Category Archives: Order
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.
Chaos as a system: New Framework
Chaos is not an unordered phenomenon. There is a certain homeostatic mechanism at play that forces a system that might have inherent characteristics of a “chaotic” process to converge to some sort of stability with respect to predictability and parallelism. Our understanding of order which is deemed to be opposite of chaos is the fact that there is a shared consensus that the system will behave in an expected manner. Hence, we often allude to systems as being “balanced” or “stable” or “in order” to spotlight these systems. However, it is also becoming common knowledge in the science of chaos that slight changes in initial conditions in a system can emit variability in the final output that might not be predictable. So how does one straddle order and chaos in an observed system, and what implications does this process have on ongoing study of such systems?
Chaotic systems can be considered to have a highly complex order. It might require the tools of pure mathematics and extreme computational power to understand such systems. These tools have invariably provided some insights into chaotic systems by visually representing outputs as re-occurrences of a distribution of outputs related to a given set of inputs. Another interesting tie up in this model is the existence of entropy, that variable that taxes a system and diminishes the impact on expected outputs. Any system acts like a living organism: it requires oodles of resources to survive and a well-established set of rules to govern its internal mechanism driving the vector of its movement. Suddenly, what emerges is the fact that chaotic systems display some order while subject to an inherent mechanism that softens its impact over time. Most approaches to studying complex and chaotic systems involve understanding graphical plots of fractal nature, and bifurcation diagrams. These models illustrate very complex re occurrences of outputs directly related to inputs. Hence, complex order occurs from chaotic systems.
A case in point would be the relation of a population parameter in the context to its immediate environment. It is argued that a population in an environment will maintain a certain number and there would be some external forces that will actively work to ensure that the population will maintain at that standard number. It is a very Malthusian analytic, but what is interesting is that there could be some new and meaningful influences on the number that might increase the scale. In our current meaning, a change in technology or ingenuity could significantly alter the natural homeostatic number. The fact remains that forces are always at work on a system. Some systems are autonomic – it self-organizes and corrects itself toward some stable convergence. Other systems are not autonomic and once can only resort to the laws of probability to get some insight into the possible outputs – but never to a point where there is a certainty in predictive prowess.
Organizations have a lot of interacting variables at play at any given moment. In order to influence the organization behavior or/and direction, policies might be formulated to bring about the desirable results. However, these nudges toward setting off the organization in the right direction might also lead to unexpected results. The aim is to foresee some of these unexpected results and mollify the adverse consequences while, in parallel, encourage the system to maximize the benefits. So how does one effect such changes?
It all starts with building out an operating framework. There needs to be a clarity around goals and what the ultimate purpose of the system is. Thus there are few objectives that bind the framework.
- Clarity around goals and the timing around achieving these goals. If there is no established time parameter, then the system might jump across various states over time and it would be difficult to establish an outcome.
- Evaluate all of the internal and external factors that might operate in the framework that would impact the success of organizational mandates and direction. Identify stasis or potential for stasis early since that mental model could stem the progress toward a desirable impact.
- Apply toll gates strategically to evaluate if the system is proceeding along the lines of expectation, and any early aberrations are evaluated and the rules are tweaked to get the system to track on a desirable trajectory.
- Develop islands of learning across the path and engage the right talent and other parameters to force adaptive learning and therefore a more autonomic direction to the system.
- Bind the agents and actors in the organization to a shared sense of purpose within the parameter of time.
- Introduce diversity into the framework early in the process. The engagement of diversity allows the system to modulate around a harmonic mean.
- Finally, maintain a well document knowledge base such that the accretive learning that results due to changes in the organization become springboard for new initiatives that reduces the costs of potential failures or latency in execution.
- Encouraging the leadership to ensure that the vector is pointed toward the right direction at any given time.
Once a framework and the engagement rules are drawn out, it is necessary to rely on the natural velocity and self-organization of purposeful agents to move the agenda forward, hopefully with little or no intervention. A mechanism of feedback loops along the way would guide the efficacy of the direction of the system. The implications is that the strategy and the operations must be aligned and reevaluated and positive behavior is encouraged to ensure that the systems meets its objective.
However, as noted above, entropy is a dynamic that often threatens to derail the system objective. There will be external or internal forces constantly at work to undermine system velocity. The operating framework needs to anticipate that real possibility and pre-empt that with rules or introduction of specific capital to dematerialize these occurrences. Stasis is an active agent that can work against the system dynamic. Stasis is the inclination of agents or behaviors that anchors the system to some status quo – we have to be mindful that change might not be embraced and if there are resistors to that change, the dynamic of organizational change can be invariably impacted. It will take a lot more to get something done than otherwise needed. Identifying stasis and agents of stasis is a foundational element
While the above is one example of how to manage organizations in the shadows of the properties of how chaotic systems behave, another example would be the formulation of strategy of the organization in responses to external forces. How do we apply our learnings in chaos to deal with the challenges of competitive markets by aligning the internal organization to external factors? One of the key insights that chaos surfaces is that it is nigh impossible for one to fully anticipate all of the external variables, and leaving the system to dynamically adapt organically to external dynamics would allow the organization to thrive. To thrive in this environment is to provide the organization to rapidly change outside of the traditional hierarchical expectations: when organizations are unable to make those rapid changes or make strategic bets in response to the external systems, then the execution value of the organization diminishes.
