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.
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Tags: CAS, Complexity, experiments, innovation, intuition, open source, slippery slope, Systems design, Systems Thinking, uncertainty, unintended consequence
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.
Posted in Business Process, Complexity, emergent systems, Innovation, Learning Organization, Learning Process, Management Models, Narratives, Order, Social Systems
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Tags: automata, CAS, cellular automata, Complexity, CPS, emergent systems, innovation, open source, platform, uncertainty
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.
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Tags: adaptive, CAS, Complexity, CPS, creativity, economics, innovation, open source, platform, Systems Thinking, transparency, uncertainty
Introduce Culture into Product Development
All products go through a life-cycle. However, the genius of an organization lies in how to manage the life-cycle of the product and extend it as necessary to serve the customers. Thus, it is not merely the wizardry in technology and manufacturing that determine the ultimate longevity of the product in the market and the mind share of the customer. The product has to respond to the diversity of demands determined by disposable income, demographics, geography, etc. In business school speak, we say that this is part of market segmentation coupled with the appropriate marketing message. However, there is not an explicit strategy formulated around identifying
- Corporate Culture
- Extended Culture
To achieve success, firms increasingly must develop products by leveraging ad coordinating broad creative capabilities and resources, which often are diffused across geographical and cultural boundaries. But what we have to explore is a lot more than that from the incipient stages that a product has imagined: How do we instill unique corporate DNA into the product that immediately marks the product with a corporate signature? In addition, how do we built out a product that is tenable across the farthest reaches of geography and cultural diversity?
Thus, an innovative approach is called for in product development … particularly, in a global context. The approach entails getting cross-disciplinary teams in liberal arts, science, business, etc. to work together to gather deeper insights into the cultural strains that drive decisions in various markets. To reiterate, there is no one particular function that is paramount: all of them have to work and improvise together while ensuring that there are channels that gather feedback. The cross disciplinary team and the institutionalization of a feedback mechanism that can be quickly acted upon are the key parameters to ensure that the right product is in the market and that it will be extended accordingly to the chatter of the crowds.
Having said that, this is hardly news! A lot of companies are well on their way to instill these factors into product design and development. Companies have created organizational architectures in the corporate structure in a manner that culturally appropriate products are developed and maintained in dispersed local markets. However, in most instances, we have also seen that the way they view this is to have local managers run the show, with the presumption that these “culturally appropriate” products will make good in those markets. But along the way, the piece that dissembles over time on account of creating the local flavor is that the product may not mirror the culture that the corporate group wants to instill. If these two are not aptly managed and balanced, islands of conflict will be created. Thus, my contention is that a top-down value mandate ought to set the appropriate parameters inside which the hotbed of collaborative activity would take place for product design and development in various markets.
Thus the necessary top down value systems that would bring culture into products would be:
- Open areas for employees to express their thoughts and ideas
- Diversity of people with different skill sets in product teams will contribute to product development
- Encouraging internal and external speakers to expound upon the product touch points in the community.
- Empowerment and recognition systems.
- Proper formulation of monetary incentives to inspire and maintain focus.
Posted in Corporate Social Responsibility, Employee Engagement, Employee retention, Extrinsic Rewards, Innovation, Intrinsic Rewards, Leadership, Learning Organization, Learning Process, Organization Architecture, Product Design, Recognition, Rewards
Tags: conversation, creativity, diversity, employee engagement, extrinsic motivation, innovation, intrinsic motivation, product design, product development, talent management, value
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.
Posted in Business Process, Chaos, Employee Engagement, Intrinsic Rewards, Leadership, Learning Organization, Learning Process, Model Thinking, Motivation, Order, Social Systems, Vision
Tags: boundaries, diversity, innovation, intrinsic motivation, learning organization, meaning, talent management, uncertainty
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.
