical imperative that states “if you desire to see, learn how to act.” I welcome the reader to check out previous posts on these concepts. This worldview supports pluralism, the idea that there can be multiple valid versions of reality. This emerges primarily because the external world being more complex than our cognitive apparatus, we maintain viability by constructing particular versions of reality rather than accessing reality directly.

Common Mischaracterizations:

A primary criticism I encounter involves misrepresenting this worldview as relativism or solipsism. Critics suggest that acknowledging multiple perspectives means that anything goes, or that nothing is shared between observers. This represents a caricature rather than a substantive critique.

Precision is necessary here. Some forms of relativism claim that all views are equally valid, including contradictory ones. In that model, if claim A asserts “only A is valid,” then relativism must also treat that assertion as valid. It has no mechanism for comparison or critique. The result is a flattening of all claims into mere equivalence, where strength, coherence, or context carry no weight.

Solipsism advances an even more extreme position. It claims that only one’s own mind is knowable, denying shared reality altogether. It discards the very possibility of meaningful intersubjectivity. No systems thinker, and certainly no pluralist, takes this position seriously.

Pluralism as a Distinct Position:

Pluralism is neither relativism nor solipsism. It does not claim all views are valid. Rather, it asserts that no view is valid by default. Pluralism insists that perspectives must be made visible, situated in context, and evaluated through dialogue. It resists automatic authority, including authority derived from its own assertions.

Consider what objectivism accomplishes by contrast. It selects a single claim and declares that only this claim is valid while all others are not. But on what basis does it make this selection? Often, no external justification is offered. The grounding remains internal, context-bound, or inherited, yet it is presented as if it were neutral, universal, and self-evident.

This selection process reveals a potential arbitrariness of objectivist claims. The view appears arbitrary because its assumptions may remain hidden from examination. Without transparent justification for why one view should be privileged, objectivism risks the appearance of arbitrariness. What presents itself as necessity may simply be preference in disguise. From a pluralist standpoint, this represents concealment rather than clarity.

The Paradox of Objectivist Authority:

Paradoxically, this form of objectivism begins to mirror the very relativism it claims to oppose. Relativism declares that all claims are valid, including any particular claim A. Objectivism declares that only claim A is valid while offering no method to interrogate why this should be so. Each approach shuts down evaluation through different mechanisms. Relativism dissolves differences into sameness. Objectivism excludes all but one view from consideration at the outset.

This dynamic reveal what objectivism risks becoming, not solipsism in the strict philosophical sense, but functional solipsism. When a worldview refuses to acknowledge its own perspective and denies legitimacy to all others, it ceases to see the world. It sees only itself, reflected and reinforced. This represents the erasure of other ways of seeing under the illusion that one’s own interpretative lens is the world itself.

The Hidden Nature of Objectivist Claims:

The danger of objectivism lies in its method: selecting a single view, designating it as truth, and treating alternatives as error, noise, or confusion. It dresses up a personal, historical, and situated position as universal and eternal. This approach is not more objective than pluralism. It is simply better concealed.

Frameworks that prioritize ontology over epistemology tend to overlook the epistemic humility that characterizes pluralism. When we claim to know what reality is before examining how we come to know it, we bypass the very process of inquiry that might reveal the limitations and situatedness of our perspective. This ontological presumption becomes particularly problematic when it denies its own epistemological foundations.

Pluralism does not collapse into solipsism. Objectivism risks this collapse precisely when it denies that it operates from a particular perspective. The refusal to acknowledge one’s interpretative framework does not eliminate that framework. It merely renders it invisible to examination.

Pluralism is not weakness, indecision, or relativistic drift. It represents a disciplined humility and a refusal to collapse complexity into certainty prematurely. It does not reject standards but demands that they be made visible, questioned, and held accountable to the context in which they arise.

Pluralism increases the space for dialogue, choice, and possibility. It reminds us that what we do not question becomes invisible, not because it is true, but because it hides within the taken-for-granted assumptions of our frameworks.

In a world increasingly polarized between loud certainties and quiet disillusionment, pluralism offers something increasingly rare: the courage to remain open, to ask how we know what we claim to know, and to stay in conversation with perspectives we might otherwise reject.

