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Almost all consumer and firm transactions are achieved using computers and as a result gives rise to increasingly large
amounts of data available for analysts. The gold standard in Economic data manipulation techniques matured during a
period of limited data access, and the new Large Data Analysis (LDA) paradigm we all face may quickly obfuscate most
tools used by Economists. When coupled with an increased availability of numerous unstructured, multi-modal data sets,
the impending 'data tsunami' could have serious detrimental effects for Economic forecasting, analysis, and research in
general. Given this reality we propose a decision-aid framework for Augmented-LDA (A-LDA) – a synergistic approach
to LDA which combines traditional supervised, rule-based Machine Learning (ML) strategies to iteratively uncover hidden
sources in large data, the artificial neural network (ANN) Unsupervised Learning (USL) at the minimum Helmholtz free
energy for isothermal dynamic equilibrium strategies, and the Economic intuitions required to handle problems
encountered when interpreting large amounts of Financial or Economic data. To make the ANN USL framework applicable
to economics we define the temperature, entropy, and energy concepts in Economics from non-equilibrium molecular
thermodynamics of Boltzmann viewpoint, as well as defining an information geometry, on which the ANN can operate
using USL to reduce information saturation. An exemplar of such a system representation is given for firm industry
equilibrium. We demonstrate the traditional ML methodology in the economics context and leverage firm financial data
to explore a frontier concept known as behavioral heterogeneity. Behavioral heterogeneity on the firm level can be
imagined as a firm's interactions with different types of Economic entities over time. These interactions could impose
varying degrees of institutional constraints on a firm's business behavior. We specifically look at behavioral heterogeneity
for firms that are operating with the label of ‘Going-Concern’ and firms labeled according to institutional influence they
may be experiencing, such as constraints on firm hiring/spending while in a Bankruptcy or a Merger procedure. Uncovering
invariant features, or behavioral data metrics from observable firm data in an economy can greatly benefit the FED, World Bank, etc. We find that the ML/LDA communities can benefit from Economic intuitions just as much as Economists can
benefit from generic data exploration tools. The future of successful Economic data understanding, modeling, simulation,
and visualization can be amplified by new A-LDA models and approaches for new and analogous models of Economic
system dynamics. The potential benefits of improved economic data analysis and real time decision aid tools are numerous
for researchers, analysts, and federal agencies who all deal with increasingly large amounts of complex data to support
their decision making.
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