A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

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.

Analysing the behavioural finance impact of ‘fake news’phenomena on financial markets:a representative agent model and empirical validation

This paper proposes an original behavioural finance representative agent model,to explain how fake news’empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis.The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news,and predicts a novel secondary impact of fake news:that fake news in a security amplifies underreactions to subsequent real news for the security.Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event,this paper finds strong qualitative validation for its model’s dynamics and predictions.

Fiscal Policy Effect Analysis Based on Agents Response Equilibrium (ARE) Model

Agents response equilibrium (ARE) model has been taken advantage of to build a multi-agent system for analyzing fiscal policy effect. Through establishing various types of economic entities and endowing them with abilities to react and make decision, the whole system will evolve to new conditions in response to policy change. Compared with different scenarios, it can be concluded that when raising taxation ratio, sectoral scale will shrink to some extent. But supported by government expenditure, certain sectors could be kept in comparatively larger production scale.

Agent行为建模与仿真研究的热点与趋势——基于CiteSpace图谱量化分析

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。

Adaptive Agent Model with Hybrid Routing Selection Strategy for Improving the Road-Network Congestion Problem

This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.

Study on the CAN Search Model based on BDI Agent

The main purpose of establishing a complex agent network （CAN） search model is to specifically model each type of the relationships between different types of Agent structure domain and make it easier to be implemented in the existing programming language environment. Under the guidance of complex Agent network method, CAN search process was analyzed, a dynamic search model description was established based on CAN search process, and then individual Agent modelling and the memory and processing of the thinking attributes such as beliefs, desires and intentions in CAN search process were mainly introduced from the individual level; all sorts of Agent conceptual models and Agent type descriptions for CAN search model were designed by introducing BDI Agent; the states and behaviors of the Agent involving in CAN search process were clearly defined.

Meaningful Contact Estimates among Children in a Childcare Centre with Applications to Contact Matrices in Infectious Disease Modelling

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.

北京市大型医院空间优化与再配置的计算实验研究

医疗资源的合理布局是构建高质量医疗体系的重要基础。随着北京疏解工作的全面启动,以往高度集中布局的北京医疗资源需要重新配置。如何实现优质医疗资源空间布局的优化,既要解决“看病难、看病贵”,还不能加剧北京的“大城市病”,这是疏解面临的新课题。研究以计算社会科学的方法为基础,北京医疗资源疏解为背景,开发基于agent的城市医疗资源空间配置和布局的计算实验平台,讨论“点轴放射”模式和“组团”模式等不同布局情景下居民的居住选择和就医行为,“动态”观察和评估不同模式在平常和应急两种状态下资源配置的合理性和有效性。计算实验结果表明,“点轴放射”模式的医院布局在空间分布上更加均匀,能够随着人口的迁移互动逐渐形成自己的服务半径,效果略好于“组团”模式。然而,在灾情疫情等应急情形下,“组团”模式的医疗资源配置表现要显著好于“点轴放射”模式。研究表明,基于agent的建模和计算实验方法能够为利用真实数据建立现实模型展开这类布局事前研究和规划提供坚实的基础。

Agent-based effectiveness evaluation method and impact analysis of airborne laser weapon system in cooperation combat

The laser weapons will play a special role in the future high-tech war.To study the impact of airborne laser weapon on the System-of-System(SoS)effectiveness in cooperative com-bat,this paper proposes an indicator construction method based on the combination of the weapon capability indicator system and the combat simulation.The indicator system of capability is divided into 4 layers by the bottom-to-up generation mechanism of indicators.It can describe the logical relationship between the indicator layers from a qualitative perspective.Together with the 4 layers capability indicator system,a hierarchical framework of airborne laser weapon is established by the agent-based modeling and simulation.Impact analyses show that the SoS effectiveness improves with the increase of the laser weapon output power,the laser launcher diameter,and the photoelectric sensor pixel.But the SoS effectiveness promotion brought by the photoelectric sensor pixel is limited.The results can be used for the development of tactical airborne laser weapon.

A framework for equipment systems-of-systems effectiveness evaluation using parallel experiments approach

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.

个人主义文化价值观对疫情控制效果的影响及其计算心理机制

COVID-19疫情是一场重大的全球健康危机,一些国家在控制COVID-19感染和死亡率上存在明显困难。我们提出,个人主义的文化价值观不利于对疫情的控制。跨文化分析结果显示,个人主义文化价值观正向预测COVID-19死亡数、每百万死亡数和死亡率,独立自我建构负向预测疫情前期控制速度。演化博弈模型和跨文化实验进一步提示,个人主义文化通过增强个体在疫情背景下的死亡恐惧,增加个体违反疫情管控的流动性倾向,从而降低了整体疫情控制的效率。我们的结果支持自然-行为-文化协同进化的理论模型,提示文化对COVID-19病毒传播管控和死亡可能性的影响,为各国应对全球公共卫生危机提供了重要科学参考。

Research on the User Interest Modeling of Personalized Search Engine

At present, how to enable Search Engine to construct user personal interest model initially, master user＇s personalized information timely and provide personalized services accurately have become the hotspot in the research of Search Engine area. Aiming at the problems of user model＇s construction and combining techniques of manual customization modeling and automatic analytical modeling, a User Interest Model （UIM） is proposed in the paper. On the basis of it, the corresponding establishment and update algorithms of User lnterest Profile （UIP） are presented subsequently. Simulation tests proved that the UIM proposed and corresponding algorithms could enhance the retrieval precision effectively and have superior adaptability.

