Category Archives: Management Models
Joseph Campbell, The Power of Myth, and the Art of Envisioning System Architecture
Joseph Campbell believed that mythology is not merely a collection of old stories; it is the human mind’s original operating system: a universal architecture that encodes how we understand change, complexity, and meaning. In The Power of Myth, his celebrated conversation with Bill Moyers ( I binged on this entire series this Saturday after a long while), Campbell argued that myths are “clues to the spiritual potentialities of human life.” Yet let’s read these myths more broadly. They are also models of systemic behavior, blueprints for how transformation unfolds, whether in an individual, an enterprise, or a technological ecosystem.
Modern system architecture, whether in finance, operations, or digital transformation, faces a challenge similar to that of mythology: to impose order without rigidity, to design for change without losing coherence, and to align many moving parts into a living, breathing whole. Seen through Campbell’s lens, architecture is not an engineering diagram but a hero’s journey in structure and function. It is a story of departure from legacy, confrontation with uncertainty, and eventual return with renewal and insight.
This essay examines how Campbell’s mythic framework can guide the way we envision and construct systems. It explores myth as the original design language, shows how the Hero’s Journey mirrors architectural transformation, and offers a practical synthesis for leaders designing resilient, meaningful, and adaptive systems.
I. Myth as the Blueprint of Human Systems
Campbell’s insight begins with a profound observation: across all civilizations, the same basic pattern repeats. Whether one reads the Odyssey, the Bhagavad Gita, or Star Wars, the storyline follows a universal topology which he calls the monomyth. The hero is called to adventure, crosses a threshold into the unknown, undergoes trials and transformation, and returns with an “elixir” that restores the community.
This pattern is not confined to literature. It is embedded in the human experience of transformation itself. Every system, be it biological, social, or organizational, must at times break its equilibrium, traverse chaos, and re-emerge at a higher level of order. Myth thus becomes the architecture of change.
In modern terms, one could call it a recursive algorithm: a self-similar process that repeats at different scales. Each subsystem, individual team, department, or platform undergoes its own hero’s journey within the larger enterprise narrative. The organization evolves as these micro-journeys interact, merge, and reinforce each other.
This recursive layering of journeys parallels how system architects think. They model modules, interfaces, and flows. Bear in mind that each operates with local autonomy while maintaining global coherence. The aim is to create a structure in which each part serves both its own function and the integrity of the whole. Myth, in essence, is the human mind’s first architecture diagram. It shows that enduring systems are not built from control alone, but from patterns of interaction guided by purpose.
II. The Hero’s Journey as a Systemic Map
To see how Campbell’s mythic model translates into architectural thinking, it helps to map the significant phases of the Hero’s Journey onto the process of system design and transformation.
1. Departure – The Call to Transformation
In the mythic narrative, the hero receives a call to adventure that disturbs the stability of the familiar world. There is usually resistance, hesitation, or denial. Similarly, in system design, the first step is to acknowledge that the current state—legacy infrastructure, static reporting, siloed processes—can no longer support the enterprise’s evolving goals.
The “call to adventure” in this context might be a strategic imperative: the need for automation, scalability, or predictive insight. Yet just as in mythology, departure demands courage. Organizations cling to legacy environments because they are stable and known. The departure phase requires both leadership and faith that what lies beyond the threshold, though uncertain, holds greater value.
In architectural terms, this is the moment of disruption: namely, when the system is deliberately unsettled so that it may evolve. It is the point at which a decision is made to move from existing architectures to adaptive, modular ones, often involving distributed systems, advanced analytics, or artificial intelligence.
2. Initiation – The Trials of Integration
The initiation phase in myth is the crucible, a period of trials, tests, and revelations. Heroes encounter helpers and enemies, face ordeals, and undergo symbolic death and rebirth. In a system architecture, this is the transformation stage, where integration, design, and implementation converge.
Architects at this stage must navigate a complex landscape: data pipelines, governance models, user adoption, and competing design philosophies. Conflicts arise between speed and control, between local autonomy and global standardization, between innovation and compliance. These are the dragons of modern enterprise.
The successful architect, like the mythic hero, learns to balance forces rather than eliminate them. Campbell called this the “coincidence of opposites”: the ability to hold dualities in creative tension. In system terms, this means designing with trade-offs in mind. One must weigh the time-space balance of computation (pre-aggregated versus real-time), the entropy of data models (flexibility versus discipline), and the complexity of governance (centralization versus decentralization).
The most powerful systems emerge not from perfect control but from simple rules that enable emergence. This aligns with complexity theory and with leadership models that empower decision-making at the edge. Just as the hero must rely on intuition and allies, architects must rely on principles rather than micromanagement. When simple, clear standards such as data schema conventions or API contracts are consistently enforced, teams can innovate within shared boundaries.
The initiation phase is therefore not a linear build but a living negotiation of a dance between structure and spontaneity, design and discovery.
3. Return – The Elixir of Integration
In Campbell’s framework, the hero’s return is not merely homecoming but integration. The hero brings back the “boon” which I think of as a gift of insight, knowledge, or capability that renews the community. The journey is complete only when this new wisdom is assimilated into ordinary life.
In architecture, this is the post-deployment phase: the system becomes operational, knowledge is institutionalized, and the organization experiences measurable improvement. Yet return is often underestimated. Many transformation efforts fail not in design but in integration. It is the inability to embed new capabilities into the daily rhythm.
For the architect, therefore, the return phase requires a self-sustaining design, a system that continues to evolve without heroic intervention. It must include feedback loops, performance metrics, and maintenance protocols that act as the organizational immune system. This is the modern equivalent of the mythic “elixir”: a living capability that strengthens the enterprise against future entropy.
When the system achieves this equilibrium, it ceases to be a project and becomes part of the organism’s identity. In mythic terms, the hero becomes king, sage, or teacher or if I may call it the new custodian of order.
III. The Mythic Mindset for System Architects
Campbell once said that myth reveals “what it means to be alive.” In the same way, a well-designed architecture reveals what it means for an organization to live and evolve. Both operate through pattern recognition, which is the ability to discern structure within chaos.
For a system architect or a finance executive overseeing transformation, adopting a mythic mindset provides several advantages.
1. Framing Transformation as a Narrative
Data flows and process diagrams rarely inspire people, but stories do. A transformation project framed as a hero’s journey resonates deeply: there is a clear beginning, a quest, obstacles, and a collective triumph. When teams understand the “why” behind change in narrative terms, resistance decreases and participation increases.
Instead of abstract technical objectives, the story might read: We are leaving behind outdated systems to seek a single source of truth. We will face integration challenges, but we will return with a platform that empowers every team to see the business clearly. This narrative coherence can align stakeholders more effectively than a dozen technical presentations.
2. Recognizing the Role of Threshold Guardians
In myth, every hero meets gatekeepers—figures who test their worthiness to enter the unknown. In organizations, these constraints include compliance requirements, data security mandates, and resource limitations. They are not enemies but necessary filters that preserve integrity. Recognizing them as part of the journey, not obstacles to it, transforms frustration into design wisdom.
3. Building for Adaptation, Not Perfection
Myths survive because they evolve. Each retelling adapts to a new context while preserving core patterns. System architecture must do the same. Designing for adaptability means embracing modularity, reusability, and continuous learning. The goal is not a flawless system but a resilient structure that can absorb change without collapsing.
4. Controlling Entropy Through Meaningful Standards
Campbell often spoke of the mythic hero’s task to bring order to chaos. In systems, chaos appears as entropy, and that is none other than data drift, process decay, or the uncontrolled proliferation of tools. The counterforce is the creation of durable “moats”: documentation, automation, standardized controls, and governance frameworks that maintain order without suffocating flexibility.
Entropy cannot be eliminated; it must be managed through renewal. Just as myths are periodically reinterpreted to stay alive, systems must be periodically refactored and retrained to remain relevant.
IV. The Architecture of Return: Sustaining Renewal
The power of Campbell’s model lies not in its sequence but in its cyclicality. The end of one journey becomes the beginning of another. Each return sows the seeds for a new departure. In systemic terms, this is the principle of continuous improvement. You have already read a few of my essays on feedback loops. Continuous Improvement is the ongoing feedback loop that transforms learning into capability.
A healthy architecture therefore, embodies the following qualities:
- Transparency: Every component knows how it connects to the whole.
- Traceability: Decisions and data can be followed back to their origins.
- Feedback: Systems collect information about their own performance.
- Redundancy: Critical functions are protected through diversity of design.
- Evolution: Components can be upgraded or replaced without destabilizing the core.
These qualities echo biological systems and myths alike. Both persist not through rigidity but through structured adaptability.
When leadership fosters the mindset of viewing every change as part of an ongoing journey rather than a discrete project, then inevitably the transformation becomes cultural rather than episodic. The system itself develops narrative intelligence: an awareness of its own history, purpose, and trajectory.
