Bayesian Statistics in Litigation Strategy

At the core of the engine of Lawptimize is a pure Bayesian principle concerning the use of probabilities in litigation strategy. The Lawptimize algorithms assume that the lawyer (or the user of the app) has enough evidence and is reasonable enough to propose an initial set of probabilities in the strategy. These probabilities can be basic, like being able to say that she believes that there is a high probability of winning this stage, or that 'the odds are in our favour but it is going to be tough'. This is enough to create a chain of probabilities in the system that are used by the AI to calculate the expected values from the first principles.

Lawptimize takes advantage of these estimates and produces expected values and expected utilities that allow lawyers not only to make the best decisions in their litigation strategy but also to re-evaluate the situation at any stage in the litigation process.

Taking things a step further, the lawptimize engine uses again Bayesian inference models that use previous predictions and estimates in order to provide better data and estimates for the lawyers.

Our clear Bayesian approach has been proven to work in hundreds of cases that is why it is trusted by law firms around the world.

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