Margaret Wheatley in her book Leadership and the New Science: Discovering Order in a Chaotic World Revised says, “Organizations lack this kind of faith, faith that they can accomplish their purposes in various ways and that they do best when they focus on direction and vision, letting transient forms emerge and disappear. We seem fixated on structures…and organizations, or we who create them, survive only because we build crafty and smart—smart enough to defend ourselves from the natural forces of destruction. Karl Weick, an organizational theorist, believes that “business strategies should be “just in time…supported by more investment in general knowledge, a large skill repertoire, the ability to do a quick study, trust in intuitions, and sophistication in cutting losses.”
We can expand the notion of a chaos in a system to embrace the bigger challenges associated with environment, globalization, and the advent of disruptive technologies.
One of the key challenges to globalization is how policy makers would balance that out against potential social disintegration. As policies emerge to acknowledge the benefits and the necessity to integrate with a new and dynamic global order, the corresponding impact to local institutions can vary and might even lead to some deleterious impact on those institutions. Policies have to encourage flexibility in local institutional capability and that might mean increased investments in infrastructure, creating a diverse knowledge base, establishing rules that govern free but fair trading practices, and encouraging the mobility of capital across borders. The grand challenges of globalization is weighed upon by government and private entities that scurry to create that continual balance to ensure that the local systems survive and flourish within the context of the larger framework. The boundaries of the system are larger and incorporates many more agents which effectively leads to the real possibility of systems that are difficult to be controlled via a hierarchical or centralized body politic Decision making is thus pushed out to the agents and actors but these work under a larger set of rules. Rigidity in rules and governance can amplify failures in this process.
Related to the realities of globalization is the advent of the growth in exponential technologies. Technologies with extreme computational power is integrating and create robust communication networks within and outside of the system: the system herein could represent nation-states or companies or industrialization initiatives. Will the exponential technologies diffuse across larger scales quickly and will the corresponding increase in adoption of new technologies change the future of the human condition? There are fears that new technologies would displace large groups of economic participants who are not immediately equipped to incorporate and feed those technologies into the future: that might be on account of disparity in education and wealth, institutional policies, and the availability of opportunities. Since technologies are exponential, we get a performance curve that is difficult for us to understand. In general, we tend to think linearly and this frailty in our thinking removes us from the path to the future sooner than later. What makes this difficult is that the exponential impact is occurring across various sciences and no one body can effectively fathom the impact and the direction. Bill Gates says it well “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.” Does chaos theory and complexity science arm us with a differentiated tool set than the traditional toolset of strategy roadmaps and product maps? If society is being carried by the intractable and power of the exponent in advances in technology, than a linear map might not serve to provide the right framework to develop strategies for success in the long-term. Rather, a more collaborative and transparent roadmap to encourage the integration of thoughts and models among the actors who are adapting and adjusting dynamically by the sheer force of will would perhaps be an alternative and practical approach in the new era.
Lately there has been a lot of discussion around climate change. It has been argued, with good reason and empirical evidence, that environment can be adversely impacted on account of mass industrialization, increase in population, resource availability issues, the inability of the market system to incorporate the cost of spillover effects, the adverse impact of moral hazard and the theory of the commons, etc. While there are demurrers who contest the long-term climate change issues, the train seems to have already left the station! The facts do clearly reflect that the climate will be impacted. Skeptics might argue that science has not yet developed a precise predictive model of the weather system two weeks out, and it is foolhardy to conclude a dystopian future on climate fifty years out. However, the alternative argument is that our inability to exercise to explain the near-term effects of weather changes and turbulence does not negate the existence of climate change due to the accretion of greenhouse impact. Boiling a pot of water will not necessarily gives us an understanding of all of the convection currents involved among the water molecules, but it certainly does not shy away from the fact that the water will heat up.
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.
Complex Physical and Adaptive Systems
There are two models in complexity. Complex Physical Systems and Complex Adaptive Systems! For us to grasp the patterns that are evolving, and much of it seemingly out of our control – it is important to understand both these models. One could argue that these models are mutually exclusive. While the existing body of literature might be inclined toward supporting that argument, we also find some degree of overlap that makes our understanding of complexity unstable. And instability is not to be construed as a bad thing! We might operate in a deterministic framework, and often, we might operate in the realms of a gradient understanding of volatility associated with outcomes. Keeping this in mind would be helpful as we deep dive into the two models. What we hope is that our understanding of these models would raise questions and establish mental frameworks for intentional choices that we are led to make by the system or make to influence the evolution of the system.
