Slotted Aloha as a game with partial information

This paper studies distributed choice of retransmission probabilities in slotted ALOHA. Both the cooperative team problem as well as the noncooperative game problem are considered. Unlike some previous work, we assume that mobiles do not know the number of backlogged packets at other nodes. A Markov chain analysis is used to obtain optimal and equilibrium retransmission probabilities and throughput. We then investigate the impact of adding retransmission costs (which may represent the disutility for power consumption) on the equilibrium and show how this pricing can be used to make the equilibrium throughput coincide with the optimal team throughput.     

基于演化博弈论的移动Ad Hoc网络中继协作机制

从演化博弈论的角度,提出一种能有效激励无线Ad Hoc网络节点参与数据分组中继协作的纳什均衡策略:G-TFT(Generous Tit for Tat).以节点中继的分组数与所需的能量开销为均衡点,建立了中继协作过程的单阶段博弈模型G,验证了节点的自私性动机,将G扩展为基于时闻序列、策略可转换的演化博弈R,并根据R的纳什均衡提出了协作激励策略G-TFT.仿真结果表明.通过调整宽容因子g,G-TFT能有效地激励节点参与中继协作.     

Non-cooperative routing in loss networks

The paper studies routing in loss networks in the framework of a non-cooperative game with selfish users. Two solution concepts are considered: the Nash equilibrium, corresponding to the case of a finite number of agents (such as service providers) that take routing decisions, and the Wardrop equilibrium, in which routing decisions are taken by a very large number of individual users. We show that these equilibria do not fall into the standard frameworks of non-cooperative routing games. As a result, we show that uniqueness of equilibria or even of utilizations at equilibria may fail even in the case of simple topology of parallel links. However, we show that some of the problems disappear in the case in which the bandwidth required by all connections is the same. For the special case of a parallel link topology, we obtain some surprisingly simple way of solving the equilibrium for both cases of Wardrop as well as Nash equilibrium.     

Numerical studies on a paradox for non-cooperative static load balancing in distributed computer systems

Numerical examples of a Braess-like paradox in which adding capacity to a distributed computer system may degrade the performance of all users in the system under non-cooperative optimization have been reported. Unlike the original Braess paradox, in the models examined, this behavior occurs only in the case of finitely many users and not in the case of infinite number of users and the degree of performance degradation can increase without bound. This study examines numerically some examples around the Braess-like paradox in a distributed computer system. In the numerical examples, it is observed that the worst-case degree of the paradox (WCDP) is largest in complete symmetry. The dependence of the WCDP on some system parameters is also examined.     

A Stackelberg game-based spectrum allocation scheme in macro/femtocell hierarchical networks

Femtocells widely deployed in a macrocell form hierarchical cell networks, which can improve indoor coverage and network capacity, and have been regarded as one of the most promising approaches. However owing to the absence of coordination between the macro and femtocells, and among femtocells, decentralized spectrum allocation between macro and femtocell users become technically challenging. In this paper, a dynamic spectrum allocation scheme based on Stackelberg game is proposed, in which macrocell base stations as leaders and femtocell base stations as followers are players, and the same spectrum is the resource that players will choose assigning to users for minimizing the affected interference among each other. The Stackelberg equilibrium is defined and proved to be existed, which is also the optimal spectrum allocation manner. Simulations were conducted to study the impact of femtocells on the macrocells regarding throughput, outage probability and spectrum efficiency. And the comparison results show that the proposed scheme might be a solution for efficiently allocating the spectrum in hierarchical cell networks, as the improvement in terms of throughput, outage probability and spectrum efficiency had been achieved.     

Selfish routing and wavelength assignment strategies with advance reservation in inter-domain optical networks

The main challenge in developing large data network in the wide area is in dealing with the scalability of the underlying routing system. Accordingly, in this work we focus on the design of an effective and scalable routing and wavelength assignment (RWA) framework supporting advance reservation services in wavelength-routed WDM networks crossing multiple administrative domains. Our approach is motivated by the observation that traffic in large optical networks spanning several domains is not controlled by a central authority but rather by a large number of independent entities interacting in a distributed manner and aiming at maximizing their own welfare. Due to the selfish strategic behavior of the involved entities, non-cooperative game theory plays an important role in driving our approach. Here the dominant solution concept is the notion of Nash equilibria, which are states of a system in which no participant can gain by deviating unilaterally its strategy. On this concept, we developed a selfish adaptive RWA model supporting advance reservation in large-scale optical wavelength-routed networks and developed a distributed algorithm to compute approximate equilibria in computationally feasible times. We showed how and under which conditions such approach can give rise to a stable state with satisfactory solutions and analyzed its performance and convergence features.     

