Resilience engineering - Concepts that can be applicable to asset management

 

Some important concepts in other fields of study often provide useful insights for asset management. For example, there is a clear trade-off between optimality and brittleness associated with engineering problems. The best optimized solutions may not accommodate large shocks, frequent change, and uncertainty. A model may break after the first time conditions change from the period used for testing and training. An over-optimized model may be fragile. For quants, this is often the problem of overfitting. A system that is overfit to the past will provide a great result on past data but will deviate from expected performance or break once there is new information or there is a change in market sentiment not seen in the past. 

Investors need models or processes that are resilient. Nassim Taleb would call this the property of being anti-fragile, but his idea or term has not gained acceptance as a structural requirement from the viewpoint of the model builder. 

Erik Hollnagel, a Scandinavian expert in the fields of resilience engineering, system safety, and cognitive systems engineering, developed the key features or cornerstone concepts behind resilience. Resilience should have relevance for all investment and hedge fund management. A resilient system or model should have the following four skills: 

1. the ability to react appropriately when new information is encountered;
2. the ability to effectively monitor the environment in order to identify the current regime and any shift to a new environment; 
3. the ability to anticipate change in regimes or in responses to market events;
4. the ability to learn from mistakes and learn from the environment faced as new information is acquired.

A resilient model or system for making decisions should have some common properties:

1. Learning to live with uncertainty - uncertainty is not feared and a point of failure but addressed in a consistent manner;
2. Maintaining internal diversity - there is not just one solution or approach but a combination of approaches that can be reweighed based on changes in the environment;
3. Combining different types of knowledge - single paths or threads to problem solving will create brittle systems so alternative data and points of view are incorporated for flexibility;
4. Create opportunities for self-organization - some form of information organization that weighs factors that impacts predictions.

Investors should ask two simple questions. Do your models show resilience skill? Do your models have resilient properties?

The principles of resilience can be incorporated with how  research and modeling is approached. New data can be tested. Models can be reviewed and trained against different environments. Models can be developed to identify regimes to support switching of information weights. Processes can be developed to prepare for failure as opposed to reacting to model failure after the fact. Perhaps holding to consistent model specifications is the right answer, but resilience engineering will always be testing this assumption in order to prepare for the worst.