V. The Meeting of Myth and Mathematics
The connection between mythology and system design might appear poetic, but it rests on a logical foundation. Campbell’s framework of transformation parallels the logic of complex adaptive systems, information theory, and control dynamics.
When a system departs from equilibrium, it enters a state of increased entropy. Through feedback and adaptation, it reorganizes into a higher level of complexity. This process mirrors the mythic initiation: chaos followed by renewal.
Turing’s concepts of time-space trade-offs apply here as well. Every system must balance computation time against storage space; every organization must balance speed of change against depth of structure. The mythic hero faces the same trade-off—venturing quickly risks failure, but hesitation costs opportunity.
Von Neumann’s idea of self-replication in systems echoes Campbell’s notion of mythic renewal: patterns that reproduce themselves across generations, adapting but never losing identity. Both imply that enduring design depends on self-similarity, which is a rule simple enough to be inherited and flexible enough to evolve.
Thus, mythology and system architecture share a mathematical symmetry: both translate chaos into pattern and time into structure.
VI. The Practical Framework: A Mythic Checklist for Architects
To translate these ideas into practice, one can structure any major architectural initiative around a mythic framework:
- Call to Adventure: Identify the disruption or opportunity demanding change. Define why the current architecture must evolve.
- Crossing the Threshold: Establish guiding principles and governance. Recognize what risks and constraints must be respected.
- Tests and Trials: Confront integration challenges, data quality issues, and cultural resistance. Allow small failures to inform larger design choices.
- Allies and Mentors: Engage cross-functional teams, experts, and governance bodies as supporting archetypes.
- The Abyss: Confront the hardest problem—the one that threatens to derail progress. Often this is not technical but human: lack of trust, clarity, or alignment.
- Revelation and Transformation: Discover the new design paradigm—simpler, modular, and resilient. Institutionalize the insight through documentation and standards.
- Return with the Elixir: Deliver measurable value—reduced cost, improved insight, faster decisions—and embed the capability into the organization’s rhythm.
- Guardians of the Moat: Establish controls and feedback loops to preserve integrity against entropy.
- Cycle of Renewal: Use metrics and retrospectives to begin the next improvement journey.
This framework is as much about psychology as it is about technology. It ensures that every stakeholder sees the architecture not as a static deliverable but as a living system, perpetually evolving toward greater coherence and value.
VII. The Leader as Architect and Storyteller
The most effective system architects and financial leaders are not just process engineers; they are storytellers of transformation. They understand that structure without story becomes sterile, while story without structure becomes chaos.
Campbell’s enduring message was that myths reveal the shared patterns of human striving. The architect’s task is similar: to design systems that honor those patterns—systems that empower, clarify, and sustain.
When a leader presents a transformation as a narrative, people locate themselves within it. They understand their role in the larger pattern. The architecture ceases to be an abstraction; it becomes a collective journey.
VIII. The Power of Myth in the Age of Systems
Today’s organizations operate in a constant state of flux and are drowning in data proliferation, algorithmic decision-making, and distributed intelligence. The temptation is to manage this complexity through control. Yet as both Campbell and complexity theorists remind us, true order arises not from rigidity but from the right balance between structure and freedom.
A mythic approach invites humility. It acknowledges that no single designer can foresee all interactions within a living system. Instead, the architect sets conditions for emergence by defining simple, consistent principles and trusting the system to self-organize.
This mindset transforms the role of the modern executive. The leader becomes less a commander and more a gardener, cultivating conditions where coherence can emerge naturally. The hero’s journey becomes not the story of one individual but the collective saga of a learning organization.
IX. The Enduring Lesson
Campbell wrote that the purpose of the hero’s journey is not the triumph of the individual but the renewal of the community. The same is true of every architectural transformation. The goal is not the perfection of a platform but the evolution of the enterprise’s capacity to learn, adapt, and thrive.
When systems are designed with this principle in mind, they become more than tools; they become living frameworks of intelligence and purpose. They reflect not only the logic of technology but the logic of life itself.
Just as myths endure because they embody the deep grammar of human meaning, great architecture endures because it represents the deep grammar of systemic integrity. Both must balance chaos and order, change and continuity, freedom and discipline.
In the end, the most elegant architecture, like the most enduring myth, is one that transcends its designer. It continues to evolve, teaching new generations how to navigate uncertainty and find coherence amid change.
To envision architecture through Joseph Campbell’s eyes is to recognize that our systems are not merely mechanical, but they are mythic. It is the expressions of our collective will to bring order to chaos, meaning to data, and story to structure. When we build with that awareness, we design not only for efficiency but for resilience, not only for output but for renewal.
We create systems that, like the great myths, stand the test of time because they speak to something universal: the perpetual journey of transformation, return, and rebirth that defines both humanity and the organizations we build.
The Finance Playbook for Scaling Complexity Without Chaos
From Controlled Growth to Operational Grace
Somewhere between Series A optimism and Series D pressure sits the very real challenge of scale. Not just growth for its own sake but growth with control, precision, and purpose. A well-run finance function becomes less about keeping the lights on and more about lighting the runway. I have seen it repeatedly. You can double ARR, but if your deal desk, revenue operations, or quote-to-cash processes are even slightly out of step, you are scaling chaos, not a company.
Finance does not scale with spreadsheets and heroics. It scales with clarity. With every dollar, every headcount, and every workflow needing to be justified in terms of scale, simplicity must be the goal. I recall sitting in a boardroom where the CEO proudly announced a doubling of the top line. But it came at the cost of three overlapping CPQ systems, elongated sales cycles, rogue discounting, and a pipeline no one trusted. We did not have a scale problem. We had a complexity problem disguised as growth.
OKRs Are Not Just for Product Teams
When finance is integrated into company OKRs, magic happens. We begin aligning incentives across sales, legal, product, and customer success teams. Suddenly, the sales operations team is not just counting bookings but shaping them. Deal desk isn’t just a speed bump before legal review, but a value architect. Our quote-to-cash process is no longer a ticketing system but a flywheel for margin expansion.
At a Series B company, their shift began by tying financial metrics directly to the revenue team’s OKRs. Quota retirement was not enough. They measured the booked gross margin. Customer acquisition cost. Implementation of velocity. The sales team was initially skeptical but soon began asking more insightful questions. Deals that initially appeared promising were flagged early. Others that seemed too complicated were simplified before they even reached RevOps. Revenue is often seen as art. But finance gives it rhythm.
Scaling Complexity Despite the Chaos
The truth is that chaos is not the enemy of scale. Chaos is the cost of momentum. Every startup that is truly growing at a pace inevitably creates complexity. Systems become tangled. Roles blur. Approvals drift. That is not failure. That is physics. What separates successful companies is not the absence of chaos but their ability to organize it.
I often compare this to managing a growing city. You do not stop new buildings from going up just because traffic worsens. You introduce traffic lights, zoning laws, and transit systems that support the growth. In finance, that means being ready to evolve processes as soon as growth introduces friction. It means designing modular systems where complexity is absorbed rather than resisted. You do not simplify the growth. You streamline the experience of growing. Read Scale by Geoffrey West. Much of my interest in complexity theory and architecture for scale comes from it. Also, look out for my book, which will be published in February 2026: Complexity and Scale: Managing Order from Chaos. This book aligns literature in complexity theory with the microeconomics of scaling vectors and enterprise architecture.
At a late-stage Series C company, the sales motion had shifted from land-and-expand to enterprise deals with multi-year terms and custom payment structures. The CPQ tool was unable to keep up. Rather than immediately overhauling the tool, they developed middleware logic that routed high-complexity deals through a streamlined approval process, while allowing low-risk deals to proceed unimpeded. The system scaled without slowing. Complexity still existed, but it no longer dictated pace.
Cash Discipline: The Ultimate Growth KPI
Cash is not just oxygen. It is alignment. When finance speaks early and often about burn efficiency, marginal unit economics, and working capital velocity, we move from gatekeepers to enablers. I often remind founders that the cost of sales is not just the commission plan. It’s in the way deals are structured. It’s in how fast a contract can be approved. It’s in how many hands a quote needs to pass through.
At one Series A professional services firm, they introduced a “Deal ROI Calculator” at the deal desk. It calculated not just price and term but implementation effort, support burden, and payback period. The result was staggering. Win rates remained stable, but average deal profitability increased by 17 percent. Sales teams began choosing deals differently. Finance was not saying no. It was saying, “Say yes, but smarter.”
Velocity is a Decision, Not a Circumstance
The best-run companies are not faster because they have fewer meetings. They are faster because decisions are closer to the data. Finance’s job is to put insight into the hands of those making the call. The goal is not to make perfect decisions. It is to make the best decision possible with the available data and revisit it quickly.
In one post-Series A firm, we embedded finance analysts inside revenue operations. It blurred the traditional lines but sped up decision-making. Discount approvals have been reduced from 48 hours to 12-24 hours. Pricing strategies became iterative. A finance analyst co-piloted the forecast and flagged gaps weeks earlier than our CRM did. It wasn’t about more control. It was about more confidence.
When Process Feels Like Progress
It is tempting to think that structure slows things down. However, the right QTC design can unlock margin, trust, and speed simultaneously. Imagine a deal desk that empowers sales to configure deals within prudent guardrails. Or a contract management workflow that automatically flags legal risks. These are not dreams. These are the functions we have implemented.
The companies that scale well are not perfect. But their finance teams understand that complexity compounds quietly. And so, we design our systems not to prevent chaos but to make good decisions routine. We don’t wait for the fire drill. We design out the fire.
Make Your Revenue Operations Your Secret Weapon
If your finance team still views sales operations as a reporting function, you are underutilizing a strategic lever. Revenue operations, when empowered, can close the gap between bookings and billings. They can forecast with precision. They can flag incentive misalignment. One of the best RevOps leaders I worked with used to say, “I don’t run reports. I run clarity.” That clarity was worth more than any point solution we bought.
In scaling environments, automation is not optional. But automation alone does not save a broken process. Finance must own the blueprint. Every system, from CRM to CPQ to ERP, must speak the same language. Data fragmentation is not just annoying. It is value-destructive.
What Should You Do Now?
Ask yourself: Does finance have visibility into every step of the revenue funnel? Do our QTC processes support strategic flexibility? Is our deal desk a source of friction or a source of enablement? Can our sales comp plan be audited and justified in a board meeting without flinching?
These are not theoretical. They are the difference between Series C confusion and Series D confidence.
Let’s Make This Personal
I have seen incredible operators get buried under process debt because they mistook motion for progress. I have seen lean finance teams punch above their weight because they anchored their operating model in OKRs, cash efficiency, and rapid decision cycles. I have also seen the opposite. A sales ops function sitting in the corner. A deal desk no one trusts. A QTC process where no one knows who owns what.
These are fixable. But only if finance decides to lead. Not just report.
So here is my invitation. If you are a CFO, a CRO, a GC, or a CEO reading this, take one day this quarter to walk your revenue path from lead to cash. Sit with the people who feel the friction. Map the handoffs. And then ask, is this how we scale with control? Do you have the right processes in place? Do you have the technology to activate the process and minimize the friction?
Navigating Chaos and Model Thinking
An inherent property of a chaotic system is that slight changes in initial conditions in the system result in a disproportionate change in outcome that is difficult to predict. Chaotic systems appear to create outcomes that appear to be random: they are generated by simple and non-random processes but the complexity of such systems emerge over time driven by numerous iterations of simple rules. The elements that compose chaotic systems might be few in number, but these elements work together to produce an intricate set of dynamics that amplifies the outcome and makes it hard to be predictable. These systems evolve over time, doing so according to rules and initial conditions and how the constituent elements work together.
Complex systems are characterized by emergence. The interactions between the elements of the system with its environment create new properties which influence the structural development of the system and the roles of the agents. In such systems there is self-organization characteristics that occur, and hence it is difficult to study and effect a system by studying the constituent parts that comprise it. The task becomes even more formidable when one faces the prevalent reality that most systems exhibit non-linear dynamics.
So how do we incorporate management practices in the face of chaos and complexity that is inherent in organization structure and market dynamics? It would be interesting to study this in light of the evolution of management principles in keeping with the evolution of scientific paradigms.
Newtonian Mechanics and Taylorism
Traditional organization management has been heavily influenced by Newtonian mechanics. The five key assumptions of Newtonian mechanics are:
- Reality is objective
- Systems are linear and there is a presumption that all underlying cause and effect are linear
- Knowledge is empirical and acquired through collecting and analyzing data with the focus on surfacing regularities, predictability and control
- Systems are inherently efficient. Systems almost always follows the path of least resistance
- If inputs and process is managed, the outcomes are predictable
Frederick Taylor is the father of operational research and his methods were deployed in automotive companies in the 1940’s. Workers and processes are input elements to ensure that the machine functions per expectations. There was a linearity employed in principle. Management role was that of observation and control and the system would best function under hierarchical operating principles. Mass and efficient production were the hallmarks of management goal.
Randomness and the Toyota Way
The randomness paradigm recognized uncertainty as a pervasive constant. The various methods that Toyota Way invoked around 5W rested on the assumption that understanding the cause and effect is instrumental and this inclined management toward a more process-based deployment. Learning is introduced in this model as a dynamic variable and there is a lot of emphasis on the agents and providing them the clarity and purpose of their tasks. Efficiencies and quality are presumably driven by the rank and file and autonomous decisions are allowed. The management principle moves away from hierarchical and top-down to a more responsibility driven labor force.
Complexity and Chaos and the Nimble Organization
Increasing complexity has led to more demands on the organization. With the advent of social media and rapid information distribution and a general rise in consciousness around social impact, organizations have to balance out multiple objectives. Any small change in initial condition can lead to major outcomes: an advertising mistake can become a global PR nightmare; a word taken out of context could have huge ramifications that might immediately reflect on the stock price; an employee complaint could force management change. Increasing data and knowledge are not sufficient to ensure long-term success. In fact, there is no clear recipe to guarantee success in an age fraught with non-linearity, emergence and disequilibrium. To succeed in this environment entails the development of a learning organization that is not governed by fixed top-down rules: rather the rules are simple and the guidance is around the purpose of the system or the organization. It is best left to intellectual capital to self-organize rapidly in response to external information to adapt and make changes to ensure organization resilience and success.
Companies are dynamic non-linear adaptive systems. The elements in the system are constantly interacting between themselves and their external environment. This creates new emergent properties that are sensitive to the initial conditions. A change in purpose or strategic positioning could set a domino effect and can lead to outcomes that are not predictable. Decisions are pushed out to all levels in the organization, since the presumption is that local and diverse knowledge that spontaneously emerge in response to stimuli is a superior structure than managing for complexity in a centralized manner. Thus, methods that can generate ideas, create innovation habitats, and embrace failures as providing new opportunities to learn are best practices that companies must follow. Traditional long-term planning and forecasting is becoming a far harder exercise and practically impossible. Thus, planning is more around strategic mindset, scenario planning, allowing local rules to auto generate without direct supervision, encourage dissent and diversity, stimulate creativity and establishing clarity of purpose and broad guidelines are the hall marks of success.
Principles of Leadership in a New Age
We have already explored the fact that traditional leadership models originated in the context of mass production and efficiencies. These models are arcane in our information era today, where systems are characterized by exponential dynamism of variables, increased density of interactions, increased globalization and interconnectedness, massive information distribution at increasing rapidity, and a general toward economies driven by free will of the participants rather than a central authority.
Complexity Leadership Theory (Uhl-Bien) is a “framework for leadership that enables the learning, creative and adaptive capacity of complex adaptive systems in knowledge-producing organizations or organizational units. Since planning for the long-term is virtually impossible, Leadership has to be armed with different tool sets to steer the organization toward achieving its purpose. Leaders take on enabler role rather than controller role: empowerment supplants control. Leadership is not about focus on traits of a single leader: rather, it redirects emphasis from individual leaders to leadership as an organizational phenomenon. Leadership is a trait rather than an individual. We recognize that complex systems have lot of interacting agents – in business parlance, which might constitute labor and capital. Introducing complexity leadership is to empower all of the agents with the ability to lead their sub-units toward a common shared purpose. Different agents can become leaders in different roles as their tasks or roles morph rapidly: it is not necessarily defined by a formal appointment or knighthood in title.
Thus, complexity of our modern-day reality demands a new strategic toolset for the new leader. The most important skills would be complex seeing, complex thinking, complex knowing, complex acting, complex trusting and complex being. (Elena Osmodo, 2012)
Complex Seeing: Reality is inherently subjective. It is a page of the Heisenberg Uncertainty principle that posits that the independence between the observer and the observed is not real. If leaders are not aware of this independence, they run the risk of engaging in decisions that are fraught with bias. They will continue to perceive reality with the same lens that they have perceived reality in the past, despite the fact that undercurrents and riptides of increasingly exponential systems are tearing away their “perceived reality.” Leader have to be conscious about the tectonic shifts, reevaluate their own intentions, probe and exclude biases that could cloud the fidelity of their decisions, and engage in a continuous learning process. The ability to sift and see through this complexity sets the initial condition upon which the entire system’s efficacy and trajectory rests.
Complex Thinking: Leaders have to be cognizant of falling prey to linear simple cause and effect thinking. On the contrary, leaders have to engage in counter-intuitive thinking, brainstorming and creative thinking. In addition, encouraging dissent, debates and diversity encourage new strains of thought and ideas.
Complex Feeling: Leaders must maintain high levels of energy and be optimistic of the future. Failures are not scoffed at; rather they are simply another window for learning. Leaders have to promote positive and productive emotional interactions. The leaders are tasked to increase positive feedback loops while reducing negative feedback mechanisms to the extent possible. Entropy and attrition taxes any system as is: the leader’s job is to set up safe environment to inculcate respect through general guidelines and leading by example.
