COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to f...COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to form an AI society.Agent modeling typically encompasses four levels:1)The autonomy features of agents,e.g.,perception,behavior,and decision-making;2)The evolutionary features of agents,e.g.,bounded rationality,heterogeneity,and learning evolution;3)The social features of agents,e.g.,interaction,cooperation,and competition;4)The emergent features of agents,e.g.,gaming with environments or regulatory strategies.Traditional modeling techniques primarily derive from ABMs(Agent-based Models)and incorporate various emerging technologies(e.g.,machine learning,big data,and social networks),which can enhance modeling capabilities,while amplifying the complexity[1].展开更多
Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management syst...Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.展开更多
The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA struc...The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.展开更多
目的:比较国内外Agent行为建模与仿真研究热点与趋势,为我国研究者更有效地应用Agent行为建模与仿真提供参考依据。方法:运用CiteSpace5.7R5软件对中国知网和Web of Science核心合集数据库2011年1月1日至2021年3月31日中有关Agent行为...目的:比较国内外Agent行为建模与仿真研究热点与趋势,为我国研究者更有效地应用Agent行为建模与仿真提供参考依据。方法:运用CiteSpace5.7R5软件对中国知网和Web of Science核心合集数据库2011年1月1日至2021年3月31日中有关Agent行为建模与仿真研究的文献进行图谱量化分析。结果:共检索到有效中文文献864篇、英文文献2323篇,国内发文量整体呈下降趋势,国外发文量整体呈上升趋势,发文量高的国家集中在发达国家,国外研究前沿已经延伸到物理学、金融学、哲学、生物学、物流学、人工智能等方面。国内研究热点主要集中在社会学、物理学、网络模型等方面。结论:Agent行为建模与仿真研究的应用范围较广泛,与国际相比国内Agent行为建模与仿真研究还存在一定的差距,研究深度和广度有待进一步拓展,国内应参考国际Agent行为建模与仿真研究的热点及前沿,探索适合我国特色的Agent行为建模与仿真系统体系,以促进我国Agent行为建模与仿真的发展。展开更多
Understanding migratory waterfowl spatiotemporal distributions is important because,in addition to their economic and cultural value,wild waterfowl can be infectious reservoirs of highly pathogenic avian influenza vir...Understanding migratory waterfowl spatiotemporal distributions is important because,in addition to their economic and cultural value,wild waterfowl can be infectious reservoirs of highly pathogenic avian influenza virus(HPAIV).Waterfowl migration has been implicated in regional and intercontinental HPAIV dispersal,and predictive capabilities of where and when HPAIV may be introduced to susceptible spillover hosts would facilitate biosecurity and mitigation efforts.To develop forecasts for HPAIV dispersal,an improved understanding of how individual birds interact with their environment and move on a landscape scale is required.Using an agent-based modeling approach,we integrated individual-scale energetics,species-specific morphology and behavior,and landscape-scale weather and habitat data in a mechanistic stochastic framework to simulate Mallard(Anas platyrhynchos)and Northern Pintail(Anas acuta)annual migration across the Northern Hemisphere.Our model recreated biologically realistic migratory patterns using a first principles approach to waterfowl ecology,behavior,and physiology.Conducting a limited structural sensitivity analysis comparing reduced models to eBird Status and Trends in reference to the full model,we identified density dependence as the main factor influencing spring migration and breeding distributions,and wind as the main factor influencing fall migration and overwintering distributions.We show evidence of weather patterns in Northeast Asia causing significant intercontinental pintail migration to North America.By linking individual energetics to landscapescale processes,we identify key drivers of waterfowl migration while developing a predictive model responsive to daily weather patterns.This model paves the way for future waterfowl migration research predicting HPAIV transmission,climate change impacts,and oil spill effects.展开更多
Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains.We introduce nanoMINER,a multi-agent system combining large language models and multimoda...Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains.We introduce nanoMINER,a multi-agent system combining large language models and multimodal analysis to extract essential information from scientific research articles on nanomaterials.This system processes documents end-to-end,utilizing tools such as YOLO for visual data extraction and GPT-4o for linking textual and visual information.At its core,the ReAct agent orchestrates specialized agents to ensure comprehensive data extraction.We demonstrate the efficacy of the system by automating the assembly of nanomaterial and nanozyme datasets previously manually curated by domain experts.NanoMINER achieves high precision in extracting nanomaterial properties like chemical formulas,crystal systems,and surface characteristics.For nanozymes,we obtain near-perfect precision(0.98)for kinetic parameters and essential features such as Cmin and Cmax.To benchmark the systemperformance,we also compare nanoMINER to several baseline LLMs,including the most recent multimodal GPT-4.1,and show consistently higher extraction precision and recall.Our approach is extensible to other domains of materials science and fields like biomedicine,advancing data-driven research methodologies and automated knowledge extraction.展开更多
