There are a few counterintuitive systemic aspects. Complex systems may be result of the repeated application of a few, simple, building blocks, rules or programs. Think of words and natural language or Lego blocks empowering the construction of practically "everything". The selection of the good over the bad results needs context, constraints, production rules…
What makes a nuclear power plant so dangerous. It's complex and tightly coupled. The high complexity makes untended events more probable and the tightly coupling reduce the time to react so drastically that there is little chance to manage it.
What risk managers keeps awake at night
The front to back responsibility - they need to understand all risk drivers.
The analytical framework - issues of methodologies, modeling challenges, data and information, communications…
Culture, government and relationship - the definition of risk "appetites", communication of risk profiles, a culture of risk-informed decision makingAnd remember, every innovation is risky itself, the following rules are recommended for quantitative innovations.
Recognize that models are needed
Acknowledge their limitations
Expect the unexpected
Understand actions and actors
Check the resources and infrastructure
Again, what is the use of an extra information if it gets lost in the algorithmic jungle, if actors cannot master the complexity, if data are not informative enough…
We assume that our innovation has clear objects of desire - better uses, better products, better making, optimal risk…they are the big takeaways for its buyers.
But there's more:
A Payoff
Assessing a quantitative innovation I nail down the major movements. The set-up, the problem, the crisis, the solution and the payoff. Has it a beginning, middle and end? In quantitative innovations the payoff should be quantifiable.
In quantitative risk management systems, it may be functions.
Simplified: it makes a big difference if you shut down a process, run it at low levels or re-adjust it to acceptable levels.
Managing risk of complex systems need
A comprehensive set-up - users, elements, actions, models, internal and external data, scenarios, simulations, test procedures…
Problems and trap checks - validate models and methods, simulate the interplay and check the simulations in backtests, stress tests…
Crisis prediction: identify elements, actions...thats interplay may drive the process towards a hazard
A framework of solutions: keep all actor's actions evident over the system's life, provide critical case analytics and simulation, perform power law analysis…
A guiding payoff - the recommendation of quantitative actions towards payoff functionsThere are a lot of conventions that empower the realization of such a system movement. Organize things orthogonally, make valuation and data management twins, apply hybrid programming, make your innovation inherently parallel, apply advanced statical methods and asymptotic mathematics, use Monte Carlo techniques…but most important make the programming power behind your innovation available to your users…symbolic languages, implemented as engines. data frameworks, computational documents….
Ok, a payoff is something I get for a hard work. Risk management is hard work and so is making tools in service of risk managers. It's maybe even harder.
So, it's not a bad idea to make it compelling.
