Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.

Daily rolling estimation of carbon emission cost of coal-fired units considering long-cycle interactive operation simulation of carbon-electricity market

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets.The carbon emission cost(CEC)of coal-fired units becomes part of the power generation cost through market coupling.The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market.Study of carbon–electricity market interaction and CEC calculations is still in its initial stages.This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets.A long-period interactive operation simulation mechanism for the carbon–electricity market is established,and operation and trading models of the carbon emissions trading market and electric power market are established.A daily rolling estimation method for the CEC of coal-fired units is proposed,along with the CEC per unit electric quantity of the coal-fired units.The feasibility and effectiveness of the proposed method are verified through an example simulation,and the factors influencing the CEC are analyzed.

An agent-based simulation with NetLogo platform to evaluate forward osmosis process(PRO Mode)

Forward osmosis(FO), as an emerging technology, is influenced by different factors such as operating conditions,module characteristics, and membrane properties. The general aim of this study was to develop a suitable(flexible,comprehensive, and convenient to use) computational tool which is able to simulate osmosis through an asymmetric membrane oriented in pressure retarded osmosis(PRO) mode in a wide variety of scenarios. For this purpose, an agent-based model was created in NetLogo platform, which is an easy-to-use application environment with graphical visualization abilities and well suited for modeling a complex system evolving over time. The simulation results were validated with empirical data obtained from literature and a great agreement was observed. The effect of various parameters on process performance was investigated in terms of temperature,cross-flow velocity, length of the module, pure water permeability coefficient, and structural parameter of the membrane. Results demonstrated that the increase in all parameters, except structural parameter of the membrane and the length of module led to the increase of average water flux. Moreover, nine different draw solutes were selected in order to assess the influence of net bulk osmotic pressure difference between the draw solution(DS) and feed solution(FS)(known as the driving force of FO process) on water flux. Based on the findings of this paper, the performance of FO process(PRO mode) can be efficiently evaluated using the NetL ogo platform.

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

A goal-based approach for modeling and simulation of different types of system-of-systems

A system of systems(SoS)composes a set of independent constituent systems(CSs),where the degree of authority to control the independence of CSs varies,depending on different SoS types.Key researchers describe four SoS types with descending levels of central authority:directed,acknowledged,collaborative and virtual.Although the definitions have been recognized in SoS engineering,what is challenging is the difficulty of translating these definitions into models and simulation environments.Thus,we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations.First,we construct the theoretical models of CS and SoS.Based on the theoretical models,we analyze the degree of authority influenced by SoS characteristics.Next,we propose a definition of SoS types by quantitatively explaining the degree of authority.Finally,we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agentbased model(ABM)simulation.This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS,so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.

Assessing the impacts of traffic calming at network level:A multimodal agent-based simulation

The reduction of speed limits in urban roads through traffic calming schemes intends to ensure safer traffic conditions among road users by reducing the probability related to the occurrence of severe accident.Looking it from a different perspective,traffic calming measures can potentially resolve congestion problems at the same time by lowering the overall accessibility and attractiveness of private cars in urban areas.This study proposes a new methodological approach to explore and assess the direct impacts of traffic calming in the transport system efficiency of a metropolitan area.The multi-agent transport simulation(MATSim)and Open-Berlin scenario are utilized to perform this simulation experiment.By developing a new external tool,the free flow speed and road capacity of each network link is updated based on new speed limits and different compliance rates,which are defined per road hierarchy level.The test scenarios that are formulated present radical conditions,where the speed limit in most urban roads of Berlin drops to 30 km/h or even 15 km/h.The findings of this study show a considerably high increase in trips,passenger hours,and passenger kilometers using public transport modes,when traffic calming links are introduced,the reserve change is observed in private cars trips.Although the speed limits are decreased in inner urban roads in most of the scenarios,the decrease of average travel speed of private cars is not so high as it was expected.Surprisingly,private cars are used for longer distances in all test scenarios.Car drivers seem to use already existed motorways and private road to commute.In simulations,driver compliance to the new speed limits seems to be a determinant factor that is strongly influenced by the design interventions applied in a traffic calming area.

Carbon Tax and Renewable Energy Diffusion in the Deregulated Texas Electricity Market: An Agent-Based Analysis

In the United States, emission regulations are enacted at a state level;individual states are allowed to define what methods they will use to mitigate their carbon emissions. The consequence of this is especially interesting in the state of Texas where new legislation has created a “deregulated” electricity market in which end-users are capable of choosing their electricity provider and subsequently the type of electricity they wish to consume (generated by fossil fuels or renewable sources). In this paper we analyze the effects of carbon tax on the development of renewable generation capacity at the utility level while taking into account expected adoption of rooftop PV systems by individual consumers using agent based modeling techniques. Monte Carlo simulations show carbon abatement trends and proffer updated renewable portfolio standards at various levels of likelihood.

Pyramid scheme in stock market:A kind of financial market simulation

Artificial stock market simulation based on agent is an important means to study financial market.Based on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes.Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main fund.Our work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

Manipulation of the Bitcoin market:an agent-based study

Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.

Simulation Technology of Environmental Impacts by Zero-energy Residential Buildings Based on Emergy Analysis Method

This paper presents a simulation technology of environmental impact for the building. By emergy analysis method,emergy costs of building( or construction engineering) can be calculated in the life cycle. It includes the engineering cost, environmental cost and social cost of building. Through integrating GIS technology with multi-agent technology,life cycle substance and energy metabolism of building( construction engineering) can be simulated and their environmental influence can be dynamically displayed. Based on the case study of entries works‘Sunny Inside'by Xiamen University in 2013 China International Solar Decathlon Competition,we discovered the changing pattern of surrounding environmental impact from waste streams of the zero-energy building and ordinary construction. The simulation results verified and showed the Odum principles of maximum power. This paper provides a new research perspective and integration approach for the environmental impact assessment in building and construction engineering. The result will help decision-making in design and construction engineering scheme.

Agent-Based Modeling: An Application to Natural Resource Management

Computer programs have been categorized as a useful tool to evaluate the complexity of systems. In fact, agent-based modeling (ABM) is considered a new method to model complex systems characterized by the role of independent and interrelating agents. Simulations contribute in estimating and comprehending emerging behaviors that require the development of new regulations for local agents that would make improvements to the system. This paper offers an example of a methodology and a process utilized to develop a simulation model named Befergyonet, an ABM used to conduct computer simulations within a spatio-intertemporal environment. The methodology discussed in this paper is intended solely to stimulate the use of innovative computer programs to simulate complex systems as an approach to represent real world events and may be a methodological guide for readers interested in developing their own ABM.

Simulation of Old Urban Residential Area Evolution Based on Complex Adaptive System

On the basis of complex adaptive system theory, this paper proposed an agent-based model of old urban residential area, in which, residents and providers are the two adaptive agents. The behaviors of residents and providers in this model are trained with back propagation and simulated with Swarm software based on environment-rules-agents interaction. This model simulates the evolution of old urban residential area and analyzes the relations between the evolution and urban management with the background of Chaozhou city. As a result, the following are obtained ： （ 1 ） Simulation without government intervention indicates the trend of housing ageing, environmental deterioration, economic depression, and social filtering-down in old urban residential area. If the development of old urban residential area is under control of developers in market, whose desire is profit maximization, and factors such as social justice, historic and culture value will be ignored. （2） If the government carries out some policies and measures which will perfectly serve their original aims, simulation reveals that old urban residential area could be adapted to environment and keep sustainable development. This conclusion emphasizes that government must act as initiator and program maker for guiding residents and other providers directly in the development of old urban residential area.

COVID-19 transmission in U.S.transit buses:A scenario-based approach with agent-based simulation modeling(ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19(SARSCoV-2)virus.Modeling disease transmission in public buses remains a challenge,especially with uncertainties in passenger boarding,alighting,and onboard movements.Although there are initial findings on the effectiveness of some of the mitigation policies(such as face-covering and ventilation),evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways,boarding and alighting patterns,and seating capacity control.This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA,in which it brings crucial insights on combating current and future epidemics.We use an agent-based simulation model(ABSM)based on standard design characteristics for urban buses in USA and two different service frequency settings(10-min and 20-min headways).We find that wearing face-coverings(surgical masks)significantly reduces onboard transmission rates,from no mitigation rates of 85%in higher-frequency buses and 75%in lower-frequency buses to 12.5%.The most effective prevention outcome is the combination of KN-95 masks,open window policies,and half-capacity seating control during higher-frequency bus services,with an outcome of nearly 0%onboard infection rate.Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design,which is crucial to ensuring passenger safety.The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

Agent-based simulation for symmetric electricity market considering price-based demand response

With the development of electricity market mechanism and advanced metering infrastructure(AMI),demand response has become an important alternative solution to improving power system reliability and effi-ciency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers’ response characteristics under time-of-use(TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly.Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles,which also helps to reduce the price.

Agent-based dynamic simulation of an electricity market with multilateral bidding

The electricity market is a complex system in which participants interact and compete with each other,which makes description of them with mathematical models difficult.To solve these difficulties,computer simulation has become one of the main methods for studying electricity market problems.How to establish a reasonable electricity market has always been a major research issue in the electric power industry,for which a key point is the bidding mechanism.Agent-based modeling and a simulation(ABMS)method are used in this paper to study the imperfect competitive electricity market.An agent-based simulation method of multilateral bargaining game theory in the dynamics of the power bidding market is presented,and a multi-agent power market bidding dynamics simulation model based on game theory is established.The dynamic bidding game behavior among the government,power grid companies,power plant companies,and consumer parties is simulated in the market,and the simulation method is realized by Anylogic software.Finally,an agent-based four-party competitive dynamic game simulation in the electricity market is implemented,which provides a theoretical reference for further understanding resource optimization problems in the electricity market.

Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.

电力现货市场交易运营的未来重大需求与关键技术

中国正在全面开展电力现货市场建设,现货试点地区结合当前市场规则及电力系统现状构建了较为完善的现货市场运营支撑体系,支撑了当前电力现货市场有效运行。随着新能源占比逐步提升的新型电力系统构建及“双碳”目标国家战略的实施,现有的运营技术支撑体系将无法适应未来市场发展需要。文中在总结中国电力现货市场技术实践的基础上,分析了电力现货市场特征发生的变化及面临的技术挑战,探讨了未来在多元主体建模接入、市场出清算法、市场风险分析、可信交易环境、市场仿真等方面的关键技术,部分技术已经取得初步应用。

An Agent-based Simulation Model of Wheat Market Operation:The Benefit of Support Price

Grain security is one of the most important issues worldwide.Many developing countries,including China,have adopted the Agriculture Support Price(ASP)program to stimulate farmers’enthusiasm for growing grain,to ensure self-sufficiency in grain and the stable development of the grain market.To propose decision support for the government in designing a more reasonable support price in the ASP program,we formulate an agent-based model to simulate the operation of the wheat market in the harvest period.To formulate the formation process of the market price influenced by farmers’expected sale price,processors’expected purchase price,and the ASP,the time series and regression methods are adopted.Based on the proposed market price model,to quantitatively analyze the grain transaction process and the ASP program’s impacts on market agents,we develop an agent-based simulation model to describe the adaptive evolution and interaction among market agents.Furthermore,we validate and implement the simulation model with public wheat market data.Finally,insights and suggestions about the decision of the ASP program are provided.