Final Words:

Not everything is permissible under pluralism. But no single view should escape questioning. The cybernetic constructivist position maintains that our constructions of reality emerge from our particular biological, cognitive, and social constraints. These constructions prove viable not because they correspond to an objective reality we cannot access, but because they enable us to navigate the complexity we encounter.

I will finish with a quote from Heinz von Foerster:

Objectivity is the delusion that observations could be made without an observer.

The task before us is not to eliminate the observer but to acknowledge the observer’s role in every observation. This acknowledgment does not lead to relativism or solipsism. It leads to a more rigorous understanding of how knowledge emerges from the interaction between observer and observed within particular contexts and constraints.

Always keep learning.

The Form of Decency

At a recent exhibition, I saw a sign that read: “Exit Only. No Re-Entry.” It seemed not just as a logistical instruction but as a metaphor. Around the same time, I came across a photo of a sign demanding that people speak the local dialect. What struck out to me was that the sign was written in English. These moments echoed something I have long been thinking about: the contradictions that arise when our distinctions fold back on themselves, what George Spencer-Brown called “reentry.”

I am a longtime admirer of Spencer-Brown’s Laws of Form, and in today’s post, I explore how his notion of reentry helps illuminate the paradoxes and blind spots in modern ideologies, especially the rise of xenophobia and extreme nationalism. These rigid ideologies depend on distinctions between us versus them, lawful versus unlawful that appear neat but collapse under their own logic when viewed recursively. We pretend we are only exiting, drawing sharp lines, while ignoring the inevitability and necessity of reentry in our sensemaking.

Drawing Distinctions

Spencer Brown opened his mathematical-philosophical treatise with a simple instruction: Draw a distinction. This simple act of marking a boundary between “this” and “that” forms the foundation of how we structure knowledge, meaning, and identity. We create categories and define what is “in” and what is “out.” This is how form arises through distinction.

In Laws of Form, he also introduced the notion of reentry: the act of folding a distinction back into itself. Simply put, this is a self-referential act. By doing this, the tidy separations we created begin to blur. This move, abstract as it sounds, has powerful consequences for how we think, live, and treat each other. Especially in a world torn by polarization, nationalism, and fear of the “other,” reentry reveals the paradoxes that rigid ideologies try to hide and points us toward a more humane way of navigating complexity.

The Pot and the Form

Let us use a simple example to understand the form better. Consider a pot of boiling water. Here, we can make three identifications:

  • Pot = the mark, or the distinction
  • Water inside the pot = what is indicated, the marked space, the inside
  • Outside the pot = the unmarked space, the outside

Together, all three constitute the form. The pot, as a boundary, plays the role of the mark in Spencer-Brown’s terms. It creates a distinction between what is inside and what is outside. The pot itself is not part of what is inside; it is what makes “inside” possible by drawing a boundary. The mark exists in a meta-position: it defines inside and outside but cannot be reduced to either. It is the operation of drawing the distinction. The pot allows us to interact with what is inside and allows what is inside to interact with the surroundings.

We can use the same example to introduce reentry. Imagine placing that pot inside another pot, creating a double boiler. The inner pot is held by the outer one. The boundary remains, but now it is nested and refers to something beyond itself. This is reentry: when a form does not just define something but begins to refer to its own act of defining. This becomes an act of second-order observation. In the double boiler metaphor, the inner pot (the reentered form) exists within the outer pot (the original distinction), creating a ‘system’ that is both distinct and self-contained.

Reentry challenges the simplicity of binary logic, revealing that ‘systems’ can be self-referential and dynamic. This concept is pivotal in understanding complex systems, where elements influence and are influenced by themselves.

The Purpose of Reentry: Revealing Cognitive Blind Spots

We love binaries: true/false, us/them, lawful/unlawful. But reentry destabilizes these neat categories. Who defines what is “lawful”? The law itself. When the law governs the making of laws (as in constitutional law), we enter a recursive loop. What is legal becomes a matter of interpretation, not clarity. The binary collapses into ambiguity. Reentry shows us that binaries are useful simplifications, not absolute truths. Dogmatic ideas rely on such binaries, and reentry becomes an effective tool for challenging dogma.

Similarly, in language, terms like “normal” are defined by cultural norms, which are themselves shaped by collective perceptions of normality. This circularity demonstrates how meanings are not fixed but evolve through self-reference. Reentry is not merely a logical twist. It reveals something crucial about how we construct meaning.

