Modeling Economic Dynamics

Examining the Problems with Traditional Risk Modeling Methods

Traditional financial risk management methods were formulated in an analogy with the early foundational principles of thermodynamics. However, traditional economic models are incomplete models of reality because economic systems are not inclined to attain equilibrium states unless we are talking about very short windows of time (similar to meteorological or most nuclear or gravitational systems).

Problems with risk modeling methods based on the laws of thermodynamics:

  • Predictability is limited to short windows, where the initial conditions varies in small amplitudes and in small frequencies
  • Complexities are dealt with once recognized, rather than as a result of structural evolution and systemic behavior of multiple-level interactions
  • Only closed systems that reach equilibrium are dealt with, no adaptive ability to an external or internal modification is allowed
  • Complex systems do not systematically expose equilibrium
  • Using Stochastic models that deal with randomness are difficult to determine 
small resonances and therefore do not tend to a long term representation

A New Way to Look at Economy and Risk

Financial systems are not wholly physical. They do not always behave in an expected manner as predicted from their patterns of past behavior. They are immature. They can sometimes exhibit unexpected and unknown behavior because we do not understand their complexity and how it changes.

To avoid future crisis in the proportions of 2008, we must identify new methods of economic risk analysis that more accurately model the dynamic reality of financial systems. To this end, we promote determinism, which is the view that every event, including human cognition, behavior, decision, and action, is causally determined by an unbroken sequence of prior occurrences.

Determinists believe the universe is fully governed by causal laws resulting in only one possible state at any point in time. Simon-Pierre Laplace’s theory is generally referred to as “scientific determinism” and predicated on the supposition that all events have a cause and effect and the precise combination of events at a particular time engender a particular outcome.

How the impact of dynamic complexity leads to economy non-equilibrium:

  • Different instruments within a portfolio have different dynamic patterns, evolution speeds, producing different impact on risk
  • But also they influence each other: in sharing, affecting, and operating in terms of both frequency and amplitude in the behavior of discriminant factors (econometrics, relation economy/finance, long term repercussion etc.)
  • In addition, each will have different reaction/interaction towards an external/ internal event.

Consequently, modeling economics dynamics is the right foundation to insure predictability of such self-organized evolutionary systems that may prevail towards even several points of singularities and larger number of degrees of freedom than the small number in traditional methods.

Using this method, we will be able to address most of the drawbacks of the traditional methods:

  • Both the need for predictable determinism and the intensive presence of high level of dynamic complexity justifies the use of Perturbation Theory
  • The condition of success to approach an exact solution at any moment of time relies on the use of deconstruction theory that will separate the constituents and find the proper mathematical expression of each prior to the deployment of the perturbed expression (i.e. two-level solution)
  • Evolutionary process guarantees wider window of representativeness and adaptability for the dynamic complexityeconomics
  • Tends to exact solution

Table: Dynamic Complexity versus Traditional Economics

Dynamic Complexity Economics Traditional Economics
Open, dynamic, non-linear in equilibrium Closed, static, linear in equilibrium
Each constituent of the system is model individually then aggregated through Perturbation Theory The system is modeled collectively in one step
No separation between micro and macro level behaviors Separation between micro and macro level behaviors
Evolutionary process guarantees wider window of representativeness and adaptability for the dynamic complexity economics Unstable for wider windows of time
Allows for continuous interactions of external and internal agents Does not allow for continuous interactions of external and internal agents
Optimal control is possible as sub product of dynamic complexity modeling Optimal control is not possible


From a scientific standpoint, the subject of financial dynamics and the best risk analysis method is still open and further mathematical, physical and engineering as well as economic risk analysis developments are necessary. A great body of contributions, covering a wide spectrum of preferences and expertise and 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 that occurred in 2008. To progress, we must continuously seek to recognize the failures of past methods and strive to find solutions.

Blockchain: Navigating the Disruption

After years of theoretical debates and abstract use cases, it is no longer a question of if blockchain will cause market disruption, but rather when and how widely the impact will be felt. Now is the time to remove any outstanding doubts about blockchain applicability and strategically manage the business and operational risks that inevitably come with innovation. The main barrier being how to best plan for and manage the disruption. In all cases, correctly quantifying the threats and opportunities is a requirement for success.

Using a range of specific cases across various markets and industries, including financial services, supply chain and healthcare, we are actively conducting research and collaborating with other industry leaders to verify how to best apply a scientific method of predictive emulation to reveal where the capabilities of blockchain are best suited to solve business problems, quantify the expected improvements and manage the risks in delivering the solution.

Blockchain technology is best known for being the magic behind Bitcoin, but there are scores of other industries that can benefit from this revolutionary technology. The benefits include driving costs savings by reducing labor-intensive processes and eliminating duplicate efforts, as well as creating new markets by exposing previously untapped sources of supply.

Funded by eager venture capitalists, start-ups can easily pursue blockchain initiatives, but convincing stakeholders of global corporations and financial institutions to go all-in on a new technology that could overturn the very fundamentals of the business and the organization’s biggest profit drivers is not easy. Alternatively, taking a wait and see approach may place laggards at a significant competitive disadvantage as the move to blockchain requires a well devised plan and sufficient time for its execution.

Given the dynamic complexity of modern systems, it can be difficult to identify across operations the right plan to manage disruption and create a sustainable business model. With technological innovations, often the most dangerous risks are posed by the unknowns that cannot be predicted with historical reference models and often escape the imagination of risk committees. A scientific method to predictively quantify opportunities and universally manage risks can help stakeholders strategically time, justify and manage a disruptive move.

X-Act® OBC Platform is useful in these cases as it is the only mathematical dynamic complexity emulator that can realistically model business services and infrastructures. We use X-Act OBC Platform to replicate the dynamics and complexity of business implementations—allowing us to predictively compute system behaviors at different points in time and under various operational conditions. These insights can then be used to plan for and manage a disruptive move by supporting the series of complex decisions necessary to make the right trade-offs between sometimes conflicting objectives, allow acceptable time to market and preserve business continuity.

Using the emulator capabilities of X-Act OBC Platform, we tested various blockchain scenarios under different patterns of initial conditions and dynamic constraints to identify the conditions under which risk will increase, as well as the possible mitigation strategies.

By modifying the parameters of each scenario within the emulator, one by one, by group, or by domain, to represent possible changes, we are able to extrapolate each time the point at which the system will hit a singularity and use the corresponding information to diagnose the case. Additional scenarios can be created to explore viable and proactive remedial options that secure an acceptable risk mitigation strategy.