Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

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.

Research Progress of Parallel Control and Management

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems.This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application demonstration.The future research,application directions,and suggestions are also discussed.

Optimization of earth observation satellite system based on parallel systems and computational experiments

Earth observation satellite system （EOSS） is the main space platform to collect ground information. Op- timization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experi- ments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.

A Modified Projection Method for Linear Feasibility Problems

In this paper, we present a modified projection method for the linear feasibility problems （LFP）. Compared with the existing methods, the new method adopts a surrogate technique to obtain new iteration instead of the line search procedure with fixed stepsize. For the new method, we first show its global convergence under the condition that the solution set is nonempty, and then establish its linear convergence rate. Preliminary numerical experiments show that this method has good performance.

About One Discrete Mathematical Model of Perfect Fluid

In work, it is constructed a discrete mathematical model of motion of a perfect fluid. The fluid is represented as an ensemble of identical so-called liquid particles, which are in the form of extended geometrical objects: circles and spheres for two-dimensional and three-dimensional cases, respectively. The mechanism of interaction between the liquid particles on a binary level and on the level of the n-cluster is formulated. This mechanism has previously been found by the author as part of the mathematical modeling of turbulent fluid motion. In the turbulence model was derived and investigated the potential interaction of pairs of liquid particles, which contained a singularity of the branch point. Exactly, this is possible to build in this article discrete stochastic-deterministic model of an ideal fluid. The results of computational experiment to simulate various kinds of flows in two-dimensional and three-dimensional ensembles of liquid particles are presented. Modeling was carried out in the areas of quadratic or cubic form. On boundary of a region satisfies the condition of elastic reflection liquid particles. The flows with spontaneous separation of particles in a region, various kinds of eddy streams, with the quite unexpected statistical properties of an ensemble of particles characteristic for the Fermi-Pasta-Ulam effect were found. We build and study the flow in which the velocity of the particles is calibrated. It was possible using the appropriate flows of liquid particles of the ensemble to demonstrate the possibility to reproduce any prescribed image by manipulating the parameters of the interaction. Calculations of the flows were performed with using MATLAB software package according to the algorithms presented in this article.

HOW TASK ALLOCATION STRATEGY AFFECTS TEAM PERFOR- MANCE： A COMPUTATIONAL EXPERIMENT

This paper studies how to determine task allocation schemes according to the status and require-ments of various teams, to achieve optimal performance for a knowledge-intensive team （KIT）, whichis different from traditional task assignment. The way to allocate tasks to a team affects task processingand, in turn, influences the team itself after the task is processed. Considering the knowledge require-ment of tasks as a driving force and that knowledge exchange is pivotal, we build a KIT system modelbased on complex adaptive system theory and agent modeling technology, design task allocation strat-egies （TASs） and a team performance measurement scale utilizing computational experiment, and an-alyze how different TASs impact the different performance indicators of KITs. The experimental re-sults show the recommend TAS varies under different conditions, such as the knowledge levels ofmembers, team structures, and tasks to be assigned, particularly when the requirements to the team aredifferent. In conclusion, we put forward a new way of thinking and methodology for real task alloca-tion problems and provide support for allocation decision makers.

Social influence and spread dynamics in social networks

Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities be- tween individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly em- pirical results. We then provide a concise description of some related social models from both macro- and micro-level per- spectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social sys- tems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.

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.

A GENERATOR AND A SIMPLEX SOLVER FOR NETWORK PIECEWISE LINEAR PROGRAMS

This is a brief report on our recent work in network piecewise linear programming (NPLP),and it consists of two parts. In the first park, we describe a generator for NPLP problems which is derived from the classical network linear program generator NETGEN. The generator creates networks of the same topological structures as NETGEN, but each arc is associated with a convex piecewise linear cost. The purpose of this program is to provide a set of standard test problems which can be used to compare the performance of various algorithms for NPLP. In the second part,we introduce a network simplex method that directly solves a network piecewise linear program without reformulating it as a network linear program of higher dimension. Forty benchmark NPLP problems are solved by this method and a reformulation method. The computational results are in favor of the direct method and show that solving an NPLP problem is not much harder than solving a network linear program of the same dimension.

An Evolutionary Adaptive System for Prediction of Strategy Influence:A Case Study of Government Regulation Guided Brand Innovation

Decision making is one of the common human activities.But in complex,interactive,and dynamic systems,it is extremely important to make decisions scientifically because the influence of the behavior after decision making is generally irreversible.The predictability of behavior influence is an effective way to improve the scientific decision making.As a new branch of computing,computational experiment is an emerging management method for research on complex systems.In this paper,based on particle swarm intelligence,an evolutionary adaptive system model of brand innovation in the toy industry cluster is constructed.By imitating the evolution process of the complex adaptive system,this method is helpful to analyze the impact of the management behavior brought to simulation system,predict the management behavior in real world,and finally choose the best management strategy.This simulation tried to figure out the affection of government regulation strategies and provide corresponding assessments and recommendations,which gives a new solution to assist the government to effectively judge the influence of the macro policy,as well as provides a new way of thinking of the related intelligent decision making.

An Optimal Resource Provision Policy in Cloud Computing Based on Customer Profiles

Conventional resource provision algorithms focus on how to maximize resource utilization and meet a fixed constraint of response time which be written in service level agreement（SLA）.Unfortunately,the expected response time is highly variable and it is usually longer than the value of SLA.So,it leads to a poor resource utilization and unnecessary servers migration.We develop a framework for customer-driven dynamic resource allocation in cloud computing.Termed CDSMS（customer-driven service manage system）,and the framework’s contributions are twofold.First,it can reduce the total migration times by adjusting the value of parameters of response time dynamically according to customers’profiles.Second,it can choose a best resource provision algorithm automatically in different scenarios to improve resource utilization.Finally,we perform a serious experiment in a real cloud computing platform.Experimental results show that CDSMS provides a satisfactory solution for the prediction of expected response time and the interval period between two tasks and reduce the total resource usage cost.