Evolutionary Game Theoretic Perspective Research Articles

How does carbon emissions trading scheme affect emission reduction decisions of coal-fired power plants? An evolutionary game theoretic perspective

Carbon emissions trading scheme (CETS) is widely regarded as a cost-effective marketbased regulation for carbon abatement. In the context of CETS, this study develops an evolutionary game model that incorporates two representative coal-fired power plants and a government. Our model captures the interplay of emission reduction strategies between coal-fired power plants and endogenously incorporates government regulatory decisions. We analyze the strategic decisions of coal-fired power plants by discussing the dynamics and equilibrium of the game. Our findings demonstrate that in the absence of government implementation of CETS, coal-fired power plants refrain from investing in carbon abatement. However, with the enforcement of CETS, along with sufficient penalties for excessive carbon emissions, coal-fired power plants become inclined to invest in emission reduction. Furthermore, the willingness of coal-fired power plants to invest in carbon abatement exhibits a negative relationship with both the quota and the cost of emission reduction.

Regulation strategies of ride-hailing market in China: an evolutionary game theoretic perspective

With the popularity of the sharing economy, ride-hailing services have greatly affected people’s travel and become a new travel mode for urban residents. However, the lack of effective industry regulation has resulted in serious operational problems and growing difficulties in the furthering development of ride-hailing services in China. Therefore, it is necessary to study the regulation strategies of multiple subjects involved in ride-hailing industry. Based on evolutionary game theory, the paper establishes the tripartite evolution game model about regulation strategies of ride-hailing industry. The theoretical research and simulation results show that the evolutionarily stable strategy of a single subject (Transportation Network Company, driver or passenger) is affected by the strategies of other two subjects together. Moreover, when making the decision, the Transportation Network Companies (TNCs) need to consider the difference between benefits and costs, user scale, incentives and penalties from the government. Drivers need to consider their benefits and costs, travel user scale and penalties from the government and the TNCs. Besides, the benefits and costs, and the harmony of ride-hailing industry need to be considered for passengers. Potential policy implications are proposed.

Understanding information interactions in diffusion: an evolutionary game-theoretic perspective

Social networks are fundamental mediums for diffusion of information and contagions appear at some node of the network and get propagated over the edges. Prior researches mainly focus on each contagion spreading independently, regardless of multiple contagions' interactions as they propagate at the same time. In the real world, simultaneous news and events usually have to compete for user's attention to get propagated. In some other cases, they can cooperate with each other and achieve more influences. In this paper, an evolutionary game theoretic framework is proposed to model the interactions among multiple contagions. The basic idea is that different contagions in social networks are similar to the multiple organisms in a population, and the diffusion process is as organisms interact and then evolve from one state to another. This framework statistically learns the payoffs as contagions interacting with each other and builds the payoff matrix. Since learning payoffs for all pairs of contagions IS almost impossible (quadratic in the number of contagions), a contagion clustering method is proposed in order to decrease the number of parameters to fit, which makes our approach efficient and scalable. To verify the proposed framework, we conduct experiments by using real-world information spreading dataset of Digg. Experimental results show that the proposed game theoretic framework helps to comprehend the information diffusion process better and can predict users' forwarding behaviors with more accuracy than the previous studies. The analyses of evolution dynamics of contagions and evolutionarily stable strategy reveal whether a contagion can be promoted or suppressed by others in the diffusion process.

Evolutionary Information Diffusion over Heterogeneous Social Networks

A huge amount of information, created and forwarded by millions of people with various characteristics, is propagating through the online social networks every day. Understanding the mechanisms of the information diffusion over the social networks is critical to various applications including online advertisement and website management. Different from most of the existing works, we investigate the information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over the heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world datasets. The theoretical results are confirmed by simulations. We also test the theory on Twitter hashtag dataset. We observe that the derived evolutionary dynamics fit the data well and can predict the future diffusion data.

An optimal time-of-use pricing for urban gas: A study with a multi-agent evolutionary game-theoretic perspective

In energy markets, regulators are often tempted to use price schedules to improve economic efficiency and promote a reasonable resource allocation. Time-of-use pricing is very popular with economists, and many researchers have been written estimating and exploring the optimal time-of-use pricing for electricity markets. Yet, such pricing has rarely been used in the natural gas sector. In this paper, we propose an optimal time-of-use pricing in urban gas market based on an evolutionary game-theoretic perspective. As the urban gas market is a nonlinear complex economic system with several interacting agents, we use a multi-agent system comprising a government agent, a local gas distribution operator agent, and different types of end-user agents. A power structure demand response program is employed to simulate the user demand response. A mixed-integer linear programming is formulated to determine the time-of-use price-signal delivering maximum gas operator profit and the optimal load pattern for end-users. In an illustrative example, we simulate and compare the time-of-use block prices and time-of-use hourly prices with traditional fixed pricing using real-world data of Wuhan in China. The numerical results indicate that time-of-use pricing schedules have significant potential for peak-shaving and load-shifting for urban gas pipeline network systems and would thus lower operating costs. Furthermore, different gas users exhibit different demand responsiveness intensity. Finally, we evaluate the impact on total social welfare of regulation scenarios and find that welfare decreases with deregulation, implying that the urban gas market is immature and reasonable regulation is still necessary.