Complex Physical Systems (CPS)
Complex Physical Systems are bounded by certain laws. If there are initial conditions or elements in the system, there is a degree of predictability and determinism associated with the behavior of the elements governing the overarching laws of the system. Despite the tautological nature of the term (Complexity Physical System) which suggests a physical boundary, the late 1900’s surfaced some nuances to this model. In other words, if there is a slight and an arbitrary variation in the initial conditions, the outcome could be significantly different from expectations. The assumption of determinism is put to the sword. The notion that behaviors will follow established trajectories if rules are established and the laws are defined have been put to test. These discoveries by introspection offers an insight into the developmental block of complex physical systems and how a better understanding of it will enable us to acknowledge such systems when we see it and thereafter allow us to establish certain toll-gates and actions to navigate, to the extent possible, to narrow the region of uncertainty around outcomes.
The universe is designed as a complex physical system. Just imagine! Let this sink in a bit. A complex physical system might be regarded relatively simpler than a complex adaptive system. And with that in mind, once again …the universe is a complex physical system. We are awed by the vastness and scale of the universe, we regard the skies with an illustrious reverence and we wonder and ruminate on what lies beyond the frontiers of a universe, if anything. Really, there is nothing bigger than the universe in the physical realm and yet we regard it as a simple system. A “Simple” Complex Physical System. In fact, the behavior of ants that lead to the sustainability of an ant colony, is significantly more complex: and we mean by orders of magnitude.
Complexity behavior in nature reflects the tendency of large systems with many components to evolve into a poised “critical” state where minor disturbances or arbitrary changes in initial conditions can create a seemingly catastrophic impact on the overall system such that system changes significantly. And that happens not by some invisible hand or some uber design. What is fundamental to understanding complex systems is to understand that complexity is defined as the variability of the system. Depending on our lens, the scale of variability could change and that might lead to different apparatus that might be required to understand the system. Thus, determinism is not the measure: Stephen Jay Gould has argued that it is virtually impossible to predict the future. We have hindsight explanatory powers but not predictable powers. Hence, systems that start from the initial state over time might represent an outcome that is distinguishable in form and content from the original state. We see complex physical systems all around us. Snowflakes, patterns on coastlines, waves crashing on a beach, rain, etc.
Complex Adaptive Systems (CAS)
Complex adaptive systems, on the contrary, are learning systems that evolve. They are composed of elements which are called agents that interact with one another and adapt in response to the interactions.
Markets are a good example of complex adaptive systems at work.
CAS agents have three levels of activity. As described by Johnson in Complexity Theory: A Short Introduction – the three levels of activity are:
- Performance (moment by moment capabilities): This establishes the locus of all behavioral elements that signify the agent at a given point of time and thereafter establishes triggers or responses. For example, if an object is approaching and the response of the agent is to run, that would constitute a performance if-then outcome. Alternatively, it could be signals driven – namely, an ant emits a certain scent when it finds food: other ants will catch on that trail and act, en masse, to follow the trail. Thus, an agent or an actor in an adaptive system has detectors which allows them to capture signals from the environment for internal processing and it also has the effectors that translate the processing to higher order signals that influence other agents to behave in certain ways in the environment. The signal is the scent that creates these interactions and thus the rubric of a complex adaptive system.
- Credit assignment (rating the usefulness of available capabilities): When the agent gathers experience over time, the agent will start to rely heavily on certain rules or heuristics that they have found useful. It is also typical that these rules may not be the best rules, but it could be rules that are a result of first discovery and thus these rules stay. Agents would rank these rules in some sequential order and perhaps in an ordinal ranking to determine what is the best rule to fall back on under certain situations. This is the crux of decision making. However, there are also times when it is difficult to assign a rank to a rule especially if an action is setting or laying the groundwork for a future course of other actions. A spider weaving a web might be regarded as an example of an agent expending energy with the hope that she will get some food. This is a stage setting assignment that agents have to undergo as well. One of the common models used to describe this best is called the bucket-brigade algorithm which essentially states that the strength of the rule depends on the success of the overall system and the agents that constitute it. In other words, all the predecessors and successors need to be aware of only the strengths of the previous and following agent and that is done by some sort of number assignment that becomes stronger from the beginning of the origin of the system to the end of the system. If there is a final valuable end-product, then the pathway of the rules reflect success. Once again, it is conceivable that this might not be the optimal pathway but a satisficing pathway to result in a better system.
- Rule discovery (generating new capabilities): Performance and credit assignment in agent behavior suggest that the agents are governed by a certain bias. If the agents have been successful following certain rules, they would be inclined toward following those rules all the time. As noted, rules might not be optimal but satisficing. Is improvement a matter of just incremental changes to the process? We do see major leaps in improvement … so how and why does this happen? In other words, someone in the process have decided to take a different rule despite their experiences. It could have been an accident or very intentional.
