Nash Equilibrium in Modeling of Complex Markets
The article “Understanding complex markets using machine learning” discusses auction theory which is a subfield of game theory used to model markets. Auctions depend on bids being submitted by several parties looking to buy product, and these parties can take an essential strategy but should be ready for the other parties to also make decisions strategically. An example of an essential strategy highlighted by the article in this case is for someone to bid less than they will spend to increase their benefits.
This article is incredibly relevant to our class discussions to date, as it highlights how important Nash equilibrium is for describing the strategic behavior of auctions. The developments of machine learning algorithms, using the principles of Nash equilibrium, have potential to calculate equilibrium strategies for complex auction models that are not solvable today.
I think it will be especially interesting to see how advances in machine learning will alter auctions considering how the article highlights both the algorithm’s potential for encouraging organizers to change auction mechanisms and for supporting bidders in creating their bidding strategies. I would also be interested to see if, in the long run, the development of these algorithms will lead to higher payoffs for the organizers or the bidders compared to the present or even if there will be improvements at all for these groups.
https://www.techexplorist.com/understanding-complex-markets-using-machine-learning/40980/