Date:
Sun, 03/04/202214:00-16:00
Location:
Elath Hall, 2nd floor, Feldman Building, Edmond Safra Campus
Lecturer:
Noam Nissan
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GAME THEORY AND MATHEMATICAL ECONOMICS RESEARCH SEMINAR
===============================================================
Speaker: Noam Nisan, HUJI
Topic: Auctions Between Regret-Minimizing Agents (joint with Yoav Kolumbus)
Place: Elath Hall, 2nd floor, Feldman Building, Edmond Safra Campus
Time: Sunday, April 3, 2022 at 14:00 p.m.
Refreshments available at 13:30 p.m.
YOU ARE CORDIALLY INVITED
Abstract:
We analyze a scenario in which software agents implemented as
regret-minimizing algorithms engage in a repeated auction on behalf of
their users. We study first price and second price auctions, as well as
their generalized versions (e.g., as those used for ad auctions). Using
both theoretical analysis and simulations, we show that, surprisingly, in
second price auctions the players have incentives to mis-report their true
valuations to their own learning agents, while in the first price auction
it is a dominant strategy for all players to truthfully report their
valuations to their agents.
See paper on arXiv: https://arxiv.org/abs/2110.11855 as well as a related companion paper: https://arxiv.org/abs/2112.07640
Google Calendar:
https://calendar.google.com/calendar/u/2?cid=cmF0aW9uYWxpdHkuaHVqaUBnbWFpbC5jb20
_______________________________________________
GAME THEORY AND MATHEMATICAL ECONOMICS RESEARCH SEMINAR
===============================================================
Speaker: Noam Nisan, HUJI
Topic: Auctions Between Regret-Minimizing Agents (joint with Yoav Kolumbus)
Place: Elath Hall, 2nd floor, Feldman Building, Edmond Safra Campus
Time: Sunday, April 3, 2022 at 14:00 p.m.
Refreshments available at 13:30 p.m.
YOU ARE CORDIALLY INVITED
Abstract:
We analyze a scenario in which software agents implemented as
regret-minimizing algorithms engage in a repeated auction on behalf of
their users. We study first price and second price auctions, as well as
their generalized versions (e.g., as those used for ad auctions). Using
both theoretical analysis and simulations, we show that, surprisingly, in
second price auctions the players have incentives to mis-report their true
valuations to their own learning agents, while in the first price auction
it is a dominant strategy for all players to truthfully report their
valuations to their agents.
See paper on arXiv: https://arxiv.org/abs/2110.11855 as well as a related companion paper: https://arxiv.org/abs/2112.07640
Google Calendar:
https://calendar.google.com/calendar/u/2?cid=cmF0aW9uYWxpdHkuaHVqaUBnbWFpbC5jb20
_______________________________________________