Category Archives: Model Thinking

Model Thinking

Model Framework

The fundamental tenet of theory is the concept of “empiria“. Empiria refers to our observations. Based on observations, scientists and researchers posit a theory – it is part of scientific realism.

A scientific model is a causal explanation of how variables interact to produce a phenomenon, usually linearly organized.  A model is a simplified map consisting of a few, primary variables that is gauged to have the most explanatory powers for the phenomenon being observed.  We discussed Complex Physical Systems and Complex Adaptive Systems early on this chapter. It is relatively easier to map CPS to models than CAS, largely because models become very unwieldy as it starts to internalize more variables and if those variables have volumes of interaction between them. A simple analogy would be the use of multiple regression models: when you have a number of independent variables that interact strongly between each other, autocorrelation errors occur, and the model is not stable or does not have predictive value.

thinking

Research projects generally tend to either look at a case study or alternatively, they might describe a number of similar cases that are logically grouped together. Constructing a simple model that can be general and applied to many instances is difficult, if not impossible. Variables are subject to a researcher’s lack of understanding of the variable or the volatility of the variable. What further accentuates the problem is that the researcher misses on the interaction of how the variables play against one another and the resultant impact on the system. Thus, our understanding of our system can be done through some sort of model mechanics but, yet we share the common belief that the task of building out a model to provide all of the explanatory answers are difficult, if not impossible. Despite our understanding of our limitations of modeling, we still develop frameworks and artifact models because we sense in it a tool or set of indispensable tools to transmit the results of research to practical use cases. We boldly generalize our findings from empiria into general models that we hope will explain empiria best. And let us be mindful that it is possible – more so in the CAS systems than CPS that we might have multiple models that would fight over their explanatory powers simply because of the vagaries of uncertainty and stochastic variations.

Popper says: “Science does not rest upon rock-bottom. The bold structure of its theories rises, as it were, above a swamp. It is like a building erected on piles. The piles are driven down from above into the swamp, but not down to any natural or ‘given’ base; and when we cease our attempts to drive our piles into a deeper layer, it is not because we have reached firm ground. We simply stop when we are satisfied that they are firm enough to carry the structure, at least for the time being”. This leads to the satisficing solution: if a model can choose the least number of variables to explain the greatest amount of variations, the model is relatively better than other models that would select more variables to explain the same. In addition, there is always a cost-benefit analysis to be taken into consideration: if we add x number of variables to explain variation in the outcome but it is not meaningfully different than variables less than x, then one would want to fall back on the less-variable model because it is less costly to maintain.

problemsol

Researchers must address three key elements in the model: time, variation and uncertainty. How do we craft a model which reflects the impact of time on the variables and the outcome? How to present variations in the model? Different variables might vary differently independent of one another. How do we present the deviation of the data in a parlance that allows us to make meaningful conclusions regarding the impact of the variations on the outcome? Finally, does the data that is being considered are actual or proxy data? Are the observations approximate? How do we thus draw the model to incorporate the fuzziness: would confidence intervals on the findings be good enough?

Two other equally other concepts in model design is important: Descriptive Modeling and Normative Modeling.

Descriptive models aim to explain the phenomenon. It is bounded by that goal and that goal only.

There are certain types of explanations that they fall back on: explain by looking at data from the past and attempting to draw a cause and effect relationship. If the researcher is able to draw a complete cause and effect relationship that meets the test of time and independent tests to replicate the results, then the causality turns into law for the limited use-case or the phenomenon being explained. Another explanation method is to draw upon context: explaining a phenomenon by looking at the function that the activity fulfills in its context. For example, a dog barks at a stranger to secure its territory and protect the home. The third and more interesting type of explanation is generally called intentional explanation: the variables work together to serve a specific purpose and the researcher determines that purpose and thus, reverse engineers the understanding of the phenomenon by understanding the purpose and how the variables conform to achieve that purpose.