One of the theories that have been presented is that of building blocks. CAS innovation is a result of reconfiguring the various components in new ways. One quips that if energy is neither created, nor destroyed …then everything that exists today or will exist tomorrow is nothing but a reconfiguration of energy in new ways. All of tomorrow resides in today … just patiently waiting to be discovered. Agents create hypotheses and experiment in the petri dish by reconfiguring their experiences and other agent’s experiences to formulate hypotheses and the runway for discovery. In other words, there is a collaboration element that comes into play where the interaction of the various agents and their assignment as a group to a rule also sets the stepping stone for potential leaps in innovation.
Another key characteristic of CAS is that the elements are constituted in a hierarchical order. Combinations of agents at a lower level result in a set of agents higher up and so on and so forth. Thus, agents in higher hierarchical orders take on some of the properties of the lower orders but it also includes the interaction rules that distinguishes the higher order from the lower order.
Short History of Complexity
Complexity theory began in the 1930’s when natural scientists and mathematicians rallied together to get a deeper understanding of how systems emerge and plays out over time. However, the groundwork of complexity theory began in the 1850’s with Darwin’s introduction to Natural Selection. It was further extended by Mendel’s genetic algorithms. Darwin’s Theory of Evolution has been posited as a slow gradual process. He says that “Natural selection acts only by taking advantage of slight successive variations; she can never take a great and sudden leap, but must advance by short and sure, though slow steps.” Thus, he concluded that complex systems evolve by leaps and the result is an organic formulation of an irreducibly complex system which is composed of many parts, all of which work together closely for the overall system to function. If any part is missing or does not act as expected, then the system becomes unwieldy and breaks down. So it was an early foray into distinguishing the emergent property of a system from the elements that constitute it. Mendel, on the other hand, laid out the property of inheritance across generations. An organic system inherits certain traits that are reconfigured over time and adapts to the environment, thus leading to the development of an organism which for our purposes fall in the realm of a complex outcome. One would imagine that there is a common thread between Darwin’s Natural Selection and Mendel’s laws of genetic inheritance. But that is not the case and that has wide implications in complexity theory. Mendel focused on how the traits are carried across time: the mechanics which are largely determined by some probabilistic functions. The underlying theory of Mendel hinted at the possibility that a complex system is a result of discrete traits that are passed on while Darwin suggests that complexity arises due continuous random variations.
In the 1920’s, literature suggested that a complex system has elements of both: continuous adaptation and discrete inheritance that is hierarchical in nature. A group of biologists reconciled the theories into what is commonly known as the Modern Synthesis. The principles guiding Modern Synthesis were: Natural Selection was the major mechanism for evolutionary change. Small random variations of genes and natural selection result in the origin of new species. Furthermore, the new species might have properties different than the elements that constitute. Modern Synthesis thus provided the framework around Complexity theory. What does this great debate mean for our purposes? Once we arrive at determining whether a system is complex, then how does the debate shed more light into our understanding of complexity. Does this debate shed light into how we regard complexity and how we subsequently deal with it? We need to further extend our thinking by looking at a few new developments that occurred in the 20th century that would give us a better perspective. Let us then continue our journey into the evolution of the thinking around complexity.
Axioms are statements that are self-evident. It serves to be a premise or starting point for further reasoning and arguments. An axiom thus is not contestable because if it, then all the following reasoning that is extended against axioms would fall apart. Thus, for our purposes and our understanding of complexity theory – A complex system has an initial state that is irreducible physically or mathematically.
One of the key elements in Complexity is computation or computability. In the 1930’s, Turing introduced the abstract concept of the Turing machine. There is a lot of literature that goes into the specifics of how the machine works but that is beyond the scope of this book. However, there are key elements that can be gleaned from that concept to better understand complex systems. A complex system that evolves is a result of a finite number of steps that would solve a specific challenge. Although the concept has been applied in the boundaries of computational science, I am taking the liberty to apply this to emerging complex systems. Complexity classes help scientists categorize the problems based on how much time and space is required to solve problems and verify solutions. The complexity is thus a function of time and memory. This is a very important concept and we have radically simplified the concept to attend to a self-serving purpose: understand complexity and how to solve the grand challenges? Time complexity refers to the number of steps required to solve a problem. A complex system might not necessarily be the most efficient outcome but is nonetheless an outcome of a series of steps, backward and forward to result in a final state. There are pathways or efficient algorithms that are produced and the mechanical states to produce them are defined and known. Space complexity refers to how much memory that the algorithm depends on to solve the problem. Let us keep these concepts in mind as we round this all up into a more comprehensive work that we will relay at the end of this chapter.
Around the 1940’s, John von Neumann introduced the concept of self-replicating machines. Like Turing, Von Neumann’s would design an abstract machine which, when run, would replicate itself. The machine consists of three parts: a ‘blueprint’ for itself, a mechanism that can read any blueprint and construct the machine (sans blueprint) specified by that blueprint, and a ‘copy machine’ that can make copies of any blueprint. After the mechanism has been used to construct the machine specified by the blueprint, the copy machine is used to create a copy of that blueprint, and this copy is placed into the new machine, resulting in a working replication of the original machine. Some machines will do this backwards, copying the blueprint and then building a machine. The implications are significant. Can complex systems regenerate? Can they copy themselves and exhibit same behavior and attributes? Are emergent properties equivalent? Does history repeat itself or does it rhyme? How does this thinking move our understanding and operating template forward once we identify complex systems?
Let us step forward into the late 1960’s when John Conway started doing experiments extending the concept of the cellular automata. He introduced the concept of the Game of Life in 1970 as a result of his experiments. His main theses was simple : The game is a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input. One interacts with the Game of Life by creating an initial configuration and observing how it evolves, or, for advanced players, by creating patterns with properties. The entire formulation was done on a two-dimensional universe in which patterns evolved over time. It is one of the finest examples in science of how a set of few simple non-arbitrary rules can result in an incredibly complex behavior that is fluid and provides a pleasing pattern over time. In other words, if one were an outsider looking in, you would see a pattern emerging from simple initial states and simple rules. We encourage you to look at several patterns that many people have constructed using different Game of Life parameters. The main elements are as follows. A square grid contains cells that are alive or dead. The behavior of each cell is dependent on the state of its eight immediate neighbors. Eight is an arbitrary number that Conway established to keep the model simple. These cells will strictly follow the rules.
Live Cells:
- A live cell with zero or one live neighbors will die
- A live cell with two or three live neighbors will remain alive
- A live cell with four or more live neighbors will die.
Dead Cells:
- A dead cell with exactly three live neighbors becomes alive
- In all other cases a dead cell will stay dead.
Thus, what his simulation led to is the determination that life is an example of emergence and self-organization. Complex patterns can emerge from the implementation of very simple rules. The game of life thus encourages the notion that “design” and “organization” can spontaneously emerge in the absence of a designer.
Stephen Wolfram introduced the concept of a Class 4 cellular automata of which the Rule of 110 is well known and widely studied. The Class 4 automata validates a lot of the thinking grounding complexity theory. He proves that certain patterns emerge from initial conditions that are not completely random or regular but seems to hint at an order and yet the order is not predictable. Applying a simple rule repetitively to the simplest possible starting point would bode the emergence of a system that is orderly and predictable: but that is far from the truth. The resultant state is that the results exhibit some randomness and yet produce patters with order and some intelligence.
Thus, his main conclusion from his discovery is that complexity does not have to beget complexity: simple forms following repetitive and deterministic rules can result in systems that exhibit complexity that is unexpected and unpredictable. However, he sidesteps the discussion around the level of complexity that his Class 4 automata generates. Does this determine or shed light on evolution, how human beings are formed, how cities evolve organically, how climate is impacted and how the universe undergoes change? One would argue that is not the case. However, if you take into account Darwin’s natural selection process, the Mendel’s law of selective genetics and its corresponding propitiation, the definitive steps proscribed by the Turing machine that captures time and memory, Von Neumann’s theory of machines able to replicate themselves without any guidance, and Conway’s force de tour in proving that initial conditions without any input can create intelligent systems – you essentially can start connecting the dots to arrive at a core conclusion: higher order systems can organically create itself from initial starting conditions naturally. They exhibit a collective intelligence which is outside the boundaries of precise prediction. In the previous chapter we discussed complexity and we introduced an element of subjective assessment to how we regard what is complex and the degree of complexity. Whether complexity falls in the realm of a first-person subjective phenomenon or a scientific third-party objective phenomenon has yet to be ascertained. Yet it is indisputable that the product of a complex system might be considered a live pattern of rules acting upon agents to cause some deterministic but random variation.
Aaron Swartz took down a piece of the Berlin Wall! We have to take it all down!
“The world’s entire scientific … heritage … is increasingly being digitized and locked up by a handful of private corporations… The Open Access Movement has fought valiantly to ensure that scientists do not sign their copyrights away but instead ensure their work is published on the Internet, under terms that allow anyone to access it.” – Aaron Swartz
Information, in the context of scholarly articles by research at universities and think-tanks, is not a zero sum game. In other words, one person cannot have more without having someone have less. When you start creating “Berlin” walls in the information arena within the halls of learning, then learning itself is compromised. In fact, contributing or granting the intellectual estate into the creative commons serves a higher purpose in society – an access to information and hence, a feedback mechanism that ultimately enhances the value to the end-product itself. How? Since now the product has been distributed across a broader and diverse audience, and it is open to further critical analyses.
The universities have built a racket. They have deployed a Chinese wall between learning in a cloistered environment and the world who are not immediate participants. The Guardian wrote an interesting article on this matter and a very apt quote puts it all together.
“Academics not only provide the raw material, but also do the graft of the editing. What’s more, they typically do so without extra pay or even recognition – thanks to blind peer review. The publishers then bill the universities, to the tune of 10% of their block grants, for the privilege of accessing the fruits of their researchers’ toil. The individual academic is denied any hope of reaching an audience beyond university walls, and can even be barred from looking over their own published paper if their university does not stump up for the particular subscription in question.
