scholarly journals Information: Price and Impact on General Welfare and Optimal Investment: An Anticipative Stochastic Differential Game Model

2010 ◽  
Author(s):  
Christian-Oliver Ewald ◽  
Yajun Xiao
Keyword(s):  
Differential Game ◽  
Optimal Investment ◽  
Game Model ◽  
Stochastic Differential Game ◽  
General Welfare

scholarly journals Information: price and impact on general welfare and optimal investment. an anticipative stochastic differential game model

2011 ◽  
Vol 43 (1) ◽  
pp. 97-120 ◽  
Author(s):  
Christian-Oliver Ewald ◽  
Yajun Xiao
Keyword(s):  
Nash Equilibria ◽  
Optimal Investment ◽  
Stochastic Calculus ◽  
Game Model ◽  
Market Game ◽  
Feedback Effects ◽  
Equilibrium Prices ◽  
General Welfare ◽  
Necessary And Sufficient Criteria ◽  
Necessary And Sufficient

Within an anticipative stochastic calculus framework, we study a market game with asymmetric information and feedback effects. We derive necessary and sufficient criteria for the existence of Nash equilibria and study how general welfare is affected by the level of information. In particular, we show that, under certain conditions in a competitive environment, an increased level of information may in fact lower the level of general welfare, leading to the so-called Hirshleifer effect (see Hirshleifer (1971)). Finally, we determine equilibrium prices for particular pieces of information, by extending our market game with a pre-stage, in which information is traded.

scholarly journals Stochastic Differential Game-Based Malware Propagation in Edge Computing-Based IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li Miao ◽  
Shuai Li
Keyword(s):  
Differential Game ◽  
Infinite Horizon ◽  
Optimal Strategies ◽  
Edge Computing ◽  
Game Model ◽  
Stochastic Differential Game ◽  
Equilibrium Solutions ◽  
Stochastic Fluctuations ◽  
Malware Propagation ◽  
Nash Equilibrium Solutions

Internet of Things (IoT) has played an important role in our daily life since its emergence. The applications of IoT cover from the traditional devices to intelligent equipment. With the great potential of IoT, there comes various kinds of security problems. In this paper, we study the malware propagation under the dynamic interaction between the attackers and defenders in edge computing-based IoT and propose an infinite-horizon stochastic differential game model to discuss the optimal strategies for the attackers and defenders. Considering the effect of stochastic fluctuations in the edge network on the malware propagation, we construct the Itô stochastic differential equations to describe the propagation of the malware in edge computing-based IoT. Subsequently, we analyze the feedback Nash equilibrium solutions for our proposed game model, which can be considered as the optimal strategies for the defenders and attackers. Finally, numerical simulations show the effectiveness of our proposed game model.

scholarly journals Information: price and impact on general welfare and optimal investment. an anticipative stochastic differential game model

2011 ◽  
Vol 43 (01) ◽  
pp. 97-120
Author(s):  
Christian-Oliver Ewald ◽  
Yajun Xiao
Keyword(s):  
Nash Equilibria ◽  
Optimal Investment ◽  
Stochastic Calculus ◽  
Game Model ◽  
Market Game ◽  
Feedback Effects ◽  
Equilibrium Prices ◽  
General Welfare ◽  
Necessary And Sufficient Criteria ◽  
Necessary And Sufficient

Within an anticipative stochastic calculus framework, we study a market game with asymmetric information and feedback effects. We derive necessary and sufficient criteria for the existence of Nash equilibria and study how general welfare is affected by the level of information. In particular, we show that, under certain conditions in a competitive environment, an increased level of information may in fact lower the level of general welfare, leading to the so-called Hirshleifer effect (see Hirshleifer (1971)). Finally, we determine equilibrium prices for particular pieces of information, by extending our market game with a pre-stage, in which information is traded.

scholarly journals Optimal Network Defense Strategy Selection Method: A Stochastic Differential Game Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yan Mi ◽  
Hengwei Zhang ◽  
Hao Hu ◽  
Jinglei Tan ◽  
Jindong Wang
Keyword(s):  
Network Security ◽  
Real Time ◽  
Differential Game ◽  
Random Effects ◽  
Game Model ◽  
Random Disturbance ◽  
Stochastic Differential Game ◽  
Defense Strategy ◽  
Random Disturbances ◽  
Attack And Defense

In a real-world network confrontation process, attack and defense actions change rapidly and continuously. The network environment is complex and dynamically random. Therefore, attack and defense strategies are inevitably subject to random disturbances during their execution, and the transition of the network security state is affected accordingly. In this paper, we construct a network security state transition model by referring to the epidemic evolution process, use Gaussian noise to describe random effects during the strategy execution, and introduce a random disturbance intensity factor to describe the degree of random effects. On this basis, we establish an attack-defense stochastic differential game model, propose a saddle point equilibrium solution method, and provide an algorithm to select the optimal defense strategy. Our method achieves real-time defense decision-making in network attack-defense scenarios with random disturbances and has better real-time performance and practicality than current methods. Results of a simulation experiment show that our model and algorithm are effective and feasible.

Sign in / Sign up

Export Citation Format

Share Document