Human-natural systems(HNS)are complex,adaptive systems where human activities and natural processes are deeply intertwined.Stochastic processes,nonlinear couplings,feedback loops,and emergent phenomena collectively sh...Human-natural systems(HNS)are complex,adaptive systems where human activities and natural processes are deeply intertwined.Stochastic processes,nonlinear couplings,feedback loops,and emergent phenomena collectively shape the interactions between human behaviors and natural dynamics.While environmental models in Earth system science are relatively well-established(equation-or process-based),modeling human systems remains insufficient.Moreover,there lacks a unified framework for effectively characterizing these complex interactions,posing significant challenges for HNS modeling and decision-making.To fill these gaps,this study proposes an integrated multi-agent deep reinforcement learning(MADRL)framework that combines Markov decision process(MDP),agent-based modeling(ABM),and deep reinforcement learning(DRL)to address modeling and decision-making challenges in HNS.The framework is structured as an MDP,defined by four core components:states of the environment(natural system),actions of agents(human system),transitions of the states(evolution of the HNS),and rewards.We introduce ABM to simulate human behaviors,decision-makings,and interactions among multi-hierarchical stakeholders,including individuals,groups,communities,governments,and non-governmental organizations.Additionally,DRL is employed to tackle the high-dimensional solving challenges of the MDP.Finally,a classic case study based on the“Tragedy of the Commons”is designed,featuring multiple fishermen operating under specific decision rules around a shared fishpond resource.The results demonstrate that under purely economic-driven incentives,fishermen tend to adopt high-intensity fishing strategies.This leads to fish populations rapidly declining from an initial 1,600 units to near-zero within 20 time steps,reproducing the classic“Tragedy of the Commons”phenomenon.In contrast,introducing sustainability penalty mechanisms or cooperative mechanisms effectively guides fishermen to adjust their fishing strategies.These mechanisms promote more stable and moderate fishing behaviors,maintaining fish populations at approximately 500 units and 1,500 units respectively throughout the simulation period.Furthermore,by incorporating behavioral parameters(greediness factors),the model effectively captures heterogeneity in fishing propensities.Highgreediness fishermen exhibit aggressive behaviors while low-greediness fishermen adopt more conservative strategies,revealing the impact of individual behavioral differences on system dynamics.These findings validate our proposed MADRL framework's ability to capture the dynamic feedback loops between heterogeneous agents and their environment,as well as emergent non-linear phenomena.By providing an integrated framework for analyzing and understanding these core mechanisms among multiple processes,agents,and activities of HNS,this study lays the foundation for future large-scale numerical experiments that address governance and decision-making challenges across multiple scales.展开更多
Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inheren...Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inherently challenging due to the unstructured,context-sensitive,and heterogeneous nature of online communication.To address these challenges,we propose a novel intelligent computational framework,Multi-domain Opinion Leader Agents Emotion Prediction(MOAEP).Our framework comprises three key components:(1)An Automatic Question Generation(AQG)module employing“Who,What,Where,When,Why,and How”(5W1H)questioning to systematically explore topic dimensions;(2)A Multi-domain Opinion Leader Agents(MOA)module that integrates enhanced Large Language Models(LLMs)with Retrieval-Augmented Generation(RAG)to produce domain-specific responses;and(3)An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses,enabling proactive social computing analysis that surpasses conventional post-event methods.Experimental results demonstrate the framework’s efficacy:the AQG module generates high-fidelity outputs,while the influencer agents maintain consistent performance,achieving an average“Generative Pre-trained Transformer 4”(GPT-4)evaluation score of 6.85(on a 0-10 scale)across multiple dimensions.In a social media conflict case study,“Russia-Ukraine War”,our framework successfully predicts key influencers’perspectives and aligns emotional forecasts with observed real-world sentiment trends.These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.展开更多
