Discrete Event Simulation Modeling of Risk Factors across Complex Systems

This is just one risk analytics approach that we take. Here we assess, using Discrete Event Simulation Models (DESM), the liklihood of a particular risk type occurring relative to all other critical risks in your decision space, and derive"steady state" probabilities that help you guage the extent to which you should make preparations for those risks.

Our Risk Management and Maturity Index (RMMI)

The RMMI is a measurement and control tool. Like any index, the intent of the RMMI is to capture the trajectory of primary risks (or all risks if you prefer) so you can monitor, manage and if necessary change behavior toward a particular risk type.

The RMMI gives you an early warning on how risks are evolving in say your portfolio, your defense  base or your asset base. We don't regard this as rocket science, but simply a painstaking attention to the details surrounding the interventions you need to make within your decision environment or a determination as to how and where you do business.

This is a data driven process - probabilities may abound but they must have traction.  Even where precious little data exists we can deepen the analysis with our DESM capabilities to provide qualified assessments of what risks permeate that space, effective real time mitigation strategies and long term modifiable control strategies.

SYSTEMIC RISK MODELING with OUTCOME PROBABILITIES

At MBDM, We see systemic risk as a macroscopic property that emerges from the nonlinear interactions of agents. This differs from a conventional view that focuses on the probability of single extreme events, e.g. earthquakes or big meteors hitting the earth, that seriously damage the system. It also differs from a perspective, for example, used in finance, where a single agent is big enough to damage the whole system - which leads to the notion of systemic importance. In addition to all these ingredients, our systemic perspective emphasizes the impact of individual failure exerted on other agents. I.e., the systemic failure can start with the failure of a few agents which is amplified both by interaction mechanisms and by systemic feedback. This can lead to failure cascades which span a significant part of the system.

We provide a general framework for modelling systemic risk which was first applied to fully connected networks and is being extended to networks with arbitrary degree distribution from a formal theoretical point of view. Since we also allow for nodes to have heterogeneous robustness, various applications and extensions of the existing models are possible. They include credit networks, supply networks or social online networks where cascades of leaving users may threaten the existence of the platform.

Our approach is based on the concept of complex networks, where agents are represented by nodes in a network, whereas their interactions are modelled by links between them. Both nodes and links can follow their own dynamics and influence each other by feedback effects. In order to understand the emergence of systemic risk, we have to model (a) the internal dynamics of the agents, which is largely neglected in other approaches, (b) the interaction dynamics of the agents (in particular the network topology), (c) macroscopic or systemic feedback, i.e. the impact of changing external conditions, (d) trend reinforcement, i.e. the fact that interactions are path dependent and depend on the history of previous interactions.

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RISK MODELING