Using scenario analysis to manage uncertainty in economic loss calculations – Litigation, Mediation & Arbitration

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Using scenario analysis to manage uncertainty in economic loss calculations

In last week’s article, I suggested that multiple “but
for” scenarios of economic loss are likely in situations where
an intervening event occurs. The need for multiple scenarios is
driven, largely, by the degree of uncertainty inherent in the
forecasting exercise. In this article, I outline how uncertainty
can be managed through scenario modelling. As lawyers reading this
article, there may be some value in thinking about the potential
implications this may have on case management, including the scope
of instructions, defining the question(s) to be solved, and the
nature of, and access to, the information required to support the
claim.

Scenario analysis recognises that there is no single picture of
the future, but rather, a range of possible outcomes. While each
economic loss claim is different, Exhibit 1 summarises some of the
steps that I typically incorporate into my approach for building
predictive financial models. As I take a contingency approach to
modelling economic loss, I use Exhibit 1 as a guide, rather than a
checklist.

While the thinking underlying Exhibit 1 has evolved over time,
reflecting my experience and continued education, here are three
lessons that particularly influenced my thinking.

I first used regression analysis to quantify the plaintiffs’
losses due to the 1997 Thredbo disaster. In that case, I built a
single scenario of economic loss founded on the strong correlation
between revenue and the quality of the ski season (duration,
snowfall). While the mathematical logic was undisputed, I’ve
since learned that providing an opinion built on sound mathematical
logic is unlikely to be sufficient on its own: providing context
(for example, against a baseline position) and a range of potential
financial outcomes (such as, through sensitivity analysis) are
among the tools I consider using to build stronger arguments and to
provide the Court with information relevant to its decision-making
process.

Fortunately, the uncertainty inherent in “but for”
scenarios of economic loss is usually – but not always
– bounded by a finite end date. While this allows us to
reasonably predict losses by looking backward, the challenge
becomes managing hindsight and associated cognitive biases that may
be inclined to recast historical events. To manage that risk, I
approach long held business “truths” with a healthy
degree of scepticism. In another instance, management informed me
that the business’s historical financial under-performance
could be explained by adverse changes in certain KPIs and cost
drivers. Initially, I spent time understanding the business and its
data. Using multi-linear regression, I was able to demonstrate to
management the flaws in their explanations, directing them instead,
towards a different set of relationships that could be relied on to
more accurately forecast cost outcomes.

And finally, I’ve learned to develop more robust predictive
models by constantly iterating between predictive analysis and
outcome assessment. This has two distinct benefits:

1. From a “build” perspective, complexity can
   be added incrementally once the more basic, and simpler assumptions
   are incorporated, justified and the outcomes assessed for
   reasonableness, and




2. More rounded and better articulated scenarios can be defined
   and considered, which should ideally assist with a more objective
   assessment of the likelihood and consequences of each particular
   scenario and reflect the range of possible ‘truths’ the
   Court may find as fact.

The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.

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