Posted in Chaos, Employee Engagement, Extrinsic Rewards, Innovation, Intrinsic Rewards, Leadership, Learning Organization, Learning Process, Motivation, Order, Organization Architecture, Vision
Tags: boundaries, communication channel, creativity, discipline, extrinsic motivation, focus, innovation, intrinsic motivation, learning organization, meaning, organization architecture, strategy, vision
MECE Framework, Analysis, Synthesis and Organization Architecture toward Problem-Solving
MECE is a thought tool that has been systematically used in McKinsey. It stands for Mutually Exclusive, Comprehensively Exhaustive. We will go into both these components in detail and then relate this to the dynamics of an organization mindset. The presumption in this note is that the organization mindset has been engraved over time or is being driven by the leadership. We are looking at MECE since it represents a tool used by the most blue chip consulting firm in the world. And while doing that, we will , by the end of the article, arrive at the conclusion that this framework alone will not be the panacea to all investigative methodology to assess a problem – rather, this framework has to reconcile with the active knowledge that most things do not fall in the MECE framework, and thus an additional system framework is needed to amplify our understanding for problem solving and leaving room for chance.
So to apply the MECE technique, first you define the problem that you are solving for. Once you are past the definition phase, well – you are now ready to apply the MECE framework.
MECE is a framework used to organize information which is:
- Mutually exclusive: Information should be grouped into categories so that each category is separate and distinct without any overlap; and
- Collectively exhaustive: All of the categories taken together should deal with all possible options without leaving any gaps.
In other words, once you have defined a problem – you figure out the broad categories that relate to the problem and then brainstorm through ALL of the options associated with the categories. So think of it as a mental construct that you move across a horizontal line with different well defined shades representing categories, and each of those partitions of shades have a vertical construct with all of the options that exhaustively explain those shades. Once you have gone through that exercise, which is no mean feat – you will be then looking at an artifact that addresses the problem. And after you have done that, you individually look at every set of options and its relationship to the distinctive category … and hopefully you are well on your path to coming up with relevant solutions.
Now some may argue that my understanding of MECE is very simplistic. In fact, it may very well be. But I can assure you that it captures the essence of very widely used framework in consulting organizations. And this framework has been imported to large organizations and have cascaded down to different scale organizations ever since.
Here is a link that would give you a deeper understanding of the MECE framework:
Now we are going to dig a little deeper. Allow me to digress and take you down a path less travelled. We will circle back to MECE and organizational leadership in a few moments. One of the memorable quotes that have left a lasting impression is by a great Nobel Prize winning physicist, Richard Feynman.
“I have a friend who’s an artist and has sometimes taken a view which I don’t agree with very well. He’ll hold up a flower and say “look how beautiful it is,” and I’ll agree. Then he says “I as an artist can see how beautiful this is but you as a scientist takes this all apart and it becomes a dull thing,” and I think that he’s kind of nutty. First of all, the beauty that he sees is available to other people and to me too, I believe. Although I may not be quite as refined aesthetically as he is … I can appreciate the beauty of a flower. At the same time, I see much more about the flower than he sees. I could imagine the cells in there, the complicated actions inside, which also have a beauty. I mean it’s not just beauty at this dimension, at one centimeter; there’s also beauty at smaller dimensions, the inner structure, also the processes. The fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting; it means that insects can see the color. It adds a question: does this aesthetic sense also exist in the lower forms? Why is it aesthetic? All kinds of interesting questions which the science knowledge only adds to theexcitement, the mystery and the awe of a flower! It only adds. I don’t understand how it subtracts.”
The above quote by Feynman lays the groundwork to understand two different approaches – namely, the artist approaches the observation of the flower from the synthetic standpoint, whereas Feynman approaches it from an analytic standpoint. Both do not offer views that are antithetical to one another: in fact, you need both to gather a holistic view and arrive at a conclusion – the sum is greater than the parts. Feynman does not address the essence of beauty that the artist puts forth; he looks at the beauty of how the components and its mechanics interact well and how it adds to our understanding of the flower. This is very important because the following dialogue with explore another concept to drive this difference between analysis and synthesis home.
There are two possible ways of gaining knowledge. Either we can proceed from the construction of the flower ( the Feynman method) , and then seek to determine the laws of the mutual interaction of its parts as well as its response to external stimuli; or we can begin with what the flower accomplishes and then attempt to account for this. By the first route we infer effects from given causes, whereas by the second route we seek causes of given effects. We can call the first route synthetic, and the second analytic.
We can easily see how the cause effect relationship is translated into a relationship between the analytic and synthetic foundation.
A system’s internal processes — i.e. the interactions between its parts — are regarded as the cause of what the system, as a unit, performs. What the system performs is thus the effect. From these very relationships we can immediately recognize the requirements for the application of the analytic and synthetic methods.
The synthetic approach — i.e. to infer effects on the basis of given causes — is therefore appropriate when the laws and principles governing a system’s internal processes are known, but when we lack a detailed picture of how the system behaves as a whole.
Another example … we do not have a very good understanding of the long-term dynamics of galactic systems, nor even of our own solar system. This is because we cannot observe these objects for the thousands or even millions of years which would be needed in order to map their overall behavior.
However, we do know something about the principles, which govern these dynamics, i.e. gravitational interaction between the stars and planets respectively. We can therefore apply a synthetic procedure in order to simulate the gross dynamics of these objects. In practice, this is done with the use of computer models which calculate the interaction of system parts over long, simulated time periods.
The analytical approach — drawing conclusions about causes on the basis of effects – is appropriate when a system’s overall behavior is known, but when we do not have clear or certain knowledge about the system’s internal processes or the principles governing these. On the other hand, there are a great many systems for which we neither have a clear and certain conception of how they behave as a whole, nor fully understand the principles at work which cause that behavior. Organizational behavior is one such example since it introduces the fickle spirits of the employees that, at an aggregate create a distinct character in the organization.
Leibniz was among the first to define analysis and synthesis as modern methodological concepts:
“Synthesis … is the process in which we begin from principles and [proceed to] build up theorems and problems … while analysis is the process in which we begin with a given conclusion or proposed problem and seek the principles by which we may demonstrate the conclusion or solve the problem.”
So we have wandered down this path of analysis and synthesis and now we will circle back to MECE and the organization. MECE framework is a prime example of the application of analytics in an organization structure. The underlying hypothesis is that the application of the framework will illuminate and add clarity to understanding the problems that we are solving for. But here is the problem: the approach could lead to paralysis by analysis. If one were to apply this framework, one would lose itself in the weeds whereas it is just as important to view the forest. So organizations have to step back and assess at what point we stop the analysis i.e. we have gathered information and at what point we set our roads to discovering a set of principles that will govern the action to solve a set of problems. It is almost always impossible to gather all information to make the best decision – especially where speed, iteration, distinguishing from the herd quickly, stamping a clear brand etc. are becoming the hallmarks of great organizations.
Applying the synthetic principle in addition to “MECE think” leaves room for error and sub-optimal solutions. But it crowd sources the limitless power of imagination and pattern thinking that will allow the organization to make critical breakthroughs in innovative thinking. It is thus important that both the principles are promulgated by the leadership as coexisting principles that drive an organization forward. It ignites employee engagement, and it imputes the stochastic errors that result when employees may not have all the MECE conditions checked off.
In conclusion, it is important that the organization and its leadership set its architecture upon the traditional pillars of analysis and synthesis – MECE and systems thinking. And this architecture serves to be the springboard for the employees that allows for accidental discoveries, flights of imagination, Nietzschean leaps that transform the organization toward the pathway of innovation, while still grounded upon the bedrock of facts and empirical observations.
Posted in Business Process, Employee Engagement, Innovation, Leadership, Learning Organization, Learning Process, Management Models, Model Thinking, Motivation, Organization Architecture, Recognition, Risk Management, Social Dynamics, Social Systems
Tags: Analysis, creativity, employee engagement, employee recognition, innovation, learning organization, mass psychology, Mental Construct, Mental Models, organization architecture, social network, social systems, Synthesis, Systems Thinking, talent management, uncertainty
Implementing Balanced Scorecard Model for Employee Engagement
The Balanced Scorecard Model (BSC) was introduced by Kaplan & Norton in their book “The Balanced Scorecard” (1996). It is one of the more widely used management tools in large organizations.