Modeling Reading and Replying Activities in a BBS Social Network

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.

Government intervention model based on behavioral heterogeneity for China’s stock market

Active government intervention is a striking characteristic of the Chinese stock market.This study develops a behavioral heterogeneous agent model(HAM)comprising fundamentalists,chartists,and stabilizers to investigate investors’dynamic switching mechanisms under government intervention.The model introduces a new player,the stabilizer,into the HAM as a proxy for the government.We use the model to examine government programs during the 2015 China stock market crash and find that it can replicate the dynamics of investor sentiment and asset prices.In addition,our analysis of two simulations,specifically the data-generating processes and shock response analysis,further corroborates the key conclusion that our intervention model not only maintains market stability but also promotes the return of risk asset prices to their fun-damental values.The study concludes that government interventions guided by the new HAM can alleviate the dilemma between reducing price volatility and improving price efficiency in future intervention programs.

Effects of herding behavior of tradable green certificate market players on market efficiency:insights from heterogeneous agent model

Tradable green certificate(TGC)scheme promotes the development of renewable energy industry which currently has a dual effect on economy and environment.TGC market efficiency is reflected in stimulating renewable energy investment,but may be reduced by the herding behavior of market players.This paper proposes and simulates an artificial TGC market model which contains heterogeneous agents,communication structure,and regulatory rules to explore the characteristics of herding behavior and its effects on market efficiency.The results show that the evolution of herding behavior reduces information asymmetry and improves market efficiency,especially when the borrowing is allowed.In addition,the fundamental strategy is diffused by herding evolution,but TGC market efficiency may be remarkably reduced by herding with borrowing mechanism.Moreover,the herding behavior may evolve to an equilibrium where the revenue of market players is comparable,thus the fairness in TGC market is improved.

Agent-oriented Aid-modeling for the Distributed Activity Network

An agent in the decision support system (DSS) has to cope with quantitative analysis processing normally. However, from the view of agent working style, the agent oriented aid modeling operations need new procedure accepted by the agent. For example, in the distributed activity planning processes, the transforming problem description has to use a unified pattern first, then to compute the activity′s 6 time parameters, activity′s duration, entity plan scheme, optimal calculation and so on. In this paper, we propose a process manner of agent oriented modeling for the distributed activity network. The goal is to introduce an agent working procedure included model usage, multi agent cooperating, communicating among agents and interaction between system and human. Also, it included the problem representation, the initial process, agent design, transform and the computing with interaction in detail.

Fire evacuation visualization in nursing homes based on agent and cellular automata

As China’s population ages and the notion of contemporary people evolves,an increasing number of elderly people opt to spend their final years in a nursing home.Recently,there have been more fires in nursing facilities for the elderly.As a result,using computer simulation technology,this paper creates an evacuation micro-simulation model for the elderly and nursing staff,investigates the impact of psychological characteristics and evacuation behavior of the elderly and nursing staffon fire escape during the fire evacuation process,and designs fire evacuation for nursing homes.Based on the nursing home’s regular evacuation paradigm,the study examines the behavioral elements.To simulate the three models and the influence of various behavioral and psychological features on fire evacuation,the authors utilized the MATLAB software,which is based on Agent theory and cellular automata.The simulation results show that the three models proposed in this study are capable of accurately describing reality.

AGENT MODEL ANALYSIS TO EXPLORE EFFECTS OF INTERACTION AND ENVIRONMENT ON INDIVIDUAL PERFORMANCE

This paper tries to find interaction rules between members and self-reform rules of members in some scientific research laboratories at a graduate school. The candidate of rules are extracted from the personality description sentences about factors： extroversion, agreeableness, conscientiousness, neuroticism and openness in the big five theory of personality psychology. In this paper, interaction and self-reform rules are not described by personalities themselves; instead, they are described by corresponding phenomena： activity, empathy, persistence, autonomy, and analytical ability which appear in the behavior side. Each member＇s initial value is determined from the main 5-factor personality investigation to the member, and the target value is given by the member＇s self-declaration. The rule selection is carried out by the genetic algorithm, where a sigmoid function is introduced in the renewal algorithm of the value, whereby a difficulty of setting the renewal parameters is avoided. An analysis is added about the obtained rules, and the validity of this approach and the subjects for future study are discussed. Finally, consideration is extended to the effect of environment on the behavior of members to reinforce the set of rules.

SPECIFYING AGENT-ORIENTED E-COMMERCE SOFTWARE AS ORGANIZATIONAL STRUCTURES

Agent architectures are gaining popularity for building open, distributed, and evolving software required by e-commerce applications. Unfortunately, despite considerable work in software architecture during the last decade, few research efforts have aimed at truly defining patterns and languages for agent architectural design. This paper proposes a modern approach based on organizational structures and architectural description languages to define and specify agent architectures notably in the case of e-commerce system design.

A review of the use of geosocial media data in agent-based models for studying urban systems

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.