Combinations of Transportation Policies to Promote BRT Usage Using Artificial Society Model

Various transportation systems have been developed in recent years.In this study,an artificial society model is developed to examine the combination of transportation policies in urban areas.In this model,each trip maker selects the primary and terminal transportation modes.An artificial society model is applied to the southeastern region of Osaka City,Japan.The effects of introducing BRT(bus rapid transit,primary transportation)and on-demand buses(terminal transportation)are investigated.The results confirm that BRT is used by a certain number of users.An increase in the use of BRT will increase the amount of walking,thus resulting in a healthy city.However,on-demand buses are rarely used as terminal transportation.Additionally,the development of bicycle parking stations near BRT stops is shown to be effective in the northern section of the BRT route.

Fostering artificial societies using social learning and social control in parallel emergency management systems

How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers？ We propose a framework for fostering artificial soci- eties using social learning mechanisms and social control ap- proaches. We present the application of fostering artificial so- cieties in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, in- cluding observational learning, reinforcement learning, imi- tation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.

ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

A Simple Game for the Study of Trust in Distributed Systems

Trust is an important aspect of the design and analysis of secure distributed systems. It is often used informally to designate those portions of a system that must function correctly in order to achieve the desired outcome. But it is a notoriously diffcult notion to formalize. What are the properties of trust? How is it learned, propagated, and utilized successfully? How can it be modeled? How can a trust model be used to derive protocols that are effcient and reliable when employed in today's expansive networks? Past work has been concerned with only a few of these issues, without concentrating on the need for a comprehensive approach to trust modeling. In this paper, we take a first step in that direction by studying an artificial community of agents that uses a notion of trust to succeed in a game against nature. The model is simple enough to analyze and simulate, but also rich enough to exhibit phenomena of real life interactive communities. The model requires agents to make decisions. To do well, the agents are informed by knowledge gained from their own past experience as well as from the experience of other agents. Communication among agents allows knowledge to propagate faster through the network, which in turn can allow for a more successful community. We analyze the model from both a theoretical and an experimental point of view.

PARALLEL SIMULATION OF LARGE-SCALE ARTIFICIAL SOCIETY WITH GPU AS COPROCESSOR

Large-scale artificial societies with millions or billions of agents call for high-performance parallel simulation.Prevailing supercomputers with thousands of CPUs and GPUs make it possible to carry out such simulation.The key is to distribute large-scale agents to massive cores of CPUs and GPUs properly for parallel computing with efficient communication and synchronization.For simplicity and efficiency,a modified discrete event system specification(DEVS)is proposed for large-scale artificial society modeling and parallelism is exploited in agent models because similar agents usually share similar behaviors.Through phased synchronization,a two-tier parallel simulation engine is designed with support of MPI and OpenCL where GPU is used as coprocessor.One-sided communication is used for reflection of remote simulation objects and message passing between processes.A general kernel function prototype is elaborately designed and conditionally compiled for execution on both CPU and GPU.An artificial society for epidemic study is used to test the performance on a supercomputer with 1024 CPU cores and 1792 GPU cores.The speedup reaches 3512 for even 2 billion agents with GPU acceleration which is far over 701 when only CPUs are used.It turns out feasible for parallel simulation of large-scale artificial society with GPU as coprocessor.

Statistical analysis and modeling of residential buildings for artificial Beijing reconstruction

Environment model is an important component of an artificial society.To level up the resolution of residence-building environment models in artificial Beijing,we divide residential buildings in Beijing into two categories,which are noncommunity residential buildings and community residential buildings,and statistically analyze the numbers of floors and areas of the real residential buildings.In this paper,we have exhibited the distribution of numbers of floors in noncommunity residential buildings and community residential buildings.Furthermore,we analyze the distribution of areas of noncommunity residential buildings and residence-use proportions of community zones by Nonlinear Regression.Results indicate that areas of noncommunity residential buildings in Beijing obey the log-normal distribution and residence-use proportions of community zones obey normal distribution.According to the statistical models,the virtual residential buildings in artificial Beijing can be endowed with rational attribute values.