This extraordinary racket is, at root, about the bewitching power of high-brow brands. Journals that published great research in the past are assumed to publish it still, and – to an extent – this expectation fulfils itself. To climb the career ladder academics must get into big-name publications, where their work will get cited more and be deemed to have more value in the philistine research evaluations which determine the flow of public funds. Thus they keep submitting to these pricey but mightily glorified magazines, and the system rolls on.”
http://www.guardian.co.uk/commentisfree/2012/apr/11/academic-journals-access-wellcome-trust
JSTOR is a not-for-profit organization that has invested heavily in providing an online system for archiving, accessing, and searching digitized copies of over 1,000 academic journals. More recently, I noticed some effort on their part to allow public access to only 3 articles over a period of 21 days. This stinks! This policy reflects an intellectual snobbery beyond Himalayan proportions. The only folks that have access to these academic journals and studies are professors, and researchers that are affiliated with a university and university libraries. Aaron Swartz noted the injustice of hoarding such knowledge and tried to distribute a significant proportion of JSTOR’s archive through one or more file-sharing sites. And what happened thereafter was perhaps one of the biggest misapplication of justice. The same justice that disallows asymmetry of information in Wall Street is being deployed to preserve the asymmetry of information at the halls of learning.
MSNBC contributor Chris Hayes criticized the prosecutors, saying “at the time of his death Aaron was being prosecuted by the federal government and threatened with up to 35 years in prison and $1 million in fines for the crime of—and I’m not exaggerating here—downloading too many free articles from the online database of scholarly work JSTOR.”
The Associated Press reported that Swartz’s case “highlights society’s uncertain, evolving view of how to treat people who break into computer systems and share data not to enrich themselves, but to make it available to others.”
Chris Soghioian, a technologist and policy analyst with the ACLU, said, “Existing laws don’t recognize the distinction between two types of computer crimes: malicious crimes committed for profit, such as the large-scale theft of bank data or corporate secrets; and cases where hackers break into systems to prove their skillfulness or spread information that they think should be available to the public.”
Kelly Caine, a professor at Clemson University who studies people’s attitudes toward technology and privacy, said Swartz “was doing this not to hurt anybody, not for personal gain, but because he believed that information should be free and open, and he felt it would help a lot of people.”
And then there were some modest reservations, and Swartz actions were attributed to reckless judgment. I contend that this does injustice to someone of Swartz’s commitment and intellect … the recklessness was his inability to grasp the notion that an imbecile in the system would pursue 35 years of imprisonment and $1M fine … it was not that he was not aware of what he was doing but he believed, as does many, that scholarly academic research should be available as a free for all.
We have a Berlin wall that needs to be taken down. Swartz started that but he was unable to keep at it. It is important to not rest in this endeavor and that everyone ought to actively petition their local congressman to push bills that will allow open access to these academic articles.
John Maynard Keynes had warned of the folly of “shutting off the sun and the stars because they do not pay a dividend”, because what is at stake here is the reach of the light of learning. Aaron was at the vanguard leading that movement, and we should persevere to become those points of light that will enable JSTOR to disseminate the information that they guard so unreservedly.
The Unbearable Lightness of Being
Where the mind is without fear and the head is held high
Where knowledge is free
Where the world has not been broken up into fragments
By narrow domestic walls
Where words come out from the depth of truth
Where tireless striving stretches its arms towards perfection
Where the clear stream of reason has not lost its way
Into the dreary desert sand of dead habit
Where the mind is led forward by thee
Into ever-widening thought and action
Into that heaven of freedom, my Father, let my country awake.
– Rabindranath Tagore
Among the many fundamental debates in philosophy, one of the fundamental debates has been around the concept of free will. The debates have stemmed around two arguments associated with free will.
1) Since future actions are governed by the circumstances of the present and the past, human beings future actions are predetermined on account of the learnings from the past. Hence, the actions that happen are not truly a consequent of free will.
2) The counter-argument is that future actions may not necessarily be determined and governed by the legacy of the present and the past, and hence leaves headroom for the individual to exercise free will.
Now one may wonder what determinism or lack of it has anything to do with the current state of things in an organizational context. How is this relevant? Why are the abstract notions of determinism and free will important enough to be considered in the context of organizational evolution? How does the meaning lend itself to structured institutions like business organizations, if you will, whose sole purpose is to create products and services to meet the market demand.
So we will throw a factual wrinkle in this line of thought. We will introduce now an element of chance. How does chance change the entire dialectic? Simply because chance is an unforeseen and random event that may not be pre-determined; in fact, a chance event may not have a causal trigger. And chance or luck could be meaningful enough to untether an organization and its folks to explore alternative paths. It is how the organization and the people are aligned to take advantage of that random nondeterministic future that could make a huge difference to the long term fate of the organization.