This last element also leads us to thinking through the other method of modeling – namely, normative modeling. Normative modeling differs from descriptive modeling because the target is not to simply just gather facts to explain a phenomenon, but rather to figure out how to improve or change the phenomenon toward a desirable state. The challenge, as you might have already perceived, is that the subjective shadow looms high and long and the ultimate finding in what would be a normative model could essentially be a teleological representation or self-fulfilling prophecy of the researcher in action. While this is relatively more welcome in a descriptive world since subjectivism is diffused among a larger group that yields one solution, it is not the best in a normative world since variation of opinions that reflect biases can pose a problem.

How do we create a representative model of a phenomenon? First, we weigh if the phenomenon is to be understood as a mere explanation or to extend it to incorporate our normative spin on the phenomenon itself. It is often the case that we might have to craft different models and then weigh one against the other that best represents how the model can be explained. Some of the methods are fairly simple as in bringing diverse opinions to a table and then agreeing upon one specific model. The advantage of such an approach is that it provides a degree of objectivism in the model – at least in so far as it removes the divergent subjectivity that weaves into the various models. Other alternative is to do value analysis which is a mathematical method where the selection of the model is carried out in stages. You define the criteria of the selection and then the importance of the goal (if that be a normative model). Once all of the participants have a general agreement, then you have the makings of a model. The final method is to incorporate all all of the outliers and the data points in the phenomenon that the model seeks to explain and then offer a shared belief into those salient features in the model that would be best to apply to gain information of the phenomenon in a predictable manner.

business model

There are various languages that are used for modeling:

Written Language refers to the natural language description of the model. If price of butter goes up, the quantity demanded of the butter will go down. Written language models can be used effectively to inform all of the other types of models that follow below. It often goes by the name of “qualitative” research, although we find that a bit limiting.  Just a simple statement like – This model approximately reflects the behavior of people living in a dense environment …” could qualify as a written language model that seeks to shed light on the object being studied.

Icon Models refer to a pictorial representation and probably the earliest form of model making. It seeks to only qualify those contours or shapes or colors that are most interesting and relevant to the object being studied. The idea of icon models is to pictorially abstract the main elements to provide a working understanding of the object being studied.

Topological Models refer to how the variables are placed with respect to one another and thus helps in creating a classification or taxonomy of the model. Once can have logical trees, class trees, Venn diagrams, and other imaginative pictorial representation of fields to further shed light on the object being studied. In fact, pictorial representations must abide by constant scale, direction and placements. In other words, if the variables are placed on a different scale on different maps, it would be hard to draw logical conclusions by sight alone. In addition, if the placements are at different axis in different maps or have different vectors, it is hard to make comparisons and arrive at a shared consensus and a logical end result.

Arithmetic Models are what we generally fall back on most. The data is measured with an arithmetic scale. It is done via tables, equations or flow diagrams. The nice thing about arithmetic models is that you can show multiple dimensions which is not possible with other modeling languages. Hence, the robustness and the general applicability of such models are huge and thus is widely used as a key language to modeling.

Analogous Models refer to crafting explanations using the power of analogy. For example, when we talk about waves – we could be talking of light waves, radio waves, historical waves, etc.  These metaphoric representations can be used to explain phenomenon, but at best, the explanatory power is nebulous, and it would be difficult to explain the variations and uncertainties between two analogous models.  However, it still is used to transmit information quickly through verbal expressions like – “Similarly”, “Equivalently”, “Looks like ..” etc. In fact, extrapolation is a widely used method in modeling and we would ascertain this as part of the analogous model to a great extent. That is because we time-box the variables in the analogous model to one instance and the extrapolated model to another instance and we tie them up with mathematical equations.

 

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.

universe

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.

ant colony

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.

cas

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:

  1. 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.
  2. 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.
  3. 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.

 

darwin statement

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?

complexity-sciences dis

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:

  1. A live cell with zero or one live neighbors will die
  2. A live cell with two or three live neighbors will remain alive
  3. A live cell with four or more live neighbors will die.

Dead Cells:

  1. A dead cell with exactly three live neighbors becomes alive
  2. 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.

turing

 

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.

Building a Lean Financial Infrastructure!

A lean financial infrastructure presumes the ability of every element in the value chain to preserve and generate cash flow. That is the fundamental essence of the lean infrastructure that I espouse. So what are the key elements that constitute a lean financial infrastructure?

And given the elements, what are the key tweaks that one must continually make to ensure that the infrastructure does not fall into entropy and the gains that are made fall flat or decay over time. Identification of the blocks and monitoring and making rapid changes go hand in hand.

lean

The Key Elements or the building blocks of a lean finance organization are as follows:

  1. Chart of Accounts: This is the critical unit that defines the starting point of the organization. It relays and groups all of the key economic activities of the organization into a larger body of elements like revenue, expenses, assets, liabilities and equity. Granularity of these activities might lead to a fairly extensive chart of account and require more work to manage and monitor these accounts, thus requiring incrementally a larger investment in terms of time and effort. However, the benefits of granularity far exceeds the costs because it forces management to look at every element of the business.
  2. The Operational Budget: Every year, organizations formulate the operational budget. That is generally a bottoms up rollup at a granular level that would map to the Chart of Accounts. It might follow a top-down directive around what the organization wants to land with respect to income, expense, balance sheet ratios, et al. Hence, there is almost always a process of iteration in this step to finally arrive and lock down the Budget. Be mindful though that there are feeders into the budget that might relate to customers, sales, operational metrics targets, etc. which are part of building a robust operational budget. var
  3. The Deep Dive into Variances: As you progress through the year and part of the monthly closing process, one would inquire about how the actual performance is tracking against the budget. Since the budget has been done at a granular level and mapped exactly to the Chart of Accounts, it thus becomes easier to understand and delve into the variances. Be mindful that every element of the Chart of Account must be evaluated. The general inclination is to focus on the large items or large variances, while skipping the small expenses and smaller variances. That method, while efficient, might not be effective in the long run to build a lean finance organization. The rule, in my opinion, is that every account has to be looked and the question should be – Why? If the management has agreed on a number in the budget, then why are the actuals trending differently. Could it have been the budget and that we missed something critical in that process? Or has there been a change in the underlying economics of the business or a change in activities that might be leading to these “unexpected variances”. One has to take a scalpel to both – favorable and unfavorable variances since one can learn a lot about the underlying drivers. It might lead to managerially doing more of the better and less of the worse. Furthermore, this is also a great way to monitor leaks in the organization. Leaks are instances of cash that are dropping out of the system. Much of little leaks amounts to a lot of cash in total, in some instances. So do not disregard the leaks. Not only will that preserve the cash but once you understand the leaks better, the organization will step up in efficiency and effectiveness with respect to cash preservation and delivery of value.  deep dive
  4. Tweak the process: You will find that as you deep dive into the variances, you might want to tweak certain processes so these variances are minimized. This would generally be true for adverse variances against the budget. Seek to understand why the variance, and then understand all of the processes that occur in the background to generate activity in the account. Once you fully understand the process, then it is a matter of tweaking this to marginally or structurally change some key areas that might favorable resonate across the financials in the future.
  5. The Technology Play: Finally, evaluate the possibilities of exploring technology to surface issues early, automate repetitive processes, trigger alerts early on to mitigate any issues later, and provide on-demand analytics. Use technology to relieve time and assist and enable more thinking around how to improve the internal handoffs to further economic value in the organization.

All of the above relate to managing the finance and accounting organization well within its own domain. However, there is a bigger step that comes into play once one has established the blocks and that relates to corporate strategy and linking it to the continual evolution of the financial infrastructure.

The essential question that the lean finance organization has to answer is – What can the organization do so that we address every element that preserves and enhances value to the customer, and how do we eliminate all non-value added activities? This is largely a process question but it forces one to understand the key processes and identify what percentage of each process is value added to the customer vs. non-value added. This can be represented by time or cost dimension. The goal is to yield as much value added activities as possible since the underlying presumption of such activity will lead to preservation of cash and also increase cash acquisition activities from the customer.