Evolutionary Game Theoretic Perspective on Analysis of Cloud Computing Security Investment

The purpose of this study is to seek feasible supply-side-chain (SSC) functions/ strategies to promote channel performance to diminish the hazard of losing efficiencies in the garment industry. We utilize the LISREL model to examine cause and effects relationship among supply-side-chain functions, service innovation and channel performance. Since the relationship between SSC function and service innovation (including supportive innovation and interactive innovation) is scant, and service innovation can effective contribute to channel performance, that the need of this study is then emerging . We employ quota sampling method to survey 400 customers and clerks from Taiwan’s top 10 garment companies, in terms of net assets, on the basis of printed questionnaires. SSC leanness in garment companies can mainly promote channel performance from the channel of supportive innovation. The supportive innovation is supposed to appear after the long usage and not discovered at the initial time period. Then, the invisible supportive innovation is supposed to achieve a certain level of opportunity benefit to restart the companies.

The land hoarding and land inspector dilemma in China: An evolutionary game theoretic perspective

China has experienced considerable economic growth since 1978, which was accompanied by unprecedented growth in urbanization and, more recently, by associated rising urban housing and land banking issues. One such issue is that of land hoarding – where real estate developers purchase land to hold unused in the rising market for a future lucrative sale, often several years later. This practice is outlawed in China, where land use is controlled by increasingly strengthened Government policies and inspectors.Despite this, land hoarding continues apace, with the main culprits being the developers and inspectors working subversively. This resembles a game between two players – the inspector and the developer – which provides the setting for this paper in developing an evolutionary game theory model to provide insights into dealing with the dilemmas faced by the players. The logic and dilemma of land banking strategy and illegal land banking issues are analysed, along with the land inspector's role from a game theory perspective by determining the replication dynamic mechanism and evolutionary stable strategies under the various conditions that the players face. The major factors influencing the actions of land inspectors, on the other hand, are the costs of inspection, no matter if it is strict or indolent, conflict costs, and income and penalties from corruption. From this, it is shown that, when the net loss for corruption (income from corruption minus the penalties for corruption and cost of strict inspections) is less than the cost of strict inspections, the final evolutionary stable strategy of the inspectors is to carry out indolent inspections. Then, whether penalising developers for hoarding is severe or not, the evolutionary strategy for the developer is to hoard. The implications for land use control mechanisms and associated developer-inspector actions and counteractions are then examined in the light of the model's properties.

Priority-Based Time-Slot Allocation in Wireless Body Area Networks During Medical Emergency Situations: An Evolutionary Game-Theoretic Perspective

In critical medical emergency situations, wireless body area network (WBAN) equipped health monitoring systems treat data packets with critical information regarding patients' health in the same way as data packets bearing regular healthcare information. This snag results in a higher average waiting time for the local data processing units (LDPUs) transmitting data packets of higher importance. In this paper, we formulate an algorithm for Priority-based Allocation of Time Slots (PATS) that considers a fitness parameter characterizing the criticality of health data that a packet carries, energy consumption rate for a transmitting LDPU, and other crucial LDPU properties. Based on this fitness parameter, we design the constant model hawk-dove game that ensures prioritizing the LDPUs based on crucial properties. In comparison with the existing works on priority-based wireless transmission, we measure and take into consideration the urgency, seriousness, and criticality associated with an LDPU and, thus, allocate transmission time slots proportionately. We show that the number of transmitting LDPUs in medical emergency situations can be reduced by 25.97%, in comparison with the existing time-division-based techniques.

A Game-Theoretic Analysis of the Baldwin Effect

The Baldwin effect is a process by which learnt traits become gradually incorporated into the genome through a Darwinian mechanism. From its inception, the Baldwin effect has been regarded with skepticism. The objective of this paper is to relativize this assessment. Our contribution is two-fold. To begin with, we provide a taxonomy of the different arguments that have been advocated in its defense, and distinguish between three justificatory dimensions—feasibility, explanatory relevance and likelihood—that have been unduly conflated. Second, we sharpen the debate by providing an evolutionary game theoretic perspective that is able to generalize previous results. The upshot of this paper is that the mechanism envisaged by Baldwin is less puzzling than commonly thought.

An evolutionary game theoretic perspective on e-collaboration: The collaboration effort and media relativeness

Abstract Studies of e-collaboration from a game-theoretical perspective are practically nonexistent. This article contributes to filling this gap by focusing on the strategic interaction between players as they decide whether and how much to collaborate with each other. We use evolutionary game theory to make predictions about a two-person e-collaboration game. More specifically, we extend the traditional Prisoners’ Dilemma and Snowdrift game theory notions to discrete-strategy e-collaboration games, by explicitly including social punishments into the players’ payoff functions. We also introduce continuous-strategy e-collaboration games with both complete and incomplete information. Finally, we provide two generic dynamic programming models for e-collaboration games with media selection.

An evolutionary game-theoretic analysis of poker strategies

In this paper we investigate the evolutionary dynamics of strategic behavior in the game of poker by means of data gathered from a large number of real world poker games. We perform this study from an evolutionary game theoretic perspective using two Replicator Dynamics models. First we consider the basic selection model on this data, secondly we use a model which includes both selection and mutation. We investigate the dynamic properties by studying how rational players switch between different strategies under different circumstances, what the basins of attraction of the equilibria look like, and what the stability properties of the attractors are. We illustrate the dynamics using a simplex analysis. Our experimental results confirm existing domain knowledge of the game, namely that certain strategies are clearly inferior while others can be successful given certain game conditions.

Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective

This paper presents the dynamics of multiple learning agents from an evolutionary game theoretic perspective. We provide replicator dynamics models for cooperative coevolutionary algorithms and for...

An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems

In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields of Multi-Agent Systems, Reinforcement Learning and Evolutionary Game Theory. We illustrate how these new insights can contribute to a better understanding of learning in MAS and to new improved learning algorithms. All three fields are introduced in a self-contained manner. Each relation is discussed in detail with the necessary background information to understand it, along with major references to relevant work.