Equipment systems-of-systems （SOS） effectiveness evaluation can provide important reference for construction and optimization of the equipment SoS. After discussing the basic theory and methods of parallel experimen...Equipment systems-of-systems （SOS） effectiveness evaluation can provide important reference for construction and optimization of the equipment SoS. After discussing the basic theory and methods of parallel experiments, we depict an SoS effectiveness analysis and evaluation method using parallel expe- riments theory in detail. A case study is carried out which takes the missile defense system as an example. An artificial system of the missile defense system is constructed with the multi-agent modeling method. Then, single factor, multiple factors and defense position deployment computational experiments are carried out and evaluated with the statistical analysis method. Experiment re- sults show that the altitude of the secondary interception missile is not the key factor which affects SoS effectiveness and putting the defense position ahead will increase defense effectiveness. The case study demonstrates the feasibility of the proposed method.展开更多
The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of inte...The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside [Current Zoology 61 （1）： 98-106, 2015].展开更多
This paper is devoted to analyze and model user reading and replying activities in a bulletin board system(BBS)social network.By analyzing the data set from a famous Chinese BBS social network,we show how some user ac...This paper is devoted to analyze and model user reading and replying activities in a bulletin board system(BBS)social network.By analyzing the data set from a famous Chinese BBS social network,we show how some user activities distribute,and reveal several important features that might characterize user dynamics.We propose a method to model user activities in the BBS social network.The model could reproduce power-law and non-power-law distributions of user activities at the same time.Our results show that user reading and replying activities could be simulated through simple agent-based models.Specifically,manners of how the BBS server interacts with Internet users in the Web 2.0 application,how users organize their reading lists,and how user behavioral trait distributes are the important factors in the formation of activity patterns.展开更多
Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that t...Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as selforganization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agentbased models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.展开更多
We present a mathematical model of a day care center in a developed country (such as Canada), in order to use it for the estimation of individual-to-individual contact rates in young age groups and in an educational g...We present a mathematical model of a day care center in a developed country (such as Canada), in order to use it for the estimation of individual-to-individual contact rates in young age groups and in an educational group setting. In our model, individuals in the population are children (ages 1.5 to 4 years) and staff, and their interactions are modelled explicitly: person-to-person and person-to-environment, with a very high time resolution. Their movement and meaningful contact patterns are simulated and then calibrated with collected data from a child care facility as a case study. We present these calibration results as a first part in the further development of our model for testing and estimating the spread of infectious diseases within child care centers.展开更多
This paper introduces a software agent as a virtual player of the business game called Bakery Game. The business game is a tool to understand business and management principles through experience in a virtual world. I...This paper introduces a software agent as a virtual player of the business game called Bakery Game. The business game is a tool to understand business and management principles through experience in a virtual world. In order to construct software agents for the gaming simulation, which is able to participate in a game and become human players' worthy rival, we combine a simple model that considers the property of Bakery Game with a strategy adjustment model. The agent has so-called strategy parameters that are updated through the experience. The agent changes his strategy depending on particular situation. The agent participates in the game that is regenerated from log data in various situations.展开更多