The principle of inductive logic states that what is true for n and n+1 would be true for n+2. The inductive logic creates predictability and hence organizations create pathways to exploit the logical extension of inductive logic. It is the most logical apparatus that exists to advance groups in a stable but robust manner to address the multitude of challenges that that they have to grapple with. After all, the market is governed by animal spirits! But let us think through this very carefully. All competition or collaboration that occurs among groups to address the market demands result in homogenous behavior with general homogeneous outcomes. Simply put, products and services become commoditized. Their variance is not unique and distinctive. However, they could be just be distinctive enough to eke out enough profits in the margins before being absorbed into a bigger whole. At that point, identity is effaced over time. Organizations gravitate to a singularity. Unique value propositions wane over time.
So let us circle back to chance. Chance is our hope to create divergence. Chance is the factoid that cancels out the inductive vector of industrial organization. Chance does not exist … it is not a “waiting for Godot” metaphor around the corner. If it always did, it would have been imputed by the determinists in their inductive world and we would end up with a dystopian homogenous future. Chance happens. And sometimes it has a very short half-life. And if the organization and people are aligned and their mindset is adapted toward embracing and exploiting that fleeting factoid of chance, the consequences could be huge. New models would emerge, new divergent paths would be traduced and society and markets would burst into a garden of colorful ideas in virtual oasis of new markets.
So now to tie this all to free will and to the unbearable lightness of being! It is the existence of chance that creates the opportunity to exercise free will on the part of an individual, but it is the organizations responsibility to allow the individual to unharness themselves from organization inertia. Thus, organizations have to perpetuate an environment wherein employees are afforded some headroom to break away. And I don’t mean break away as in people leaving the organization to do their own gigs; I mean breakaway in thought and action within the boundaries of the organization to be open to element of chance and exploit it. Great organizations do not just encourage the lightness of being … unharnessing the talent but rather – the great organizations are the ones that make the lightness of being unbearable. These individuals are left with nothing but an awareness and openness to chance to create incredible values … far more incredible and awe inspiring and momentous than a more serene state of general business as usual affairs.
Reality Distortion Field: A Powerful Motivator in Organizations!
“The reality distortion field was a confounding mélange of a charismatic rhetorical style, an indomitable will, and an eagerness to bend any fact to fit the purpose at hand. If one line of argument failed to persuade, he would deftly switch to another. Sometimes, he would throw you off balance by suddenly adopting your position as his own, without acknowledging that he ever thought differently. “
– Andy Hertzfield on Steve Jobs’ Reality Distortion Field.
Many of us have heard the word – Reality Distortion Field. The term has been attributed to Steve Jobs who was widely known to have communicated messages to his constituency in a manner such that the reality of the situation was supplanted by him packaging the message so that people would take the bait and pursue paths that would, upon closer investigation, be dissonant from reality. But having been an avid acolyte of Jobs, I would imagine that he himself would be disturbed and unsettled by the label. Since when did the promise of a radiant future constitute a Reality Distortion Field? Since when did the ability of a person to embrace what seemingly is impossible and far-fetched and instill confidence in the troops to achieve it constitute a Reality Distortion Field? Since when did the ability of leadership to share in the wonders of unique and disruptive creations constitute a Reality Distortion Field? Since when did dreams of a better future underpinned with executable actions to achieve it constitute a Reality Distortion Field?
The Reality Distortion Field usage reflects the dissonance between what is and what needs to be. It is a slapstick term which suggests that you are envisioning tectonic dissonance rifts between reality and possibilities and that you are leading the awestruck starry-eyed followers off a potential cliff. Some people have renamed RDF as hype of Bulls*#t. They believe that RDF is extremely bad for organizations because it pushes the people outside the comfort zone of physical and logical constraints and is a recipe for disaster. The argument continues that organizations that are grounded upon the construct of reality and to communicate the same are essential to advance the organization. I beg to differ.
So let me address this on two fronts: RDF label and if we truly accept what RDF means … then my position is that it is the single most important attribute that a strong leader ought to embrace in the organization.
The RDF label:
We all know this to be true: A rose by any other name is still a rose. We just happen to call this rose in this context a RDF. It is presumed to be the ability of a person to cast possibilities in a different light … so much so that the impossibilities are reduced to elements just within the grasp of reality. Now I ask you – What is wrong with that? For a leader to be able to cast their vision within the inimitable grasp of an organization is a huge proxy for the faith of the leader of the people in the organization. If a project realistically would take 3 months but a RDF is cast to get a project done in 15 days – that is a tall order – but think of the consequences if people are “seduced” into the RDF and hence acts upon it. It immediately unfolds new pathways of collaboration, unforeseen discoveries into super-efficient and effective methods, it creates trench camaraderie, it distills focus into singularity points to be executed against, it instills and ignites a passion and an engagement around the new stakes in the ground, people become keepers of one another for a consequential and significant conquest, it brings out the creative energies and the limitless possibilities, once the goal is accomplished, of disruptive innovation in means and ends. Of course, one could also counter-argue a plethora of incidental issues in such cases: employees would burn out under the burden of unrealistic goals, employees are set more for failing than succeeding, it would create a disorderly orientation upon groups working together to meet RDF standards, and if one were to fall short …it would be a last straw that may break the camel’s back. So essentially this speaks to the ordinal magnitude of the RDF schema that is being pushed out by leadership.
