New AI algorithm as a tool for economists.
Auction theory is an essential part of game theory. It is mainly used in economic theory to model markets. Auctions depend on the attached rule: multiple parties place bids to buy products. The parties can pursue one essential strategy, such as bidding less than they will spend, in order to increase their profits. However, you need to be prepared for other parties to act strategically as well.
Martin Bichler, Professor of Decision Sciences and Systems at TUM said: “Machine learning is not yet widely used in auction theory. Using neural networks, we were able to compute equilibrium strategies for complex auction models that were previously unsolvable.”
“For common auction models, we can prove mathematically that the results of the NPGA method reliably converge to the equilibrium strategy. We also showed in experiments that our process delivers extremely close approximations to equilibrium strategies for markets.”
“The new algorithm will help economists to analyze more complex markets and their equilibria. But real-world applications are also conceivable: Since the mid-1990s, governments worldwide have sold wireless spectrum through auctions. The Nobel laureates Robert Wilson and Paul Milgrom have developed auction formats for this purpose.”
“Spectrum auctions are an exciting real-world example. NPGA can help identify strategic issues in advance that could lead to undesirable results – for example, a high likelihood of bidding strategies resulting in spectrum licenses being purchased by inefficient bidders. In this case, the organizers could opt for a different auction mechanism. And conversely, the algorithm could also support bidders in developing their bidding strategies.”
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