Complex Knowing: Leadership is tasked with formulating simple rules to enable learned and quicker decision making across the organization. Leaders must provide a common purpose, interconnect people with symbols and metaphors, and continually reiterate the raison d’etre of the organization. Knowing is articulating: leadership has to articulate and be humble to any new and novel challenges and counterfactuals that might arise. The leader has to establish systems of knowledge: collective learning, collaborative learning and organizational learning. Collective learning is the ability of the collective to learn from experiences drawn from the vast set of individual actors operating in the system. Collaborative learning results due to interaction of agents and clusters in the organization. Learning organization, as Senge defines it, is “where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspirations are set free, and where people are continually learning to see the whole together.”
Complex Acting: Complex action is the ability of the leader to not only work toward benefiting the agents in his/her purview, but also to ensure that the benefits resonates to a whole which by definition is greater than the sum of the parts. Complex acting is to take specific action-oriented steps that largely reflect the values that the organization represents in its environmental context.
Complex Trusting: Decentralization requires conferring power to local agents. For decentralization to work effectively, leaders have to trust that the agents will, in the aggregate, work toward advancing the organization. The cost of managing top-down is far more than the benefits that a trust-based decentralized system would work in a dynamic environment resplendent with the novelty of chaos and complexity.
Complex Being: This is the ability of the leaser to favor and encourage communication across the organization rapidly. The leader needs to encourage relationships and inter-functional dialogue.
The role of complex leaders is to design adaptive systems that are able to cope with challenging and novel environments by establishing a few rules and encouraging agents to self-organize autonomously at local levels to solve challenges. The leader’s main role in this exercise is to set the strategic directions and the guidelines and let the organizations run.
Chaos and the tide of Entropy!
We have discussed chaos. It is rooted in the fundamental idea that small changes in the initial condition in a system can amplify the impact on the final outcome in the system. Let us now look at another sibling in systems literature – namely, the concept of entropy. We will then attempt to bridge these two concepts since they are inherent in all systems.
Entropy arises from the law of thermodynamics. Let us state all three laws:
- First law is known as the Lay of Conservation of Energy which states that energy can neither be created nor destroyed: energy can only be transferred from one form to another. Thus, if there is work in terms of energy transformation in a system, there is equivalent loss of energy transformation around the system. This fact balances the first law of thermodynamics.
- Second law of thermodynamics states that the entropy of any isolated system always increases. Entropy always increases, and rarely ever decreases. If a locker room is not tidied, entropy dictates that it will become messier and more disorderly over time. In other words, all systems that are stagnant will inviolably run against entropy which would lead to its undoing over time. Over time the state of disorganization increases. While energy cannot be created or destroyed, as per the First Law, it certainly can change from useful energy to less useful energy.
- Third law establishes that the entropy of a system approaches a constant value as the temperature approaches absolute zero. Thus, the entropy of a pure crystalline substance at absolute zero temperature is zero. However, if there is any imperfection that resides in the crystalline structure, there will be some entropy that will act upon it.
Entropy refers to a measure of disorganization. Thus people in a crowd that is widely spread out across a large stadium has high entropy whereas it would constitute low entropy if people are all huddled in one corner of the stadium. Entropy is the quantitative measure of the process – namely, how much energy has been spent from being localized to being diffused in a system. Entropy is enabled by motion or interaction of elements in a system, but is actualized by the process of interaction. All particles work toward spontaneously dissipating their energy if they are not curtailed from doing so. In other words, there is an inherent will, philosophically speaking, of a system to dissipate energy and that process of dissipation is entropy. However, it makes no effort to figure out how quickly entropy kicks into gear – it is this fact that makes it difficult to predict the overall state of the system.
Chaos, as we have already discussed, makes systems unpredictable because of perturbations in the initial state. Entropy is the dissipation of energy in the system, but there is no standard way of knowing the parameter of how quickly entropy would set in. There are thus two very interesting elements in systems that almost work simultaneously to ensure that predictability of systems become harder.
Another way of looking at entropy is to view this as a tax that the system charges us when it goes to work on our behalf. If we are purposefully calibrating a system to meet a certain purpose, there is inevitably a corresponding usage of energy or dissipation of energy otherwise known as entropy that is working in parallel. A common example that we are familiar with is mass industrialization initiatives. Mass industrialization has impacts on environment, disease, resource depletion, and a general decay of life in some form. If entropy as we understand it is an irreversible phenomenon, then there is virtually nothing that can be done to eliminate it. It is a permanent tax of varying magnitude in the system.
Humans have since early times have tried to formulate a working framework of the world around them. To do that, they have crafted various models and drawn upon different analogies to lend credence to one way of thinking over another. Either way, they have been left best to wrestle with approximations: approximations associated with their understanding of the initial conditions, approximations on model mechanics, approximations on the tax that the system inevitably charges, and the approximate distribution of potential outcomes. Despite valiant efforts to reduce the framework to physical versus behavioral phenomena, their final task of creating or developing a predictable system has not worked. While physical laws of nature describe physical phenomena, the behavioral laws describe non-deterministic phenomena. If linear equations are used as tools to understand the physical laws following the principles of classical Newtonian mechanics, the non-linear observations marred any consistent and comprehensive framework for clear understanding. Entropy reaches out toward an irreversible thermal death: there is an inherent fatalism associated with the Second Law of Thermodynamics. However, if that is presumed to be the case, how is it that human evolution has jumped across multiple chasms and have evolved to what it is today? If indeed entropy is the tax, one could argue that chaos with its bounded but amplified mechanics have allowed the human race to continue.
Let us now deliberate on this observation of Richard Feynmann, a Nobel Laurate in physics – “So we now have to talk about what we mean by disorder and what we mean by order. … Suppose we divide the space into little volume elements. If we have black and white molecules, how many ways could we distribute them among the volume elements so that white is on one side and black is on the other? On the other hand, how many ways could we distribute them with no restriction on which goes where? Clearly, there are many more ways to arrange them in the latter case.
We measure “disorder” by the number of ways that the insides can be arranged, so that from the outside it looks the same. The logarithm of that number of ways is the entropy. The number of ways in the separated case is less, so the entropy is less, or the “disorder” is less.” It is commonly also alluded to as the distinction between microstates and macrostates. Essentially, it says that there could be innumerable microstates although from an outsider looking in – there is only one microstate. The number of microstates hints at the system having more entropy.
In a different way, we ran across this wonderful example: A professor distributes chocolates to students in the class. He has 35 students but he distributes 25 chocolates. He throws those chocolates to the students and some students might have more than others. The students do not know that the professor had only 25 chocolates: they have presumed that there were 35 chocolates. So the end result is that the students are disconcerted because they perceive that the other students have more chocolates than they have distributed but the system as a whole shows that there are only 25 chocolates. Regardless of all of the ways that the 25 chocolates are configured among the students, the microstate is stable.
So what is Feynmann and the chocolate example suggesting for our purpose of understanding the impact of entropy on systems: Our understanding is that the reconfiguration or the potential permutations of elements in the system that reflect the various microstates hint at higher entropy but in reality has no impact on the microstate per se except that the microstate has inherently higher entropy. Does this mean that the macrostate thus has a shorter life-span? Does this mean that the microstate is inherently more unstable? Could this mean an exponential decay factor in that state? The answer to all of the above questions is not always. Entropy is a physical phenomenon but to abstract this out to enable a study of organic systems that represent super complex macrostates and arrive at some predictable pattern of decay is a bridge too far! If we were to strictly follow the precepts of the Second Law and just for a moment forget about Chaos, one could surmise that evolution is not a measure of progress, it is simply a reconfiguration.
Theodosius Dobzhansky, a well known physicist, says: “Seen in retrospect, evolution as a whole doubtless had a general direction, from simple to complex, from dependence on to relative independence of the environment, to greater and greater autonomy of individuals, greater and greater development of sense organs and nervous systems conveying and processing information about the state of the organism’s surroundings, and finally greater and greater consciousness. You can call this direction progress or by some other name.”
Harold Mosowitz says “Life is organization. From prokaryotic cells, eukaryotic cells, tissues and organs, to plants and animals, families, communities, ecosystems, and living planets, life is organization, at every scale. The evolution of life is the increase of biological organization, if it is anything. Clearly, if life originates and makes evolutionary progress without organizing input somehow supplied, then something has organized itself. Logical entropy in a closed system has decreased. This is the violation that people are getting at, when they say that life violates the second law of thermodynamics. This violation, the decrease of logical entropy in a closed system, must happen continually in the Darwinian account of evolutionary progress.”
Entropy occurs in all systems. That is an indisputable fact. However, if we start defining boundaries, then we are prone to see that these bounded systems decay faster. However, if we open up the system to leave it unbounded, then there are a lot of other forces that come into play that is tantamount to some net progress. While it might be true that energy balances out, what we miss as social scientists or model builders or avid students of systems – we miss out on indices that reflect on leaps in quality and resilience and a horde of other factors that stabilizes the system despite the constant and ominous presence of entropy’s inner workings.
