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Project Information

COLLABORATIVE RESEARCH: TIME-CONSISTENT RISK-AVERSE CONTROL OF MARKOV SYSTEMS

Agency:
NSF

National Science Foundation

Project Number:
1312016
Contact PI / Project Leader:
RUSZCZYNSKI, ANDRZEJ
Awardee Organization:
RUTGERS THE STATE UNIV OF NJ NEWARK

Description

Abstract Text:
Ruszczynski, 1312016
Dentcheva, 1311978

The project is concerned with optimal control of multi-dimensional dynamic stochastic systems with risk aversion. Two approaches to risk aversion are considered: dynamic risk measures and stochastic orders. The investigators seek to advance their work on risk-averse discrete-time models and to develop general methodology for incorporating risk models into continuous-time optimal control problems of Markov structure. Three major challenges are associated with this project. First, the investigators develop proper mathematical tools for measuring risk in a time-consistent manner that would be suitable for continuous-time Markov systems. Second, the investigators develop optimality theory for control problems involving time-consistent dynamic models of risk. This includes the analysis of the structure of the control models, existence of solutions, and properties of the solutions. When developing risk-averse control models the third challenge has to be taken into account: the possibility to solve the problems numerically in an efficient way.

Decision and control problems under uncertainty arise in many areas: energy production and distribution, telecommunication, insurance and finance, logistics, medicine, security and military applications. In most cases decisions have to be made over time: decisions, as well as the random environment, influence evolution of a system, which creates the need to make new decisions, etc. Therefore, a policy has to be designed that incorporates rules for responding to future states of the system. So far, most theoretical models of such control processes have been based on average performance criteria. The investigators propose to take into account risk, that is, the possibility of occurrence of highly undesirable scenarios. The project develops mathematical models for quantifying risk in dynamical systems that evolve in a continuous way. It also provides methods to determine the best policy, when risk aversion is essential.
Project Terms:
Accounting; Area; base; design; Environment; Evolution; Future; Insurance; Logistics; mathematical model; Measures; Medicine; Methodology; Methods; Military Personnel; Modeling; Performance; Policies; Problem Solving; Process; Production; Property; Research; Research Personnel; Risk; Security; Solutions; Structure; System; Telecommunications; Theoretical model; theories; Time; tool; Uncertainty; Work

Details

Contact PI / Project Leader Information:
Name:  RUSZCZYNSKI, ANDRZEJ
Other PI Information:
Not Applicable
Awardee Organization:
Name:  RUTGERS THE STATE UNIV OF NJ NEWARK
City:  NEWARK    
Country:  UNITED STATES
Congressional District:
State Code:  NJ
District:  10
Other Information:
Fiscal Year: 2013
Award Notice Date: 11-Sep-2013
DUNS Number: 130029205
Project Start Date: 01-Sep-2013
Budget Start Date:
CFDA Code: 47.049
Project End Date: 31-Aug-2016
Budget End Date:
Agency: ?

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National Science Foundation
Project Funding Information for 2013:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

FY Total Cost
2013 NSF

National Science Foundation

$240,000

Results

i

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