Business networks dynamics and agent-based simulation

The main purpose of this article is to build a general platform for Unmanned Aerial Vehicle (UAV) simulation especially for vision algorithms. This general simulation platform can also connect to realistic UAVs for further tests. The platform enables UAVs controlled by a variety of advanced vision algorithms to accomplish certain tasks. Unlike the previous simulation systems, which can only achieve tasks with certain drones, we are committed to build a platform to support a variety of vision based UAVs simulation platform. And if the simulation test is successful, we can apply vision algorithms to realistic UAVs' tests based on our platform with little adaption. We use open source Gazebo as a simulator and combined with ROS (Robot Operating System) to implement UAVs simulation. Besides, we build a general UAVs link interface to connect realistic UAVs with ROS. All these works construct the general simulation platform.

Deploying wireless sensor networks (WSN) along a barrier line to provide surveillance against illegal intruders is a fundamental sensor-allocation problem. To maximize the detection probability of intruders with a limited number of sensors, we propose an integer non-linear program optimization model which considers multiple types of sensors and targets, probabilistic detection functions and sensor-reliability issues. An agent-based simulation (ABS) model is used to validate the analytic results and evaluate the performance of the WSN under more realistic conditions, such as intruders moving along random paths. Our experiment shows that the results from the optimization model are consistent with the results from the ABS model. This increases our confidence in the ABS model and allows us to conduct a further experiment using moving intruders, which is more realistic, but it is challenging to find an analytic solution. This experiment shows the complementary benefits of using optimization and ABS models.

GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform

There is little research concerning comparisons and combination of System Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm used in many levels of abstraction, including those levels covered by SDS. We believe that the establishment of frameworks for the choice between these two simulation approaches would contribute to the simulation research. Hence, our work aims for the establishment of directions for the choice between SDS and ABS approaches for immune system-related problems. Previously, we compared the use of ABS and SDS for modelling agents' behaviourin an environment with no movement or interactions between these agents. We concluded that for these types of agents it is preferable to use SDS, as it takes up less computational resources and produces the same results as those obtained by the ABS model. In order to move this research forward, our next research question is: if we introduce interactions between these agents will SDS still be the most appropriate paradigm to be used? To answer this question for immune system simulation problems, we will use, as case studies, models involving interactions between tumour cells and immune effector cells. Experiments show that there are cases where SDS and ABS can not be used interchangeably, and therefore, their comparison is not straightforward.

Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation

Scene simulation technology has been the basal component for building 3D simulation application now. Constructing a general scene simulation platform based on multiple format 3D models can greatly speed developing process of simulation application and promote developing efficiency. According to the idea above, a method of building scene simulation platform based on models data fusion is put forward. With this method, multiple 3D model data format such as FLT, 3DS, X, WRL and GLS are fused and driven by OpenGL API. The layered structure and framework based on HLA are also presented to design the platform. Key technologies involving model data fusion and simulation driving are detailed. Finally, challenges associating with realization of the platform named plug-in framework and simulation examples based on the platform are brought forward. It shows greatest traits on building similar large-scale simulation application system.

Agent based simulation (ABS) is efficient for the simulation of objects whose mathematical modeling is not easy. There are many studies on ABS and several frameworks for ABS have already been published. However, there are few frameworks that can enable agent-based simulation using large numbers of agents. We have been developing a Java-based platform for Massive Agent-Based Simulation (MABS) called “Zillions of Agents-based Simulation Environment” or ZASE. The purpose of ZASE is to develop MABS applications on multiple general computers such as PCs or workstations where they are connected with a high performance but generally available network such as gigabit Ethernet. ZASE is designed to host over millions of agents. In this paper, we introduce ZASE and explain the fundamental capabilities for MABS provided by ZASE. We evaluated ZASE for the agent-based auction simulation where the number of agents varied from ten to a million. The results indicate that the number of agents affects the final bid prices and their distributions. Performance measurement results on both an SMP computer environment and a multiple-computer environment are shown. The results in the both cases were about 3.5 millions of times of that agents handle messages in a second. The scalability ratio on the SMP computer and the multiple-computers environment were 78% and 85% respectively. Those scalability ratios are such good values for the simulations.KeywordsAgent-based SimulationMassively Multi-Agent Systems

An important challenge within the field of Criminology is to investigate the spatio-temporal dynamics of crime. Typical questions in this area are how the behaviour of offenders, targets, and guardians, and the emergence and displacement of criminal hot spots can be predicted. This paper presents an agent-based simulation model that can be used as an experimental tool to address such questions. The simulation model particularly focuses on the interplay between hot spots and reputation. Using the model, a number of simulation experiments have been performed, of which results have been analysed using formal techniques. The results indicate that the presented approach is able to adequately reproduce displacement patterns as described in the literature.

Modern healthcare reforms are required to be financially, environmentally and socially sustainable in order to address the additional constraints of financial resources shrinkage, pressure to reduce the environmental impacts and demand for improving the quality of healthcare services. Decision makers face the challenge of balancing all three aspects when planning. However, implementing such an approach, particularly in healthcare, is not a trivial task. Modeling & simulation is a valuable tool for studying complex systems. This paper investigates the application of a hybrid approach that combines Agent-based Modeling & Simulation (ABMS) and Discrete-Event Simulation (DES) for analyzing sustainable planning strategies for Emergency Medical Services. The paper presents a case study that shows how combined ABMS and DES models can support strategic planning and simulation analytics, respectively. The generated data from the ABMS is fed to the DES model in order to analyze the different strategies and the preliminary results are promising.