One of the major strengths of the BSC model is how the key categories in the BSC model links to corporate missions and objectives. The key categories which are referred to as “perspectives” illustrated in the BSC model are:
Kaplan and Norton do not disregard the traditional need for financial data. Timely and accurate data will always be a priority, and managers will do whatever necessary to provide it. In fact, often there is more than enough handling and processing of financial data. With the implementation of a corporate database, it is hoped that more of the processing can be centralized and automated. But the point is that the current emphasis on financials leads to the “unbalanced” situation with regard to other perspectives. There is perhaps a need to include additional financial-related data, such as risk assessment and cost-benefit data, in this category.
Recent management philosophy has shown an increasing realization of the importance of customer focus and customer satisfaction in any business. These are leading indicators: if customers are not satisfied, they will eventually find other suppliers that will meet their needs. Poor performance from this perspective is thus a leading indicator of future decline, even though the current financial picture may look good. In developing metrics for satisfaction, customers should be analyzed in terms of kinds of customers and the kinds of processes for which we are providing a product or service to those customer groups
Internal Business Process Perspective
This perspective refers to internal business processes. Metrics based on this perspective allow the managers to know how well their business is running, and whether its products and services conform to customer requirements (the mission). These metrics have to be carefully designed by those who know these processes most intimately; with our unique missions these are not necessarily something that can be developed by outside consultants. My personal opinion on this matter is that the internal business process perspective is too important and that internal owners or/and teams take ownership of understanding the process.
Learning and Growth Perspective
This perspective includes employee training and corporate cultural attitudes related to both individual and corporate self-improvement. In a knowledge-worker organization, people — the only repository of knowledge — are the main resource. In the current climate of rapid technological change, it is becoming necessary for knowledge workers to be in a continuous learning mode. Metrics can be put into place to guide managers in focusing training funds where they can help the most. In any case, learning and growth constitute the essential foundation for success of any knowledge-worker organization.
Kaplan and Norton emphasize that ‘learning’ is more than ‘training’; it also includes things like mentors and tutors within the organization, as well as that ease of communication among workers, the engagement of the workers, the potential of cross-training that would create pockets of bench strength and switch hitters, and other employee specific programs that allows them to readily get help on a problem when it is needed. It also includes technological tools; what the Baldrige criteria call “high performance work systems.”
This perspective was appended to the above four by Bain and Company. It refers to the vitality of the organization and its culture to provide the appropriate framework to encourage innovation. Organizations have to innovate. Innovation is becoming the key distinctive element in great organizations, and high levels of innovation or innovative thinking are talent magnets.
Taking the perspectives a step further, Kaplan and Cooper instituted measures and targets associated with each of those targets. The measures are geared around what the objective is associated with each of the perspectives rather than a singular granule item. Thus, if the objective is to increase customer retention, an appropriate metric or set of metrics is around how to measure the objective and track success to it than defining a customer.
One of the underlying presumptions in this model is to ensure that the key elements around which objectives are defined are done so at a fairly detailed level and to the extent possible – defined so much so that an item does not have polymorphous connotations. In other words, there is and can be only a single source of truth associated with the key element. That preserves the integrity of the model prior to its application that would lead to the element branching out into a plethora of objectives associated with the element.
Objectives, Measures, Targets and Initiatives
Within each of the Balance Scorecard financial, customer, internal process, learning perspectives and innovation perspectives, the firm must define the following:
Strategic Objectives – what the strategy is to achieve in that perspective
Measures – how progress for that particular objective will be measured
Targets – the target value sought for each measure
Initiatives – what will be done to facilitate the reaching of the target?
As in models and analytics, the information that the model spouts could be rife with a cascade of metrics. Metrics are important but too many metrics associated with the perspectives may diffuse the ultimate end that the perspectives represent.
