A Practice-Based Artificial Society Approach to Exploring the Evolution of Trust

2020 ◽  
pp. 273-284
Author(s):  
Michael Heidt ◽  
Andreas Bischof
Keyword(s):  
Artificial Society

scholarly journals The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Zhang ◽  
Bin Chen ◽  
Liang Ma ◽  
Zhen Li ◽  
Zhichao Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Ebola Virus ◽  
Virus Disease ◽  
Propagation Model ◽  
Propagation Mechanism ◽  
Chinese Government ◽  
Emergency Plans ◽  
Artificial Society ◽  
The City

Ebola virus disease (EVD) distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals’ behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals’ behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

Simulation of Skulduggery in a Multi-Agent System

2015 ◽  
Vol 7 (1) ◽  
pp. 17-31
Author(s):  
Rasoul Ramezanian ◽  
Akram Emdadi
Keyword(s):  
Utility Function ◽  
Test Session ◽  
Testing Session ◽  
Multi Agent System ◽  
Agent System ◽  
Factors Affecting ◽  
Artificial Society ◽  
Control Factors ◽  
Multi Agent ◽  
Level Of Knowledge

In a testing session, students may want to use the information of other students, which is cheating. The authors of this paper develop an artificial society to model and simulate this situation. They consider two control factors to increase the incentive of students to not cheat. The first factor is the penalty for similarity between responses (as much as two answer-sheets of two students are the same, their final grades decrease). The second factor is the observers who look into the students and do not allow the observed students to cheat. In this model, agents participate in a test based on their level of knowledge, location and two above factors, deciding whether or not to cheat. These components are used to formulate the utility function. Taking advantage of the developed artificial society, the authors now study the above factors affecting the amount of cheating in a test session.

Le modèle du choix éducatif interdépendant

2007 ◽  
Vol 48 (1) ◽  
pp. 3-53 ◽  
Author(s):  
Gianluca Manzo
Keyword(s):  
Theoretical Model ◽  
Computational Approach ◽  
Point Of View ◽  
Educational Opportunities ◽  
Educational Choice ◽  
Absolute Point ◽  
Educational Stratification ◽  
Artificial Society ◽  
Generative Mechanisms ◽  
Multi Agents

A theoretical model is presented that aims to explain the generative mechanisms of educational stratification. The rational educational choice of the Ego is supposed to be influenced by the choices of others. The model explores various forms of direct and indirect interdependent choices between actors. In order to demonstrate the empirical consequences, use is made of an artificial society built upon a “multi agents” computational approach. The dynamic of this virtual society is proved to fit well with empirical French and Italian data from an absolute point of view (distribution of diplomas) as well as a relative one (educational opportunities).

scholarly journals A meta-modeling framework in public health emergency management

SIMULATION ◽  
2017 ◽  
Vol 94 (5) ◽  
pp. 401-419
Author(s):  
Bin Chen ◽  
Peng Zhang
Keyword(s):  
Public Health ◽  
Emergency Management ◽  
Code Generation ◽  
Large Scale ◽  
Public Health Emergency ◽  
Modeling Framework ◽  
Artificial Society ◽  
Novel Approach ◽  
Meta Modeling ◽  
Emergency Scenarios

Epidemic transmission is a common type of public health emergency that is difficult to forecast and often causes substantial harm. Artificial societal models provide a novel approach to the study of public health problems. However, public health emergency management (PHEM) always involves multi-disciplinary and multi-hierarchical models that complicate the work of modeling. Models are also made more complex by the consideration of new requirements and interactions. Therefore, we propose a domain-specific methodology to guide the modeling process in PHEM. By analyzing domain characteristics and modeling requirements, a meta-modeling framework can be constructed, containing the basic elements with which to construct an artificial society to study epidemic transmission. In this paper, the designs of meta-models are discussed in detail, and domain models are implemented by code generation, which enables the support of large-scale, agent-based computational experiments on the KD-ACP platform. Case studies of Ebola are outlined, emergency scenarios are reconstructed based on pre-designed meta-models, and “scenario-response” experiments are presented. This study provides a valuable framework and methodology with which to study complex social problems in PHEM. The proposed method has been verified effectively and efficiently.

Weighted social networks for a large scale artificial society

2016 ◽  
Vol 30 (02) ◽  
pp. 1550276 ◽  
Author(s):  
Zong Chen Fan ◽  
Wei Duan ◽  
Peng Zhang ◽  
Xiao Gang Qiu
Keyword(s):  
Social Networks ◽  
Social Structure ◽  
Social Relationships ◽  
Large Scale ◽  
Census Data ◽  
Clustering Coefficient ◽  
Generation Model ◽  
Social Phenomenon ◽  
Artificial Society ◽  
Synthetic Population

The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

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