by Andrew G. Watters, Esq.
This is a work in progress article that will be revised and updated as I continue to use these techniques in my cases.
A discussion of the issues presented in most complex cases, irrespective of actual size.
An overview of technical computation is provided.
I present my use of the computation program Mathematica in two cases.
Regarding support and property division, the community business had sales of over $1 million in the preceding twelve months, but the couple was carrying large amounts of unsecured debt. Husband took the position that there was simply no money available and that the community residence should be sold to clear out all the debt and provide cash for the parties to go their separate ways. I prepared a cash flow analysis in Mathematica based on bank statements provided by Husband, producing the following table:
When plotted on a graph, the data came alive and revealed an obvious trend: the net profit of the community business over the last twelve months was essentially zero (the green region is net cash flow each month).
The overall picture became even worse when financial statements were prepared, revealing that payables had increased along with the unsecured debt, which made the business lose significant money over the same period due to non-payment or late payment of vendors. This showed that the couple could not sustain its lifestyle in California, and I used the information to justify a motion to sell the community residence in order to provide quick cash, and also justify certain positions taken in the case.
I expect to publish additional analyses and visualizations in the near future showing how I am exploiting technical computation to advocate for my client on the issues of property division, support, and attorney fees.
This is an interesting one because of the complicated acquisition and transfers of several real properties and portions thereof via 1031 exchange and gifts, as well as Husband's 401(k). I will show how I used Mathematica to visualize and analyze the characterization and appreciation of these real properties over time, including a Moore/Marsden analysis, as well as estimate the separate property portion of Husband's 401(k). In addition, I expect to improve on the cash flow analysis and visualization performed in Dissolution A. The use was for the client's benefit in a pre-filing discussion so the client knew what kind of numbers to propose to the other party versus what could be expected in court.
I am pleased to announce the development of D3 Analyzer, which will be an alternative to the DissoMaster Suite of applications based on my research. The new application will be an interactive computation system that takes actual data as inputs and produces results that are justifiable and easy to understand. In addition, project/case management for aggressively litigated complex family law matters is achieved in an integrated web application.
D3 Analyzer provides a comprehensive software platform and interface for running various financial computations on high-asset, high-earner, and/or other types of complex dissolutions of marriage. D3 Analyzer is based on original, interdisciplinary research that our team has done in the areas of data science, data visualization, decision theory, predictive modeling, behavioral science, and complex family law. The above print ad/announcement shows a basic list of the features of D3 Analyzer as well as some sample graphs. The input consists of data points from documents such as bank statements, tax filings, business financial reports, and similar matter. The output consists of (1) stunning, effective, high-resolution, and interactive graphs and charts; and (2) proposed property divisions, reimbursements/offsets, support, attorney fees, and other figures that are all justified by the underlying data. In addition, we are experimenting with machine learning and computer vision to automatically map financial transactions shown on bank statements and canceled checks for tracing purposes. We also plan to integrate predictive modeling of decisions based on multivariate regression of such factors as the parties’ personality traits, the community’s financial practices, separate property ownership of either party, opposing counsel ratings, judicial officer temperament and past decisions, and more.
If you want custom analyses and/or charts for your case, email me at my law firm address for a consultation.
Last updated: March 18, 2018
© Andrew G. Watters