The Tyranny of Uncertainty

Through established risk management practices, organizational leaders attempt to optimize business outcomes. However, management is handicapped by an inability to accurately predict the future behavior of corporate, economic, or industrial systems. Due to the deficiencies of current risk management approaches, organizations are often forced to reactively respond to risk because they are unable to rapidly diagnose and consequently fix problems before a risk leads to a crisis. The cause of this modern day predicament is the growing dynamic complexity that occurs over time and the negative effects dynamic complexity plays in a systemic implementation.

Before we can effectively manage risk, we must have reliable methods to identify, assess, and prioritize risks. If we fail to identify a major source of risk, then all plans to minimize, monitor, and control the probability and/or impact of unfortunate events will likely fail. At some point, the unidentified risk will be exposed as a surprise and we will be forced to reactively manage the risk. Surprises like the 2007 economic crisis or Fukushima Daiichi nuclear disaster, which were caused by the unknown impacts of dynamic complexity, demonstrate the shortcomings of current risk management practices.

Throughout history we have dedicated excessive amounts of time and energy preparing ourselves for some future threat. However, despite all of our modern technological advances, we are often unaware of all the risk factors or unable to predict when something dangerous may happen and in what form a risk may materialize. Over time metaphysical certainties have been gradually replaced by more scientific understanding of risk. But we still have more work to do. Growing levels of dynamic complexity dictate that we must further evolve risk management into a science of predicting, controlling, and mitigating the unintended consequences caused by continuous system changes.

During any crisis, part of the perception of risk is subjective and evolves as new insights become available. Risk awareness and even the reaction to occurrences of risk—and so the adopted attitude towards risk mitigation—differ depending on the predominant personality dynamics of the decision maker. This is a dimension that should be considered in risk management, and in particular for those risk classes that represent safety, security, and business continuity related risks. This subjectivity in risk management represents a certain concern that should be addressed.

Most business leaders want to limit the shock of surprises and be prepared to act to avoid any crisis. There are a number of well-defined risk management methodologies that are used to define and judge possible risk scenarios. Some of the methodologies follow a scientific approach to identify possible interdependencies, while others aim to minimize the impact of a disaster on the system. To identify this impact and the probability of such an occurrence, statistical methods are typically used or sometimes decision makers guess based on past experiences. However, none of the common established business risk management practices incorporate analytical modeling and parameter analysis.

Partial risk analysis represents in most cases a waste of investment that will deliver the wrong actions at the wrong time. Ill-defined analytics and models can only lead to a partial understanding of risk and incomplete requirements for any necessary remedial efforts. When we hear that a risk came as an unexpected surprise, we should be certain that inefficient and partial risk management capabilities were to blame. Often the impacts of dynamic complexity are completely missing from risk analysis and mitigation plans. Dynamic complexity is produced through an evolving process and accumulates due to an increase in aging and complex dependency topologies that produce variations towards higher risk, which become more difficult to manage over time. Therefore, the absence of risk traceability from the outset makes it more difficult in later stages to mitigate the risk that corresponds to more sophisticated and evolved structures.

The main goal of any risk management practice is to be able to insure an acceptable level of predictability in order to gain a lead-time to mitigate a possible risk. However, until new risk management approaches are employed to fill the gap between the known and unknown, most crises will continue to come as a surprise. Risk is latent until an external event or an internal process will reveal its existence. Predictive analysis is an indispensable tool that can help decision makers preemptively test all possible or even some perceived impossible operational scenarios before a risk transforms into crisis or disaster. To be prepared is a better position than to discover the risk too late for business continuity or disaster recovery measures. In this way, predictive emulation becomes necessary for appropriate risk mitigation.

In spite of the sophistication of modern risk management tools, we still underestimate a variety of risk, which will then reveal itself by surprise. Current risk management methods rely on stochastic methods and past experience for eventual risk mitigation. The practice is supported using data collected over time or during a crisis that was considered to be exceptional. The analysis is always partial and therefore the corresponding probabilistic models are only appropriate and representative of known data. In this case, a portion of unknowns remain hidden. If unknowns can be revealed or better understood using improved predictive capabilities, there is no doubt that risk management practices will significantly improve in the timely mitigation of risk.