When we draw a distinction between “lawful” and “unlawful,” we assume clarity. But as soon as we ask who defines the law and realize it is the law itself, we see that the boundary is recursive. It defines itself. This is not a flaw but a feature of complexity.

The Second-Order View: Observing Observation

This leads us to second-order thinking: the act of observing the act of observing. In logic, when a ‘system’ includes itself in its model, it can become unstable. However, it also owns its position. Blind spots can be revealed, opening the door to creativity, paradox, and deeper understanding. Reentry is how we shift from first-order systems (clear categories, fixed forms) to second-order ones (reflexivity, contradiction, emergence). It is how we move from saying “we are right” to asking “how do we know?”

As the cybernetician Heinz von Foerster observed: “The observer must be included in the observed system.”

This represents the leap from first-order thinking (observing the world) to second-order thinking (observing how we observe). Reentry is the mechanism of that leap. Recognizing and thinking along the lines of reentry is deeply needed today because some of the most dangerous ideas we face rely on distinctions that collapse under their own logic.

Reentry and the Illogic of Xenophobia

Xenophobic ideologies often define “us” versus “them,” asserting superiority or purity. However, when these distinctions undergo reentry, when the criteria for inclusion are applied to the in-group, they often fail to hold consistently. Similar to the sign that demanded the use of the local dialect but was written in English, xenophobic logic contradicts itself when examined through reentry.

What does it mean to be a person from country “X”? Is it geography? Culture? Language? Legal status? Values? The more we examine these criteria, the fuzzier they become. Yet we use such labels as if they were clean boundaries, pots that perfectly contain identity. Reentry challenges this assumption by turning the form inward.

If being from country “X” means standing for freedom, justice, and decency, how can one uphold those values while treating outsiders with cruelty? If your culture preaches respect, how can you use that culture to justify disrespect? If your national identity is built on moral ideals, then those ideals must apply to how you treat everyone, not just those inside your imaginary boundaries.

Bigotry collapses under reentry. Its internal logic folds in on itself. The principle violates the practice. The mirror reflects itself and reveals the contradiction. Racism, xenophobia, and nationalism, when examined through the lens of reentry, are not just morally wrong. They are logically incoherent.

The Ethical Need for Redundancy

In complex systems, one of the most powerful safeguards is redundancy. In engineering, redundancy prevents collapse. In ethics, it serves the same function.

Hope is redundancy in action, as are other humanistic notions such as kindness, compassion, and forgiveness. These are not luxuries; they are second-order buffers. They activate when logic stalls. They hold the ‘system’ together when paradox threatens to tear it apart. Reentry exposes the instability of our forms. Redundancy helps us live with that instability.

Ethical redundancy functions like the inner pot in a double boiler. It buffers the heat. It allows care to emerge where rigidity would cause harm. It creates space for ambiguity, reflection, and repair. This is why, in the face of bigotry and rigid ideologies, we must design for ethical reentry. We must build in second chances. We must speak gently even when the logic breaks.

Final Words

In a world obsessed with efficiency, clarity, and being right, reentry is a radical act. It turns the ‘system’ inward. It reveals our blind spots. It shows us where our ideals betray themselves. But reentry does more than expose contradictions; it opens pathways to wisdom. When we embrace reentry, we move from the arrogance of first-order certainty to the humility of second-order inquiry.

The rise of extreme nationalism and xenophobia reflects our collective failure to practice reentry. These ideologies thrive on the illusion of clear boundaries, pure identities, and simple answers. They collapse when subjected to their own logic, but only if we have the courage to apply that logic. Only if we are willing to let our mirrors reflect.

Reentry teaches us that our most cherished distinctions are provisional, our certainties are constructed, and our boundaries are more porous than we dare admit. This is not cause for despair but for hope. It means we can rebuild. We can redesign. We can choose compassion over cruelty, and in that act, we can stay human.

In the end, reentry invites us to remain human and to include kindness as a design principle, building ‘systems’ that can reflect on themselves without breaking. It asks us to hold our beliefs lightly enough that they do not harden into weapons, yet firmly enough that they can guide us toward justice. This is the form of decency: recursive and reflective.

