Search

Cyberwar is no longer subject to "if" but "when." Despite growing interest in cyberspace and cyber war readiness and resilience among academics, researchers, policymakers, and the media, the area needs to be more robust with different terminology that strategically captures the activities of state actors in cyberspace. The paper aims to provide a strategic classification of the activities of state actors in cyberspace. A typology is developed to encapsulate the strategic complexity of the activities of state actors in this terrain. The typology can illuminate the current global proportion and payoff of each type. [ABSTRACT FROM AUTHOR]

Negative frequency-dependent selection (i.e., declining fitness with increased frequency in the population) is thought to be one of the factors that maintains biological diversity. In this paper, we give a concrete mathematical argument supporting this. Our model is as follows: A collection of species derive their fitnesses via a rock-paper-scissors-type game whose precise payoffs are a function of the environment. The new aspect of our model lies in adding a feedback loop: the environment changes according to the relative fitnesses of the species (hence, payoffs change as a function of fitness, which in turn changes as a function of payoffs). The changes in the payoffs are in keeping with the principle of negative frequency-dependent selection, which is widespread in nature. In order to model our game as a continuous time dynamical system, we cast it in the setting of a differential game. We show that for certain parameters, this dynamics cycles, i.e., no species goes extinct and diversity is maintained. We believe that our techniques can be applied to optimization and machine learning to show that first order methods (e.g., gradient descent/ascent) do cycle even in online settings in which the loss function changes with time. [ABSTRACT FROM AUTHOR]

Venture capital is a risky and long-term equity investment activity, and venture capitalists widely use the syndication, a jointly investment model, to finance entrepreneurial start-ups. And, the syndication's successful operation is inseparable from trust between participants. Our paper focuses on trust governance between the leader and the follower, players in the syndication, through constructing an evolutionary game model. In the model, both players adopt Replicator-Dynamics-based learning rules and their strategy space is {Trust, No-Trust}. The trust governance mechanism contains two aspects: punishment and reward, both of whose intensity are categorized into three levels, low, middle, and high level. After comprehensively computing and comparing players' evolutionary stable strategy (ESS), we find relationships between the trust governance mechanism and players' ESS. If both of the intensities of reward and punishment are at low level, then both the leader and the follower choose "No-Trust". If both of the intensities of reward and punishment are at high level, then both the leader and the follower choose "Trust". If the intensity of punishment is at low or middle level, and the intensity of reward is at middle level, then the leader chooses "Trust" and the follower does not. In the intensity of punishment is at middle level, and the intensity of reward is at low or middle level, then the leader chooses "Trust" and the follower does not. If the intensity of punishment is at low level and the intensity of reward is at high level, or if the intensity of punishment is at high level and the intensity of reward is at low level, player's ESS combination shows a rather varied changes. [ABSTRACT FROM AUTHOR]

Venture capital is a risky and long-term equity investment activity, and venture capitalists widely use the syndication, a jointly investment model, to finance entrepreneurial start-ups. And, the syndication's successful operation is inseparable from trust between participants. Our paper focuses on trust governance between the leader and the follower, players in the syndication, through constructing an evolutionary game model. In the model, both players adopt Replicator- Dynamics-based learning rules and their strategy space is {Trust, No-Trust}. The trust governance mechanism contains two aspects: punishment and reward, both of whose intensity are categorized into three levels, low, middle, and high level. After comprehensively computing and comparing players' evolutionary stable strategy (ESS), we find relationships between the trust governance mechanism and players' ESS. If both of the intensities of reward and punishment are at low level, then both the leader and the follower choose "No-Trust". If both of the intensities of reward and punishment are at high level, then both the leader and the follower choose "Trust". If the intensity of punishment is at low or middle level, and the intensity of reward is at middle level, then the leader chooses "Trust" and the follower does not. In the intensity of punishment is at middle level, and the intensity of reward is at low or middle level, then the leader chooses "Trust" and the follower does not. If the intensity of punishment is at low level and the intensity of reward is at high level, or if the intensity of punishment is at high level and the intensity of reward is at low level, player's ESS combination shows a rather varied changes. [ABSTRACT FROM AUTHOR]

This paper focuses on integrated optical network and IT infrastructures in support of the Future Internet and its new emerging applications. In this context, the concept of virtualization of the physical infrastructure is proposed and the process of virtual infrastructure planning is discussed. A novel optimization scheme suitable to adaptively plan virtual infrastructures employing evolutionary game theory is presented and compared to conventional centralized approaches. Our evolutionary game theory modelling results clearly show, that given sufficient time to learn the status of the underlying physical topology the virtual infrastructures planned have similar performance to those generated through traditional global optimization approaches such as integer linear programming [ABSTRACT FROM PUBLISHER]

This paper addresses the joint pricing and network selection problem in cognitive radio networks, considering both the point of view of network users and the Primary Operator. The problem is formulated as a Stackelberg (leader-follower) game where first the PO sets the network subscription price to maximize its revenue. Then, users perform the network selection process, deciding whether to pay for having a guaranteed service, or use a cheaper, best-effort secondary network, where congestion and low throughput may be experienced. Such process is modeled as a population game to study the strategic interactions among a large number of agents. For our pricing and network selection game, we provide equilibrium and convergence properties, and derive optimal stable price and network selection settings. Numerical results illustrate that our game model captures the main factors behind cognitive network pricing and channel selection, thus representing a promising framework for the design and understanding of cognitive radio systems. [ABSTRACT FROM PUBLISHER]

Many social systems consist of multiple populations of selfish players. In such systems, a problem called a social dilemma may occur. In order to resolve the dilemma, the government tries to control the multiple populations by imposing taxes and offering subsidies to players. Dynamical behaviors of the controlled systems have been discussed by using multipopulation replicator dynamics. Previous work assumed that the taxes and the subsidies of a player depend on which population he/she belongs to. In general, however, the government cannot identify the members of each population. In this paper, we introduce a government that controls multiple populations under such incomplete information, and formulate replicator dynamics. We also derive the stabilization conditions of a target output in the replicator dynamics. [ABSTRACT FROM PUBLISHER]

In distributed optimization problems, each agent can get information only from a neighborhood defined by a network topology to minimize a global objective function in a networked system. To solve this problem, we present a local strategy based on population dynamics, in order to perform a dynamic resource allocation in a system described by connected graph. The local replicator equation (LRE) is applied to a lighting control system to show that the achieved equilibrium point is an optimal solution to certain distributed optimization problems. We present some simulation results with different conditions, and a stability analysis to guarantee the convergence of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

There are more than 300 secondary lead enterprises in China, serious environmental pollution is a major issue which must be resolved. This paper establishes a 2 × 2 asymmetric evolution game model between secondary lead enterprises and environmental protection departments, and analyses evolutionary process of the secondary lead enterprises whether to take measures to avoid environmental pollution or not, environmental protection departments whether to monitor or not. This paper gives evolutionary stability conditions of the two game players, and shows effect of some key parameters of the evolution game model on the ESS by means of numerical experiments. We indicate some important factors, including costs and benefits of secondary lead enterprises, tax incentives and penalties of environmental protection departments, rewards and penalties to environmental protection departments as well as cost of supervision. [ABSTRACT FROM PUBLISHER]

In the context of smart energy grids, demand-side management refers to the ability of dynamically controlling and scheduling energy-consuming tasks. In one potential deployment scenario, smart appliances are controlled by a local intelligent software agents, which implement a given optimization algorithm for scheduling such tasks. The higher the fraction of users adopting such technology, the higher the advantage for the energy operator, due to the ability to control load curve and smooth peaks. At the same time, single users may incur some penalties, related to the fact that energy-consuming tasks may be deferred, thereby causing inconveniences. In this paper we take a game-theoretical perspective at demand-side management techniques. Tools and solution concepts from evolutionary games are employed: we are interested in the dynamics of the adoption of demand-side management schemes by intelligent software agents. We focus on distributed control schemes that can be enforced by the operator through pricing schemes. Agent-based numerical simulations are provided to validate our theoretical results. [ABSTRACT FROM PUBLISHER]

The replicator dynamics model is an evolutionary game concept that describes the state of a population in a process inspired by natural selection. This model is used to analyze a resource allocation problem in distributed networked systems. The main properties of the replicator equation are analyzed to propose a novel technique based only on the available information of a system modeled as a connected graph. Likewise, we analyze the stability of the equilibrium points as a function of a certain class of fitness. Finally, an economic dispatch in distributed generation systems is presented in order to illustrate the theoretical results. [ABSTRACT FROM PUBLISHER]