Risk analysis should be supported by a deterministic view of the world that consists of a strict relation between the cause and effect. This analysis should be based on the fact that risk is latent and will be revealed once the dynamic complexity is provoked through the impact of external and internal influencers. Such analysis will not impose a search for limited categories of risk, prejudge a risk severity, or search for a particular risk amplitude, but instead analyze and model any risk, the conditions of its occurrence, time of appearance, amplitude, and distance from the actual system characteristics at a point in time.

If we want to reduce uncertainty, we must find methods to reveal and analyze the contribution of dynamic complexity. This requires new technological innovations and methods to represent dynamic complexity, analyze its associated risks, as well as discover the limits of systems, possible singularities, and measure the distance in time or change in initial conditions.

When organizations are able to analyze and control dynamic complexity, they can establish market advantage by becoming better managed and more agile than their competitors. We envision a new era of risk management, in which our proposed methods of universal risk management enable organizations to more accurately predict the future and take strategic actions to improve business outcomes. The ways and means of achieving this goal are based upon our own real-life experiences, backed by scientific principles, and the proven results we have achieved through consulting engagements with over 350 global organizations.

As businesses change at an ever-increasing rate, so does the growth and severity of its dynamic complexity. Today the dynamic complexity that is generated due to business dynamics is reaching a much faster speed than the current business definition of stability can manage. Faster change in dynamics, and consequently faster growth in dynamic complexity, causes the eventual singularity to come faster and therefore imposes a requirement on management to face the dilemma, anticipate the impact, and manage closely something that is hidden and can be suddenly revealed at any point in time.

Once a system hits a singularity point, any corrective actions will be taken under panic conditions. Reactively fixing systems once a risk has manifested itself has become the standard management practice over the last two decades. The undiscovered dynamic complexity leads to a surprise for which management is unprepared to act upon, eventually leading to unsuccessful remediation. Towards the goal of predictively managing environments, not only will risk appreciation be improved by adding the dynamic complexity dimension, but it is also important to recognize that dynamic complexity may also affect the knowns. Risk often evolves in time and space to a point where risk becomes multi-form in terms of the window of influence and pandemic contamination, involving areas that were not necessarily consequential if dynamic complexity did not exist.

In risk management a great body of contributions, covering a wide spectrum of preferences and expertise—from deeply theoretical to profoundly pragmatic—currently exists today. All show the interest, but also the urgency to find a solution that can help us avoid the singularities like those that occurred as a result of 2007 economic meltdown. To progress towards these ends, we must continuously seek to recognize the failures of past methods and strive to find alternative solutions. Mathematical emulation allows us to discover which components can cause risk, whatever the risk may be.

Our proposed risk management process involves a number of stages that support a scientific methodology of risk management. A number of phases are presented in this chapter. The objective of these phases is to acquire a good level of pattern-based knowledge improvement that can then be referenced and stored. Going through the same full cycle a number of times will allow better understanding of the different patterns a system will expose. Over time the risk management will be able to apply rapid fixing accompanied by faster detection and theretofore more effective overall management.

The use of patterns can improve risk management practices if at any moment in time we can predict a new pattern and react accordingly. However, the dynamic dimension of systems presents a challenge in the accurate identification and classification of patterns. For this reason, the dynamic characteristics of a system are often discovered too late within the development process for effective remediation. To overcome this challenge, we use emulative deconstruction to mathematically reproduce the dynamic behavior and predictively identify the singularities. Through the use of perturbation theory, we are able to breakdown the environment or system into its constituents and create the dynamic signatures necessary to predictively emulate system behaviors under various conditions. This affords system stakeholders considerable insights—providing the ability to discover system limits, identify chaotic boundaries, and proactively prepare for action to systemically face an eventual crisis.

Over the last three decades, a technical discipline has been evolving to reactively answer the growing need for better management practices supported by predictive tools that deal with the inflation of system complexity. Unfortunately, the speed of developing this discipline has been much slower than the speed at which the sophistication of the problem is progressing. Pressures to diagnose and solve an even wider spectrum of challenges are increasing. Many contributions (some serious and some less serious) have been proposed to support risk management—often with increasing levels of sophistication varying from frequently misleading measurement-based methods, spreadsheets, and mean value analysis (MVA) to queuing-based modeling and many other numerical methods. We favor the use of mathematical techniques that can be applied to reduce the uncertainty (a.k.a. risk) that we see within business systems and on the macro scale in financial systems.