Always keep learning…

Get a Grip on It:

Complexity is a matter of degree and not a kind. – Glenda Eoyang

In today’s post, I am exploring the importance of incorporating diversity when navigating complex environments. I have written previously about the seductive appeal of efficiency and how its blind pursuit can leave us exposed. Efficiency asks us to optimize for known outcomes. It assumes a world where inputs are controlled and variation is minimized. But reality is rarely that generous. It is textured, layered, and in motion. It offers few clean edges and rarely repeats itself in neat loops. As leaders, we are asked to shape structures that can stay viable in this kind of world. The overuse of efficiency in such contexts does not make us leaner or smarter—it often makes us brittle.

We often design as though the ground is level. As if everyone begins from the same place, with the same tools, the same reach, the same slack. But the ground is not level. It has never been. Some people begin with more; more access, more time, more tolerance from the structure. Others begin already contending with friction. Not because they lack capability, but because the design was not shaped with them in mind. This is not just an ethical issue. It is a design one.

In complexity, uniformity fails fast. In simple, symbolic systems—code, logic, procedures—uniformity can be a virtue. The terrain is controlled. The inputs are known. The environment is stable enough to reward sameness. But in complexity—where causes are fuzzy, signals are noisy, and context moves mid-sentence—we need something else. We need grip.

Reality Requires Grip

Reality rarely presents itself in tidy ways. It offers no singular handle for us to grab. Instead, it throws contradictions, mismatched signals, and unexpected constraints. We cannot hold it with one kind of mind, one kind of framework, or one kind of experience. The more varied the terrain, the more varied our grasp must be.

That grip—our capacity to make meaningful contact with complexity—comes from difference. It comes from a range of perspectives, a mix of sensibilities, a spread of lived experiences. It comes from people who notice different things, who ask different questions, who move through the world in different ways. This does come with a cost. Uniform structures may look clean and run fast, but they tend to crack under pressure. Diverse structures take longer to build, but they flex, adapt, and hold when things shift.

Ross Ashby reminded us that only variety can absorb variety. If the environment can surprise us in a hundred ways, then our ‘systems’ must be able to respond in at least a hundred ways. If not, the environment ‘wins’.

We often treat diversity as an accessory, something to be added after the main frame is in place. But in complexity, diversity is not decorative. It becomes load-bearing. The differences give the structure grip, not inward but outward, allowing it to hold against the irregularities of reality. They create structural tension and enable edge awareness. This awareness helps us notice early signals, those subtle cues that something is shifting. The presence of difference prevents the system from becoming complacent, blind, or brittle. Diversity introduces stretch that resists premature closure, while expanding the system’s capacity to perceive what is happening at its limits, where breakdowns often tend to begin.

A monoculture in nature may appear efficient. For example, fields of identical crops may offer predictability, ease of control, and optimized yield when conditions remain stable. But this sameness introduces a hidden fragility. A single disease, an unexpected frost, or a sudden shift in climate can cause the entire network to fail, because uniformity amplifies vulnerability. In contrast, a wild field may seem chaotic or inefficient, yet its diversity in root structures, growth patterns, and tolerances create resilience. When conditions change, not everything is affected in the same way. Some parts fail, while others adapt. The ‘system’ bends, but it does not break.

This is more than an ecological insight. It is a way of thinking about how we organize and sustain ourselves. When a team, a community, or a structure relies on sameness, it may function smoothly in predictable conditions, but it lacks the range to respond when reality becomes more complex. Diversity—cognitive, experiential, and demographic—broadens a group’s capacity to interpret change, adjust course, and stay viable over time. In environments where uncertainty is the rule and control is limited, it is this range that gives the whole arrangement a better grip on reality.

Designing From the Blind Spot

We tend to build from what is visible, measurable, and familiar. We optimize for what is easy to test. But what gets left out often matters more than what gets built in. And too often, the people left out are the ones already carrying the most structural friction. We tend to think of inclusion as a moral gesture. A choice to be kind or fair. When we design only for those already well-positioned, we do not just exclude, we weaken the design itself.

