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.

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。

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.

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.

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。

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.

Green neighbourhoods in low voltage networks:measuring impact of electric vehicles and photovoltaics on load profiles

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.

Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing

The re-emerging outbreak of COVID-19 in Beijing,China,in the summer of 2020 originated from a SARSCoV-2-infested wholesale food supermarket.We postulated that the Xinfadi market outbreak has links with food-trade activities.Our Susceptible to the disease,Infectious,and Recovered coupled Agent Based Modelling(SIR-ABM)analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number(R0).The retail shop closure strategy reduced the number of visitors to the market by nearly half.In addition,the buy-local policy option reduced the infection by more than 70%in total.Therefore,retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.

Urban growth models： progress and perspective

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.

Persistent variation in spatial behavior affects the structure and function of interaction networks

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].

COVID-19: Analytics of contagion on inhomogeneous random social networks

Motivated by the need for robust models of the Covid-19 epidemic that adequately reflect the extreme heterogeneity of humans and society,this paper presents a novel framework that treats a population of N individuals as an inhomogeneous random social network(IRSN).The nodes of the network represent individuals of different types and the edges represent significant social relationships.An epidemic is pictured as a contagion process that develops day by day,triggered by a seed infection introduced into the population on day 0.Individuals’social behaviour and health status are assumed to vary randomly within each type,with probability distributions that vary with their type.A formulation and analysis is given for a SEIR(susceptible-exposed-infective-removed)network contagion model,considered as an agent based model,which focusses on the number of people of each type in each compartment each day.The main result is an analytical formula valid in the large N limit for the stochastic state of the system on day t in terms of the initial conditions.The formula involves only one-dimensional integration.The model can be implemented numerically for any number of types by a deterministic algorithm that efficiently incorporates the discrete Fourier transform.While the paper focusses on fundamental properties rather than far ranging applications,a concluding discussion addresses a number of domains,notably public awareness,infectious disease research and public health policy,where the IRSN framework may provide unique insights.