Does missing my plane mean that I got my risk assessment wrong?

A while ago I planned to take the last aeroplane of the evening to fly out for a client meeting the next day. Unfortunately the plane was cancelled and I had to miss the meeting. On speaking to the client, he said dryly: “But you are supposed to be a risk manager!”

I smiled but did not respond. However, as he was saying this (and indeed well before in anticipation), the following thoughts went through my head: First, the reason that I planned to take the last plane was that the client was particularly unwilling to pay my normal charge out rate, and after protracted negotiations we had just about come to a satisfactory solution. As a result to make the whole situation worthwhile, I had to do a full day’s chargeable work on the day before visiting the client. Second, in any case the only way of guaranteeing with close to 100% certainty that I would have been at the meeting would have been to take a flight two days earlier, and lose even more productive work time. Third, the client and I had prepared well for the meeting in advance, and he was well able to represent our joint project to his colleagues without my presence.

In other words, risk management is not really about risk elimination at all costs, but rather about balancing the costs of risk mitigation (e.g. high cost associated with travelling to a meeting two days early) with the consequences if risks materialise (costs associated with my not being at the meeting). Effective risk management is about finding the optimal balance between these. Even if all risks could be eliminated, generally doing so would be too costly.

In many everyday situations, we already optimise our actions to reflect this balance. When crossing the road, we look both ways, perhaps several times (an action which costs little but reduces the probability of an accident), but we do not build a bridge (an action that could eliminate the risk, but only at great cost). We intuitively find the optimum point and accept residual risk.  More formally one can approach optimisation problems by building quantitative risk models (e.g. using Monte Carlo simulation or decision tree analysis). Often such analysis is most easily implemented by the use of risk software such as @RISK, PrecisionTree or RISKOptimizer (which uses a genetic algorithm to optimise within the context of a Monte Carlo simulation).

The topic of residual risk (and optimisation) is an interesting one, as it does pose challenges from a communication perspective. Management may wish rather to hear that all risks have been eliminated (rather than their being a residual which is too costly to eliminate). In addition, the notion of an optimum does open the discussion of which criteria are used to assess that (e.g. maximising the total average profit after including the cost of risk mitigation measures, or minimising the losses in the worst 10% of cases etc.). In more complex cases (such as in some agricultural, veterinary, or environmental risk analysis situations), the costs of risk mitigation and the benefits associated with this are borne by different parties. Such externalities can make an appropriate assessment even more complex; perhaps there will be more on this in a later blog.

Dr. Michael Rees
Director of Training and Consulting

Steve Hunt, LSSBB

Steve Hunt, LSSBB
Whether in DMAIC, Design for Six Sigma (DFSS), Lean projects, or Design of
Experiments (DOE), uncertainty and variability lie at the core
of any Six Sigma analysis. I’m interested in how Monte Carlo simulation can be used to identify,
measure, and root out the causes of variability in production and service
processes and designs.

Actuaries Tune Risk Analysis to Improve Decision Making under Uncertainty

Actuaries are more risk-averse than you and I because they advise insurers and pension funds that have a lot at risk.  So they need to be better at decision making under uncertainty.   When they calculate requirements for pension fund investment, life insurance, or long-term care they try to account not only for the uncertainties–say, growth rates for bonds– but the factors that influence variation in the uncertainties, key economic indices.

A recent research study by noted authorities in actuarial practice Kevin Ahlgrim, Illinois State University, and  Steven D’Arcy and Richard Gorvett of the University of Illinois, Champaign-Urbana, provides a risk analysis model for projecting economic indices such as interest rates, equity price levels, inflation rates, unemployment rates, and real estate price levels.  The researchers undertook the project for the Society of Actuaries and the Casualty Actuary Society.  Their goal was not simply to create a model, but to help bring practicing actuaries up to date on current thinking and techniques for economic modeling and to lay the foundation for future advances. Their model was developed using @RISK, the popular Monte Carlo software for Excel, and if you want to take a look at it, it is available on the website for the Casualty Actuary Society (the model is a link to Appendix D).

Holly Bailey

Holly BaileyPublic Relations Representative for Palisade

I specialize in communications for technical and scientific companies. During my work for Palisade Corporation over the past decade I have kept a close eye on trends in quantitative decision-making techniques.  I’m keeping this blog to report where and how I find these techniques–such as risk analysis, risk optimization, decision analysis, neural networks, and statistical analysis–being applied.

Risk Analysis in Clinical Practice

As medical practice has become more and more “evidence-based,” the role of risk analysis and Monte Carlo simulation has expanded rapidly.  Nowhere is this more evident than in recent software introductions that incorporate Monte Carlo software to calculate dosages with extreme precision.  If “dosage” brings to mind a teaspoon and a bottle of cough syrup, you need to expand that image to include laser targeting and photon radiation.  As a recent study of treatment strategies for hepatitis C demonstrates, the what-if scenarios churned out by these specialized risk analysis algorithms allow clinical practitioners to compare likely treatment outcomes of different dosages without real-life trial and error.

Why Use Decision Tree Analysis?

Conducting analysis of decision making under uncertainty using decision trees serves several purposes.

First, a decision tree is a visual representation of a decision situation (and hence aids communication).

Second, the branches of a tree explicitly show all those factors within the analysis that are considered relevant to the decision (and implicitly those that are not).

Third, and more subtly, a decision tree generally captures the idea that if different decisions were to be taken then the structural nature of a situation (and hence of the model) may have changed dramatically. This is in contrast to an Excel model with sensitivity analysis (or a Monte Carlo simulation model) in which a change of parameters in the model does not represent any structural change to the situation. Capturing the logic and conditionality that is present in a tree would be complex to do in such modelling environments.

Fourth, and arguably the most powerful, a decision tree allows for forward and backward calculation paths to happen (taken care of automatically when using the PrecisionTree decision tree software) and hence the choice of the correct decision to take (optimality of decision making, or optimal exercise if embedded real options) is made automatically.

Dr. Michael Rees
Director of Training and Consulting