Exact and approximate equilibria for optimal group network formation

We consider a process called the Group Network Formation Game, which represents the scenario when strategic agents are building a network together. In our game, agents can have extremely varied connectivity requirements, and attempt to satisfy those requirements by purchasing links in the network. We show a variety of results about equilibrium properties in such games, including the fact that the price of stability is 1 when all nodes in the network are owned by players, and that doubling the number of players creates an equilibrium as good as the optimum centralized solution. For the general case, we show the existence of a 2-approximate Nash equilibrium that is as good as the centralized optimum solution, as well as how to compute good approximate equilibria in polynomial time. Our results essentially imply that for a variety of connectivity requirements, giving agents more freedom can paradoxically result in more efficient outcomes.     

Protecting shared information in networks: A network security game with strategic attacks

A digital security breach, by which confidential information is leaked, does not only affect the agent whose system is infiltrated but is also detrimental to other agents socially connected to the infiltrated system. Although it has been argued that these externalities create incentives to underinvest in security, this presumption is challenged by the possibility of strategic adversaries that attack the least protected agents. In this paper we study a new model of security games in which agents share tokens of sensitive information in a network of contacts. The agents have the opportunity to invest in security to protect against an attack that can be either strategically or randomly targeted. We show that, in the presence of random attack, underinvestments always prevail at the Nash equilibrium in comparison with the social optimum. Instead, when the attack is strategic, either underinvestments or overinvestments are possible, depending on the network topology and on the characteristics of the process of the spreading of information. Actually, agents invest more in security than socially optimal when dependencies among agents are low (which can happen because the information network is sparsely connected or because the probability that information tokens are shared is small). These overinvestments pass on to underinvestments when information sharing is more likely (and therefore, when the risk brought by the attack is higher). In order to keep our analysis tractable, some of our results on strategic attacks make an assumption of homogeneity in the network, namely, that the network is vertex‐transitive. We complement these results with an analysis on star graphs (which are nonhomogeneous), which confirms that the essential lines of our findings can remain valid on general networks.     

Cooperative car parking using vehicle-to-vehicle communication: An agent-based analysis

In this paper, we introduce a decentralized car parking approach for vast car park areas based on cooperation among vehicles through vehicle-to-vehicle communication, called Cooperative Car Parking (CoPark). In CoPark, the task of finding car parking spaces inside a large car parking area is done via smart agents in vehicles opportunistically cooperating with each other to locate parking spaces as near as possible to the final destination with reduced searching time. We comprehensively investigate a range of car parking circumstances and situations in our simulations to evaluate the proposed CoPark approach. We show that by strategic cooperation between agents in the CoPark approach, greater satisfaction can be achieved in terms of individual and social benefits, in the form of reduced search times for car park spaces and reduced walking distances from where cars are parked to a destination building.     

Emergent Properties of a Market-based Digital Library with Strategic Agents

The University of Michigan Digital Library (UMDL) is designed as an open system that allows third parties to build and integrate their own profit-seeking agents into the marketplace of information goods and services. The profit-seeking behavior of agents, however, risks inefficient allocation of goods and services, as agents take strategic stances that might backfire. While it would be good if we could impose mechanisms to remove incentives for strategic reasoning, this is not possible in the UMDL. Therefore, our approach has instead been to study whether encouraging the other extreme—making strategic reasoning ubiquitous—provides an answer.Toward this end, we have designed a strategy (called the p-strategy) that uses a stochastic model of the market to find the best offer price. We have then examined the collective behavior of p-strategy agents in the UMDL auction. Our experiments show that strategic thinking is not always beneficial and that the advantage of being strategic decreases with the arrival of equally strategic agents. Furthermore, a simpler strategy can be as effective when enough other agents use the p-strategy. Consequently, we expect the UMDL is likely to evolve to a point where some agents use simpler strategies and some use the p-strategy.     

Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals’ degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores (“indirect reciprocity”), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. “tit-for-tat”) as well as indirect reciprocity (helping strangers in order to increase one’s reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level.     

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

     

Endogenous games with goals: side-payments among goal-directed agents

Boolean games have been developed as a paradigm for modelling societies of goal-directed agents. In boolean games agents exercise control over propositional variables and strive to achieve a goal formula whose realization might require the opponents’ cooperation. The presence of agents that are goal-directed makes it difficult for an external authority to be able to remove undesirable properties that are inconsistent with agents’ goals, as shown by recent contributions in the multi-agent literature. What this paper does is to analyse the problem of regulation of goal-direct agents from within the system, i.e., what happens when agents themselves are given the chance to negotiate the strategies to be played with one another. Concretely, we introduce endogenous games with goals, obtained coupling a general model of goal-directed agents (strategic games with goals) with a general model of pre-play negotiations (endogenous games) coming from game theory. Strategic games with goals are shown to have a direct correspondence with strategic games (Proposition 1) but, when side-payments are allowed in the pre-play phase, display a striking imbalance (Proposition 4). The effect of side-payments can be fully simulated by taxation mechanisms studied in the literature (Proposition 7), yet we show sufficient conditions under which outcomes can be rationally sustained without external intervention (Proposition 5). Also, integrating taxation mechanisms and side-payments, we are able to transform our starting models in such a way that outcomes that are theoretically sustainable thanks to a pre-play phase can be actually sustained even with limited resources (Proposition 8). Finally, we show how an external authority incentivising a group of agents can be studied as a special agent of an appropriately extended endogenous game with goals (Proposition 11).     

