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DTSTAMP:20250312T213349
DTSTART;TZID=America/Detroit:20250319T160000
DTEND;TZID=America/Detroit:20250319T170000
SUMMARY:Workshop / Seminar:Student AIM Seminar: Spatial Pattern Formation in Eco-Evolutionary Games with Environment-Driven Motion
DESCRIPTION:The sustainable management of common resources often leads to a social dilemma known as the tragedy of the commons: individuals benefit from rapid extraction of resources\, while communities as a whole benefit from more sustainable extraction strategies. In this talk\, we explore a PDE model of evolutionary game theory with environmental feedback\, describing how the spatial distribution of resource extraction strategies and environmental resources evolve due to reaction terms describing eco-evolutionary game-theoretic dynamics and spatial terms describing diffusion of environmental resources and directed motion of resource harvesters towards regions of greater environmental quality. Through linear stability\, we show that this biased motion towards higher-quality environments can lead to spatial patterns in the distribution of extraction strategies\, creating local regions with improved environmental quality and increase payoff for resource extractors. However\, by measuring the average payoff and environmental quality across the spatial domain\, we see that this pattern-forming mechanism can actually decrease the overall success of the population relative to the equilibrium outcome in the absence of spatial motion. This suggests that environmental-driven motion can produce a spatial social dilemma\, in which biased motion towards more beneficial regions can produce emergent patterns featuring a worse overall environment for the population.
UID:131412-21868443@events.umich.edu
URL:https://events.umich.edu/event/131412
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Applied Mathematics,Mathematics
LOCATION:East Hall - 3088
CONTACT:
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DTSTAMP:20250224T160613
DTSTART;TZID=America/Detroit:20250319T160000
DTEND;TZID=America/Detroit:20250319T170000
SUMMARY:Workshop / Seminar:Uncertain Games
DESCRIPTION:This work provides a unified framework for exploring games. In existing literature\, strategies of players are typically assigned scalar values\, and the concept of Nash equilibrium is used to identify compatible strategies. However\, this approach lacks the internal structure of a player\, thereby failing to accurately model observed behaviors in reality. To address this limitation\, we propose to characterize players by their learning algorithms\, and as their estimations intrinsically induce a distribution over strategies\, we introduced the notion of equilibrium in terms of characterizing the recurrent behaviors of the learning algorithms. This approach allows for a more nuanced understanding of players\, and brings the focus to the challenge of learning that players face. While our explorations in discrete games\, mean-field games\, and reinforcement learning demonstrate the framework's broad applicability\, they also set the stage for future research aimed at specific applications. This is a joint work with Erhan Bayraktar.
UID:133106-21872393@events.umich.edu
URL:https://events.umich.edu/event/133106
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1360
CONTACT:
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