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CHILWIN CHENG
Moneyball Law
Using statistical modelling to improve litigation forecasting
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ost litigators who have benefitted from participating as counsel in a few trials can assess, with reasonable confidence, whether their client has a “strong” or a “weak” case. However, if one asked them whether they could frame their forecasts as a range of likelihoods of an acquittal, a fine range, or the length of incarceration, probation, or a suspension, most litigators demur from making such a forecast. Yet, increasingly, sophisticated parties demand these forecasts. Insurers ask for realistic estimates rather than “worst case” estimates of reserves they need to set aside. Litigation finance must assess the likelihood of recovering a sum of damages that reflects the risks of loss. Our trade publications are rife with articles about clients who demand greater transparency into how litigators develop their forecasts. THE ROLE OF DECISION ANALYSIS Decision analysis is a discipline with universal use in other fields such as operations research, finance, project management, among many others. Whenever a person needs to forecast an event that depends on a chain of events, each link of the chain having a range of likelihood of occurrence, those professionals will employ decision analysis tools. A wonder that lawyers, whose stock and trade is in the analysis of uncertain events,
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have not embraced these techniques. It’s high time we did. THE DECISION TREE A decision tree is a model of event points having a range of potential outcomes. Lawyers have defined “decision points” for generations. For example, with a prosecution of a business contrary to section 6(1) of the Environmental Management Act, the Crown must prove beyond a reasonable doubt that: (1) the accused “introduce, cause, or allow” waste to be introduced into the environment; (2) the accused introduced “waste”; (3) the waste introduced into the “environment”; (4) the accused engaged in a “prescribed industry, trade of business”; and (5) the accused failed to execute due diligence to prevent the contravention. Knowing the evidence, lawyers can identify the likelihood that each element can be proven. With that capability, each question becomes a point in a decision tree. Here is an example of such a tree:
In this example, each node on the tree represents an element of the Crown’s case.
But you may say, “It’s impossible to be so precise in putting a number at each of those nodes! It’s more like a range of probabilities!” Decision analysis allows for this: the Monte Carlo simulation — a technique developed by theoretical physicists trying to develop the atomic bomb. The word “Monte Carlo” refers to the codename that US government scientists gave to the technique rather than to the suggestion that the technique results in random results or is a form of gambling. The lawyer assigns to each node a range of reasonable outcomes for that node. Usually, a node represents a discrete and often binary condition: “Yes” or “No”. As in, did the Plaintiff prove that the Defendant owed the Plaintiff a duty of care: “Yes” or “No” — unlike lawyers, a judge does not have the luxury of “Maybe”. In our example, the lawyer has defined the likelihood that a judge will decide that the Defendant owes the Plaintiff a duty of care as being 70% plus or minus 20%. In this model, we establish probability ranges for each node of the tree, recognizing that we are uncertain how a court will rule on each point along the analysis. For example, we have modelled whether the Crown can prove the accused’s actions caused the introduction of waste into the environment with this probability range:
