基于囚徒困境策略的改进HK网络上的合作博弈

为模拟现实世界的合作行为,本文在HK网络模型基础上提出了一种具有高聚类幂律可调性质的新的网络模型,并分析了囚徒困境博弈在此网络上的演化。通过仿真实验,研究了该网络的高聚类特性对合作行为的影响。大量实验表明,网络的高聚类特性可以极大促进合作现象的涌现。同时研究也发现,随着诱惑参数的变大,合作水平也会随之下降,但幅度不大。总之,该演化博弈模型可以促进合作现象的涌现并抵御背叛策略的传播。     

COVERAGE: detecting and reacting to worm epidemics using cooperation and validation

Cooperative defensive systems communicate and cooperate in their response to worm attacks, but determine the presence of a worm attack solely on local information. Distributed worm detection and immunization systems track suspicious behavior at multiple cooperating nodes to determine whether a worm attack is in progress. Earlier work has shown that cooperative systems can respond quickly to day-zero worms, while distributed detection systems allow detectors to be more conservative (i.e., paranoid) about potential attacks because they manage false alarms efficiently. In this paper we present our investigation into the complex tradeoffs in such systems between communication costs, computation overhead, accuracy of the local tests, estimation of viral virulence, and the fraction of the network infected before the attack crests. We evaluate the effectiveness of different system configurations in various simulations. Our experiments show that distributed algorithms are better able to balance effectiveness against worms and viruses with reduced cost in computation and communication when faced with false alarms. Furthermore, cooperative, distributed systems seem more robust against malicious participants in the immunization system than earlier cooperative but non-distributed approaches.     

A Kohonen Self-Organizing Map Approach to Modeling Growth Pole Dynamics

Economic growth poles remain an important concept in regional development policy analysis. Recent attention has focused for example on the emergence and promotion of technopoles, or agglomerations of high technology activities within a region. This paper presents a neural network approach for analyzing these evolutionary processes. Kohonen self-organizing maps are used to simulate the spread and backwash effects associated with the emergence of regional growth poles. Unlike other models of growth pole development, which rely heavily on conventional economic theory, the approach outlined in this paper allows for increasing returns, multiple equilibria, `satisficing' behavior, imperfect information and stochasticity. The model could be used to explore spatio-temporal dynamics under different assumptions relating to industrial composition, interindustry linkages, and the spatial juxtaposition of firms (industries). The model is applied to the Washington, D.C. metropolitan area, and the patterns of development that have arisen from the location behavior of high technology firms in this region.     

Cooperation in the evolutionary iterated prisoner’s dilemma game with risk attitude adaptation

The Iterated Prisoner’s Dilemma (IPD) game has been commonly used to investigate the cooperation among competitors. However, most previous studies on the IPD focused solely on maximizing players’ average payoffs without considering their risk preferences. By introducing the concept of income stream risk into the IPD game, this paper presents a novel evolutionary IPD model with agents seeking to balance between average payoffs and risks with respect to their own risk attitudes. We build a new IPD model of multiple agents, in which agents interact with one another in the pair-wise IPD game while adapting their risk attitudes according to their received payoffs. Agents become more risk averse after their payoffs exceed their aspirations, or become more risk seeking after their payoffs fall short of their aspirations. The aspiration levels of agents are determined based on their historical self-payoff information or the payoff information of the agent population. Simulations are conducted to investigate the emergence of cooperation under these two comparison methods. Results indicate that agents can sustain a highly cooperative equilibrium when they consider only their own historical payoffs as aspirations (called historical comparison) in adjusting their risk attitudes. This holds true even for the IPD with a short game encounter, for which cooperation was previously demonstrated difficult. However, when agents evaluate their payoffs in comparison with the population average payoff (called social comparison), those agents with payoffs below the population average tend to be dissatisfied with the game outcomes. This dissatisfaction will induce more risk-seeking behavior of agents in the IPD game, which will constitute a strong deterrent to the emergence of mutual cooperation in the population.     

时间尺度与选择倾向性协同作用下的演化博弈模型

针对合作行为的涌现与维持问题，基于演化博弈理论和网络理论，提出了一种促进合作的演化博弈模型。该模型同时将时间尺度、选择倾向性引入到演化博弈中。在初始化阶段，根据持有策略的时间尺度将个体分为两种类型：一种个体在每个时间步都进行策略更新；另一种个体在每一轮博弈后，以某种概率来决定是否进行策略更新。在策略更新阶段，模型用个体对周围邻居的贡献来表征他的声誉，并假设参与博弈的个体倾向于学习具有较好声誉邻居的策略。仿真实验结果表明，所提出的时间尺度与选择倾向性协同作用下的演化博弈模型中，合作行为能够在群体中维持；惰性个体的存在不利于合作的涌现，但是个体的非理性行为反而能够促进合作。     

