NDLI: Optimal halting policies in Markov population decision chains with constant risk posture

Content Provider	Springer Nature Link
Author	Canbolat, Pelin G.
Copyright Year	2013
Abstract	This paper concerns infinite-horizon Markov population decision chains with finite state-action space, where one exerts control on a population of individuals in different states by assigning an action to each individual in the system in each period. In every transition, each individual earns a random reward and generates a random progeny vector. The objective of the decision maker is to maximize expected (infinite-horizon) system utility under the following assumptions: (i) The utility function exhibits constant risk posture, (ii) the (random) progeny vectors of distinct individuals are independent, and (iii) the progeny vectors of individuals in a state who take the same action are identically distributed. The paper deals with the problem of finding an optimal stationary halting policy and shows that this problem can be solved efficiently using successive approximations with the original state-action space without enlarging it to include information about the population in each state or any other aspect of the system history in a state. The proposed algorithm terminates after a finite number of iterations with an optimal stationary halting policy or with proof of nonexistence.
Starting Page	227
Ending Page	237
Page Count	11
File Format	PDF
ISSN	02545330
Journal	Annals of Operations Research
Volume Number	222
Issue Number	1
e-ISSN	15729338
Language	English
Publisher	Springer US
Publisher Date	2013-01-12
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Markov decision chains Stochastic control Markov population decision chains Constant risk posture Dynamic programming Halting policies Operations Research/Decision Theory Combinatorics Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research

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