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Our Research

We’re a small business specializing in:

 

-> near-real time data extraction

 

-> behavioral analysis

 

-> threat forecasting

 

->effects assessment

 

 

Theoretical Framework

  • Conflict and cooperation outcomes are a function of the strategic decisions that governments, dissidents, and the larger population make within constrained environments.
  • Ultimate victory resides with who gains the support of the larger population/society.
  • Government & dissident behavior is a function of their desire to win support and gain/remain in power (This theory transports well to businesses and competitors trying to win support of consumers)
  • They use a variety of tactics to do so (actions & rhetoric)

Data Needed to Specify/Test Theory

  • Events Data – “who does what to whom”
  • Sentiment/Emotions Data – “who says what to whom or about what ” and “feels what about whom/what”
  • Discourse Data – “how dissidents communicate ”

Automated Data Extraction Tools

  • Praxis: Automated Events Data Engine
  • Pathos: Automated Sentiment & Emotions Data Engine
  • Logos: Automated Rhetorical Discourse Data Engine
  • Taxis: Automated Document and Topic Classifier
  • HuGo: Heuristic Geo-location tool

Models Needed to Specify/Test Theory

Models deliberately simply reality to highlight key causal relationships, filter out extraneous/secondary effects, and focus on how motivations and actions are interrelated.

 

  • Statistical Models estimate parameters (effects) for the impact that:
      •  strategic interactions among key players in a situation
      • emotions and support for those actions/actors
      • ) the rhetoric and intent of actors have on outcomes (4) within a constrained environment.
  • Once we understand the relationships, we can use those parameters to forecast future outcomes.
  • There is no silver bullet model; thus we create multiple models that highlight and weight accordingly different aspects of a situation to produce the best ensemble forecast.

Our Modeling Tools

  • Time-Series Models
    • estimate parameters of a model that describe the stochastic process underlying a temporal series of data
  • Hierarchical Mixed Effects Models
    • Allows the estimation of different effects for variables across spatial units (cities, regions, countries, continents, etc.)
  • Random Forest Models
    • Ensemble learning method for classification and regression which constructs a multitude of decision trees to optimize the fit of a variety of variables
  • Geo-Spatial Models
    • Spatial autoregressive models
    • Distance decay models
  • Ensemble Bayesian Model Aggregator (eBMA)
    • Uses outputs from multiple models to better forecast behavior (e.g., similar to hurricane track forecasting)

Extract

Model

Understand

Predict

Assess

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