Distribution Economics
Distribution is a method to get products and services to the maximum number of customers efficiently.
Complexity science is the study of complex systems and the problems that are multi-dimensional, dynamic and unpredictable. It constitutes a set of interconnected relationships that are not always abiding to the laws of cause and effect, but rather the modality of non-linearity. Thomas Kuhn in his pivotal essay: The Structure of Scientific Revolutions posits that anomalies that arise in scientific method rise to the level where it can no longer be put on hold or simmer on a back-burner: rather, those anomalies become the front line for new methods and inquiries such that a new paradigm necessarily must emerge to supplant the old conversations. It is this that lays the foundation of scientific revolution – an emergence that occurs in an ocean of seeming paradoxes and competing theories. Contrary to a simple scientific method that seeks to surface regularities in natural phenomenon, complexity science studies the effects that rules have on agents. Rules do not drive systems toward a predictable outcome: rather it sets into motion a high density of interactions among agents such that the system coalesces around a purpose: that being necessarily that of survival in context of its immediate environment. In addition, the learnings that follow to arrive at the outcome is then replicated over periods to ensure that the systems mutate to changes in the external environment. In theory, the generative rules leads to emergent behavior that displays patterns of parallelism to earlier known structures.
For any system to survive and flourish, distribution of information, noise and signals in and outside of a CPS or CAS is critical. We have touched at length that the system comprises actors and agents that work cohesively together to fulfill a special purpose. Specialization and scale matter! How is a system enabled to fulfill their purpose and arrive at a scale that ensures long-term sustenance? Hence the discussion on distribution and scale which is a salient factor in emergence of complex systems that provide the inherent moat of “defensibility” against internal and external agents working against it.
Distribution, in this context, refers to the quality and speed of information processing in the system. It is either created by a set of rules that govern the tie-ups between the constituent elements in the system or it emerges based on a spontaneous evolution of communication protocols that are established in response to internal and external stimuli. It takes into account the available resources in the system or it sets up the demands on resource requirements. Distribution capabilities have to be effective and depending upon the dynamics of external systems, these capabilities might have to be modified effectively. Some distribution systems have to be optimized or organized around efficiency: namely, the ability of the system to distribute information efficiently. On the other hand, some environments might call for less efficiency as the key parameter, but rather focus on establishing a scale – an escape velocity in size and interaction such that the system can dominate the influence of external environments. The choice between efficiency and size is framed by the long-term purpose of the system while also accounting for the exigencies of ebbs and flows of external agents that might threaten the system’s existence.
Since all systems are subject to the laws of entropy and the impact of unintended consequences, strategies have to be orchestrated accordingly. While it is always naïve to assume exactitude in the ultimate impact of rules and behavior, one would surmise that such systems have to be built around the fault lines of multiple roles for agents or group of agents to ensure that the system is being nudged, more than less, toward the desired outcome. Hence, distribution strategy is the aggregate impact of several types of channels of information that are actively working toward a common goal. The idea is to establish multiple channels that invoke different strategies while not cannibalizing or sabotaging an existing set of channels. These mutual exclusive channels have inherent properties that are distinguished by the capacity and length of the channels, the corresponding resources that the channels use and the sheer ability to chaperone the system toward the overall purpose.
The complexity of the purpose and the external environment determines the strategies deployed and whether scale or efficiency are the key barometers for success. If a complex system must survive and hopefully replicate from strength to greater strength over time, size becomes more paramount than efficiency. Size makes up for the increased entropy which is the default tax on the system, and it also increases the possibility of the system to reach the escape velocity. To that end, managing for scale by compromising efficiency is a perfectly acceptable means since one is looking at the system with a long-term lens with built-in regeneration capabilities. However, not all systems might fall in this category because some environments are so dynamic that planning toward a long-term stability is not practical, and thus one has to quickly optimize for increased efficiency. It is thus obvious that scale versus efficiency involves risky bets around how the external environment will evolve. We have looked at how the systems interact with external environments: yet, it is just as important to understand how the actors work internally in a system that is pressed toward scale than efficiency, or vice versa. If the objective is to work toward efficiency, then capabilities can be ephemeral: one builds out agents and actors with capabilities that are mission-specific. On the contrary, scale driven systems demand capabilities that involve increased multi-tasking abilities, the ability to develop and learn from feedback loops, and to prime the constraints with additional resources. Scaling demand acceleration and speed: if a complex system can be devised to distribute information and learning at an accelerating pace, there is a greater likelihood that this system would dominate the environment.
Scaling systems can be approached by adding more agents with varying capabilities. However, increased number of participants exponentially increase the permutations and combinations of channels and that can make the system sluggish. Thus, in establishing the purpose and the subsequent design of the system, it is far more important to establish the rules of engagement. Further, the rules might have some centralized authority that will directionally provide the goal while other rules might be framed in a manner to encourage a pure decentralization of authority such that participants act quickly in groups and clusters to enable execution toward a common purpose.
In business we are surrounded by uncertainty and opportunities. It is how we calibrate around this that ultimately reflects success. The ideal framework at work would be as follows:
- What are the opportunities and what are the corresponding uncertainties associated with the opportunities? An honest evaluation is in order since this is what sets the tone for the strategic framework and direction of the organization.
- Should we be opportunistic and establish rules that allow the system to gear toward quick wins: this would be more inclined toward efficiencies. Or should we pursue dominance by evaluating our internal capability and the probability of winning and displacing other systems that are repositioning in advance or in response to our efforts? At which point, speed and scale become the dominant metric and the resources and capabilities and the set of governing rules have to be aligned accordingly.
- How do we craft multiple channels within and outside of the system? In business lingo, that could translate into sales channels. These channels are selling products and services and can be adding additional value along the way to the existing set of outcomes that the system is engineered for. The more the channels that are mutually exclusive and clearly differentiated by their value propositions, the stronger the system and the greater the ability to scale quickly. These antennas, if you will, also serve to be receptors for new information which will feed data into the organization which can subsequently process and reposition, if the situation so warrants. Having as many differentiated antennas comprise what constitutes the distribution strategy of the organization.
- The final cut is to enable a multi-dimensional loop between external and internal system such that the system expands at an accelerating pace without much intervention or proportionate changes in rules. In other words, system expands autonomously – this is commonly known as the platform effect. Scale does not lead to platform effect although the platform effect most definitely could result in scale. However, scale can be an important contributor to platform effect, and if the latter gets root, then the overall system achieves efficiency and scale in the long run.
Network Theory and Network Effects
Complexity theory needs to be coupled with network theory to get a more comprehensive grasp of the underlying paradigms that govern the outcomes and morphology of emergent systems. In order for us to understand the concept of network effects which is commonly used to understand platform economics or ecosystem value due to positive network externalities, we would like to take a few steps back and appreciate the fundamental theory of networks. This understanding will not only help us to understand complexity and its emergent properties at a low level but also inform us of the impact of this knowledge on how network effects can be shaped to impact outcomes in an intentional manner.
There are first-order conditions that must be met to gauge whether the subject of the observation is a network. Firstly, networks are all about connectivity within and between systems. Understanding the components that bind the system would be helpful. However, do keep in mind that complexity systems (CPS and CAS) might have emergent properties due to the association and connectivity of the network that might not be fully explained by network theory. All the same, understanding networking theory is a building block to understanding emergent systems and the outcome of its structure on addressing niche and macro challenges in society.
Networks operates spatially in a different space and that has been intentionally done to allow some simplification and subsequent generalization of principles. The geometry of network is called network topology. It is a 2D perspective of connectivity.
Networks are subject to constraints (physical resources, governance constraint, temporal constraints, channel capacity, absorption and diffusion of information, distribution constraint) that might be internal (originated by the system) or external (originated in the environment that the network operates in).
Finally, there is an inherent non-linearity impact in networks. As nodes increase linearly, connections will increase exponentially but might be subject to constraints. The constraints might define how the network structure might morph and how information and signals might be processed differently.
Graph theory is the most widely used tool to study networks. It consists of four parts: vertices which represent an element in the network, edges refer to relationship between nodes which we call links, directionality which refers to how the information is passed ( is it random and bi-directional or follows specific rules and unidirectional), channels that refer to bandwidth that carry information, and finally the boundary which establishes specificity around network operations. A graph can be weighted – namely, a number can be assigned to each length to reflect the degree of interaction or the strength of resources or the proximity of the nodes or the ordering of discernible clusters.
The central concept of network theory thus revolves around connectivity between nodes and how non-linear emergence occurs. A node can have multiple connections with other node/nodes and we can weight the node accordingly. In addition, the purpose of networks is to pass information in the most efficient manner possible which relays into the concept of a geodesic which is either the shortest path between two nodes that must work together to achieve a purpose or the least number of leaps through links that information must negotiate between the nodes in the network.
Technically, you look for the longest path in the network and that constitutes the diameter while you calculate the average path length by examining the shortest path between nodes, adding all of those paths up and then dividing by the number of pairs. Significance of understanding the geodesic allows an understanding of the size of the network and throughput power that the network is capable of.