We create brittle solutions, ones that quietly assume access, literacy, capacity, forgiveness. We optimize for efficiency and familiarity, and miss the parts that strain under real-world pressure. But when we start from the edges—from those who live with constraint—we see what the structure hides. We start to notice the steps that are too steep, and that the protocols assume too much. Fixing for them is not just being humane. It becomes diagnostic work. It is how we surface the assumptions that compromise integrity. It is how we build arrangements that do not crack when things get uneven, which they always do.

Final Words

Heinz v on Foerster said:

Act always so as to increase the number of choices.

Maybe the corollary to that is:

Design as if you might be the one with the least choice.

That is not a political statement. It is a practical one. When we build for those with the least slack, we tend to uncover the most insight. And when we design from the blind spot, we do not just fill a gap—we often strengthen the whole. Designing for the most vulnerable builds in the redundancy that makes a structure resilient. When you build in space for the person who cannot read the form, who does not have time to wait, who misses the signal the first time—we are not just helping them. We are making the whole arrangement more resilient.

This is because the real world is not clean. Things fail, contexts shift and people miss a step. And if our design cannot bend in those moments, it will break.

In complex arrangements, redundancy is what keeps the structure whole. Not all paths will be smooth. Not all users will match the ideal profile. Not all steps will land perfectly the first time. This means that we should build space for detours, retries, and second chances. That is not inefficiency. That is how we build resilience. Redundancy is not the opposite of elegance or efficiency. It is the thing that lets the design bend without breaking.

I will finish with one of my favorite quotes from Doctor Who:

Human progress is not measured by industry. It is measured by the value you place on a life. An unimportant life. A life without privilege. The boy who died on the river, that boy’s value is your value. That is what defines an age, that is… what defines a species.

Always keep learning…

Absurdity in Systems Thinking

In today’s post, I am looking at absurdity in Systems Thinking. Absurdity is an official term used in the school of philosophy called existentialism. An existentialist believes that existence precedes essence. This means that our essence is not pregiven. Our meaning and purpose are that which we create. In existentialism, the notion of absurdity comes from the predicament that we are by nature meaning makers, and we are thrown into a world devoid of meaning. We do not have direct access to the external world; therefore, our cognitive framework has been tweaked by evolution to seek meaning in all perturbations we encounter. We are forever trying to make sense of a world devoid of any sense or meaning.

We like to imagine that there is greater meaning to this all and that there is a “system” of objective truths in this world. In this framework, we all have access to an objective reality where we can use 2 x 2 matrices to solve complex problems. In the existentialist framework, we see that instead of a “system” of objective truths, we have multiplicity of subjective truths. Soren Kierkegaard, one of the pioneers of existentialism, viewed subjective truth as the highest truth attainable.

When we talk about a “system” we are generally talking about a collection of interrelated phenomena that serves a purpose. From the existentialism standpoint, every “system” is a construction by someone to make sense of something. For example, when I talk about the healthcare system, I have a specific purpose in mind – one that I constructed. The parts of this system serve the purpose of working together for a goal. However, this is my version and my construction. I cannot act as if everyone has the same perspective as me. I could be viewing this as a patient, while someone else, say a doctor, could see an entirely different “system” from their viewpoint. Systems have meaning only from the perspective of a participant or an observer. We are talking about systems as if they have an inherent meaning that is grasped by all. When we talk about fixing “systems”, we again treat a conceptual framework as if they are real things in the world like a machine.  The notion of absurdity makes sense here. The first framework is like what Maurice Merleau-Ponty, another existential philosopher, called “high-altitude thinking”.  Existentialism rejects this framework. In existentialism, we see that all “systems” are human systems – conceptual frameworks unique to everyone who constructed them based on their worldviews and living experiences. Each “system” is thus highly rich from all aspects of the human condition.

Kevin Aho wrote about this beautifully in the essay, “Existentialism”:

By practicing what Merleau-Ponty disparagingly calls, “high-altitude thinking”, the philosopher adopts a perspective that is detached and impersonal, a “God’s eye view” or “view from nowhere” uncorrupted by the contingencies of our emotions, our embodiment, or the prejudices of our time and place. In this way the philosopher can grasp the “reality” behind the flux of “appearances,” the essential and timeless nature of things “under the perspective of eternity” (sub specie aeternitatis). Existentialism offers a thoroughgoing rejection of this view, arguing that we cannot look down on the human condition from a detached, third-person perspective because we are already thrown into the self-interpreting event or activity of existing, an activity that is always embodied, felt, and historically situated. 

