What would be the ideal node types and edge relationships to model the business world in a way you can run simulations of events?
Has this already been done? Seems like it would be helpful.
"That's another thing we've learned from your Nation," said Mein Herr, "map-making. But we've carried it much further than you. What do you consider the largest map that would be really useful?"
"About six inches to the mile."
""Only six inches!"exclaimed Mein Herr. "We very soon got to six yards to the mile. Then we tried a hundred yards to the mile. And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to the mile!"
"Have you used it much?" I enquired.
"It has never been spread out, yet," said Mein Herr: "the farmers objected: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well.
Even modeling a single, non-trivial business would probably be exceptionally difficult
The question is what is an appropriate ontology to have some basic sense of how a catalyst can affect a company.
If a catalyst affects nodes, how many steps until it hits the "company node" in question.
OP then lists at least 6 prominent factors to add onto "merely" your overly-simplified b2b relationship :)
Simulating the entire publicly-traded economy is - effectively - on the order of simulating the the universe :)
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[0] https://www.benzinga.com/news/20/10/18026067/the-number-of-c...
OWL from the RDF world has quite a few reason it hasn’t gotten traction but one is that it doesn’t have answers for many problems of ‘agentic’ reasoning which would be useful to agents that are doing things in the world.
There are problems with the management of a database in practice. A document database lets you update a document at a time, a relational database a row at a time. If you are adding edges and nodes to a graph there needs to be a transaction facility so that you never see inconsistent states. Deleting a ‘record’ is not just a matter of deleting a ‘node’ or an ‘edge’ but may involve deleting some set of nodes and edges which graph databases don’t have in their model.
In the case of OWL I think you need a transaction mechanism to make the data and ontology checking abilities of the standard useful. In a normal OWL database you put junk in and now you have an invalid database, error messages that don’t make any sense, and no systematic answer to fix it. Really in that case it should revert the transaction or maybe let you work in an invalid branch but never be able to commit an invalid branch. So far as I know most OWL products today have no real answer for this problem but it seems to me they could.
Generally in common sense reasoning there are many issues like ‘what was the state of the world on Nov 4: 1975?’ Or ‘what does John believe about X?’ or ‘it is necessarily true that…’ or ‘what would happen if?’ that go beyond first order logic.
Many knowledge graphs build structures that let you query who was president at a particular time because there are records that say ‘X was president between date A and date B’ but this only works when it is spelled out explicitly, you can’t ask questions about the world as a whole at a certain time.
Some of these problems could be solved by the use of persistent data structures, that is, fork the world when you put in facts so you can’t break the world, fork it to do a simulation, etc.
There is also literature in international trade on trade networks (sourcing components for a final product, for example). Here I don't have names for you.
There is also Matt Jackson at Stanford who has worked on many, many topics in networks. On the empirics (which are very challenging) you may want to look up Bryan Graham and coauthors.
From my limited exposure the work on financial crises and trade, it doesn't seem that interesting but it does exist. The empirical work on networks exposes a lot of challenges. Graham (IIRC) has a recent survey in the Handbook of Econometrics if you'd like to learn more.
And ... is networks worth a solo prize? If not, I am not sure who else he would share it with? Or how they would shoehorn this into a "Networks and..." prize. [If you are a micro theorist please feel free to correct me.]
I actually think that we need government to enforce this data collection and it needs to take advantage of some decentralized systems for it to be workable. Primarily because we need "hard" (usable) data about resources, wealth (inequality) and crops etc. in order to have a realistic (and indisputable) view of what's happening. Combining that type of decentralized megastream with advanced cryptocurrency smart contracts could change economics from being a cult to a useful science.
What are the node types, properties of the nodes, and the edge types to best model a catalyst (like the Ukraine war).
2 types of nodes
1) Company
<-> Owners ( Company|Person )
Incorporation Date
Name
Type (llc, c-corp,s-corp, publicly traded, etc)
2) Person
<-> Companies
Name
A mostly un-edited, totally unpolished, and probably erroneous - don't judge too hard :) - version here:
- All goals are to some extent intermediate - a means to achieving a further goal - directed but not acyclic
- Some goals are largely measured by how useful they are achieving others, exemplified by stocks and tokens
- Node values are the measurement of the goal (in what?) and the edge values are the percentage split (like a Sankey diagram) but inclusive of factors less than 0 or greater than 1 (i.e any value) to be added to the value in the node, like y = y + (xf)
- (The x-f relationship could also be exponential - (x
f^n) is a better equation)- Maybe, by measuring the values of x and y empirically over time, we can try to calculate f. f has units to balance out the units of x and y, so no problem with incompatible units
- What are the nodes? Every damn thing that can be measured - prices of everything sold on the market, population statistics like literacy rates, time spent on Khan Academy, anything that can be quantitatively measured (quality of the measurement doesn't matter as each f value is completely independent of other f values)
- And we have a tech tree! You can choose the measurements that you want to optimize for and use it to prioritize your resources towards progress. Can also be used to intelligently guess at the inputs and outputs of progress in a specific goal
- Better for quantifying the current economy and scientifically deploying investment for the near future. The long term is obviously unpredictable (think https://twitter.com/robert_zubrin/status/1278681124944793611), however can be used to analyze changes during previous paradigm shifts with historical data
- This is 99% dependent upon price signals (which I believe will be almost all of the useful data)
Having a general idea of what can be affected is the goal, not an accurate prediction of the future universe
How can the Ukraine war affect Subway (restaurant)
Ukraine war (catalyst) -> Key exports of Ukraine -> Grain -> Acme Grain To Flour Co -> Subway
Supply/price of flour may change for Subway.
Node Types: - Country/Region (Ukraine) - Raw Material (grain) - Company (subway / acme grain to flour co)
Relationships - Export - Input to - Supplier to
A release of a sanitized public version of the materialized knowledge graph (sans internal company data) is planned for the near future. If you'd like to work with us please get in touch!
The idea was that you could later run simulations and what-if scenarios. Lots of agent-based modeling happening too once we had the structure up and running.
(this was done around the Lehman Brothers period, and there was a lot of interest in these kinds of works in Complexity Sciences).
[1]https://www.amazon.com/Monetary-Economics-Integrated-Approac...
I am wondering what a "company-centered" approach ontology would be.
Agent based economic models were a really hot idea 15-20 years ago. They had interesting properties but to my knowledge nobody ever could calibrate them to generate real-world testable predictions.
If you really want to get exotic peek at what the Cybersyn people were trying to achieve.
What would be the simplest ontology definition to get a rudimentary idea of what's going on?
I'm working on this.DM me if you're interested
It was called network of functions.
Products (Raw Material <> Application use) Products to company (Supplier <> Buyer) People connected via companies and products
We are already seeing benefits of this in being able to easily discover new connections across products and companies. Our focus is right now on few verticals in manufacturing sector and hope to expand to wider manufacturing space at some point.
I forget exactly the details but it was a cool article.