跨洋航空网络的频谱共享博弈模型

提出一种跨洋航空网络中主干网络的频谱共享博弈模型。该模型综合考虑了主用户和所有次级用户的收益,主用户可在保证自身收益最大的前提下对各次级用户进行有效的频谱分配;各次级用户根据主用户的最佳单位定价改变请求带宽以获得最大收益。给出了该模型的静态博弈纳什均衡与动态博弈收敛结果,通过与次级用户收益的最优化模型进行比较,验证了用该模型解决跨洋航空网络中主干网络上的频谱分配问题具有公平性。     

A hybrid systems model for power control in multicell wireless data networks

We present a power control scheme based on noncooperative game theory, using a fairly broad class of convex cost functions. The multicell CDMA wireless data network is modeled as a switched hybrid system where handoffs of mobiles between different cells correspond to discrete switching events between different subsystems. Under a set of sufficient conditions, we prove the existence of a unique Nash equilibrium for each subsystem, and prove global exponential stability of an update algorithm. We also establish the global convergence of the dynamics of the multicell power control game to a convex superset of Nash equilibria for any switching (handoff) scheme satisfying a mild condition on average dwell-time. Robustness of these results to feedback delays as well as to quantization is investigated. In addition, we consider a quantization scheme to reduce the communication overhead between mobiles and the base stations. Finally, we illustrate the power control scheme developed through simulations.     

Multi-channel power allocation based on market competitive equilibrium in cognitive radio networks

Cognitive radios such as intelligent phones and Bluetooth devices have been considered essential goods in next-generation communication systems.Such devices will have to share the same frequency band owing to the limited bandwidth resource.To improve spectrum efciency,we formulate multi-channel power allocation as a market competitive equilibrium(CE)problem,and prove that its solution exists and is unique under the condition of weak interference.We then propose two distributed power allocation algorithms achieving CE,namely the fast convergent power allocation algorithm(FCPAA)and the social-fairness-aware FCPAA(SFAF).Theoretical analysis and simulation results demonstrate that the proposed algorithms lead to better system performance in terms of the guaranteed interference temperature constraint using the price mechanism instead of a strategy based on the Nash equilibrium.Moreover,it is shown that the FCPAA maximizes total utility,and converges more quickly than the method addressed in prior research with the help of improved round-robin rules.However,the FCPAA cannot ensure the same social fairness among secondary users as the SFAF scheme in both the non-fading channel and Rayleigh fading channel;the SFAF balances the individual utility by adjusting each user’s budget at the expense of a small quantity of system total throughput.     

AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents

Two minimal requirements for a satisfactory multiagent learning algorithm are that it 1. learns to play optimally against stationary opponents and 2. converges to a Nash equilibrium in self-play. The previous algorithm that has come closest, WoLF-IGA, has been proven to have these two properties in 2-player 2-action (repeated) games—assuming that the opponent’s mixed strategy is observable. Another algorithm, ReDVaLeR (which was introduced after the algorithm described in this paper), achieves the two properties in games with arbitrary numbers of actions and players, but still requires that the opponents' mixed strategies are observable. In this paper we present AWESOME, the first algorithm that is guaranteed to have the two properties in games with arbitrary numbers of actions and players. It is still the only algorithm that does so while only relying on observing the other players' actual actions (not their mixed strategies). It also learns to play optimally against opponents that eventually become stationary. The basic idea behind AWESOME (Adapt When Everybody is Stationary, Otherwise Move to Equilibrium) is to try to adapt to the others' strategies when they appear stationary, but otherwise to retreat to a precomputed equilibrium strategy. We provide experimental results that suggest that AWESOME converges fast in practice. The techniques used to prove the properties of AWESOME are fundamentally different from those used for previous algorithms, and may help in analyzing future multiagent learning algorithms as well. Editors: Amy Greenwald and Michael Littman     

