An agent-based simulation model for analyzing the governance of the Brazilian Financial System

Regulation can play an important role in effectively managing systemic risk while providing accountability to all affected governments. IMF points out weak governance structures as one of the main causes for financial/economical crisis. However, research in this area is still limited. One of the reasons is the inherent complexity of the public sector governance notion. In this research, the regulatory governance of the financial sector is conceived as a complex system, in which governance is perceived as a phenomenon resulting from the interactions among all the actors that influence or are influenced by regulatory activities within the financial sector. An agent-based simulation was developed to analyze and evaluate the emergent behaviors from the governance in the Brazilian finance sector under different macroeconomics variables and different attitudes, perceptions and desires of economic and political actors. The agent-based model is combined with an econometric model, which is intended to characterize the macroeconomic environment. The regulatory environment is modeled by computational agents using BDI (beliefs–desires–intentions) architecture. The agents have beliefs about their environment and desires they want to satisfy, thus leading them to create intentions to act. The agents’ behavior was modeled using fuzzy rules built by means of content analysis of newspapers and in-depth interviews with experts from the financial area. Computational experiments demonstrate the potential of the agent-based model simulation in the study of complex environments involving regulatory governance.     

人群应急疏散可视仿真研究进展和问题

人群应急疏散可视仿真是用智能体来模拟具有自主感知、情绪和行为能力的人群个体,并采用3维可视的方式来直观呈现人群应急疏散情景,可以为制定人群应急预案提供形象直观的分析方法。本文从人群仿真数据的来源、人群导航模型的构建、人群行为模型、人群情绪感染、人群渲染5个方面概述目前研究的进展,然后从仿真模型的可验证性、人群疏散导航模型的构建、人与环境的物理模型、动物逃生实验与仿真、疏散中的社会行为表现以及人群情绪的可视计算6个角度讨论需要进一步研究的问题。针对需要深入研究的问题,指出借助于紧急事件的视频监控分析和虚拟人群情景的用户调查,有助于完善人群仿真模型。结合物理模型,可以更准确地描述人群应急疏散场景。开展动物逃生实验分析,有助于完善人群运动导航算法。建立人群社会行为模型,可以更详细描述疏散中人群行为的多样性。构建基于多通道感知的人群情绪感染计算方法,可以详尽描述情绪感染的过程。人群应急疏散行为的可视仿真研究在城市的安全管理方面具有重要的应用前景,但其研究仍存在很多亟待解决的问题,综合地运用多学科知识,完善实验手段是进一步推动研究的关键所在。     

Management decision support using long-term market simulation

In recent years Agent-based Computational Economics (ACE) has become an increasingly important method in market simulation. After liberalization of many former governmental owned or controlled industries the used operations research models are not longer sufficient to simulate market behavior due to individual action and increasing competition. Agent-based simulation appears to be an alternative approach considering also individual behavior and competition. Some short-term simulation approaches have shown promising results for the simulation in the domain of electricity markets. Picking up the desire for a long-term oriented simulation, this paper presents a basic agent-based model considering the investment decision within long-term planning of electricity markets. Additionally, regulatory agents are introduced as a third side in the market simulation to represent governmental decisions. This results in the definition of three types of agents representing electricity generating companies, consumers and governmental instances.     

Modeling culture in intelligent virtual agents

This work addresses the challenge of creating virtual agents that are able to portray culturally appropriate behavior when interacting with other agents or humans. Because culture influences how people perceive their social reality it is important to have agent models that explicitly consider social elements, such as existing relational factors. We addressed this necessity by integrating culture into a novel model for simulating human social behavior. With this model, we operationalized a particular dimension of culture—individualism versus collectivism—within the context of an interactive narrative scenario that is part of an agent-based tool for intercultural training. Using this scenario we conducted a cross-cultural study in which participants from a collectivistic country (Portugal) were compared with participants from an individualistic country (the Netherlands) in the way they perceived and interacted with agents whose behavior was either individualistic or collectivistic, according to the configuration of the proposed model. In the obtained results, Portuguese subjects rated the collectivistic agents more positively than the Dutch but both countries had a similarly positive opinion about the individualistic agents. This experiment sheds new light on how people from different countries differ when assessing the social appropriateness of virtual agents, while also raising new research questions on this matter.     

Shadow obstacle model for realistic corner-turning behavior in crowd simulation

This paper describes a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a corner. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic corner-turning behavior by comparison with real data obtained from the experiments. Finally, we perform parameter analysis to show the believability of our model through a series of experiments.     

基于用户偏好特征挖掘的个性化推荐算法

为了提升社交网络个性化推荐能力,结合用户行为分布进行个性化推荐设计,文中提出基于用户行为特征挖掘的个性化推荐算法,构建社交网络的用户行为信息特征挖掘模型,采用显著数据分块检测方法对社交网络用户特征的行为信息进行融合处理,提取反映用户偏好的语义信息特征量。从情感、关键词和结构等方面根据用户行为特征组,结合模糊信息感知方法进行社交网络个性化推荐过程中的信息融合处理,在关联规则约束控制下,构建社交网络用户偏好特征的混合推荐模型,实现用户偏好特征挖掘,根据语义分布和用户的行为偏好实现社交网络的个性化信息推荐。仿真结果表明,采用所提方法进行社交网络个性化推荐的特征分辨能力较好,对用户行为特征的准确识别能力较强,提高了社交网络推荐输出的准确性。     

A model of agents (robots) command behavior: The cognitive approach

The paper suggests a mathematical model of agents’ cooperation in dynamics, which employs agents’ utility functions and cognitive dissonance of their relations. The model is based on the theories of social psychologists investigating the behavior of people in small social groups and explaining the principles of their functioning and stability. We illustrate the suggested model via simulation of virtual soccer game of agents (robots). The performed simulation allows to model diverse aspects of agents’ cooperation and selfish behavior.     

基于用户行为挖掘的融合社交网络推荐模型

采用大数据处理技术和并行计算方法进行融合社交网络的用户行为特征的挖掘,实现社交网络智能推荐,提出一种基于用户行为挖掘的融合社交网络推荐模型。采用关联规则分布模型进行融合社交网络的用户行为特征检测,提取融合社交网络的用户行为的本体信息和关联规则项,构建社交网络的联合推荐的模糊决策模型,计算融合社交网络用户行为的联合信息熵特征值,采用模糊C均值聚类方法对提取的特征量进行分类识别,根据分类识别结果实现用户行为挖掘和融合社交网络的自适应推荐。仿真结果表明,采用该方法进行融合社交网络的用户行为特征挖掘的查准率较高,推荐的置信度水平较高。     

INFLUENCE OF ENVIRONMENTAL CHANGES ON COOPERATIVE BEHAVIOR IN THE PRISONER'S DILEMMA GAME ON AN ARTIFICIAL SOCIAL MODEL

In this paper, by simulations on an artificial social model, we analyze cooperative behavior of agents playing the prisoner's dilemma game, in which each of the agents has the two strategies: cooperate and defect. Because defect yields a better payoff whichever strategy an opponent chooses, it is rational for an agent to choose defect in a single game or a finite number of games. However, it is known that a pair of cooperates can also be a Nash equilibrium pair if the players do not know when the game is over or the game is infinitely repeated. To investigate such cooperative behavior, we employ an artificial social model called the Sugarscape and carry out simulations on the model. Arranging three kinds of environments in the Sugarscape, we examine cooperative behavior of agents who are essentially selfish, in a sense that they maximize their payoffs, and investigate influence of environmental changes on the cooperative behavior.     

THE STROBE MODEL: DYNAMIC SERVICE GENERATION ON THE GRID

This article presents the STROBE model: both an agent representation and an agent communication, model based on a social approach, which means interaction centered. This model represents how agents may realize the interactive, dynamic generation of services on the Grid. Dynamically generated services embody a new concept of service implying a collaborative creation of knowledge, i.e., learning; services are constructed interactively between agents depending on a conversation. The approach consists of integrating selected features from multi-agent systems and agent communication, language interpretation in applicative/functional programming and e-learning/human-learning into a unique, original, and simple view that privileges interactions, including control. The main characteristic of STROBE agents is that they develop a language (environment + interpreter) for each of their interlocutors. The model is inscribed within a global approach, defending a shift from the classical algorithmic (control based) view to problem solving in computing to an interaction-based view of social informatics, where artificial as well as human agents operate by communicating as well as by computing. The paper shows how the model may not only account for the classical communicating agent approaches, but also represent a fundamental advance in modeling societies of agents in particular in dynamic service generation scenarios such as those necessary today on the Web and proposed tomorrow for the Grid. Preliminary concrete experimentations illustrate the potential of the model; they are significant examples for a very wide class of computational and learning situations.     

Autonomous agents and human cultures in the trust–revenge game

Autonomous agents developed by experts are embedded with the capability to interact well with people from different cultures. When designing expert agents intended to interact with autonomous agents developed by non-game theory agents (NGTE), it is beneficial to obtain insights on the behavior of these NGTE agents. Is the behavior of these NGTE agents similar to human behavior from different cultures? This is an important question as such a quality would allow an expert agent interacting with NGTE agents to model them using the same methods that are used to model humans from different cultures. To study this point, we evaluated NGTE agents behavior using a game called the Trust–Revenge game, which is known in social science for capturing different human tendencies. The Trust–Revenge game has a unique subgame-perfect equilibrium strategy profile, however, very rarely do people follow it. We compared the behavior of autonomous agents to the actions of several human demographic groups—one of which is similar to the designers of the autonomous agents. We claim that autonomous agents are similar to human players from various cultures. This enables the use of approaches, developed for handling cultural diversity among humans, to be applied for interaction with NGTE agents. This paper also analyzes additional aspects of autonomous agents behavior and whether composing autonomous agents affects human behavior.     

用人工社会方法研究社会行为演化问题

人类社会系统是一个复杂适应系统,社会行为演化问题单纯应用静态、还原思想为主导的理论框架难以解决,实验研究也因为风险和道德等问题很难实际进行.在社会心理学和复杂系统领域研究成果基础上,利用计算机技术构造人工社会系统进行仿真,可以提供一个重要的研究方法,有助于研究社会系统复杂现象背后的基本机制、人的行为对系统演化的影响等问题.文章主要工作包括建立社会系统行为演化仿真模型,讨论实现方法.对以社会组织行为演化为背景的仿真结果分析表明该研究方法是有效的.     

基于Logistic模型的社会安全治理公众参与意愿实证分析

社会安全与公众的利益息息相关，因此社会安全的治理离不开公众的参与。为了分析社会安全治理中公众参与意愿的影响因素，本文以知信行理论和计划行为理论为理论基础，设计相关问题进行实地调研。运用Logistic回归方法研究分析发现：公众的个体特征对公众参与社会安全治理的意愿影响具有差异性；公众对社会安全的认知、态度、主观规范、知觉行为控制对公众参与社会安全治理的意愿具有正向影响。藉此探索公众在社会安全治理方面参与维度及途径，为公众有序合理地参与社会安全治理工作提供参考。     

Role of obligations in multiagent coordination

Carrying out distributed business processes over networks is rapidly shifting the nature of application architectures from the simple command and control client-server model to complex peer-to-peer models supporting dynamic patterns of social interaction and behavior among autonomous, proactive, goal oriented agents. Trusting agents to autonomously make decisions and execute actions on behalf of humans, as part of global business processes, requires both understanding and modeling of the social laws that govern collective behavior and a practically useful operationalization of the models into agent programming tools. In this article we present a solution to these problems based on a representation of obliged and forbidden behavior in an organizational framework, together with an inference method that also decides which obligations to break in conflicting situations. These are integrated into an operational, practically useful agent development language that covers the spectrum from the definition of organizations, roles, agents, obligations, goals, and conversations to inferring and executing coordinated agent behaviors in multiagent applications. The major strength of the approach is the way it supports coordination by exchanging constraints about obliged and forbidden behavior among agents. We illustrate this and the entire system with solution examples to the feature interaction problem in the telecommunications industry and to integrated supply chain management.     

Towards social behavior in virtual-agent navigation

We present Social Groups and Navigation (SGN), a method to simulate the walking behavior of small pedestrian groups in virtual environments. SGN is the first method to simulate group behavior on both global and local levels of an underlying planning hierarchy. We define quantitative metrics to measure the coherence and the sociality of a group based on existing empirical data of real crowds. SGN does not explicitly model coherent and social formations, but it lets such formations emerge from simple geometric rules. In addition to a previous version, SGN also handles group-splitting to smaller groups throughout navigation as well as social sub-group behavior whenever a group has to temporarily split up to re-establish its coherence. For groups of four, SGN generates between 13% and 53% more socially-friendly behavior than previous methods, measured over the lifetime of a group in the simulation. For groups of three, the gain is between 15% and 31%, and for groups of two, the gain is between 1% and 4%. SGN is designed in a flexible way, and it can be integrated into any crowd-simulation framework that handles global path planning and any path following as separate steps. Experiments show that SGN enables the simulation of thousands of agents in real time.     

Does Social Welfare Preference Always Promote Cooperation on Barabási and Albert Networks?

Existing models of evolutionary prisoner’s dilemma game always assume that agents are self-interest only. But more and more evidences show that agents may have other-regarding preference. The present article extents the model of prisoner’s dilemma on Barabási and Albert networks to include heterogeneous social welfare preference agents and studies its effects on the cooperation emergence on networks. The simulation results show that social welfare preference may promote cooperation in many cases, especially in the situation where the defection attraction is high. However, there are some situations where social welfare preference does not favor cooperation on complex networks. Simulations also display that it is not always true that the higher weight on social welfare preference corresponds with the higher cooperation frequency. Neighborhood size and initial cooperation also have important effects on the cooperation frequency as well, the features which are not observed in the situations without social welfare preference agents. These results are all related to the locality of social welfare preference. This article reveals the complex relationship between social preference and cooperation frequency in complex networks, which may help researchers reconsider the role of pro-social preference in the evolution of cooperation on complex networks.     

炼油厂常压塔侧线质量实时监测模糊专家系统

通过对炼油厂分馏塔侧线质量进行机理分析，并根据实际流程利用ＡＳＰＥＮ对过程进行模拟得到各控制变量与侧线质量的对应结果，利用数据统计方法，得到带有可信度ＣＦ量度的规则。利用正向推理，构置了一个基于规则在线监测炼油厂分馏塔侧线质量指标的模糊专家系统ＳＱＰＥＳ，并利用工厂采集的实时数据对所建立的专家系统进行了验证。结果表明，这种规则获取手段对建立炼油厂分馏塔质量指标监测的专家系统是适用的，它的建立，即可     

A study of evolution strategy based cooperative behavior in collective agents

The following paper introduces an evolution strategy on the basis of cooperative behaviors in each group of agents. The evolution strategy helps each agent to be self-defendable and self-maintainable. To determine an optimal group behavior strategy under dynamically varying circumstances, agents in same group cooperate with each other. This proposed method use reinforcement learning, enhanced neural network, and artificial life. In the present paper, we apply two different reward models: reward model 1 and reward model 2. Each reward model is designed as considering the reinforcement or constraint of behaviors. In competition environments of agents, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained as subtracting the values. And we propose an enhanced neural network to add learning behavior of an artificial organism-level to artificial life simulation. In future, the system models and results described in this paper will be applied to the framework of healthcare systems that consists of biosensors, healthcare devices, and healthcare system.     

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.     

Realization of emergent behavior in collective autonomous mobile agents using an artificial neural network and a genetic algorithm

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents emergent behavior during simulation.This paper was supported by WonKwang University in 2004.