Causal deconstruction provides a significant step-forward in understanding the behavior of complex systems. In the coming years, it will become a cornerstone of complex system analysis for all businesses with systems that are impacted by dynamic complexity. Causal deconstruction allows us to uncover results that often defy the common wisdom that stops at the wrong level of analysis and usually produces a host of misleading conclusions. Using this method we can promote the right approach of analysis and mathematics capable of solving the problem within an environment where dynamic complexity has become the major risk.

Dynamic complexity is a major inhibitor to the predictive performance of a business and its systems. The dynamic complexity of a system increases and its effects drive performance and efficiencies further and further away from the original design criteria definitions. To control the effects of dynamic complexity, the processes of causal deconstruction and predictive emulation should be applied. Through deconstruction, we can apply multiple possible input scenarios, multiple processing scenarios, and multiple output scenarios to better guide risk management practices.

The only way to discover the full effects of dynamic complexity is to use mathematics to accurately project the amplitude and characteristics of gradually increasing complexity. Perturbation theory allows us to solve complex problems around an exact solution by adding a number of inequalities that represent the disturbing effects on the exact solution. These inequalities represent direct or indirect perturbations exerted on the characteristics or the behavior of the exact solution. The presented formulae demonstrate how mathematics can be applied to uncover and predict the future effects of dynamic complexity.

Emulative deconstruction theory is a predictive new approach that calls upon a number of concepts including perturbation theory and causal deconstruction to collectively ensure representative, accurate, and replicable estimations. Analytically computed estimations allows us to diagnose the system dynamics, find the most efficient action for improvement to transform or innovate its operations, and discover and fix chaotic boundaries before a crisis occurs.

The ability to predict chaos and singularities presents businesses with new opportunities to move from a reactive to proactive risk management posture. Mathematics can be applied to identify the conditions under which dynamic complexity will produce singularities and explore what actions can be taken to contain the risk. Ultimately businesses will benefit by identifying the path towards a point of chaos in order to make the decision to either re-route away from the dangerous course, push it as far as possible, or start a process of transformation and innovation. While it is useful to understand how mathematics supports a goal towards better control of dynamic complexity, and therefore better risk management, commercial technologies and new business practices will be required to fully capitalize on these discoveries.

The perturbation theory approach involves a dynamic system of Lagrange-like partial differential equations that represent the dynamic behavior of a cost function and a solution that will capture both direct and indirect perturbations around a base of the un-perturbed solution. The presented deterministic mathematical treatment of risk prediction, diagnosis, and remediation involves the coverage of both the known (Newton’s Laws) and the perturbed impact considered as unknowns due to multiple orders of perturbations (due to dynamic complexity).

Constantly evolving business demands and technology advancements provide the perfect breeding ground for dynamic complexity. As a result, today’s information technology (IT) systems expose an increasing number of risks that must be managed in order to ensure the achievement of business objectives. System obsolescence is occurring at an ever-increasing rate and if the risk is not anticipated, managed, and controlled at the right time, then business growth will be severely restricted. Under these circumstances, traditional methods used to manage the risk typically fail. Using a perturbation theory-based emulator, we can successfully manage the risk by discovering the eventual singularity and taking actions to avoid its occurrence.

With increasing competition, almost every company faces some level of competitive risk. Under conditions of economic uncertainty and growing competitive threats, business leaders are increasingly called upon to discover new ways to innovate, contain costs, and grow. Within this competitive environment, disruptions caused by technology and business model innovations are becoming commonplace. In the late 1990s, the credit card processing industry was caught off guard when new market entrants created pricing pressures, which in turn led to margin erosion for the established market leaders. In this case and many others since, the complexity of modern business created obstacles that hindered the optimal execution of business strategy and plans. However, predictive emulation can help decision makers apply forward-looking insights and remedial actions at the right time to ensure the highest level of performance.

Changing market factors often force entire industries to redefine their business strategy or face extinction. The postal industry today faces such a challenge, as postal organizations worldwide must evolve their strategies to survive the rising popularity of digital communications, the decline in volume of physical/traditional mail, and growing competition from private companies. Market disruption quickly moved postal organizations from attractive economic and industrial models to an unbalanced financial corporate position with a major risk of obsolescence. To survive a market disruption of this magnitude, executive leaders of France’s postal services provider, La Poste, used predictive emulation technologies to identify and implement the right strategies to compensate for major shifts in customer demands—while managing costs without negatively impacting the quality of services.