As demonstrated during the COVID-19 pandemic, non-pharmaceutical interventions, such as case isolation, are an important element of pandemic response. The overall impact of case isolation on epidemic dynamics depends ...As demonstrated during the COVID-19 pandemic, non-pharmaceutical interventions, such as case isolation, are an important element of pandemic response. The overall impact of case isolation on epidemic dynamics depends on a number of factors, including the timing of isolation relative to the onset of contagiousness for each individual instructed to isolate by public health authorities. While there is an extensive literature examining the importance of minimising the delay from exposure to direction to isolate in determining the impact of case isolation policy, less is known about how underlying epidemic dynamics may also contribute to that impact. Empirical observation and modelling studies have shown that, as an epidemic progresses, the distribution of viral loads among cases changes systematically. In principle, this may allow for more targeted and efficient isolation strategies to be implemented. Here, we describe a multi-scale agent-based model developed to investigate how isolation strategies that account for cases viral loads could be incorporated into policy. We compare the impact and efficiency of isolation strategies in which all cases, regardless of their viral load, are required to isolate to strategies in which some cases may be exempt from isolation. Our findings show that, following the epidemic peak, the vast majority of cases identified with a low viral load are in the declining phase of their infection and so contribute less to overall contagiousness. This observation prompts the question about the potential public health value of discontinuing isolation for such individuals. Our numerical investigation of this ‘adaptive’ strategy shows that exempting individuals with low viral loads from isolation following the epidemic peak leads to a modest increase in new infections. Surprisingly, it also leads to a drop in efficiency, as measured by the average number of infections averted per isolated case. Our findings therefore suggest caution in adopting such flexible or adaptive isolation policies. Our multi-scale modelling framework is sufficiently flexible to enable extensive numerical evaluation of more complex isolation strategies that incorporate more disease-specific biological and epidemiological features, supporting the development and evaluation of future public health pandemic response plans.展开更多
Urban growth models have been developed and extensively adopted to study urban expansion and its impact on the ambient environment. These models can be employed in urban policymaking or analyses of development scenari...Urban growth models have been developed and extensively adopted to study urban expansion and its impact on the ambient environment. These models can be employed in urban policymaking or analyses of development scenarios. In this paper, we provide a systematic review of urban growth models, including the evolution of urban models and associated theories and the common framework of different models and their applications. Three typical classes of urban growth models, namely, the land use/transportation model, the cellular automata （CA） model and the agent-based model, were introduced. Their relationships were explained, considering their modelling mechanisms, data requirements and application scales. Based on the extensively utilized urban CA models, we proposed four perspectives for improvements, including the adjustment of the basic spatial unit, the incorporation of temporal contexts, public platforms to support model comparison, and scenario analyses. New opportunities （e.g., open social data and integrated assessment models） have emerged to assist model development and application.展开更多
In the near future, various types of low-carbon technologies(LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the ex...In the near future, various types of low-carbon technologies(LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage(LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods,where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood.This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.展开更多
基金supported in part by National Key Research and Development Program of China(2021YFF0900800)National Natural Science Foundation of China(62472306,62441221,62206116)+2 种基金Tianjin University’s 2024 Special Project on Disciplinary Development(XKJS-2024-5-9)Tianjin University Talent Innovation Reward Program for Literature&Science Graduate Student(C1-2022-010)Shanxi Province Social Science Foundation(2020F002).
文摘COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to form an AI society.Agent modeling typically encompasses four levels:1)The autonomy features of agents,e.g.,perception,behavior,and decision-making;2)The evolutionary features of agents,e.g.,bounded rationality,heterogeneity,and learning evolution;3)The social features of agents,e.g.,interaction,cooperation,and competition;4)The emergent features of agents,e.g.,gaming with environments or regulatory strategies.Traditional modeling techniques primarily derive from ABMs(Agent-based Models)and incorporate various emerging technologies(e.g.,machine learning,big data,and social networks),which can enhance modeling capabilities,while amplifying the complexity[1].
文摘Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.
文摘The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.
