98 research outputs found
Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games
Markov decision process (MDP) congestion game is an extension of classic
congestion games, where a continuous population of selfish agents solves Markov
decision processes with congestion: the payoff of a strategy decreases as more
population uses it. We draw parallels between key concepts from capacitated
congestion games and MDP. In particular, we show that population mass
constraints in MDP congestion games are equivalent to imposing tolls/incentives
on the reward function, which can be utilized by social planners to achieve
auxiliary objectives. We demonstrate such methods in a simulated Seattle
ride-share model, where tolls and incentives are enforced for two separate
objectives: to guarantee minimum driver density in downtown Seattle, and to
shift the game equilibrium towards a maximum social output.Comment: 7 pages, 6 figures, accepted to American Control Conference 201
Sensitivity Analysis for Markov Decision Process Congestion Games
We consider a non-atomic congestion game where each decision maker performs
selfish optimization over states of a common MDP. The decision makers optimize
for their own expected costs, and influence each other through congestion
effects on the state-action costs. We analyze on the sensitivity of MDP
congestion game equilibria to uncertainty and perturbations in the state-action
costs by applying an implicit function type analysis. The occurrence of a
stochastic Braess paradox is defined, analyzed based on sensitivity of game
equilibria and demonstrated in simulation. We further analyze how the
introduction of stochastic dynamics affects the magnitude of Braess paradox in
comparison to deterministic dynamics
RECURRENT APHTHOUS ULCERATION OF ORAL MUCOUS MEMBRANE AND GENITALS ASSOCIATED WITH RECURRENT HYPOPYON IRITIS (BEHCET'S SYNDROME), REPORT OF THREE CASES
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