A Nash equilibrium solution in an oligopoly market: The search for Nash equilibrium solutions with replicator equations derived from the gradient dynamics of a simplex algorithm

The present analysis applies continuous time replicator dynamics to the analysis of oligopoly markets. In the present paper, we discuss continuous game problems in which decision-making variables for each player are bounded on a simplex by equalities and non-negative constraints. Several types of problems are considered under conditions of normalized constraints and non-negative constraints. These problems can be classified into two types based on their constraints. For one type, the simplex constraint applies to the variables for each player independently, such as in a product allocation problem. For the other type, the simplex constraint applies to interference among all players, creating a market share problem. In the present paper, we consider a game problem under the constraints of allocation of product and market share simultaneously. We assume that a Nash equilibrium solution can be applied and derive the gradient system dynamics that attain the Nash equilibrium solution without violating the simplex constraints. Models assume that three or more firms exist in a market. Firms behave to maximize their profits, as defined by the difference between their sales and cost functions with conjectural variations. The effectiveness of the derived dynamics is demonstrated using simple data. The present approach facilitates understanding the process of attaining equilibrium in an oligopoly market.     

CDMA系统中基于时延花费的功率速率联合控制

针对CDMA系统中的多业务处理的飞速发展,采用非合作博弈（Non—cooperative Game,NG）理论实现功率与速率的联合控制（joint control of power and rate）。将功率与速率控制问题等效为非合作博弈（Non—cooperative Game,NG）问题,并且提出了一种基于时延花费的代价函数,接着证明了该联合算法纳什均衡（Nash Equilibrium,NE）的存在性与唯一性。仿真结果表明,该算法使得用户在较小的功率下获得较高效用值,并且减少了用户的传输延时。     

An optimal cooperation in a team of agents subject to partial information

The objective of this work is to design a controller for a team of agents to accomplish cohesive motion with consensus on an agreed upon trajectory in both leaderless and modified leader–follower structures. The agent's dynamical model is considered to be governed by a general linear representation instead of a point-mass model which is commonly used in the literature. A semi-decentralised optimal control strategy is designed based on minimisation of individual cost functions over a finite horizon using local information. Decentralisation is achieved through incorporating interaction terms in the team members model. Minimisation of our proposed cost function results in a modified version of the consensus algorithm. Corresponding to the leader–follower structure, the utilisation of a corrective feedback from followers to the leader provides potential advantages in terms of improving robustness of the team to faults in individual agents. Simulation results are presented to demonstrate the effectiveness and capabilities of our proposed method in achieving user predefined requirements and specifications.     

Efficient wireless packet scheduling in a non-cooperative environment: Game theoretic analysis and algorithms

In many practical scenarios, wireless devices are autonomous and thus, may exhibit non-cooperative behaviors due to self-interests. For instance, a wireless cellular device may be programmed to report bogus channel information to gain resource allocation advantages. Such non-cooperative behaviors are highly probable as the device’s software can be modified by the user. In this paper, we first analyze the impact of these rationally selfish behaviors on the performance of packet scheduling algorithms in time-slotted wireless networks. Using a mixed strategy game model, we show that the traditional maximum rate packet scheduling algorithm can cause non-cooperative devices to converge to highly inefficient Nash equilibria, in which the wireless channel resources are significantly wasted. By using a repeated game to enforce cooperation, we further propose a novel game theoretic algorithm that can lead to an efficient equilibrium.     

Combining data mining and Game Theory in manufacturing strategy analysis

The work presented in this paper is result of a rapid increase of interest in game theoretical analysis and a huge growth of game related databases. It is likely that useful knowledge can be extracted from these databases. This paper argues that applying data mining algorithms together with Game Theory poses a significant potential as a new way to analyze complex engineering systems, such as strategy selection in manufacturing analysis. Recent research shows that combining data mining and Game Theory has not yet come up with reasonable solutions for the representation and structuring of the knowledge in a game. In order to examine the idea, a novel approach of fusing these two techniques has been developed in this paper and tested on real-world manufacturing datasets. The obtained results have been indicated the superiority of the proposed approach. Some fruitful directions for future research are outlined as well.     

