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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.
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