An Adaptive Strategy via Reinforcement Learning for the Prisoner's Dilemma Game

The iterated prisoner's dilemma (IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.      

关于多主体系统的可视化技术研究

多主体系统的可视化是一个非常复杂的工作,它将多个主体复杂的协作和交互活动过程可视化,以便用户对其进行监控。该文主要从这一复杂问题入手,分析多主体系统可视化需要研究的内容和亟待解决的技术难点,然后从多主体系统的结构、主体的通信语言出发,结合多主体系统的任务和目标,提出了一种多主体系统可视化的解决方案。     

Endogenous Neighborhood Selection and the Attainment of Cooperation in a Spatial Prisoner’s Dilemma Game

There is a large literature in economics and elsewhere on the emergence and evolution of cooperation in the repeated Prisoner’s Dilemma. Recently this literature has expanded to include games in a setting where agents play only with local neighbors in a specified geography. In this paper we explore how the ability of agents to move and choose new locations and new neighbors influences the emergence of cooperation. First, we explore the dynamics of cooperation by investigating agent strategies that yield Markov transition probabilities. We show how different agent strategies yield different Markov chains which generate different asymptotic behaviors in regard to the attainment of cooperation. Second, we investigate how agent movement affects the attainment of cooperation in various networks using agent-based simulations. We show how network structure and density can affect cooperation with and without agent movement.     

A strategic investment model for phased implementation of flexible manufacturing systems

This paper describes a set of strategic, tactical, and operational models that can be used to analyze the phased implementation of flexible technology in a manufacturing system. The strategic model represents capital investment decisions, the tactical model represents aggregate production decisions, and the operational model represents the functional form of the production costs. Learning effects and other nonlinearities (such as setup costs, economies of scale, and congestion effects) can be quantified in the models. An efficient solution procedure (using dynamic programming and minimum cost network flow optimization) is described. The efficiency of the solution method permits a host of 'what-if' scenarios to be examined by the decision maker.     

基于多代理协作的卫星网络故障检测算法

根据卫星网络拓扑时变性特点及管理域划分策略,提出一种卫星网络故障检测算法。在卫星节点能够响应网管指令的情况下,采用网络管理方法对卫星节点的各个接口进行检测,在卫星节点对网管指令不响应的情况下,启动域内协作或域间协作,通过域内或域间多个卫星上故障检测代理的协作,对疑似故障卫星进行检测。仿真实验结果表明,在故障率较低时,该算法的协作时间短,故障诊断效率高。     

How different connectivity patterns of individuals within an organization can speed up organizational learning

Knowledge sharing within a cooperative organization is an important issue since the power of its outcome has been the principal source of competitive advantage over the competitors in the market. However, without a proper collective knowledge management, its utilization as a strategic weapon or competitive advantage becomes difficult and inefficient. From an organizational perspective, the most important aspect of knowledge management is to transfer knowledge. In this regards, organizations must adopt structures that allow them to create and transfer more knowledge. Organizational communication structure affects the nature of human interactions and information flow which in its own turn can lead to a competitive advantage in the knowledge economy. However, in addition to that, social relationships between individuals in an organization can also be utilized to produce positive returns. In this article we emphasize the role of individual structural importance within an organizational informal communication structure as a mechanism for knowledge flow and speeding up organizational learning. Our experimental results indicate the fact that structural position of individuals within their informal communication networks can help the network members to have a better access to ongoing information exchange processes in the organization. The results of our analyses also show that organizational learning through an informal communication network of people in the form of scale-free connectivity pattern is faster comparing to the small-world connectivity style.     

Capturing an intruder in product networks

In this paper, we propose a solution to the problem of capturing an intruder in a product network. This solution is derived based on the assumption of existing algorithms for basic member graphs of a graph product. In this problem, a team of cleaner agents are responsible for capturing a hostile intruder in the network. While the agents can move in the network one hop at a time, the intruder is assumed to be arbitrarily fast in a way that it can traverse any number of nodes contiguously as far as no agents reside in those nodes. Here, we consider a version of the problem where each agent can replicate new agents. Thus, the algorithm starts with a single agent and new agents are created on demand. We propose a novel method for deriving intrusion capturing algorithms based on the abstract idea of spanning search trees. Later, we utilize this method for deriving capturing algorithms for Cartesian product graphs.     

Decision strategies and design of agent interactions in hierarchical manufacturing systems

Decisions in manufacturing planning environments involve various interdependent hierarchical levels, ranging from shop-floor operations over production planning to enterprise coordination. Distributed decision making across these hierarchies reduces the complexity compared to an otherwise monolithic planning approach. We develop a stochastic model with two decision makers (agents) and analyze their strategic interaction using game theory. The mutual influence between agents depends on organizational parameters, which, in a first case, agents themselves can regulate, and, in a second case, an organizational designer can control. The model provides managerial insights on how to achieve cooperation among self-interested agents and how decisions and preferences of agents can be aligned with the goals of the organization. Moreover, we investigate the degree and fidelity of information necessary for optimal decision making. The model is applied to a manufacturing enterprise context.     