Emerging Cooperation With Minimal Effort: Rewarding Over Mimicking

This paper compares supervised and unsupervised learning mechanisms for the emergence of cooperative multiagent spatial coordination using a top-down approach. By observing the global performance of a group of homogeneous agents-supported by a nonglobal knowledge of their environment-we attempt to extract information about the minimum size of the agent neurocontroller and the type of learning mechanism that collectively generate high-performing and robust behaviors with minimal computational effort. Consequently, a methodology for obtaining controllers of minimal size is introduced and a comparative study between supervised and unsupervised learning mechanisms for the generation of successful collective behaviors is presented. We have developed a prototype simulated world for our studies. This case study is primarily a computer games inspired world but its main features are also biologically plausible. The two specific tasks that the agents are tested in are the competing strategies of obstacle-avoidance and target-achievement. We demonstrate that cooperative behavior among agents, which is supported only by limited communication, appears to be necessary for the problem's efficient solution and that learning by rewarding the behavior of agent groups constitutes a more efficient and computationally preferred generic approach than supervised learning approaches in such complex multiagent worlds     

Cios' Perspectives on Competitive Intelligence

Competitive intelligence (CI) has become an important source of information for business planning and other activities because it provides information about the present and future behavior of competitors, suppliers, customers, technologies, acquisitions, markets, and the general business environment. This article discusses the results of a recent survey of CIOs and CEOs about competitive intelligence practices in their firms. the authors anticipate an increase in competitive intelligence activities that will require the increased use of IT and suggest a heightened awareness that this spiraling situation is likely to place increasing demand on the IT function and the CIO.     

Modeling and convergence analysis of distributed coevolutionary algorithms

A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyzes are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.     

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.     

A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies

Managers allocating budgets using scarce economic resources increasingly invest in consumer information to set prices. This highlights the importance of developing methods to efficiently use consumer information in firm pricing strategies so as to increase profits. In this article we utilize such a method which includes information about consumer consideration sets. Extant pricing strategies assume that all brands in the market are included in this competitive set while setting prices. Through an empirical illustration in the ketchup market we demonstrate that firms can improve profits substantially by including consumer consideration sets in arriving at their optimal pricing strategies. We find that the ability of a brand to increase prices seems to depend on both the number of competitors it faces as well as the proportion of households for whom there are competitors in their consideration sets. Failure to incorporate consideration sets leads to biased equilibrium margin inferences and a substantial reduction in net profits.     

How useful is old information?

We consider the problem of load balancing in dynamic distributed systems in cases where new incoming tasks can make use of old information. For example, consider a multiprocessor system where incoming tasks with exponentially distributed service requirements arrive as a Poisson process, the tasks must choose a processor for service, and a task knows when making this choice the processor queue lengths from T seconds ago. What is a good strategy for choosing a processor in order for tasks to minimize their expected time in the system? Such models can also be used to describe settings where there is a transfer delay between the time a task enters a system and the time it reaches a processor for service. Our models are based on considering the behavior of limiting systems where the number of processors goes to infinity. The limiting systems can be shown to accurately describe the behavior of sufficiently large systems and simulations demonstrate that they are reasonably accurate even for systems with a small number of processors. Our studies of specific models demonstrate the importance of using randomness to break symmetry in these systems and yield important rules of thumb for system design. The most significant result is that only small amounts of queue length information can be extremely useful in these settings; for example, having incoming tasks choose the least loaded of two randomly chosen processors is extremely effective over a large range of possible system parameters. In contrast, using global information can actually degrade performance unless used carefully; for example, unlike most settings where the load information is current, having tasks go to the apparently least loaded server can significantly hurt performance     

Gossip-Based Self-Organising Agent Societies and the Impact of False Gossip

The objective of this work is to demonstrate how cooperative sharers and uncooperative free riders can be placed in different groups of an electronic society in a decentralised manner. We have simulated an agent-based open and decentralised P2P system which self-organises itself into different groups to avoid cooperative sharers being exploited by uncooperative free riders. This approach encourages sharers to move to better groups and restricts free riders into those groups of sharers without needing centralised control. Our approach is suitable for current P2P systems that are open and distributed. Gossip is used as a social mechanism for information sharing which facilitates the formation of groups. Using multi-agent based simulations we demonstrate how the adaptive behaviour of agents lead to self-organisation. We have tested with varying the gossip level and checked its impact in the system’s behaviour. We have also investigated the impact of false gossip in this system where gossip is the medium for information sharing which leads to self-organisation.     

Supporting evolution in a multi-agent cooperative design environment

New architectures for next-generation of computer-aided design environments need to be distributed, evolutive, and cooperative. This paper presents a multi-agent design environment that supports cooperative and evolutionary design by cooperation of a group of agents. Multi-agent system architecture offers a promising framework with its novel approach for dynamically creating and managing agent communities in widely distributed and ever-changing design environments; while the genetic algorithm based approach provides a foundation for supporting evolutionary and innovative design abilities. The computational and information processing processes involved in evolutionary and distributed environment are discussed in this paper.     

