Abstract

The objective of this paper is to study and characterize the role and the importance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information</i> in achieving a feedback (Nash) equilibrium strategy in linear quadratic (LQ) differential games whenever the underlying players are distributed over a (physical or logic) network. It is assumed that each player should achieve a desired goal, quantified by an individual cost functional, in a competitive dynamic environment - captured by an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interconnection network</i> - by relying only on the information and data exchanged with other players according to a prescribed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information network:</i> the objective of the paper is to establish the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">value</i> of such an information exchange pattern towards achieving a more favorable (social) equilibrium. Interestingly, it is not assumed that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interconnection network</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information network</i> coincide. Moreover, since the ability of achieving a certain Nash equilibrium strategy may be lost even by removing a single communication link in the network - thus partially limiting the use of the metrics discussed above - in the second part of the paper we also consider the value of the information in forming approximate Nash equilibrium strategies, namely the so-called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -Nash equilibria. Finally, the newly defined metrics are corroborated - together with a few suggested constructive results to characterize such values - by means of numerical examples.

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