Comparative dynamics in an overlapping-generations model: the effects of quasi-rational discrete choice on finding and maintaining Nash equilibrium

Many models of Nash Equilibrium are complex enough that it becomes difficult to ascertain if and under what conditions the economic players can find and maintain this equilibrium. Using an analytical overlapping- generations model of goods, labor, and banking markets and quasi-rational discrete choice decision making, we find through agent-based simulations that Nash Equilibrium in goods market prices is stable when firms are sufficiently sensitive to changes in profits. In addition to verifying the analytical Nash outcome, the simulations verify that their economic agents, decision rules, and other protocols correspond to and maintain consistency with the analytical theory and identify important bounds of the analytical model.      

利润-CVaR准则下的二级供应链定价与订货策略研究

传统的定价与订货策略研究多是建立在风险中性的假设之上,近来也有利用风险度量CVaR研究风险厌恶对库存的影响.因此,以期望利润和CVaR的加权平均为目标函数,研究零售商的订货策略,并在此基础上研究上游供应商的定价策略.这样的利润-CVaR目标既反映了决策者追求高利润的愿望,又反映了其对潜在风险的控制.     

Statistical Analysis and Modeling of Jobs in a Grid Environment

The existence of good probabilistic models for the job arrival process and the delay components introduced at different stages of job processing in a Grid environment is important for the improved understanding of the Grid computing concept. In this study, we present a thorough analysis of the job arrival process in the EGEE infrastructure and of the time durations a job spends at different states in the EGEE environment. We define four delay components of the total job delay and model each component separately. We observe that the job inter-arrival times at the Grid level can be adequately modelled by a rounded exponential distribution, while the total job delay (from the time it is generated until the time it completes execution) is dominated by the computing element’s register and queuing times and the worker node’s execution times. Further, we evaluate the efficiency of the EGEE environment by comparing the job total delay performance with that of a hypothetical ideal super-cluster and conclude that we would obtain similar performance if we submitted the same workload to a super-cluster of size equal to 34% of the total average number of CPUs participating in the EGEE infrastructure. We also analyze the job inter-arrival times, the CE’s queuing times, the WN’s execution times, and the data sizes exchanged at the kallisto.hellasgrid.gr cluster, which is node in the EGEE infrastructure. In contrast to the Grid level, we find that at the cluster level the job arrival process exhibits self-similarity/long-range dependence. Finally, we propose simple and intuitive models for the job arrival process and the execution times at the cluster level.     

Developing a design support system for the exterior form of running shoes using partial least squares and neural networks

The function of sport shoes is to improve sport performance and reduce sports-related injuries. They are commercial products developed by combining sports technology and marketing activities. Numerous studies on research and development, material application, production process improvements, and human physiological measurements for the sport shoe market exist. However, few studies have conducted an in-depth investigation on the design of forms and external appearance for sports shoes.     

A joint model for cash and inventory management for a retailer under delay in payments

As retail companies continue to navigate through the economy downturn, it becomes critical to find innovative cost reduction methods. Cash management is a cost-intensive process for retailers, who are currently focusing on effective cash management, such as deciding on the maximum cash level to keep in their business accounts and how much to borrow to finance inventories and pay suppliers. In this paper, we consider the problem of finding the optimal operational (how much to order and when to pay the supplier) and financial decisions (maximum cash level and loan amount) by integrating the cash management and inventory lot sizing problems. We consider a supplier offering a retailer an interest-free credit period for settling the payment. Beyond this period, the supplier charges interest on the outstanding balance. Whenever the cash exceeds a certain limit, it will be invested in purchasing financial securities. At the time when the retailer pays the supplier for the received order, cash is withdrawn from the account, incuring various financial costs. If the cash level becomes zero or not sufficient, the retailer obtains an asset-based loan at interest. We model this problem as a nonlinear program and propose a solution procedure for finding the optimal solution. We perform a numerical study to analyze the impact of optimal cash management on the inventory decisions. The results indicate that the optimal order quantity decreases as the retailer’s return on cash increases. We compare our model to a model that ignores financial considerations of cash management, and show numerically that our model lowers the retailer’s cost. Also, we illustrate the effect of changing various model parameters on the optimal solution and obtain managerial insights.