Nodes are the atomic elements in the network. It is presumed that its degree of significance is related to greater number of connections. There are other factors that are important considerations: how adjacent or close are the nodes to one another, does some nodes have authority or remarkable influence on others, are nodes positioned to be a connector between other nodes, and how capable are the nodes in absorbing, processing and diffusing the information across the links or channels. How difficult is it for the agents or nodes in the network to make connections? It is presumed that if the density of the network is increased, then we create a propensity in the overall network system to increase the potential for increased connectivity.
As discussed previously, our understanding of the network is deeper once we understand the elements well. The structure or network topology is represented by the graph and then we must understand size of network and the patterns that are manifested in the visual depiction of the network. Patterns, for our purposes, might refer to clusters of nodes that are tribal or share geographical proximity that self-organize and thus influence the structure of the network. We will introduce a new term homophily where agents connect with those like themselves. This attribute presumably allows less resources needed to process information and diffuse outcomes within the cluster. Most networks have a cluster bias: in other words, there are areas where there is increased activity or increased homogeneity in attributes or some form of metric that enshrines a group of agents under one specific set of values or activities. Understanding the distribution of cluster and the cluster bias makes it easier to influence how to propagate or even dismantle the network. This leads to an interesting question: Can a network that emerges spontaneously from the informal connectedness between agents be subjected to some high dominance coefficient – namely, could there be nodes or links that might exercise significant weight on the network?
The network has to align to its environment. The environment can place constraints on the network. In some instances, the agents have to figure out how to overcome or optimize their purpose in the context of the presence of the environmental constraints. There is literature that suggests the existence of random networks which might be an initial state, but it is widely agreed that these random networks self-organize around their purpose and their interaction with its environment. Network theory assigns a number to the degree of distribution which means that all or most nodes have an equivalent degree of connectivity and there is no skewed influence being weighed on the network by a node or a cluster. Low numbers assigned to the degree of distribution suggest a network that is very democratic versus high number that suggests centralization. To get a more practical sense, a mid-range number assigned to a network constitutes a decentralized network which has close affinities and not fully random. We have heard of the six degrees of separation and that linkage or affinity is most closely tied to a mid-number assignment to the network.
We are now getting into discussions on scale and binding this with network theory. Metcalfe’s law states that the value of a network grows as a square of the number of the nodes in the network. More people join the network, the more valuable the network. Essentially, there is a feedback loop that is created, and this feedback loop can kindle a network to grow exponentially. There are two other topics – Contagion and Resilience. Contagion refers to the ability of the agents to diffuse information. This information can grow the network or dismantle it. Resilience refers to how the network is organized to preserve its structure. As you can imagine, they have huge implications that we see. How do certain ideas proliferate over others, how does it cluster and create sub-networks which might grow to become large independent networks and how it creates natural defense mechanisms against self-immolation and destruction?
Network effect is commonly known as externalities in economics. It is an effect that is external to the transaction but influences the transaction. It is the incremental benefit gained by an existing user for each new user that joins the network. There are two types of network effects: Direct network effects and Indirect network effect. Direct network effects are same side effects. The value of a service goes up as the number of users goes up. For example, if more people have phones, it is useful for you to have a phone. The entire value proposition is one-sided. Indirect networks effects are multi-sided. It lends itself to our current thinking around platforms and why smart platforms can exponentially increase the network. The value of the service increases for one user group when a new user group joins the network. Take for example the relationship between credit card banks, merchants and consumers. There are three user groups, and each gather different value from the network of agents that have different roles. If more consumers use credit cards to buy, more merchants will sign up for the credit cards, and as more merchants sign up – more consumers will sign up with the bank to get more credit cards. This would be an example of a multi-sided platform that inherently has multi-sided network effects. The platform inherently gains significant power such that it becomes more valuable for participants in the system to join the network despite the incremental costs associated with joining the network. Platforms that are built upon effective multi-sided network effects grow quickly and are generally sustainable. Having said that, it could be just as easy that a few dominant bad actors in the network can dismantle and unravel the network completely. We often hear of the tipping point: namely, that once the platform reaches a critical mass of users, it would be difficult to dismantle it. That would certainly be true if the agents and services are, in the aggregate, distributed fairly across the network: but it is also possible that new networks creating even more multi-sided network effects could displace an entrenched network. Hence, it is critical that platform owners manage the quality of content and users and continue to look for more opportunities to introduce more user groups to entrench and yet exponentially grow the network.
Managing Scale
| I think the most difficult thing had been scaling the infrastructure. Trying to support the response we had received from our users and the number of people that were interested in using the software. – Shawn Fanning |
Froude’s number? It is defined as the square of the ship’s velocity divided by its length and multiplied by the acceleration caused by gravity. So why are we introducing ships in this chapter? As I have done before, I am liberally standing on the shoulder of the giant, Geoffrey West, and borrowing from his account on the importance of the Froude’s number and the practical implications. Since ships are subject to turbulence, using a small model that works in a simulated turbulent environment might not work when we manufacture a large ship that is facing the ebbs and troughs of a finicky ocean. The workings and impact of turbulence is very complex, and at scale it becomes even more complex. Froude’s key contribution was to figure out a mathematical pathway of how to efficiently and effectively scale from a small model to a practical object. He did that by using a ratio as the common denominator. Mr. West provides an example that hits home: How fast does a 10-foot-long ship have to move to mimic the motion of a 700-foot-long ship moving at 20 knots. If they are to have the same Froude number (that is, the same value of the square of their velocity divided by their length), then the velocity has to scale as the square root of their lengths. The ratio of the square root of their lengths is the the square of 700 feet of the ship/10 feet of the model ship which turns out to be the square of 70. For the 10-foot model to mimic the motion of a large ship, it must move at the speed of 20 knots/ square of 70 or 2.5 knots. The Froude number is still widely used across many fields today to bridge small scale and large-scale thinking. Although this number applies to physical systems, the notion that adaptive systems can be similarly bridged through appropriate mathematical equations. Unfortunately, because of the increased number of variables impacting adaptive systems and all of these variables working and learning from one another, the task of establishing a Froude number becomes diminishingly small.
The other concept that has gained wide attention is the science of allometry. Allometry essentially states that as size increases, then the form of the object would change. Allometric scaling governs all complex physical and adaptive systems. So the question is whether there are some universal laws or mathematics that can be used to enable us to better understand or predict scale impacts. Let us extend this thinking a bit further. If sizes influence form and form constitute all sub-physical elements, then it would stand to reason that a universal law or a set of equations can provide deep explanatory powers on scale and systems. One needs to bear in mind that even what one might consider a universal law might be true within finite observations and boundaries. In other words, if there are observations that fall outside of those boundaries, one is forced into resetting our belief in the universal law or to frame a new paradigm to cover these exigencies. I mention this because as we seek to understand business and global grand challenges considering the existence of complexity, scale, chaos and seeming disorder – we might also want to embrace multiple laws or formulations working at different hierarchies and different data sets to arrive at satisficing solutions to the problems that we want to wrestle with.
Physics and mathematics allow a qualitatively high degree of predictability. One can craft models across different scales to make a sensible approach on how to design for scale. If you were to design a prototype using a 3D printer and decide to scale that prototype a 100X, there are mathematical scalar components that are factored into the mechanics to allow for some sort of equivalence which would ultimately lead to the final product fulfilling its functional purpose in a complex physical system. But how does one manage scale in light of those complex adaptive systems that emerge due to human interactions, evolution of organization, uncertainty of the future, and dynamic rules that could rapidly impact the direction of a company?
Is scale a single measure? Or is it a continuum? In our activities, we intentionally or unintentionally invoke scale concepts. What is the most efficient scale to measure an outcome, so we can make good policy decisions, how do we apply our learning from one scale to a system that operates on another scale and how do we assess how sets of phenomena operate at different scales, spatially and temporally, and how they impact one another? Now the most interesting question: Is scale polymorphous? Does the word scale have different meanings in different contexts? When we talk about microbiology, we are operating at micro-scales. When we talk at a very macro level, our scales are huge. In business, we regard scale with respect to how efficiently we grow. In one way, it is a measure but for the following discussion, we will interpret scale as non-linear growth expending fewer and fewer resources to support that growth as a ratio.
As we had discussed previously, complex adaptive systems self-organize over time. They arrive at some steady state outcome without active intervention. In fact, the active intervention might lead to unintended consequences that might even spell doom for the system that is being influenced. So as an organization scales, it is important to keep this notion of rapid self-organization in mind which will inform us to make or not make certain decisions from a central or top-down perspective. In other words, part of managing scale successfully is to not manage it at a coarse-grained level.