We are each thrown here into the world devoid of any meaning, and we try to make meaning. In the act of making sense and meaning, we tend to believe that our version of world and systems are real. We often forget to see the world from others’ viewpoints.

Every post about Systems Thinking must contain the wonderful quote from West Churchman – the systems approach begins when first you see the world through the eyes of another. This beautifully captures the essence of Systems Thinking. Existentialism teaches us to realize the absurdity of seeking meaning in a world devoid of any meaning, while at the same time realizing that the act of seeking meaning itself is meaningful for us.

Always keep on learning!

References:

[1] Aho, Kevin, “Existentialism”, The Stanford Encyclopedia of Philosophy (Summer 2023 Edition), Edward N. Zalta & Uri Nodelman (eds.), URL = <https://plato.stanford.edu/archives/sum2023/entries/existentialism/&gt;.

An Existentialist’s View of Complexity:

Art by NightCafe

In my post today, I am looking at the idea of complexity from an existentialist’s viewpoint. An existentialist believes that we, humans, create meanings for ourselves. There is no meaning out there that we do not create. An existentialist would say, from this viewpoint, that complexity is entirely dependent upon an observer, a meaning maker.

We are meaning makers, and we assign meanings to things or situations in terms of possibilities. In other words, the what-is is defined by an observer in terms of what-it-can-be. For example, a door is described by an observer in terms of what it can be used for, in relation to other things in its environment. The door’s meaning is generated in terms of its possibilities. For example, it is something for me to enter or exit a building. The door makes sense to me when it has possibilities in terms of action or relation to other things. This is very similar to the ideas of Gibson, in terms of “affordances”.

In existentialism, there are two concepts that go hand in hand that are relevant here. These are “facticity” and “transcendence”. Facticity refers to the constraints a subject is subjected to. For example, I am a middle-aged male living in the 21st century. I could very well blame my facticity for pretty much any situation in life. Transcendence is realizing that I have freedom to make choices to stand up for myself to transcend my facticity and make meaning of my own existence. We exist in terms of facticity and transcendence. We are thrown into this world and we find ourselves situated amidst the temporal, physical, cultural and social constraints. We could very well say that we have a purpose in this world, one that is prescribed to us as part of facticity or we can refer to ourselves to enable us to transcend our facticity and create our own purposes in the world.

In the context of the post, I am using “facticity” to refer to the constraints and “transcendence” to refer to the possibilities. Going back to complexity and an observer, managing complexity is making sense of “what-is” as the constraints, in terms of “what-it-can-be” as the possibilities. We describe a situation in terms of complexity, when we have to make meaning out of it. We do so to manage the situation – to get something out of it. This is a subject-object relationship in many regards. What the object is, is entirely dependent on what the subject can afford. When one person calls something as complex, they are indicating that the variety of the situation is manifold than what they can absorb. Another subject (observer) can describe the same object as something simple. That subject may choose to focus on only certain attributes of the situation, the attributes that the subject is familiar with. Anything can be called as complex or simple from this regard. As I have noted before, a box of air can be as complex as it can get when one considers the motion of an air particle inside, or as simple as it can get when one considers it as a box of “nothing”. In other words, complexity has no meaning without an observer because the meaning of the situation is introduced by the observer.

A social realm obviously adds more nuance to this simply because there are other meaning-makers involved. Going back to existentialism, we are the subject and at the same time objects for the others in the social realm. Something that has a specific meaning to us can have an entirely different meaning to another person. When we draw a box and call that as a “system”, another person can draw a different box that includes only a portion of my box, and call that as the same “system”. In the social realm, meaning-making should be a social activity as well. It will be a wrong approach to use a prescribed framework to make sense because each of us have different facticities and what possibilities lie within a situation are influenced by these facticities. The essence of these situations cannot be prescribed simply because the essence is brought forth in the social realm by different social beings. A situation is as-is with no complexity inherent to it. It is us who interact with it, and utilize our freedom to assign meaning to it. I will finish off with a great quote from Sartre:

Human reality everywhere encounters resistance and obstacles which it has not created, but these resistances and obstacles have meaning only in and through the free choice which human reality is.

Stay safe and always keep on learning…

In case you missed it, my last post was Plurality of Variety: