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.

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.

The Application and Modification of Delegation-Agent Model in Agricultural Insurance

The delegation-agent models in agricultural insurance are established both under the circumstances of information symmetry and information asymmetry.Insurers choose effort level-a* according to the first order optimal condition of ∫{v(π-s(π))+λ11[u(s(π))]fa(π,a*)}dπ=λ11c'(a*)u(s(π)) at the present stage when the information is symmetric.While the information is asymmetric,the first order optimal condition changed into v'(π-s(π))u'(s(π))=λ21+μ21(1-fa(π,a)f(π,a)).In other words,the higher the output,the more and more income of insured.The paper also modifies the models,when the information is symmetric,the insurers determine the effort level of insured-a* based on the first order optimal condition of ∫{v(π-s(π))+λ12[u(s(π))]fa(π,a*)}dπ=λ12h'(a*)u(s(π));to the contrary,the first order optimal condition would change into v'(π*-s(π*))u'(s(π*))=λ22+μ22(1-fa(π,a)f(π,a))-λh(a)f(π,a)-μh'(a)f(π,a).The results show that the insured and the insurers would both benefit from the insurance when the effort cost function related to the expectation of the insured(agricultural producers).If the insured manage the objects of insurance more seriously,the rate of disasters would be lowered.Therefore,the insurance claimed against the insured would be lessened,and the benefits of the insurers would be increased at last.

Immune multi-agent model using vaccine for cooperative air-defense system of systems for surface warship formation based on danger theory

Aiming at the problem on cooperative air-defense of surface warship formation,this paper maps the cooperative airdefense system of systems (SoS) for surface warship formation (CASoSSWF) to the biological immune system (BIS) according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS,and then designs the models of components and the architecture for a monitoring agent,a regulating agent,a killer agent,a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system,the multi-agent system (MAS),the vaccine and the danger theory (DT).Moreover a new immune multi-agent model using vaccine based on DT (IMMUVBDT) for the cooperative air-defense SoS is advanced.The immune response and immune mechanism of the CASoSSWF are analyzed.The model has a capability of memory,evolution,commendable dynamic environment adaptability and self-learning,and embodies adequately the cooperative air-defense mechanism for the CASoSSWF.Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.

The Multi-Agent Modeling and Calculated Analysis of Shop Logistics Scheduling in the Manufacturing Enterprises

In this paper,the multi-agent model about shop logistics is set up. This model has 8 agents: raw materials stock agent,process agent,testing agent,transition agent,production information agent,scheduling agent,process agent and stock agent. The scheduling agent has three subagents: manager agent (MA),resource agent (RA) and part agent (PA). MA,PA and RA are communicating equally that guarantees agility of the whole MAS system. The part tasks pass between MA,RA and PA as an integer,which can guarantee the consistency of the data. We use a detailed example about shop logistics scheduling in a semiconductor company to explain the principle. In this example,we use two scheduling strategies: FCFS and SPT. The result data indicates that the average flow time and lingering ratio are changed using different strategy. It is proves that the multi-agent scheduling is useful.

Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

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.

A Cautionary Note on the Application of GIS in Spatial Optimization Modeling

Spatial optimization as part of spatial modeling has been facilitated significantly by integration with GIS techniques. However, for certain research topics, applying standard GIS techniques may create problems which require attention. This paper serves as a cautionary note to demonstrate two problems associated with applying GIS in spatial optimization, using a capacitated p-median facility location optimization problem as an example. The first problem involves errors in interpolating spatial variations of travel costs from using kriging, a common set of techniques for raster files. The second problem is inaccuracy in routing performed on a graph directly created from polyline shapefiles, a common vector file type. While revealing these problems, the paper also suggests remedies. Specifically, interpolation errors can be eliminated by using agent-based spatial modeling while the inaccuracy in routing can be improved through altering the graph topology by splitting the long edges of the shapefile. These issues suggest the need for caution in applying GIS in spatial optimization study.

基于多Agent传动关系的股市趋势预测

股市趋势预测是机器学习领域中一个具有挑战性的任务。由于一些因素对于股市的影响是动态且不确定的,导致股市趋势难以预测。针对已有方法在股市预测时存在的灵敏性差、适应力弱等问题,从快变量和慢变量的传动关系出发,利用Agent技术对股市中的快周期和慢周期进行联合建模,提出一种多Agent传动影响图(MATID)股市趋势预测方法。给出股市中快周期和慢周期的划分标准,并引入周期能量的概念;通过对相关趋势指标的特征融合,给出周期能量的量化计算方法;通过分析快周期和慢周期的动态作用过程,给出传动因子的表示方法;将快周期和慢周期分别对应成不同的Agent,利用多Agent影响图模型建模快周期和慢周期的传动过程;利用股市振子模型表示快Agent和慢Agent之间的传动效用,利用联合树的自动推理技术对股市趋势进行预测。在不同样本数量和不同股市趋势下进行实验,结果表明,与门控循环单元、S-LSTM和Hybrid-RNN预测方法相比,MATID方法预测精确率提升1.5%~7.0%,召回率提升5.4%~6.7%,F1值提升3.7%~6.2%,具有良好的灵敏性和适应力。

面向任务的装备保障体系多Agent建模与评估方法

随着战争形态向着体系化、智能化转变,装备保障体系有了新挑战。同时,动态变化的战场环境要求装备体系要适应战场态势,动态支撑作战任务。目前,装备保障体系建模研究存在与作战任务联系不紧密、与装备体系不匹配,不能充分体现装备保障体系自主性等问题。首先,基于多Agent方法,从结构和流程两个方面对装备保障体系的任务层、基地层、装备层建模,重点考虑保障任务中时间和空间约束问题;其次,提出面向任务的装备保障体系评价指标,构建指标模型;最后,通过对典型作战任务对应保障活动进行仿真,验证模型的合理性和有效性,并完成装备保障体系的效能评估。本研究可为装备保障体系结构优化、流程设计等工作提供参考。

Development of Simulation System for the Disaster Evacuation Based on Multi-Agent Model Using

This paper presents a simulation system for the disaster evacuation based on multi-agent model considering geographical information. This system consists of three parts, the modeling for the land and buildings using GIS data, the analysis of disaster evacuation using multi-agent model, and the visualization for the numerical results using the virtual reality technique. By introducing the numerical solver of the natural disaster to the present system, it is possible to evaluate not only the damage of structure but also the damage of human being. Furthermore, it is possible to investigate the appropriate evacuation route by the simulation. The Dijkstra algorithm is used to obtain shortest route to the refuge. In addition, the visualization using virtual reality technique is curried out to understand the feeling of refugee. The present system is applied to the evacuation analysis by the flood flow in urban area and is shown to be a useful tool to investigate the damage by natural disasters.

技术创新难度对高科技企业最优创新投入的影响——基于Agent-Based Model的分析

通过Agent-Based Model模拟了高技术企业的发展环境,分析了不同技术创新门槛下企业技术的发展状况。研究发现,高技术创新门槛阻碍了市场最高技术水平的发展,但能提升市场平均技术水平及公司数目。同时,企业研发活动存在能实现最大技术增长的最优创新研发投入比例,并且随着技术创新门槛的提升,企业的研发政策向模仿性投入倾斜。由此可知,针对有高技术创新门槛的高科技企业,政府应提升专利保护强度以影响企业的最优研发政策,保证高新技术产业的健康发展。

Amelioration of Alzheimer's disease pathology and cognitive deficits by immunomodulatory agents in animal models of Alzheimer's disease

The most common age-related neurodegenerative disease is Alzheimer's disease(AD) characterized by aggregated amyloid-β(Aβ) peptides in extracellular plaques and aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles,together with loss of cholinergic neurons,synaptic alterations,and chronic inflammation within the brain.These lead to progressive impairment of cognitive function.There is evidence of innate immune activation in AD with microgliosis.Classically-activated microglia(M1 state) secrete inflammatory and neurotoxic mediators,and peripheral immune cells are recruited to inflammation sites in the brain.The few drugs approved by the US FDA for the treatment of AD improve symptoms but do not change the course of disease progression and may cause some undesirable effects.Translation of active and passive immunotherapy targeting Aβ in AD animal model trials had limited success in clinical trials.Treatment with immunomodulatory/anti-inflammatory agents early in the disease process,while not preventive,is able to inhibit the inflammatory consequences of both Aβ and tau aggregation.The studies described in this review have identified several agents with immunomodulatory properties that alleviated AD pathology and cognitive impairment in animal models of AD.The majority of the animal studies reviewed had used transgenic models of early-onset AD.More effort needs to be given to creat models of late-onset AD.The effects of a combinational therapy involving two or more of the tested pharmaceutical agents,or one of these agents given in conjunction with one of the cell-based therapies,in an aged animal model of AD would warrant investigation.

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-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System

Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.

Multi-agent Blind Model and Its Application to Regional Eco-environmental Quality Assessment

Through denoting each expert as an agent and viewing a multiple criteria decision-making as a synthesis problem of aggregating experts’ ratings, a multi-agent blind model (MABM) is developed for regional eco- environmental quality assessment. In this model, the ratings of the evaluated object under an index, given by expert group, are first utilized to construct a series of blind numbers. In general, each index will correspond to different blind numbers. On the basis of aggregating index weights, the rank score in the form of a blind number is obtained for the evaluated object. Then, by means of calculating expected value of the above blind number, its rank score is further converted into a crisp value. By way of comparing the expected value with classification standards, eco-environmental quality of the evaluated sample could be identified successfully in the end. As a case, the MABM is used to evaluate the eco-environmental quality of Chaohu Lake basin. Study result shows that the MABM is a useful model for regional eco-environmental quality assessment.

基于多Agent模型的宁东基地水-能源-粮食系统协同演化研究

随着淡水资源逐渐减少、能源需求量逐渐增长和粮食供应结构不断变化,加之三者之间既相互依存又相互竞争的特点,水-能源-粮食的交互关系变得异常复杂。为确保水安全、能源安全和粮食安全,实现三者的协同优化管理、资源的高效利用和可持续发展,以集资源型缺水和季节性缺水为一体的干旱敏感区域宁东基地为例,提出了基于多Agent模型的宁东基地水-能源-粮食系统模拟模型,将宁东基地水-能源-粮食系统看成是由若干个相对独立的自主Agent构成的一个合作共生网络体系,构建了宁东基地水-能源-粮食模拟框架,定义了各Agent的属性,设计了Agent之间以及Agent和环境要素之间的交互规则,基于Python语言的多Agent建模工具MESA库开发了宁东基地水-能源-粮食仿真模型,以模拟宁东能源化工基地的系统演化过程。结果表明:随着人口的增长,生活部门配水量、能源消耗和粮食消耗量均呈现上升趋势;粮食部门配水量呈现出与农作物产量变化相对应的变化趋势;宁东基地的能源部门配水量呈现上升趋势,预测年的配水量每年增长约为0.08亿m^(3);随着生态绿化覆盖面积的逐年增长,生态部门的配水量也呈现出逐年增长的趋势;每年可以向基地外输送的能源,实测年增长率约为19.85%,预测年增长率约为3.53%,预计到2025年可增加到4.96×107t标准煤。

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

这篇论文试着在一个研究生院在一些科学研究实验室发现在成员的成员和 self-reformrules 之间的相互作用规则。候选人 ofrules 关于因素从个性描述句子被提取：外翻，适合，良心， neu-roticism 和坦诚在大 personalitypsychology 的五个理论。在这篇论文，相互作用和自我改革规则没被 personalitiesthemselves 描述；相反，他们被相应现象描述：出现在行为的活动，神入，坚持，自治，和分析能力站在一起。每个成员“ s 到成员的从主要 5 因素个性调查的起始的价值 isdetermined，和目标由成员珍视 isgiven ” s 自我声明。在一个 S 字形的函数在值的更新算法被介绍的地方，规则选择被基因算法执行，设置更新参数的 difficultyof 由此被避免。分析关于这条途径的获得的规则，和 thevalidity 被增加，为未来学习的主语被讨论。最后， considerationis 在成员的行为上延长了到环境的效果增强规则的集合。

Agent Based Multiviews Requirements Model

Based on the current researches of viewpoints oriented requirements engineering and intelligent agent, we present the concept of viewpoint agent and its abstract model based on a meta-language for multiviews requirements engineering. It provided a basis for consistency checking and integration of different viewpoint requirements, at the same time, these checking and integration works can automatically realized in virtue of intelligent agent’s autonomy, proactiveness and social ability. Finally, we introduce the practical application of the model by the case study of data flow diagram.

A New Formula for Partitions in a Set of Entities into Empty and Nonempty Subsets, and Its Application to Stochastic and Agent-Based Computational Models

In combinatorics, a Stirling number of the second kind S (n,k)? is the number of ways to partition a set of n objects into k nonempty subsets. The empty subsets are also added in the models presented in the article in order to describe properly the absence of the corresponding type i of state in the system, i.e. when its “share” Pi =0?. Accordingly, a new equation for partitions P (N, m)? in a set of entities into both empty and nonempty subsets was derived. The indistinguishableness of particles (N identical atoms or molecules) makes only sense within a cluster (subset) with the size?0≤ni ≥N. The first-order phase transition is indeed the case of transitions, for example in the simplest interpretation, from completely liquid state?typeL = {n1 =N, n2 = 0} to the completely crystalline state??typeC= {n1 =0, n2 = N }. These partitions are well distinguished from the physical point of view, so they are ‘typed’ differently in the model. Finally, the present developments in the physics of complex systems, in particular the structural relaxation of super-cooled liquids and glasses, are discussed by using such stochastic cluster-based models.