The second element of successfully managing scale is to understand the constraints that prevent scale. There is an entire chapter dedicated to the theory of constraints which sheds light on why this is a fundamental process management technique that increases the pace of the system. But for our purposes in this section, we will summarize as follows: every system as it grows have constraints. It is important to understand the constraints because these constraints slow the system: the bottlenecks have to be removed. And once one constraint is removed, then one comes across another constraint. The system is a chain of events and it is imperative that all of these events are identified. The weakest links harangue the systems and these weakest links have to be either cleared or resourced to enable the system to scale. It is a continuous process of observation and tweaking the results with the established knowledge that the demons of uncertainty and variability can reset the entire process and one might have to start again. Despite that fact, constraint management is an effective method to negotiate and manage scale.
The third element is devising the appropriate organization architecture. As one projects into the future, management might be inclined toward developing and investing in the architecture early to accommodate the scale. Overinvestment in the architecture might not be efficient. As mentioned, cities and social systems that grow 100% require 85% investment in infrastructure: in other words, systems grow on a sublinear scale from an infrastructure perspective. How does management of scale arrive at the 85%? It is nigh impossible, but it is important to reserve that concept since it informs management to architect the infrastructure cautiously. Large investments upfront could be a waste or could slow the system down: alternative, investments that are postponed a little too late can also impact the system adversely.
The fourth element of managing scale is to focus your lens of opportunity. In macroecology, we can arrive at certain conclusions when we regard the system from a distance versus very closely. We can subsume our understanding into one big bucket called climate change and then we figure out different ways to manage the complexity that causes the climate change by invoking certain policies and incentives at a macro level. However, if we go closer, we might decide to target a very specific contributor to climate change – namely, fossil fuels. The theory follows that to manage the dynamic complexity and scale of climate impact – it would be best to address a major factor which, in this case, would be fossil fuels. The equivalence of this in a natural business setting would be to establish and focus the strategy for scale in a niche vertical or a relatively narrower set of opportunities. Even though we are working in the web of complex adaptive systems, we might devise strategies to directionally manage the business within the framework of complex physical systems where we have an understanding of the slight variations of initial state and the realization that the final outcome might be broad but yet bounded for intentional management.
The final element is the management of initial states. Complex physical systems are governed by variation in initial states. Perturbation of these initial states can lead to a wide divergence of outcomes, albeit bounded within a certain frame of reference. It is difficult perhaps to gauge all the interactions that might occur from a starting point to the outcome, although we agree that a few adjustments like decentralization of decision making, constraint management, optimal organization structure and narrowing the playing field would be helpful.
Model Thinking
| Model Framework |
The fundamental tenet of theory is the concept of “empiria“. Empiria refers to our observations. Based on observations, scientists and researchers posit a theory – it is part of scientific realism.
A scientific model is a causal explanation of how variables interact to produce a phenomenon, usually linearly organized. A model is a simplified map consisting of a few, primary variables that is gauged to have the most explanatory powers for the phenomenon being observed. We discussed Complex Physical Systems and Complex Adaptive Systems early on this chapter. It is relatively easier to map CPS to models than CAS, largely because models become very unwieldy as it starts to internalize more variables and if those variables have volumes of interaction between them. A simple analogy would be the use of multiple regression models: when you have a number of independent variables that interact strongly between each other, autocorrelation errors occur, and the model is not stable or does not have predictive value.
Research projects generally tend to either look at a case study or alternatively, they might describe a number of similar cases that are logically grouped together. Constructing a simple model that can be general and applied to many instances is difficult, if not impossible. Variables are subject to a researcher’s lack of understanding of the variable or the volatility of the variable. What further accentuates the problem is that the researcher misses on the interaction of how the variables play against one another and the resultant impact on the system. Thus, our understanding of our system can be done through some sort of model mechanics but, yet we share the common belief that the task of building out a model to provide all of the explanatory answers are difficult, if not impossible. Despite our understanding of our limitations of modeling, we still develop frameworks and artifact models because we sense in it a tool or set of indispensable tools to transmit the results of research to practical use cases. We boldly generalize our findings from empiria into general models that we hope will explain empiria best. And let us be mindful that it is possible – more so in the CAS systems than CPS that we might have multiple models that would fight over their explanatory powers simply because of the vagaries of uncertainty and stochastic variations.
Popper says: “Science does not rest upon rock-bottom. The bold structure of its theories rises, as it were, above a swamp. It is like a building erected on piles. The piles are driven down from above into the swamp, but not down to any natural or ‘given’ base; and when we cease our attempts to drive our piles into a deeper layer, it is not because we have reached firm ground. We simply stop when we are satisfied that they are firm enough to carry the structure, at least for the time being”. This leads to the satisficing solution: if a model can choose the least number of variables to explain the greatest amount of variations, the model is relatively better than other models that would select more variables to explain the same. In addition, there is always a cost-benefit analysis to be taken into consideration: if we add x number of variables to explain variation in the outcome but it is not meaningfully different than variables less than x, then one would want to fall back on the less-variable model because it is less costly to maintain.
Researchers must address three key elements in the model: time, variation and uncertainty. How do we craft a model which reflects the impact of time on the variables and the outcome? How to present variations in the model? Different variables might vary differently independent of one another. How do we present the deviation of the data in a parlance that allows us to make meaningful conclusions regarding the impact of the variations on the outcome? Finally, does the data that is being considered are actual or proxy data? Are the observations approximate? How do we thus draw the model to incorporate the fuzziness: would confidence intervals on the findings be good enough?
Two other equally other concepts in model design is important: Descriptive Modeling and Normative Modeling.
Descriptive models aim to explain the phenomenon. It is bounded by that goal and that goal only.
There are certain types of explanations that they fall back on: explain by looking at data from the past and attempting to draw a cause and effect relationship. If the researcher is able to draw a complete cause and effect relationship that meets the test of time and independent tests to replicate the results, then the causality turns into law for the limited use-case or the phenomenon being explained. Another explanation method is to draw upon context: explaining a phenomenon by looking at the function that the activity fulfills in its context. For example, a dog barks at a stranger to secure its territory and protect the home. The third and more interesting type of explanation is generally called intentional explanation: the variables work together to serve a specific purpose and the researcher determines that purpose and thus, reverse engineers the understanding of the phenomenon by understanding the purpose and how the variables conform to achieve that purpose.
This last element also leads us to thinking through the other method of modeling – namely, normative modeling. Normative modeling differs from descriptive modeling because the target is not to simply just gather facts to explain a phenomenon, but rather to figure out how to improve or change the phenomenon toward a desirable state. The challenge, as you might have already perceived, is that the subjective shadow looms high and long and the ultimate finding in what would be a normative model could essentially be a teleological representation or self-fulfilling prophecy of the researcher in action. While this is relatively more welcome in a descriptive world since subjectivism is diffused among a larger group that yields one solution, it is not the best in a normative world since variation of opinions that reflect biases can pose a problem.
How do we create a representative model of a phenomenon? First, we weigh if the phenomenon is to be understood as a mere explanation or to extend it to incorporate our normative spin on the phenomenon itself. It is often the case that we might have to craft different models and then weigh one against the other that best represents how the model can be explained. Some of the methods are fairly simple as in bringing diverse opinions to a table and then agreeing upon one specific model. The advantage of such an approach is that it provides a degree of objectivism in the model – at least in so far as it removes the divergent subjectivity that weaves into the various models. Other alternative is to do value analysis which is a mathematical method where the selection of the model is carried out in stages. You define the criteria of the selection and then the importance of the goal (if that be a normative model). Once all of the participants have a general agreement, then you have the makings of a model. The final method is to incorporate all all of the outliers and the data points in the phenomenon that the model seeks to explain and then offer a shared belief into those salient features in the model that would be best to apply to gain information of the phenomenon in a predictable manner.
There are various languages that are used for modeling:
Written Language refers to the natural language description of the model. If price of butter goes up, the quantity demanded of the butter will go down. Written language models can be used effectively to inform all of the other types of models that follow below. It often goes by the name of “qualitative” research, although we find that a bit limiting. Just a simple statement like – This model approximately reflects the behavior of people living in a dense environment …” could qualify as a written language model that seeks to shed light on the object being studied.
Icon Models refer to a pictorial representation and probably the earliest form of model making. It seeks to only qualify those contours or shapes or colors that are most interesting and relevant to the object being studied. The idea of icon models is to pictorially abstract the main elements to provide a working understanding of the object being studied.
Topological Models refer to how the variables are placed with respect to one another and thus helps in creating a classification or taxonomy of the model. Once can have logical trees, class trees, Venn diagrams, and other imaginative pictorial representation of fields to further shed light on the object being studied. In fact, pictorial representations must abide by constant scale, direction and placements. In other words, if the variables are placed on a different scale on different maps, it would be hard to draw logical conclusions by sight alone. In addition, if the placements are at different axis in different maps or have different vectors, it is hard to make comparisons and arrive at a shared consensus and a logical end result.
Arithmetic Models are what we generally fall back on most. The data is measured with an arithmetic scale. It is done via tables, equations or flow diagrams. The nice thing about arithmetic models is that you can show multiple dimensions which is not possible with other modeling languages. Hence, the robustness and the general applicability of such models are huge and thus is widely used as a key language to modeling.
