Positive Negotiations, Part 10: Appreciating Risk Using The Risk Assessment Matrix
In this video, I introduce and explain the Risk Assessment Matrix, a tool people use in many different scenarios to assess different levels of risk. It is a tool people can use to evaluate - or “appreciate” - the different levels of risk they face in a civil dispute. These disputes will be resolved by a judge or jury, typically through a trial, if the parties are unable to resolve them by themselves and through negotiations. Using this Risk Assessment Matrix, either before or during negotiations, can help each side make a reason-based assessment and appreciation of their respective risks, and this can help them in negotiating a resolution that effectively manages those risks.
Video Transcript
Hello, and welcome.
In my last video, I discussed what I call Appreciation Questions. Those are the open-ended, “so what” questions mediators can use to guide each party into considering all the circumstances of their dispute. They help each party through the sometimes challenging process of making a fair yet candid appreciation of the risks each side faces in their particular dispute.
But, as I noted in that video, risk itself can be a difficult concept to grasp. This can be especially true for individuals who are unfamiliar with judicial process and trials, and who have not had the benefit of going through this kind of risk appreciation process before. There are, however, tools and techniques that we can use to help people with this.
In this video, I introduce the first of these: the Risk Assessment Matrix. This is a risk assessment tool people use in many different environments, and it is worth considering in the context of dispute resolution and trials.
The premise underlying the Risk Assessment Matrix is that any “risk” is a combination of the “probability” of a given event or occurrence and the “consequence” of that occurrence. This example is a 5 cell by 5 cell Risk Assessment Matrix. The Y axis represents the “Probability” or “Likelihood” of an occurrence - and this ranges progressively upwards from Rare through Unlikely, Possible, and Likely before ending with Almost Certain. The X axis represents the “Consequence” or “Severity” of the occurrence - and this starts with Negligible and then increases through Minor, Moderate, Major and up to Catastrophic.
The matrix assigns levels of risk, ranging from Low to Extreme, based on different combinations of Probability and Consequence. So starting from the bottom left corner, it considers occurrences that are Rare, Unlikely, or Possible that have Negligible consequences, and it designates these as LOW risk occurrences. Similarly, it then considers Rare occurrences that are Negligible, Minor, or Moderate, and it also designates these as LOW risk events.
It does the same thing in considering events that are Likely or Almost Certain to occur but also have Negligible consequences, but due to the increased probability it designates these as Moderate risks. It then goes back to occurrences that are Unlikely or Possible that have Minor consequences, and assigns those a Moderate risk. It does the same for Unlikely occurrences that have Moderate consequences. And then it assigns Moderate risk levels for Rare occurrences that have Major or Catastrophic consequences.
At this point we can start to see how risks rise in relation to increases in probability and increases in consequences. So instead of leaving “risk” as some abstract and ill-defined concept that is both hard to grasp and easy to ignore, this simple visual tool gives us a way to both assess and categorize different risks. This can be immensely helpful for anyone who has to grapple with different risks.
As we move further up and across the matrix, we see the combinations of:
Likely and Almost Certain probabilities with Minor consequences;
Possible and Likely probabilities with Moderate consequences;
Unlikely and Possible probabilities with Major consequences; and
Unlikely probabilities with Catastrophic consequences;
All constitute High risks.
Similarly:
Almost Certain probabilities with Moderate consequences;
Likely and Almost Certain probabilities with Major consequences; and
Possible, Likely, and Almost Certain probabilities with Catastrophic consequences;
All constitute Extreme risks.
This Risk Assessment Matrix works very well when the probabilities and consequences are well-defined and easy to measure.
For example, I first learned about this in an avalanche awareness lecture given by Bruce Tremper, who wrote the go-to avalanche book for backcountry skiers. Forecasters use this tool when they issue avalanche hazard warnings, ranging anywhere from Low to Extreme, on the North American Public Avalanche Danger Scale. There are measurable physical conditions within a snowpack that allow a fairly precise assessment of the probability of an avalanche occurring in a given location. In addition, there are measurable physical conditions - such as the depth of the snowpack - that allow forecasters to grade the consequences of any avalanche that may occur at that location.
When it comes to litigated disputes and trials, there is a huge array of variables that can impact the probabilities and consequences of any given occurrence, and this inevitably blurs the lines between various levels of risk.
Nonetheless, even if this matrix doesn’t allow us to be quite as precise as avalanche forecasters, it still provides us with a useful tool for assessing risk and predicting probable results.
Let’s, for example, assume we have a plaintiff who was a pedestrian in a crosswalk when she was struck by a car and suffered clear, disabling and life-changing injuries from that impact. Let’s also assume there is credible evidence the defendant had a blood/alcohol level at twice the legal limit at the time of this incident. In this case, it would be reasonable to conclude a jury will be Likely, if not Almost Certain, to find both the defendant is liable and the injuries are Catastrophic. In this case, it is reasonable to conclude the defendant faces an Extreme risk of a significant verdict. If we add more details regarding the plaintiff’s claimed damages, we could add probable ranges, in dollars, of any verdict.
Another example could be a plaintiff claiming he suffered soft tissue injuries after slipping on ice and falling in his friend’s driveway. In this example, let us assume the plaintiff has no lost income, and he was discharged from care after eight weeks of physical therapy and reporting he had no further symptoms. The defense in this case may conclude the probability of a plaintiff’s verdict on liability is no more than Rare, Unlikely, or perhaps Possible, and they may also conclude the damages exposure - or consequence - of any liability verdict will be Negligible, Minor, or Moderate. In these circumstances, the defense may see this as a Low risk case.
There can, of course, be many other possibilities. For example, some claims may be significant but not extraordinary, or the evidence may not be so clearcut, and the probabilities and consequences may end up being weighted more toward the middle of the matrix.
What this matrix does is it gives people a reason-based tool for evaluating each side’s risk. It also gives them a reason-based way to explain to others why they assign any particular risk level to any particular potential outcome. Instead of people declaring something is risky, they can say this is the level of risk we associate with that potential outcome, and this is why.
Like any forecasting tool, it is not perfect. It will not predict results with absolute certainty, and sometimes trial verdicts end up being dramatically different from well-founded and reasonable predictions.
The main challenge is in coming up with well-reasoned levels of probability - from Rare to Almost Certain - and of Consequences - from Negligible to Catastrophic - for this matrix based on the evidence of each particular case.
There are three questions we can ask about that evidence to help with this, and I will discuss those in my next video.
Until then, goodbye.