RDF and the beneficial impact to an organization:
It is the sine qua non of great leadership to be able to push organizations beyond the boundaries of plain convenience. I have, in my career, been fortunate to have been challenged and on many occasions, forced out of my comfort zone. But in having done so successfully on many occasions, it has also given me the confidence to scale mountains. And that confidence is a perquisite that the organization leadership has to provide on a daily basis. After all, one of the biggest assets that an employee in an organization ought to have is pride and sense of accomplishment to their work. RDF unfolds that possibility.
We hear of disruptive innovations. These are defined as innovations that leapfrog the bounds of technology inertia. How does a company enable that? It is certainly not incremental thinking. It is a vision that marginally lies outside our aggregated horizon of sight. The age today which is a result of path breaking ideas and execution have been a result of those visionaries that have aimed beyond the horizons, instilled faith amongst the line men to align and execute, and made the impossible possible. We ought to thank our stars for having leaders that emit an RDF and lead us off our tenebrous existence in our diurnal professional lives.
There is absolutely no doubt that such leadership would create resistance and fierce antipathy among some. But despite some of the ill effects, the vector that drives great innovations lies in the capacity of the organization to embrace degrees of RDF to hasten and make the organizations competitive, distinctive and powerful.
Medici Effect – Encourage Innovation in the Organization
“Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things. And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people.”
– Steve Jobs
What is the Medici Effect?
Frans Johanssen has written a lovely book on the Medici Effect. The term “Medici” relates to the Medici family in Florence that made immense contributions in art, architecture and literature. They were pivotal in catalyzing the Renaissance, and some of the great artists and scientists that we revere today – Donatello, Michelangelo, Leonardo da Vinci, and Galileo were commissioned for their works by the family.
Renaissance was the resurgence of the old Athenian democracy. It merged distinctive areas of humanism, philosophy, sciences, arts and literature into a unified body of knowledge that would advance the cause of human civilization. What the Medici effect speaks to is the outcome that is the result of creating a system that would incorporate what on first glance, may seem distinctive and discrete disciplines, into holistic outcomes and a shared simmering of wisdom that permeated the emergence of new disciplines, thoughts and implementations.
Supporting the organization to harness the power of the Medici Effect
We are past the industrial era, the Progressive era and the Information era. There are no formative lines that truly distinguish one era from another, but our knowledge has progressed along gray lines that have pushed the limits of human knowledge. We are now wallowing in a crucible wherein distinct disciplines have crisscrossed and merged together. The key thesis in the Medici effect is that the intersections of these distinctive disciplines enable the birth of new breakthrough ideas and leapfrog innovation.
So how do we introduce the Medici Effect in organizations?
Some of the key ways to implement the model is really to provide the support infrastructure for
1. Connections: Our brains are naturally wired toward associations. We try to associate a concept with contextual elements around that concept to give the concept more meaning. We learn by connecting concepts and associating them, for the most part, with elements that we are conversant in. However, one can create associations within a narrow parameter, constrained within certain semantic models that we have created. Organizations can hence channelize connections by implementing narrow parameters. On the other hand, connections can be far more free-form. That means that the connector thinks beyond the immediate boundaries of their domain or within certain domains that are “pre-ordained”. In those cases, we create what is commonly known as divergent thinking. In that approach, we cull elements from seemingly different areas but we thread them around some core to generate new approaches, new metaphors, and new models. Ensuring that employees are able to safely reach out to other nodes of possibilities is the primary implementation step to generate the Medici effect.
2. Collaborations: Connecting different streams of thought in different disciplines is a primary and formative step. To advance this further, organization need to be able to provide additional systems wherein people can collaborate among themselves. In fact, the collaboration impact accentuates the final outcome sooner. So enabling connections and collaboration work in sync to create what I would call – the network impact on a marketplace of ideas.
3. Learning Organization: Organizations need to continuously add fuel to the ecosystem. In other words, they need to bring in speakers, encourage and invest in training programs, allow exploration possibilities by developing an internal budget for that purpose and provide some time and degree of freedom for people to mull over ideas. This enables collaboration to be enriched within the context of diverse learning.
4. Encourage Cultural Diversity: Finally, organizations have to invest in cultural diversity. People from different cultures have varied viewpoints and information and view issues from different perspectives and cultures. Given the fact that we are more globalized now, the innate understanding and immersion in cultural experience enhances the Medici effect. It also creates innovation and ground-breaking thoughts within a broader scope of compassion, humanism, social and shared responsibilities.
Implementing systems to encourage the Medici effect will enable organizations to break out from legacy behavior and trammel into unguarded territories. The charter toward unknown but exciting possibilities open the gateway for amazing and awesome ideas that engage the employees and enable them to beat a path to the intersection of new ideas.
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