Endogenous Explanation for Random Fluctuation of Stock Price and Its Application: Based on the View of Repeated Game with Asymmetric Information

In this paper, we characterize the players’ behavior in the stock market by the repeated game model with asymmetric information. We show that the discount price process of stock is a martingale driven by Brownian motion, and give an endogenous explanation for the random fluctuation of stock price: the randomizations in the market is due to the randomizations in the strategy of the informed player which hopes to avoid revealing his private information. On this basis, through studying the corresponding option pricing problem furtherly, we can give the expression of function φ.

Zero-determinant strategy:An underway revolution in game theory

Repeated games describe situations where players interact with each other in a dynamic pattern and make decisions ac- cording to outcomes of previous stage games. Very recently, Press and Dyson have revealed a new class of zero-determinant （ZD） strategies for the repeated games, which can enforce a fixed linear relationship between expected payoffs of two play- ers, indicating that a smart player can control her unwitting co-player＇s payoff in a unilateral way [Proc. Acad. Natl. Sci. USA 109, 10409 （2012）]. The theory of ZD strategies provides a novel viewpoint to depict interactions among players, and fundamentally changes the research paradigm of game theory. In this brief survey, we first introduce the mathematical framework of ZD strategies, and review the properties and constrains of two specifications of ZD strategies, called pinning strategies and extortion strategies. Then we review some representative research progresses, including robustness analysis, cooperative ZD strategy analysis, and evolutionary stability analysis. Finally, we discuss some significant extensions to ZD strategies, including the multi-player ZD strategies, and ZD strategies under noise. Challenges in related research fields are also listed.

Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric games.In the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the leader.However,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of states.To this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected utility.The authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE strategy.Then the authors turn to multi-player models,where there exists one player adopting an equalizer ZD strategy.The authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE strategy.Finally,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

Preventing Dropping Packets Attack in Sensor Networks:A Game Theory Approach

Focusing on dropping packets attacks in sensor networks, we propose a model of dropping packets attack-resistance as a repeated game based on such an assumption that sensor nodes are rational. The model prevents malicious nodes from attacking by establishing punishment mechanism, and impels sensor networks to reach a collaborative Nash equilibrium. Simulation results show that the devised model can effectively resist the dropping packets attacks（DPA） by choosing reasonable configuration parameters.

The Research of QoS Guarantee Mechanism of the Secondary Users in Cognitive Radio Networks

Cognitive radio(CR) is a promising solution to improve the spectrum utilization.The cognitive radio networks includes the primary user(PU) system with authorized spectrum and the secondly user(SU) system without authorized spectrum. When the SUs want to use the spectrum, they have to find the idle channels that are not occupied by the PUs. So the QoS of the SUs will be affected not only by the characteristic of their own business, but also by the behavior of the PUs.Currently, in order to ensure the quality of the SU services, the M-LDWF algorithm is widely used in scheduling. However, the M-LWDF algorithm didn't fully consider the difference among the SUs. For those SUs who are in the process of communication but have to change channel due to the return of the PU, they should have higher scheduling priority. In this paper, we put forward an improved algorithm based on M-LWDF. In order to guarantee the QoS of the SUs those were in the processing of communication, we gave the higher scheduling priority. Simulation results show that the improved algorithm can effectively decrease the dropping rate and improve the QoS of the SUs and the performance of the whole system.

自组织网络中一种基于博弈论的协商TDMA机制(英文)

This paper proposes a negotiation-based TDMA scheme for ad hoc networks, which was modeled as an asynchronous myopic repeated game. Compared to the traditional centralized TDMA schemes, our scheme operates in a decentralized manner and is scalable to topology changes. Simulation results show that, with respect to the coloring quality, the performance of our scheme is close to that of the classical centralized algorithms with much lower complexity.

Playing repeated security games with multiple attacker types:a Q-iteration on a linear programming approach

This paper investigates infinite horizon repeated security games with one defender and multiple attacker types.The incomplete information brings uncertainty of attackers’behaviour for the defender.Under the uncertainty of attackers’behaviours,we take the worst-case analysis to minimise the defender’s regret w.r.t.each attacker type.We wish to keep the regret especially small w.r.t.one attacker type,at the cost of modest additional overhead compared to others.The tradeoff among the objectives requires us to build a Multi-Objective Repeated SecurityGame(MORSG)model.To parameterise the regret Pareto frontier,we combine the different weight vectors with different objectives and build a linear programming approach.By running the Q-iteration procedure on linear programming for each weight vector,the optimal regret Pareto frontier can be computed.We also propose an approximate approach to approximate it.The approximation analysis proves the effectiveness of the approximation approach.

Research on pricing game strategy for load-balancing in VANET

In vehicle Ad-hoc netwok （VANET）, traffic load is often unevenly distributed among access points （APs）. Such load imbalance hampers the network from fully utilizing the network capacity. To alleviate such imbalance, the paper introduces a novel pricing game model. The research scene is at the intersection when the traffic light is green. As vehicles are highly mobile and the network typology changes dynamically, the paper divides the green light time into equal slots and calculates APs＇ prices with the presented pricing game in each time slot. The whole process is a repeated game model. The final equilibrium solution set is APs＇ pricing strategy, and the paper claim that this equilibrium solution set can affect vehicles＇ selection and ensure APs＇ load-balancing. Simulation results based on a realistic vehicular traffic model demonstrate the effectiveness of the game method.

Learning implicit information in Bayesian games with knowledge transfer

In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.

A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information

The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.