Hence, one has to exercise restraint and rigor in defining a few key metrics that are most relevant and roll up to corporate objectives. As an example, outlined below are examples of metrics associated with the perspectives:
Financial performance (revenues, earnings, return on capital, cash flow);
Customer value performance (market share, customer satisfaction measures, customer loyalty);
Internal business process performance (productivity rates, quality measures, timeliness);
Employee performance (morale, knowledge, turnover, use of best demonstrated practices);
Innovation performance (percent of revenue from new products, employee suggestions, rate of improvement index);
To construct and implement a Balanced Scorecard, managers should:
- Articulate the business’s vision and strategy;
- Identify the performance categories that best link the business’s vision and strategy to its results (e.g., financial performance, operations, innovation, and employee performance);
- Establish objectives that support the business’s vision and strategy;
- Develop effective measures and meaningful standards, establishing both short-term milestones and long-term targets;
- Ensure company wide acceptance of the measures;
- Create appropriate budgeting, tracking, communication, and reward systems;
- Collect and analyze performance data and compare actual results with desired performance;
- Take action to close unfavorable gaps.
Source : http://www.ascendantsmg.com/blog/index.cfm/2011/6/1/Balanced-Scorecard-Strategy-Map-Templates-and-Examples
The link above contains a number of templates and examples that you may find helpful.
I have discussed organization architecture and employee engagement in our previous blogs. The BSC is a tool to encourage engagement while ensuring a tight architecture to further organizational goals. You may forget that as an employee, you occupy an important place in the ecosystem; the forgetting does not speak to your disenchantment toward the job, neither to your disinclination toward the uber-goals of the organization. The forgetting really speaks to potentially a lack of credible leadership that has not taken the appropriate efforts to engage the organization by pushing this structure that forces transparency. The BSC is one such articulate model that could be used, even at its crudest form factor, to get employees informed and engaged.
Posted in Business Process, Employee Engagement, Employee retention, Financial Metrics, Financial Process, Innovation, Leadership, Learning Organization, Learning Process, Management Models, Organization Architecture, Recognition, Risk Management, Social Dynamics, Talent Management
Tags: bain and company, balanced scorecard, business process, communication channel, employee engagement, employee recognition, extrinsic motivation, financial process, innovation, intrinsic motivation, learning organization, management tools, mass psychology, model, organization architecture, rewads, risk management, social systems, strategy, talent management, value, value management
Applying Gamification in the Workplace
Wikipedia defines gamification as the use of game mechanics and game design techniques in non-game contexts. It applies to non-game applications and processes, in order to encourage people to adopt them, or to influence how they are used. It makes technology use more exciting and engaging, and encourages users to engage in desired behaviors with fruitful consequences to the environment where these techniques and processes are being deployed.
Many years ago, I took a series of courses at Cal Tech in Pasadena at the School of Industrial Relations. One of the courses was applying tools to encourage teamwork and participation. Thereafter, I have attended field trips in organizations in strategic off-sites where we had to do rope walking, free fall, climbing bamboo structures strung together to retrieve flags, etc. Thus, in those days – we applied sports and board games to fuel a shared success environment. Now things have become more technology oriented, and we have thus seamlessly transitioned to some extent from those environments to consumer web based experiences. This does not suggest that the other alternatives are less rewarding; they draw upon other types of triggers but gamification through technology is more accessible and generally less expensive with less overhead in the long run.
What are the four key elements in Gamification?
Games generally tend to have four elements that are closely intertwined. Absent any of these four elements and the jury would be out on whether the application could suitably be considered gamified. Clearly, when these elements are being applied to non-gaming contexts, you will find that some of these elements are more watered down or cruder representation of game design principles or applications to actual game play environments. Regardless, all of these elements are necessary conditions that must come into play.
Games have narratives. They must be able to tell a story. They must place the player or user in a context, make them aware of the context, create a temporal dimension of a past, present and future and provide a theme or a set of themes that the players pursue.
These constitute the provision of tools and use cases that create PvP (Player vs. Player) or PvE (Player vs. Environment) experience. Common tools like teleporting, cockpit load (number of player controls), in-game user interaction, human-computer interaction, etc. come into play. The mechanics must aptly support the narrative.
People look for rich experiences. In MMORPG, the aesthetics are extremely rich and immersive. In gamified applications, it need not be so. Regardless, users have continued to raise the bar on aesthetics and richness of media to support their interaction. So the trend toward aesthetics will continue, albeit at a lower benchmark than would be in the extreme case of a high quality MMORPG game.
Finally, games have to have a purpose. The narratives have to have a light at the end of the tunnel. There is a carrot and stick principle in game design. It is a very important component to either persuade people to behave or not behave in a certain manner. Rewards are vanity points awarded for achieving goals that are user-driven or context driven. Either way, it is and will continue to remain the key element in game design.
The myth of rewards!
In one my earlier blogs, I laid out the distinction between intrinsic and extrinsic motivation. This has bearing on the concept of rewards and recognition in the workplace. You can find the details in my blog – “Intrinsic and Extrinsic Motivation: Impact on Employee Engagement”. (https://linkedstarsblog.com/2012/10/16/intrinsic-and-extrinsic-motivation-impact-on-employee-engagement/
Designing an application with rewards to fuel engagement in the workplace is a good idea. But rewards have to follow a narrative, a storyline. For example, an application that simply awards points and badges based on transactions without a narrative cannot be considered an application that applies all of the elements of the gamification process. It only addresses one element, and in fact, for some it is the least important component. When one focuses the product design around this single component, I contend that you are not really gamifying; you are in fact drawing upon some temporary impulses that are not sustainable and enduring.
Hence, the narrative and craftsmanship is quite critical to gamifying an application and making it relevant for employees in the workplace.
One must adopt the right mix of the gaming elements to ultimately create ends such as stickiness, re-engagement, and deeper levels of interaction, fun, challenge, promoting options to cooperate and also compete, and broadcast success.
So now we arrive at assessing the scales to benchmark each of those ends. I am being particular by not calling these tools, since tools are to support mechanics whereas scales are manifestations of an end result. For practical design and implementation purposes, here are a few scales that are common across all games, some of which are quite relevant for gamification in an employee setting. Some of the more common scales to assess or broadcast success are:
2) Achievement levels and measures of achievements
3) Challenges between users
4) Progress Bars
5) Reward Points that have redemption value
To reiterate, for the final outcomes associated with the scales to be meaningful, the narrative is extremely important. Storyboarding the experience in various settings is the key to designing relevant gamified applications. In fact, applying the appropriate narratives concerning particular industries is a very interesting architectural initiative that can be pursued.
Thus, in the case of workplace engagement, if the nuances of the work and the industry were emulated around themes with contextual narratives, it would truly make for wonderful experiences that ignite employee engagement while furthering corporate objectives.
Posted in Employee Engagement, Extrinsic Rewards, Gamification, Intrinsic Rewards, Product Design, Recognition, Rewards, Social Dynamics, Social Gaming
Tags: employee engagement, extrinsic motivation, game design, gamification, innovation, intrinsic motivation, leaderboards, product design, relevance, social network, social systems
Walled Garden: Mirage or Oasis?
A walled garden in the context of the internet relays to full control of the user experience. In other words, it is a methodology that is deployed to ensure closed or exclusive content for consumption by a set of users.
One of the prime examples of the walled garden in the technology world is the Apple ecosystem. This constitutes the interplay or hardware and software intricately tied in a manner that precludes any legal way to contaminate the harmony of user experience. Well, at least that is what Apple has vociferously claimed over the ages. They have argued that adopting the walled garden has served to be a bedrock of innovation, and the millions of applications on the Appstore bears out that fact. But the open source folks argue otherwise.
Posted in Chaos, Innovation, Order, Social Network, Walled Garden
Tags: Android, ecosystem, innovation, iOS, open source, order, pay to play, walled garden