Subscribe to DSC Newsletter

Relevancy Modeling with Symbolic AI

At time of crisis, everyone is scrambling for the latest news for signals of changes and make decisions to mitigate risk. Average person uses common sense to draw inference on relevancy. For example, as the Coronavirus Crisis evolves, many decided to hog up bathroom tissue. As much as it is common sense for many, it simply does not make sense. However, when it is evaluated against aggregated common sense, we can eliminate many of these signals.

 

In financial modeling, analyst rely on known variables to model outcomes. For example, bond yields, dollar strength, value vs growth, risks, liquidity etc.

 

This kind of modeling works well when life is normal and the causation of financial indigestion is due to mistakes in execution. In a connected world, where supply and demand are intertwined, these models get more complicated. The outcome of these models can deviate much further from the real world when variables does not match with the real world or when behavior of these variables is impacted by other dependencies that are unknown.

 

For example, we have observed many experts come into the scene to compare the current economic turmoil with prior downturn, and soon found that they are wrong. In fact, the current crisis is not caused by financial indigestion or malfunction, it is the result caused by the force of nature – a pandemic that no apparent cure is at hand.

 

For this reason, we turn to relevancy modeling with Symbolic Logic. In relevancy modeling, we listen to textual communications for signals with Symbolic AI, then apply aggregated Context Discriminant against crowd sourced common sense to determine relevancy on these signals. The few signals with the highest relevancy at a given time is then used as basis to predict the behavior and reaction of the critical mass.

 

Relevancy factor is a composite vector. It exists in a 3-D space having properties of these scalars - challenge, momentum, work-in-progress. Using differential calculus, we can project relevancy on different industry specific surfaces to obtain the slope or rate of change with regard to a particular industry such as travel, aerospace, medical, thereby determining the reaction of investor’s behavior toward an industry.

 

Views: 225

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Videos

  • Add Videos
  • View All

© 2020   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service