The complexity of modern supply chains and their critical role have businesses and governments increasingly concerned about managing major disruptions. This calls for a better resiliency strategy that includes building agile, transparent, and diversified systems. To meet these objectives, risk management must be an explicit and integral part of supply chain governance. This case shows how supply chain stakeholders can use predictive emulation to achieve better preparedness and transparency of constraints at the right time so plans can be executed to control multiple risk dimensions that often precipitate major supply chain disruptions.

Currently many governments are struggling to contain the cost of reliable and equitable healthcare systems. The efficiency of the system is necessary to support the wellness of citizens as well as the economic and social progress of the country. By applying the universal risk management methods presented in this book, healthcare system stakeholders can gain the insights needed to create continuous cost management programs by identifying opportunities to improve cost efficiency without reducing the quality of care or reach out to individuals who need access to universal healthcare services. These are ambitious goals, but achievable as the dynamic complexity inherent in healthcare systems becomes understood and the subsequent determinism is fully managed.

Today the financial industry is just as likely to be shocked as it was in 2007. As we discovered that the main cause of the financial meltdown was a severe example of dynamic complexity, nothing changed then to reduce its effect. As systems progress and new product and services are introduced, the complexities and the rate of change will further increase, thereby escalating the risk of another financial crisis. Undoubtedly, financial markets will continue to pose grave risks to the welfare of the global economy as long as economic stakeholders are unable to accurately measure dynamic complexity or understand all the steps required to protect their assets. We must test and expand upon the presented universal risk management methods to define a better way forward that allows economic stakeholders to predict a potential singularity with sufficient time to act to avoid a future crisis of this magnitude.

The underlying factors of the Greek crisis that started more than 10 years ago and continue to haunt the country today can be summarized in one word as risk. The European Union is mainly political and the Eurozone is financial. No economic coordination or oversight has been put in place. Because there is no oversight, there is no transparency in the interrelations internal or external to the community. For this reason, a considerable amount of dynamic complexity was built in from the start. Using our causal deconstruction theory to understand the risk and its roots, we were able to conclude that the Greek crisis is different from the 2008 economic crisis. In the case of Greece, it is the role of external influencers within the system rather than the dynamic complexity of 2008 that provoked the crisis and created the surprise.

If we fail in our mission to promote the development and use of adequate methods to understand and avoid dynamic complexity risks, it should be clear that we face an uncertain future. Increasingly, businesses, governments, and citizens will be shocked by the manifestation of hidden risks that are exposed too late for appropriate remediation. In this case, we will live in a reactionary world of chronic crisis. If we want to build a better world, where accountability is the safety net for a sound economy, viable systems, and greater human prosperity, we must challenge our current risk management practices, expand our ability to predict risk, and ready ourselves for action.

We know that conventional risk management approaches are not sufficient to fully master all risk dimensions. In many cases when risk manifests itself, it appears as a surprise due to unknowns that are produced by dynamic complexity. As our experience and insights mature, the practice of risk management will mature and become a scientific discipline in its own right. Dynamic complexity emulation supports this move by revealing how a common component characteristic or behavior will impact a process with respect to another under different scenarios.

When risk becomes chronic or when it is produced by structural or environmental causes that cannot be sufficiently improved or remediated, disruption may be the only viable option for the survival of a business, government, healthcare, or other type of system. In these cases, we can use predictive emulation to support the decisions necessary to force a disruption and thereby avoid a disaster or stop the cycle of chronic crisis. A robust mathematical platform that represents dependencies and emulates the perturbations generated by the changing dynamics allows us to test a host of scenarios to discover sensitivities and identify limits. For this reason, mathematical emulation is the best recourse to manage the disruption project and ensure the project delivers the expected outcome. With these insights, decision makers can define the right path without compromising on the disruptive goals or risk management imperatives.

As we attempt to control risk within renewable environments that continuously morph due to evolutionary as well as revolutionary forces, there can be no doubt that management by analogy and case-experience has dramatically failed us. In the wake of past decades’ catastrophic economic, environmental, business, and other related disasters, it has been proven that current risk management practices are unable to universally reveal major risks at the point in time organizations need to proactively avoid a singularity or point of chaos. The most dangerous risks are often posed by the unknowns that cannot be predicted with historical reference models and often escape the imagination of risk committees.