Efficiency and Equilibrium in Network Games: An Experiment

The tension between efficiency and equilibrium is a central feature of economic systems. We examine this trade-off in a network game with a unique Nash equilibrium in which agents can achieve a higher payoff by following a “collaborative norm”. Subjects establish and maintain a collaborative norm in the circle, but the norm weakens with the introduction of one hub connected to everyone in the wheel. In complex and asymmetric networks of 15 and 21 nodes, the norm disappears and subjects’ play converges to Nash. We provide evidence that subjects base their decisions on their degree, rather than the overall network structure.

Stochastic Graphon Games: I. The Static Case

We consider static finite-player network games and their continuum analogs graphon games. Existence and uniqueness results are provided as well as convergence of the finite-player network game optimal strategy profiles to their analogs for the graphon games. We also show that equilibrium strategy profiles of a graphon game provide approximate Nash equilibria for the finite-player games. Connections with mean field games are discussed. A motivating application of Cournot competition is presented, and explicit computation of its Nash equilibrium is provided.

Complex Network Game Model Simulation of Arctic Sustainable Fishery Trade Cooperation under COVID-19

Under the background of COVID-19, the conditions and environment of international cooperation in Arctic sustainable fisheries have changed. Accurately predicting the impact of environmental changes on the evolution of Arctic fishery cooperation, identifying its key influencing elements, and formulating appropriate corresponding measures have practical value for the sustainable development of Arctic fisheries. Based on the collection of fisheries trade data in Arctic sustainable fisheries trade cooperation countries, this paper builds a trade cooperation network, identifies key influencing factors, establishes a network game model, and uses simulation methods to verify the variables. The results show that the reward value given by neighboring countries has a positive effect on such cooperation. The higher the reward value is, the more countries choose to cooperate. The cooperation cost has the opposite effect, the lower the cost, the more countries choose to cooperate. The impact of cost on cooperation is greater than the incentives. The game structure also affects the outcome of cooperation, and the number of countries participating in cooperation based on the Stag Hunt Model is the largest. The change of network structure will have an impact on cooperation, and the COVID-19 pandemic has influenced cooperation on Arctic sustainable fisheries trade cooperation in the short term.

Transitional dynamics in network game with heterogeneous agents: stochastic case

In this paper, stochastic parameters are introduced into the network games model with production and knowledges externalities. This model was formulated by V. Matveenko and A. Korolev and generalized two-period Romer model. Agents' productivities have deterministic and Wiener components. The research represents the dynamics of a single agent and the dynamics in a triangle which occurs in the process of unifying agents. Explicit expressions of the dynamics of a single agent and dyad agents in the form of Brownian random processes were obtained. A qualitative analysis of the solutions of stochastic equations and systems was carried out.

Optimal Network Defense Strategy Selection Method Based on Evolutionary Network Game

The basic hypothesis of evolutionary game theory is that the players in the game possess limited rationality. The interactive behavior of players can be described by a learning mechanism that has theoretical advantages in modeling the network security problem in a real society. The current network security evolutionary game model generally adopts a replicator dynamic learning mechanism and assumes that the interaction between players in the group conforms to the characteristics of uniform mixed distribution. However, in an actual network attack and defense scenario, the players in the game have limited learning capability and can only interact with others within a limited range. To address this, we improved the learning mechanism based on the network topology, established the learning object set based on the learning range of the players, used the Fermi function to calculate the transition probability to the learning object strategy, and employed random noise to describe the degree of irrational influence in the learning process. On this basis, we built an attack and defense evolutionary network game model, analyzed the evolutionary process of attack and defense strategy, solved the evolution equilibrium, and designed a defense strategy selection algorithm. The effectiveness of the model and method is verified by conducting simulation experiments for the transition probability of the players and the evolutionary process of the defense group strategy.