文摘目的:比较国内外Agent行为建模与仿真研究热点与趋势,为我国研究者更有效地应用Agent行为建模与仿真提供参考依据。方法:运用CiteSpace5.7R5软件对中国知网和Web of Science核心合集数据库2011年1月1日至2021年3月31日中有关Agent行为建模与仿真研究的文献进行图谱量化分析。结果:共检索到有效中文文献864篇、英文文献2323篇,国内发文量整体呈下降趋势,国外发文量整体呈上升趋势,发文量高的国家集中在发达国家,国外研究前沿已经延伸到物理学、金融学、哲学、生物学、物流学、人工智能等方面。国内研究热点主要集中在社会学、物理学、网络模型等方面。结论:Agent行为建模与仿真研究的应用范围较广泛,与国际相比国内Agent行为建模与仿真研究还存在一定的差距,研究深度和广度有待进一步拓展,国内应参考国际Agent行为建模与仿真研究的热点及前沿,探索适合我国特色的Agent行为建模与仿真系统体系,以促进我国Agent行为建模与仿真的发展。
文摘Understanding migratory waterfowl spatiotemporal distributions is important because,in addition to their economic and cultural value,wild waterfowl can be infectious reservoirs of highly pathogenic avian influenza virus(HPAIV).Waterfowl migration has been implicated in regional and intercontinental HPAIV dispersal,and predictive capabilities of where and when HPAIV may be introduced to susceptible spillover hosts would facilitate biosecurity and mitigation efforts.To develop forecasts for HPAIV dispersal,an improved understanding of how individual birds interact with their environment and move on a landscape scale is required.Using an agent-based modeling approach,we integrated individual-scale energetics,species-specific morphology and behavior,and landscape-scale weather and habitat data in a mechanistic stochastic framework to simulate Mallard(Anas platyrhynchos)and Northern Pintail(Anas acuta)annual migration across the Northern Hemisphere.Our model recreated biologically realistic migratory patterns using a first principles approach to waterfowl ecology,behavior,and physiology.Conducting a limited structural sensitivity analysis comparing reduced models to eBird Status and Trends in reference to the full model,we identified density dependence as the main factor influencing spring migration and breeding distributions,and wind as the main factor influencing fall migration and overwintering distributions.We show evidence of weather patterns in Northeast Asia causing significant intercontinental pintail migration to North America.By linking individual energetics to landscapescale processes,we identify key drivers of waterfowl migration while developing a predictive model responsive to daily weather patterns.This model paves the way for future waterfowl migration research predicting HPAIV transmission,climate change impacts,and oil spill effects.
基金supported by the Priority 2030 Federal Academic Leadership Program.Wewould like to thank S.Danilova,M.Reykina,and S.Shevtsova for their valuable contribution to the annotation of the dataset used in this study.
文摘Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains.We introduce nanoMINER,a multi-agent system combining large language models and multimodal analysis to extract essential information from scientific research articles on nanomaterials.This system processes documents end-to-end,utilizing tools such as YOLO for visual data extraction and GPT-4o for linking textual and visual information.At its core,the ReAct agent orchestrates specialized agents to ensure comprehensive data extraction.We demonstrate the efficacy of the system by automating the assembly of nanomaterial and nanozyme datasets previously manually curated by domain experts.NanoMINER achieves high precision in extracting nanomaterial properties like chemical formulas,crystal systems,and surface characteristics.For nanozymes,we obtain near-perfect precision(0.98)for kinetic parameters and essential features such as Cmin and Cmax.To benchmark the systemperformance,we also compare nanoMINER to several baseline LLMs,including the most recent multimodal GPT-4.1,and show consistently higher extraction precision and recall.Our approach is extensible to other domains of materials science and fields like biomedicine,advancing data-driven research methodologies and automated knowledge extraction.
基金supported by the National Natural Science Foundation of China(Grant Nos.42301545,42430112)China Postdoctoral Science Foundation(Grant No.2023M733606)。
文摘Human-natural systems(HNS)are complex,adaptive systems where human activities and natural processes are deeply intertwined.Stochastic processes,nonlinear couplings,feedback loops,and emergent phenomena collectively shape the interactions between human behaviors and natural dynamics.While environmental models in Earth system science are relatively well-established(equation-or process-based),modeling human systems remains insufficient.Moreover,there lacks a unified framework for effectively characterizing these complex interactions,posing significant challenges for HNS modeling and decision-making.To fill these gaps,this study proposes an integrated multi-agent deep reinforcement learning(MADRL)framework that combines Markov decision process(MDP),agent-based modeling(ABM),and deep reinforcement learning(DRL)to address modeling and decision-making challenges in HNS.The framework is structured as an MDP,defined by four core components:states of the environment(natural system),actions of agents(human system),transitions of the states(evolution of the HNS),and rewards.We introduce ABM to simulate human behaviors,decision-makings,and interactions among multi-hierarchical stakeholders,including individuals,groups,communities,governments,and non-governmental organizations.Additionally,DRL is employed to tackle the high-dimensional solving challenges of the MDP.Finally,a classic case study based on the“Tragedy of the Commons”is designed,featuring multiple fishermen operating under specific decision rules around a shared fishpond resource.The results demonstrate that under purely economic-driven incentives,fishermen tend to adopt high-intensity fishing strategies.This leads to fish populations rapidly declining from an initial 1,600 units to near-zero within 20 time steps,reproducing the classic“Tragedy of the Commons”phenomenon.In contrast,introducing sustainability penalty mechanisms or cooperative mechanisms effectively guides fishermen to adjust their fishing strategies.These mechanisms promote more stable and moderate fishing behaviors,maintaining fish populations at approximately 500 units and 1,500 units respectively throughout the simulation period.Furthermore,by incorporating behavioral parameters(greediness factors),the model effectively captures heterogeneity in fishing propensities.Highgreediness fishermen exhibit aggressive behaviors while low-greediness fishermen adopt more conservative strategies,revealing the impact of individual behavioral differences on system dynamics.These findings validate our proposed MADRL framework's ability to capture the dynamic feedback loops between heterogeneous agents and their environment,as well as emergent non-linear phenomena.By providing an integrated framework for analyzing and understanding these core mechanisms among multiple processes,agents,and activities of HNS,this study lays the foundation for future large-scale numerical experiments that address governance and decision-making challenges across multiple scales.
