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BEGIN:VEVENT
DTSTAMP:20260428T074444Z
DTSTART;VALUE=DATE-TIME:20251022T160000
DTEND;VALUE=DATE-TIME:20251022T173000
SUMMARY:CRETA Theory Seminar - Eva Tardos (Cornell)
TZID:Europe/London
UID:20251022-8ac672c4990a69aa0199100571304adf@warwick.ac.uk
CREATED:20251021T142052Z
DESCRIPTION:Title: Learning in investment markets. https://arxiv.org/pdf/
 2502.08597 Abstract: We analyze the performance of heterogeneous learnin
 g agents in asset markets with stochastic payoffs. Our main focus is on 
 comparing Bayesian learners and no-regret learners who compete in market
 s and identifying the conditions under which each approach is more effec
 tive. Surprisingly\, we find that low regret is not sufficient for survi
 val: an agent can have regret as low as O(log T) but still vanish when c
 ompeting against a Bayesian with a finite prior and any positive prior p
 robability on the correct model. On the other hand\, we show that Bayesi
 an learning is fragile\, while no-regret learning requires less knowledg
 e of the environment and is therefore more robust. Motivated by the stre
 ngths and weaknesses of both approaches\, we propose a balanced strategy
  for utilizing Bayesian updates that improves robustness and adaptabilit
 y to distribution shifts\, providing a step toward a best-of-both-worlds
  learning approach. The method is general\, efficient\, and easy to impl
 ement. Finally\, we formally establish the relationship between the noti
 ons of survival and market dominance studied in economics and the framew
 ork of regret minimization\, thus bridging these theories. More broadly\
 , our work contributes to the understanding of dynamics with heterogeneo
 us types of learning agents and their impact on markets.
LOCATION:S2.79
CATEGORIES:CRETA Seminar
LAST-MODIFIED:20251021T142052Z
ORGANIZER;CN=Gill Gudger:
END:VEVENT
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