Risk Architecture: Application of Complexity Science Neil Cantle, Principal, Milliman [email protected]Risk Architecture, Washington, DC, April18, 2012 2 Table of Contents Background The nature of risk The sciences of complexity Risk Appetite Emerging Risk
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Application of Complexity Science - Confex of Complexity Science Neil Cantle, ... Introduction to Systems ... evolution and therefore emerging risks Identify branches that have the
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Risk Architecture: Application of Complexity Science
Systems theory helps us to make sense of complex problemsHelps to uncover “complex” patterns…not chaosMany alleged “black swans” are just complex risks we didn't understand early in their developmentGain insights into future developmentSupport for the experts…spot the next crisisSciences developed across many disciplinesSystems tools help us to:– Identify and understand emergent properties– Describe how the system works in terms of the key
Has a purposeEmergence – the whole has properties not held by sub components Self Organisation – structure and hierarchy but few leverage pointsInteracting feedback loops – causing highly non-linear behaviourCounter-intuitive and non-intended consequencesHas tipping point or critical complexity limit before collapseEvolves and history is importantCause and symptom separated in time and space
Uncertainty = absence of precise and complete knowledge leading to consensus of future stateRisk = state of uncertainty for a participant where some of the possibilities involve an undesirable outcome (e.g. loss)Risk Appetite = “our comfort and preference for accepting a series of interconnected uncertainties related to achieving our strategic goals”Risk Limits = operational restrictions intended to maintain performance within risk appetite
Also need to specify which sources of uncertainty are un/acceptable (risk preferences)Need to understand how various factors cause variation in the outcome
Agree business goals for which uncertainty mattersDescribe how much uncertainty you are comfortable withIdentify the possible sources of uncertaintyDescribe how that system worksEstablish limits which maintain performance within desired range of uncertaintyCycle of measuring risk capacity and resource utilisation against appetite
Use a combination of cognitive and data-driven methodsLeverage expert knowledge (using cognitive mapping)Technique can be easily embedded within ORSA/planning processResulting model remains in the language/style of the contributorsExplicitly links to Internal Model for Solvency II
Proposed approach:– Embraces systems thinking approach– Is scalable from small/simple to large/complex– Can apply to any type of firm– Reacts naturally to emerging information– Provides a basis for setting AND monitoring limits– Can make use of expert knowledge until data available– Retains a form of use and interest to business people– Translates “risk” into business terms
Risk registers typically force the assignment of a label to each entryBut the entries are often not that simpleBy using a more granular labeling approach it is still possible to aggregate the informationTechnique from biology permits analysis of:– Which entries are “like” each other– Understanding of how risk scenario characteristics evolve– Clues about potential future scenarios
Enterprise risk as an evolutionary processHow can we model the risk evolution process What insight can evolution of risks provide– A rigorous classification system with relationships– A guide to emerging, dynamic and systemic risks– A unique organizational risk lineage
This methodology identifies small groups of highly related risks which share a common ancestorThe evolutionary history of each of these groups can then be accurately tracedThen their relation to other groups investigatedBy understanding the phylogeny of the risks we can:– Determine where evolution is most prolific – Detail path dependency and co-evolution of risk– Identify the most active characteristics to manage– Create focused scenarios for emerging risk modelling
Systems approach helps to study the complexity before making simplificationsHelps to triangulate multiple insights (data, experts, etc.)Helps to incorporate adaptation and non-linearityThink of companies as a collection of people not machinesFocus on outcomes not just the “how”