基金supported in part by the National Natural Science Foundation of China(Nos.62301510,62271455,and 72474198)the Public Computing Cloud,CUC.
文摘Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inherently challenging due to the unstructured,context-sensitive,and heterogeneous nature of online communication.To address these challenges,we propose a novel intelligent computational framework,Multi-domain Opinion Leader Agents Emotion Prediction(MOAEP).Our framework comprises three key components:(1)An Automatic Question Generation(AQG)module employing“Who,What,Where,When,Why,and How”(5W1H)questioning to systematically explore topic dimensions;(2)A Multi-domain Opinion Leader Agents(MOA)module that integrates enhanced Large Language Models(LLMs)with Retrieval-Augmented Generation(RAG)to produce domain-specific responses;and(3)An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses,enabling proactive social computing analysis that surpasses conventional post-event methods.Experimental results demonstrate the framework’s efficacy:the AQG module generates high-fidelity outputs,while the influencer agents maintain consistent performance,achieving an average“Generative Pre-trained Transformer 4”(GPT-4)evaluation score of 6.85(on a 0-10 scale)across multiple dimensions.In a social media conflict case study,“Russia-Ukraine War”,our framework successfully predicts key influencers’perspectives and aligns emotional forecasts with observed real-world sentiment trends.These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.
文摘Equipment systems-of-systems （SOS） effectiveness evaluation can provide important reference for construction and optimization of the equipment SoS. After discussing the basic theory and methods of parallel experiments, we depict an SoS effectiveness analysis and evaluation method using parallel expe- riments theory in detail. A case study is carried out which takes the missile defense system as an example. An artificial system of the missile defense system is constructed with the multi-agent modeling method. Then, single factor, multiple factors and defense position deployment computational experiments are carried out and evaluated with the statistical analysis method. Experiment re- sults show that the altitude of the secondary interception missile is not the key factor which affects SoS effectiveness and putting the defense position ahead will increase defense effectiveness. The case study demonstrates the feasibility of the proposed method.
文摘The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside [Current Zoology 61 （1）： 98-106, 2015].
基金supported in part by the National Natural Science Foundation of China under Grant No.60972010the Beijing Natural Science Foundation under Grant No.4102047+1 种基金the Major Program for Research on Philosophy&Humanity Social Sciences of the Ministry of Education of China under Grant No.08WL1101the Service Business of Scientists and Engineers Project under Grant No.2009GJA00048
文摘This paper is devoted to analyze and model user reading and replying activities in a bulletin board system(BBS)social network.By analyzing the data set from a famous Chinese BBS social network,we show how some user activities distribute,and reveal several important features that might characterize user dynamics.We propose a method to model user activities in the BBS social network.The model could reproduce power-law and non-power-law distributions of user activities at the same time.Our results show that user reading and replying activities could be simulated through simple agent-based models.Specifically,manners of how the BBS server interacts with Internet users in the Web 2.0 application,how users organize their reading lists,and how user behavioral trait distributes are the important factors in the formation of activity patterns.
