This research develops a game theoretic network design model which considers three scenarios: (i) perfect competition between the hub ports, (ii) perfect cooperation between the hub ports, and (iii) cooperation between the shipping companies and the hub ports as a whole.
In this context, three main approaches can be distinguished: dynamic Bayesian networks, information-theoretic networks and ordinary differential equations.
We study the dynamics of a game-theoretic network formation model that yields large-scale small-world networks.
A large body of work on game-theoretic network formation models exists in the computer science and economics literature.
Our game-theoretic network formation model is mainly inspired by Even-Dar and Kearns (EK model) [1].
Hence, we refer to a class of game-theoretic network formation, also known as strategic network formation (see [6,7] for comprehensive surveys).
To assist clinicians in diagnosing and treating patients with pneumonia, a decision-theoretic network had been designed with the help of domain experts.
On the other hand, it is natural to seek for game-theoretic network formation models in which links are formed due to strategic behaviors of individuals rather than based on probabilities.
Development of contrast-independent network extraction algorithms has dramatically improved our ability to characterise these dynamic macroscopic networks and promises to bridge the gap between experiments in realistic experimental microcosms and graph-theoretic network analysis, greatly facilitating quantitative description of their complex behaviour.
To study this problem, a simple game-theoretic network protection model is considered, in which the adversary decides whether to intrude on the network to inflict maximal damage or to perform a reconnaissance mission, and based on this decision an intrusion strategy is designed.
The information-theoretic network distance is therefore a useful macroscopic descriptor of similarity.