Simulation of COVID-19 Outbreak in Nanjing Lukou Airport Based on Complex Dynamical Networks

The Corona Virus Disease 2019(COVID-19)pandemic is still imposing a devastating impact on public health,the economy,and society.Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years.However,the spread simulation of COVID-19 in the dynamic social system is relatively unexplored.To address this issue,considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021,we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies,Computational experiments,and Parallel execution(ACP)approach.Specifically,the artificial society includes an environmental model,population model,contact networks model,disease spread model,and intervention strategy model.To reveal the dynamic variation of individuals in the airport,we first modeled the movement of passengers and designed an algorithm to generate the moving traces.Then,the mobile contact networks were constructed and aggregated with the static networks of staff and passengers.Finally,the complex dynamical network of contacts between individuals was generated.Based on the artificial society,we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies.Learned from the reproduction of the outbreak,it is found that the increase in cumulative incidence exhibits a linear growth mode,different from that(an exponential growth mode)in a static network.In terms of mitigation measures,promoting unmanned security checks and boarding in an airport is recommended,as to reduce contact behaviors between individuals and staff.

Agent-based Simulation Systems for Emergency Management

Emergencies, which are very difficult to be forecasted, would always bring about huge harm to people. Therefore, to find ways to reduce such devastating effects, researches on emergency management have turned to be paramount. Nowadays, the rapid development of computer technology has supplied a new and effective idea for the researches of emergency management, namely that the researches can be done in computers by performing simulation experiments according to the artificial societies, computational experiments, parallel execution （ACP） approach. Guided by this approach, this paper has proposed one agent-based prototype simulation system to research emergency management. Firstly, structure of the simulation system oriented to emergency management was analyzed and designed. Then a simulation system oriented to public health emergency management was constructed to study the transmission of infectious diseases. Finally, several experiments were carried out based on the system, with several significant conclusions having also been obtained.

Role of new ideas in the mobile phone market share

In this work,dynamics of diffusion of innovation in the smartphone markets is modeled by using an agent-based simulation in a period of 28 quarters starting from 2009.Rapid changes in smartphone technology affected the consumer preferences and two operating systems,namely Android and iOS,out of over 10 operating systems,and dominated the smartphone markets in a period of 6 years.The model aims to study the conditions of competition and adoption among similar high technology products.Relative roles of the essential parameters,namely affordability and social effects have been the main interest points in these studies.For this end,a simple,Cobb–Douglas production relation-based model on simple square lattice is introduced.The model parameters are adjusted to match the simulation results with the actual market shares data obtained from Statista.A clear relation between the model parameter values and global sales of different operating systems is observed.

A VIRTUAL FIELD-BASED CONCEPTUAL FRAMEWORK FOR THE SIMULATION OF COMPLEX SOCIAL SYSTEMS

This highly interdisciplinary research paper discusses some new insights into the fundamentalproperties of information-rich social networks.It mainly focuses on:i)Postulating the generalproperties of an information-based networking economy;ii)Modeling emergent and self-organizing featuresof social networks;iii)Discussing how to simulate complex social systems using a field-basedapproach and multi-agent platforms.Additionally,this paper gives some ideas of how to construct avirtual field-based communications network of intelligent agents using currently available computationalintelligence methods.A new simulation paradigm offers some useful concepts to transform multidimensionalfactor space(representing a multiplicity of phenomenal forms and interactions)into the mostuniversal spectral coding system.This paper gives some ideas of how not only the communicationmechanism but also the social agents can be simulated as oscillating processes.

Multi-Agent Based Stochastic Dynamical Model to Measure Community Resilience

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters.Recognized as a key metric of community resilience is the social well-being of a community during a disaster,which is made up of mental and physical social health.Other factors influencing community resilience directly or indirectly are emotional health,emergency services,and the availability of critical infrastructures services,such as food,agriculture,water,transportation,electric power,and communications system.It turns out that in computational social science literature dealing with community resilience,the role of these critical infrastructures along with some important social characteristics is not considered.To address these weaknesses,we develop a new multi-agent based stochastic dynamical model,standardized by overview,design concepts,details,and decision(ODD+D)protocol and derived from neuro-science,psychological and social sciences,to measure community resilience in terms of mental and physical well-being.Using this model,we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear,risk perception,informationseeking behaviour,cooperation,flexibility,empathy,and experience,in an artificial society.Furthermore,we simulate this model in two case studies and show that a high level of flexibility,experience,and cooperation enhances community resilience.Implications for both theory and practice are discussed.