文摘Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as selforganization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agentbased models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.
文摘We present a mathematical model of a day care center in a developed country (such as Canada), in order to use it for the estimation of individual-to-individual contact rates in young age groups and in an educational group setting. In our model, individuals in the population are children (ages 1.5 to 4 years) and staff, and their interactions are modelled explicitly: person-to-person and person-to-environment, with a very high time resolution. Their movement and meaningful contact patterns are simulated and then calibrated with collected data from a child care facility as a case study. We present these calibration results as a first part in the further development of our model for testing and estimating the spread of infectious diseases within child care centers.
基金The original version was presented at the congress of the IFSR2005
文摘This paper introduces a software agent as a virtual player of the business game called Bakery Game. The business game is a tool to understand business and management principles through experience in a virtual world. In order to construct software agents for the gaming simulation, which is able to participate in a game and become human players' worthy rival, we combine a simple model that considers the property of Bakery Game with a strategy adjustment model. The agent has so-called strategy parameters that are updated through the experience. The agent changes his strategy depending on particular situation. The agent participates in the game that is regenerated from log data in various situations.
基金the Australian Research Council for funding this research through Discovery Project DP210101920JMM is supported by an ARC Laureate Fellowship(FL240100126)This research was supported by The University of Melbourne's Research Computing Services and the Petascale Campus Initiative and an allocation on the Australian Research Data Commons NECTAR cloud computing resource.
文摘As demonstrated during the COVID-19 pandemic, non-pharmaceutical interventions, such as case isolation, are an important element of pandemic response. The overall impact of case isolation on epidemic dynamics depends on a number of factors, including the timing of isolation relative to the onset of contagiousness for each individual instructed to isolate by public health authorities. While there is an extensive literature examining the importance of minimising the delay from exposure to direction to isolate in determining the impact of case isolation policy, less is known about how underlying epidemic dynamics may also contribute to that impact. Empirical observation and modelling studies have shown that, as an epidemic progresses, the distribution of viral loads among cases changes systematically. In principle, this may allow for more targeted and efficient isolation strategies to be implemented. Here, we describe a multi-scale agent-based model developed to investigate how isolation strategies that account for cases viral loads could be incorporated into policy. We compare the impact and efficiency of isolation strategies in which all cases, regardless of their viral load, are required to isolate to strategies in which some cases may be exempt from isolation. Our findings show that, following the epidemic peak, the vast majority of cases identified with a low viral load are in the declining phase of their infection and so contribute less to overall contagiousness. This observation prompts the question about the potential public health value of discontinuing isolation for such individuals. Our numerical investigation of this ‘adaptive’ strategy shows that exempting individuals with low viral loads from isolation following the epidemic peak leads to a modest increase in new infections. Surprisingly, it also leads to a drop in efficiency, as measured by the average number of infections averted per isolated case. Our findings therefore suggest caution in adopting such flexible or adaptive isolation policies. Our multi-scale modelling framework is sufficiently flexible to enable extensive numerical evaluation of more complex isolation strategies that incorporate more disease-specific biological and epidemiological features, supporting the development and evaluation of future public health pandemic response plans.
文摘Urban growth models have been developed and extensively adopted to study urban expansion and its impact on the ambient environment. These models can be employed in urban policymaking or analyses of development scenarios. In this paper, we provide a systematic review of urban growth models, including the evolution of urban models and associated theories and the common framework of different models and their applications. Three typical classes of urban growth models, namely, the land use/transportation model, the cellular automata （CA） model and the agent-based model, were introduced. Their relationships were explained, considering their modelling mechanisms, data requirements and application scales. Based on the extensively utilized urban CA models, we proposed four perspectives for improvements, including the adjustment of the basic spatial unit, the incorporation of temporal contexts, public platforms to support model comparison, and scenario analyses. New opportunities （e.g., open social data and integrated assessment models） have emerged to assist model development and application.
基金supported by Scottish and Southern Electricity Networks through the New Thames Valley Vision Project (SSET203 New Thames Valley Vision)funded by the Low Carbon Network Fund established by Ofgem
文摘In the near future, various types of low-carbon technologies(LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage(LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods,where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood.This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.