Swords and plowshares: Information technology for collaborative advantage

Competitors who view information technology exclusively as a strategic weapon for competitive advantage risk missing a valuable opportunity: the use of information technology in conjunction with competitors for collaborative advantage. Collaborative advantage is defined as the benefit gained by a group of participants as the result of their cooperation rather than their competition. The development of a community health information network by seven competing hospitals in Dayton, Ohio illustrates how information technology can provide such an advantage. It also provides lessons for information systems and other managers who might consider such a use of information technology for their organization. Finally, it suggests further research on the subject.     

Trust-based evolutionary game model assisting AODV routing against selfishness

Self-organizing networks such as wireless ad hoc networks present a number of problems and paradoxes towards trustworthiness and consistency of the network. Lack of cooperation between nodes drastically brings down the throughput of the network. Selfish behavior of the nodes is one of the issues, which deter the trust among them. This paper explores the multiple modes to build trust and proposes an effective, dynamic and distributed framework using evolutionary game theory. Strategic, non-cooperative and evolutionary game theory has been utilized to model the dynamic behavior of selfish nodes in a self-policing ad hoc network. AODV routing assisted by the proposed game model enlightens a cogent scenario, which demonstrates that it can stimulate cooperation among selfish nodes, albeit without negotiations. The proposed model encompasses two distinct modes to learn and predict the behavior of neighbors namely deterministic and random. Deterministic mode is a generic one which helps to analyze the behavior of the network for standard strategic patterns whereas the random strategy profiles are explored using randomized analysis based on genetic algorithm. This paper investigates the simulated results for the two methods and asserts that if nodes use evolved strategies and trust evaluation mechanism, then the model supports in accomplishing the cooperation level to reach the maximum possible.     

A strategic conflict avoidance approach based on cooperative coevolutionary with the dynamic grouping strategy

Conflict avoidance plays a crucial role in guaranteeing the safety and efficiency of the air traffic management system. Recently, the strategic conflict avoidance (SCA) problem has attracted more and more attention. Taking into consideration the large-scale flight planning in a global view, SCA can be formulated as a large-scale combinatorial optimisation problem with complex constraints and tight couplings between variables, which is difficult to solve. In this paper, an SCA approach based on the cooperative coevolution algorithm combined with a new decomposition strategy is proposed to prevent the premature convergence and improve the search capability. The flights are divided into several groups using the new grouping strategy, referred to as the dynamic grouping strategy, which takes full advantage of the prior knowledge of the problem to better deal with the tight couplings among flights through maximising the chance of putting flights with conflicts in the same group, compared with existing grouping strategies. Then, a tuned genetic algorithm (GA) is applied to different groups simultaneously to resolve conflicts. Finally, the high-quality solutions are obtained through cooperation between different groups based on cooperative coevolution. Simulation results using real flight data from the China air route network and daily flight plans demonstrate that the proposed algorithm can reduce the number of conflicts and the average delay effectively, outperforming existing approaches including GAs, the memetic algorithm, and the cooperative coevolution algorithms with different well-known grouping strategies.     

Self-organizing and adaptive peer-to-peer network.

In this paper, an algorithm that forms a dynamic and self-organizing network is demonstrated. The hypothesis of this work is that in order to achieve a resilient and adaptive peer-to-peer (P2P) network, each network node must proactively maintain a minimum number of edges. Specifically, low-level communication protocols are not sufficient by themselves to achieve high-service availability, especially in the case of ad hoc or dynamic networks with a high degree of node addition and deletion. The concept has been evaluated within a P2P agent application in which each agent has a goal to maintain a preferred number of connections to a number of service providing agents. Using this algorithm, the agents update a weight value associated with each connection, based on the perceived utility of the connection to the corresponding agent. This utility function can be a combination of several node or edge parameters, such as degree k of the target node, or frequency of the message response from the node. This weight is updated using a set of Hebbian-style learning rules, such that the network as a whole exhibits adaptive self-organizing behavior. The principal result is the finding that by limiting the connection neighborhood within the overlay topology, the resulting P2P network can be made highly resilient to targeted attacks on high-degree nodes, while maintaining search efficiency.     

社会情感算法优化神经网络的短时交通流预测

针对反向传播(BP)神经网络用于交通流预测易陷入局部最优且寻优速度慢的问题,采用了社会情感优化(SEO)BP神经网络的参数,以SEO中的个体为一个BP神经网络,以3种情绪为表现形式,通过个体间的合作竞争进行寻优.运用Levy、正态、柯西分布3种情绪随机选择策略,通过不同方式实现了以不同的概率选择不确定的情绪,使SEO中情绪更好地模拟人的正常心理变化.实验表明:该模型较其他模型更有利于搜寻全局最优解,能有效提高短时交通流的预测精度.