Entrepreneurship as design: A design process for the emergence and development of entrepreneurial opportunities

Although firms constantly seek opportunities to launch new products, services, or business models, little is known about the way opportunities emerge and develop. In particular, despite current ontological and epistemological knowledge of entrepreneurial opportunities, the process that drives their emergence and development remains understudied. To enrich our understanding, we conduct an exploratory multiple-case study of six design agencies that supported firms in developing their entrepreneurial endeavours by leveraging design sprint, a hybrid method combining design and entrepreneurship. A primary contribution of our study is the conceptualization of a process model illustrating how design (sprint) can support the emergence and development of entrepreneurial opportunities. The model advances four actions that enable translating insights into opportunities: defining, framing, experimenting, and learning. Our findings also offer interesting insights on the role of third-party agents in this process. Indeed, design agencies can act as facilitators in enacting entrepreneurship as design by supporting the emergence and development of entrepreneurial opportunities. Our research also contributes to the debate on the timing of entrepreneurial endeavours, offering an empirical portrayal of their chronology. In this sense, our model also contributes to managerial practice, providing a sequence of actions that can guide the emergence and development of entrepreneurial opportunities.     

Behaviors for physical cooperation between robots for mobility improvement

A team of small, low-cost robots instead of a single large, complex robot is useful in operations such as search and rescue, urban exploration etc. However, performance of such a team is limited due to restricted mobility of the team members. We propose solutions based on physical cooperation among mobile robots to improve the overall mobility. Our focus is on the development of the low level system components. Recognizing that small robots need to overcome discrete obstacles, we develop specific analytical maneuvers to negotiate each obstacle where a maneuver is built from a sequence of fundamental cooperative behaviors. In this paper we present cooperative behaviors that are achieved by interactions among robots via un-actuated links thus avoiding the need for additional actuation. We analyze the cooperative lift behavior and demonstrate that useful maneuvers such a gap crossing can be built using this behavior. We prove that the requirements on ground friction and wheel torques set fundamental limits for physical cooperation. Using the design guidelines based on static analysis we have developed simple and low cost hardware to illustrate cooperative gap crossing with two robots. We have developed a complete dynamic model of two-robot cooperation which leads to control design. A novel connecting link design is proposed that can change the system configuration with no additional actuators. A decentralized control architecture is designed for the two-robot system, where each robot controls its own state with no information about the state of the other robot thus avoiding the need of continuous communication between the two robots. Simulation and hardware results demonstrate a successful implementation with the gap crossing example. We have analytically proved that robot dynamics can be used to reduce the friction requirements and have demonstrated, with simulations, the implementation of this idea for the cooperative lifting behavior.

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.     

A multi‐agent system for analyzing the effect of information on prediction markets

Prediction markets have been shown to be a useful tool for forecasting the outcome of future events by aggregating public opinion about the event's outcome. In this paper, we investigate an important aspect of prediction markets—the effect of different information‐related parameters on the behavior of the traders in the market. We have developed a multi‐agent based system that incorporates different information‐related aspects including the arrival rate of information, the reliability of information, the penetration or accessibility of information among the different traders, and the perception or impact of information by the traders. We have performed extensive simulations of our agent‐based prediction market for analyzing the effect of information‐related parameters on the traders' behaviors expressed through their trading prices, and compared our agents' strategies with another agent‐based pricing strategy used in prediction markets called the zero intelligence strategy. Our results show that information‐related parameters have a significant impact on traders' beliefs about event outcomes, and, frequent, reliable information about events improves the utilities that the traders receive. Overall, our work provides a better understanding of the effect of information on the operation of prediction markets and on the strategies used by the traders in the market. © 2011 Wiley Periodicals, Inc.     

Preventing undesirable effects of mutual trust and the development of skepticism in virtual groups by applying the knowledge and information awareness approach

Empirical studies have proven the effectiveness of the knowledge and information awareness approach of Engelmann and colleagues for improving collaboration and collaborative problem-solving performance of spatially distributed group members. This approach informs group members about both their collaborators’ knowledge structures and their collaborators’ information. In the current study, we investigated whether this implicit approach reduces undesirable effects of mutual trust and mutual skepticism. Trust is an important influencing factor with regard to behavior and performance of groups. High mutual trust can have a negative impact on group effectiveness because it reduces mutual control and, as a result, the detection of the others’ mistakes. In an empirical study, 20 triads collaborating with the knowledge and information awareness approach were compared with 20 triads collaborating without this approach. The members of a triad were spatially distributed and participated in a computer-supported collaboration. The results demonstrated that the availability of the knowledge and information awareness approach overrides the negative impact of too much mutual trust and counteracts the development of mutual skepticism. This study contributes to further clarifying the impact of trust on effectiveness and efficiency of virtual groups depending upon different situational contexts.     

基于CADCON的群体AUV三维仿真

近年的AUV仿真工作更多地集中于对AUV水动力学特性仿真数学模型的建立上，不能满足AUV群体协作策略级仿真的需要。CADCON是AUSI基于Internet架构开发的群体协作AUV三维水下环境分布式仿真平台，提供了可定制的虚拟海洋环境和一系列可灵活配置的AUV模型以及AUV群体行为组件开发接口。该文介绍了CADCON的功能和体系结构，归纳了基于CADCON平台进行AUV群体协作仿真的基本流程和参数配置方法，给出了Visual C＋＋环境下的AUV群体行为组件开发的基本方法，该方法的可行性已得到实验验证。