EconPapers    
Economics at your fingertips  
 

A Numerical Algorithm to Calculate the Unique Feedback Nash Equilibrium in a Large Scalar LQ Differential Game

Jacob Engwerda ()

Dynamic Games and Applications, 2017, vol. 7, issue 4, 635-656

Abstract: Abstract In this paper, we study scalar linear quadratic differential games with state feedback information structure. We present a numerical algorithm which determines whether this game will have no, one, or multiple equilibria. Furthermore, in case there is a unique equilibrium, the algorithm provides this equilibrium. The algorithm is efficient in the sense that it is capable of handling a large number of players. The analysis is restricted to the case the involved cost depend only on the state and control variables.

Keywords: Linear quadratic differential games; Linear feedback Nash equilibria; Coupled algebraic Riccati equations (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s13235-016-0201-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: A numerical algorithm to calculate the unique feedback nash equilibrium in a large scalar LQ differential game (2017) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:dyngam:v:7:y:2017:i:4:d:10.1007_s13235-016-0201-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13235

Access Statistics for this article

Dynamic Games and Applications is currently edited by Georges Zaccour

More articles in Dynamic Games and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2019-05-21
Handle: RePEc:spr:dyngam:v:7:y:2017:i:4:d:10.1007_s13235-016-0201-7