Analogous Models refer to crafting explanations using the power of analogy. For example, when we talk about waves – we could be talking of light waves, radio waves, historical waves, etc. These metaphoric representations can be used to explain phenomenon, but at best, the explanatory power is nebulous, and it would be difficult to explain the variations and uncertainties between two analogous models. However, it still is used to transmit information quickly through verbal expressions like – “Similarly”, “Equivalently”, “Looks like ..” etc. In fact, extrapolation is a widely used method in modeling and we would ascertain this as part of the analogous model to a great extent. That is because we time-box the variables in the analogous model to one instance and the extrapolated model to another instance and we tie them up with mathematical equations.
The Law of Unintended Consequences
The Law of Unintended Consequence is that the actions of a central body that might claim omniscient, omnipotent and omnivalent intelligence might, in fact, lead to consequences that are not anticipated or unintended.
The concept of the Invisible Hand as introduced by Adam Smith argued that it is the self-interest of all the market agents that ultimately create a system that maximizes the good for the greatest amount of people.
Robert Merton, a sociologist, studied the law of unintended consequence. In an influential article titled “The Unanticipated Consequences of Purposive Social Action,” Merton identified five sources of unanticipated consequences.
Ignorance makes it difficult and impossible to anticipate the behavior of every element or the system which leads to incomplete analysis.
Errors that might occur when someone uses historical data and applies the context of history into the future. Linear thinking is a great example of an error that we are wrestling with right now – we understand that there are systems, looking back, that emerge exponentially but it is hard to decipher the outcome unless one were to take a leap of faith.
Biases work its way into the study as well. We study a system under the weight of our biases, intentional or unintentional. It is hard to strip that away even if there are different bodies of thought that regard a particular system and how a certain action upon the system would impact it.
Weaved with the element of bias is the element of basic values that may require or prohibit certain actions even if the long-term impact is unfavorable. A good example would be the toll gates established by the FDA to allow drugs to be commercialized. In its aim to provide a safe drug, the policy might be such that the latency of the release of drugs for experiments and commercial purposes are so slow that many patients who might otherwise benefit from the release of the drug lose out.
Finally, he discusses the self-fulfilling prophecy which suggests that tinkering with the elements of a system to avert a catastrophic negative event might in actuality result in the event.
It is important however to acknowledge that unintended consequences do not necessarily lead to a negative outcome. In fact, there are could be unanticipated benefits. A good example is Viagra which started off as a pill to lower blood pressure, but one discovered its potency to solve erectile dysfunctions. The discovery that ships that were sunk became the habitat and formation of very rich coral reefs in shallow waters that led scientists to make new discoveries in the emergence of flora and fauna of these habitats.
If there are initiatives exercised that are considered “positive initiative” to influence the system in a manner that contribute to the greatest good, it is often the case that these positive initiatives might prove to be catastrophic in the long term. Merton calls the cause of this unanticipated consequence as something called the product of the “relevance paradox” where decision makers thin they know their areas of ignorance regarding an issue, obtain the necessary information to fill that ignorance gap but intentionally or unintentionally neglect or disregard other areas as its relevance to the final outcome is not clear or not lined up to values. He goes on to argue, in a nutshell, that unintended consequences relate to our hubris – we are hardwired to put our short-term interest over long term interest and thus we tinker with the system to surface an effect which later blow back in unexpected forms. Albert Camus has said that “The evil in the world almost always comes of ignorance, and good intentions may do as much harm as malevolence if they lack understanding.”
An interesting emergent property that is related to the law of unintended consequence is the concept of Moral Hazard. It is a concept that individuals have incentives to alter their behavior when their risk or bad decision making is borne of diffused among others. For example:
If you have an insurance policy, you will take more risks than otherwise. The cost of those risks will impact the total economics of the insurance and might lead to costs being distributed from the high-risk takers to the low risk takers.
How do the conditions of the moral hazard arise in the first place? There are two important conditions that must hold. First, one party has more information than another party. The information asymmetry thus creates gaps in information and that creates a condition of moral hazard. For example, during 2006 when sub-prime mortgagors extended loans to individuals who had dubitable income and means to pay. The Banks who were buying these mortgages were not aware of it. Thus, they ended up holding a lot of toxic loans due to information asymmetry. Second, is the existence of an understanding that might affect the behavior of two agents. If a child knows that they are going to get bailed out by the parents, he/she might take some risks that he/she would otherwise might not have taken.
To counter the possibility of unintended consequences, it is important to raise our thinking to second-order thinking. Most of our thinking is simplistic and is based on opinions and not too well grounded in facts. There are a lot of biases that enter first order thinking and in fact, all of the elements that Merton touches on enters it – namely, ignorance, biases, errors, personal value systems and teleological thinking. Hence, it is important to get into second-order thinking – namely, the reasoning process is surfaced by looking at interactions of elements, temporal impacts and other system dynamics. We had mentioned earlier that it is still difficult to fully wrestle all the elements of emergent systems through the best of second-order thinking simply because the dynamics of a complex adaptive system or complex physical system would deny us that crown of competence. However, this fact suggests that we step away from simple, easy and defendable heuristics to measure and gauge complex systems.
Emergent Systems: Introduction
The whole is greater than the sum of its parts. “Emergent properties” refer to those properties that emerge that might be entirely unexpected. As discussed in CAS, they arise from the collaborative functioning of a system. In other words, emergent properties are properties of a group of items, but it would be erroneous for us to reduce such systems into properties of atomic elements and use those properties as binding elements to understand emergence Some common examples of emergent properties include cities, bee hives, ant colonies and market systems. Out thinking attributes causal effects – namely, that behavior of elements would cause certain behaviors in other hierarchies and thus an entity emerges at a certain state. However, we observe that a process of emergence is the observation of an effect without an apparent cause. Yet it is important to step back and regard the relationships and draw lines of attribution such that one can concur that there is an impact of elements at the lowest level that surfaces, in some manner, at the highest level which is the subject of our observation.
Jochenn Fromm in his paper “Types and Forms of Emergence” has laid this out best. He says that emergent properties are “amazing and paradox: fundamental but familiar.” In other words, emergent properties are changeless and changing, constant and fluctuating, persistent and shifting, inevitable and unpredictable. The most important note that he makes is that the emergent property is part of the system and at the same time it might not always be a part of the system. There is an undercurrent of novelty or punctuated gaps that might arise that is inexplicable, and it is this fact that renders true emergence virtually irreducible. Thus, failure is embodied in all emergent systems – failure being that the system does not behave according to expectation. Despite all rules being followed and quality thresholds are established at every toll gate at the highest level, there is still a possibility of failure which suggests that there is some missing information in the links. It is also possible that the missing information is dynamic – you do not step in the same water twice – which makes the study to predict emergent systems to be a rather difficult exercise. Depending on the lens through which we look at, the system might appear or disappear.
There are two types of emergence: Descriptive and Explanatory emergence. Descriptive emergence means that properties of wholes cannot be necessarily defined through the properties of the pasts. Explanatory emergence means laws of complex systems cannot be deduced from the laws of interaction of simpler elements that constitute it. Thus the emergence is a result of the amount of variety embodied in the system, the amount of external influence that weights and shapes the overall property and direction of the system, the type of resources that the system consumes, the type of constraints that the system is operating under and the number of levels of sub-systems that work together to build out the final system. Thus, systems can be benign as in the system is relatively more predictable whereas a radical system is a material departure of a system from expectation. If the parts that constitute a system is independent of its workings from other parts and can be boxed within boundaries, emergent systems become more predictable. A watch is an example of a system that follows the different mechanical elements in a watch that are geared for reading the time as it ultimate purpose. It is a good example of a complex physical system. However, these systems are very brittle – a failure in one point can cascade into a failure of the entire system. Systems that are more resilient are those where the elements interact and learn from one another. In other words, the behavior of the elements excites other elements – all of which work together to create a dance toward a more stable state. They deploy what is often called the flocking trick and the pheromone trick. Flocking trick is largely the emulation of the particles that are close to each other – very similar to the cellular automata as introduced by Neumann and discussed in the earlier chapter. The Pheromone trick reflects how the elements leave marks that are acted upon as signals by other elements and thus they all work together around these signal trails to behave and thus act as a forcing function to create the systems.
There are systems that have properties of extremely strong emergence. What does Consciousness, Life, and Culture have in common? How do we look at Climate? What about the organic development of cities? These are just some examples of system where determinism is nigh impossible. We might be able to tunnel through the various and diverse elements that embody the system, but it would be difficult to coherently and tangibly draw all set of relationships, signals, effectors and detectors, etc. to grapple with a complete understanding of the system. Wrestling a strong emergent system would be a task that might even be outside the purview of the highest level of computational power available. And yet, these systems exist, and they emerge and evolve. Yet we try to plan for these systems or plan to direct policies to influence the system, not fully knowing the impact. This is also where the unintended consequences of our action might take free rein.
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