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.

Importance of Heroes and Narratives in Organizations

“My own heroes are the dreamers, those men and women who tried to make the world a better place than when they found it, whether in small ways or great ones. Some succeeded, some failed, most had mixed results… but it is the effort that’s heroic, as I see it. Win or lose, I admire those who fight the good fight.” – George Martin

 

Stories, like people and butterflies and songbirds’ eggs and human hearts and dreams, are also fragile things, made up of nothing stronger or more lasting than twenty-six letters and a handful of punctuation marks. Or they are words on the air, composed of sounds and ideas-abstract, invisible, gone once they’ve been spoken-and what could be more frail than that? But some stories, small, simple ones about setting out on adventures or people doing wonders, tales of miracles and monsters, have outlasted all the people who told them, and some of them have outlasted the lands in which they were created.” – Neil Gaiman

Heroes are not born. Circumstance and happenstance create heroes. In some cases, heroes are individuals who walk into a minefield of uncertainty that threatens their natural inclination for self-preservation in the interest of value systems and people that are alien to the individual. Thus, a private in an army is a hero already in the fact that he/she is walking into possible harm’s way and serving a cause to serve and protect people not necessarily related to him/her. One has heard the adage – one man’s freedom fighter is another person’s terrorist.  Thus, someone whom we call a terrorist may be perceived a hero by someone else. Thus, in this case …it all becomes a matter of a point of view, but the fundamental point remains – a hero is considered a person who abnegates and abjures their rights to self-preservation for some greater perceived good.

Sustaining innovation is a vital yet difficult task. Innovation requires the coordinated efforts of many actors to facilitate (1) the recombination of ideas to generate novelty, (2) real-time problem solving, and (3) linkages between present innovation efforts with past experiences and future aspirations. Innovation narratives are cultural mechanisms that address these coordination requirements by enabling translation. Specifically, innovation narratives are powerful mechanisms for translating ideas across the organization so that they are comprehensible and appear legitimate to others. Narratives also enable people to translate emergent situations that are ambiguous or equivocal so as to promote real-time problem solving. With their accumulation, innovation narratives provide a generative memory for organizations that enable people to translate ideas accumulated from particular instances of past innovation to inform current and future efforts.

The concept of collective identity has gained prominence within organizational theory as researchers have studied how it consequentially shapes organizational behavior. However, much less attention has been paid to the question of how nascent collective identities become legitimated. Although it is conventionally argued that membership expansion leads to collective identity legitimacy, one draws on the notion of cultural entrepreneurship to argue that the relationship is more complex and is culturally mediated by the stories told by group members. Legitimacy is more likely to be achieved when members articulate a clear defining collective identity story that identifies the group’s orienting purpose and core practices. Although membership expansion can undermine legitimation by introducing discrepant actors and practices to a collective identity, this potential downside is mitigated by compelling narratives, which help to coordinate expansion. And that is where the heroes can be interwoven into organizational theory and behavior. It is important to create environments that by happenstance and circumstance create heroes. The architecture of great organizations imputes heroes and narratives in their tapestry.

Heroes and narratives are instrumental in organizations that forge a pathway to long-term sustenance and growth. Hence, we are quick to idolize figures – Iacocca, Welch, Jobs, Ellison, Gates, Benioff, Gerstner, Branson, Bezos, Zuckerberg, Brin and Page, etc.  We learn narratives through case studies, news print, scholarly books on successful companies; and we emulate and steal and copy and parody and so much more … not necessarily because we want to be them but we want to create our identity in our own lair in ecosystems that move with or against the strongest currents.

So it is essential to celebrate the heroes and the narratives of great companies as an additional instrument to ignite engagement and foray into uncharted territories and conquer the unknown. Hence, personally I have also found solace in reading biographies of people who have made a difference, and a great pleasure in vicariously living through the ebbs and troughs of great companies

 

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.