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My hunch is this, and I'll put it in simple terms.   The original thesis was that Le Chatelier, which deals with relative instability could be applied to markets. This much we know is true and comprises the field of thermoeconomics. We know it works from our back-test last year. (The QAS)   What do we know?  Any given system is comprised of quantifiable entities or collections of constituents, or factors.   The information comprises data.  It doesn't matter which data we feed the machine, the algo weighs each constituent factor differently and produce a signal. In our case it does this on a constituent basis and in an time series.   In the case of virology, there are endogenous sources of information, or factors, all the heavy science involved in the virus itself--that's one data set.  The same rules applies.   The market, reduced to a stock goes up or down, depending on volatility, over a given time and is attended by signals and the news cycle. People buy or sell based on rumors. Some signals are reliable, others are not. Companies are comprised of sets of both kinds of information. Inside/outside, essentially.  Pandemics goes up or down, reduced to one area, even a zip code, over a given time, and is attended by a news cycle. People panic, or don't base on signals and rumors. One virus is comprised of both kinds of information.  There are exogenous information sources--mostly qualitative, in this case, media reports, conspiracy theories, etc.  What I am formulating is that taken together, structured and unstructured, hard science and soft media--will comprise a system and that system is, or is not in balance.   Informational instability is a reality and people would pay to know.  Qs:   Can we detect it, demonstrate the instabilities?   Who would be interested in this kind of system?

Hackathons

Technologies

ajax

Devpost Software Identifier

254470