This work aims to investigate the role of individual scientists and their collaborations in enhancing the innovative performance of the Canadian nanotechnology innovation network. The study uses real data that consists of all the journal articles in nanotechnology field published within 1980-2012 by the authors affiliated to Canadian institutions, which were collected from Scopus database. The scientific networks have been created based on the co-authorship of the articles and an agent-based simulation model has been developed to study the innovation networks in their dynamic context. This research argues that the individual performance of authors with different network properties distinctively affect the overall efficiency and structure of the network.

The issue of national disintegration is one of the most crucial in the defense of Indonesia. One of the causes of national disintegration is the existence of Radicalism, Extremism, and Terrorism (RET). Radical entities can change using extreme means including violence through acts of terror in achieving their goals. Efforts continue to be made by the Government in anticipating the spread of this RET. Using agent-based simulation modeling techniques, some characteristics in the RET such as numbers are affected, driving factors, the number of agents in a region, and effective strategies. Therefore, this study is aimed at evaluating the spread of RET in Indonesia using agent-based simulations. This research is qualitative descriptive by trying to collect qualitative data as an initial input for the creation of flowchart designs and agent-based simulation models that will eventually be verified and validated. The results of this study illustrate the agent-based simulation in the spread of RET using Netlogo software by paying attention to several parameters such as incubation period, possible exposure to radicalism, extremist severity, availability of rehabilitation centers, and the rate of terrorism infection. From the results of the simulation, it can be said that the parameter greatly affects the percentage of radical agents, agents who are extremists until they become terrorists. This factor can be evidence for policymakers to reduce the spread of RET effectively by increasing religious understanding, strengthening the sense of nationalism and defending the country, and implementing community empowerment strategies. The evaluation of the spread of this RET can eventually strengthen Indonesia's non-military defense. Thus, good defense will have implications for the continuous development and achievement of Indonesia's goals.

One of the most widely used techniques for the analysis of crowds such as evacuations, pedestrian flow or vehicular traffic among others is agent-based simulation. Reasoning and decision making are crucial processes but modeling them correctly requires knowledge representation; the methodologies used to represent knowledge and manage interactions between agents are fundamental to creating effective and realistic models. Multi-agent models often oversimplify human or social behaviors, which can result in unrealistic representations. The lack of modeling accuracy and the complex interaction between agents can limit the validity of the results, being one of the biggest challenges in multi-agent models. We propose making use of the collective intelligence genome proposed by Thomas Malone as a methodology for the representation of crowds through agent-based simulation. The definitions that compose it can be understood as an ontology for crowd representation. We have related the components of the genome directly to the components of an agent-based simulation model: What=agent actions, why=behavior motivators, how = how they perform actions and describe it as an ontology. We have also developed case studies to test the methodology.

Agent-based modeling and simulation is a simulation paradigm that allows focusing on individuals, their interactions, and the complex behavior that emerges from them. Agent-based simulations are typically developed in simulation platforms that provide features related to agents. One such platform is NetLogo, to which a reinforcement learning extension was made available recently. The extension provides commands for using the Q-Learning algorithm, but no evaluation on whether it simplifies the development of simulations is available. This paper presents a quantitative evaluation on using the extension in two simulations: the classic cliff walking problem; and a real-world, adaptive traffic signal control (ATSC) simulation. Results show that the size of simulations source code developed using the extension is smaller than those developed without using it, giving evidence that the extension simplifies the development of simulations

Incorporating dynamic realistic human behaviors in population-scale computational models has been challenging. While some efforts have leveraged behavioral theories from social science, validated theories specifically applicable to Agent-based modeling remain limited. Existing approaches lack a comprehensive framework to model the situated, adaptive nature of human cognition and choice. To address these challenges, this paper proposes a novel framework, Psychologically-Valid Generative Agents. These agents consist of a Cognitive Architecture that provides data-driven and cognitively-constrained decision-making functionality, and a Large Language Model that generates human-like linguistic data. In addition, our framework benefits from Stance Detection, a Natural Language Processing technique, that allows highly personalize initialization of the agents, based on real-world data, within Agent-based modeling simulations. This combination provides a flexible yet structured approach to endogenously represent how people perceive, deliberate, and respond to social or other types of complex decision-making dynamics. Previous work has demonstrated promising results by using a subset of the components of our proposed architecture. Our approach has the potential to exhibit highly-realistic human behavior and can be used across a variety of domains (e.g., public health, group dynamics, social and psychological sciences, and financial markets).

Although the problem of a lack of an adequate denominator of crime rates has been widely recognized, relatively few solutions have been found. This paper proposes a sophisticated method of analyzing crime hot spots using global positioning system (GPS) tracking data and agent-based simulation modeling. The goal of the proposed analysis technique is to estimate a population at risk across time and space at the micro-level, such as streets and locations (e.g. parks and playgrounds). In order to create valid estimates of a population at risk and to illustrate how the estimated population at risk can be used in crime analysis, this paper presents a series of three sub-studies. First, GPS tracking data were obtained for two weeks for 80 children in the second and fifth grades from one school located in a suburb of Tokyo, Japan. GIS software was used to analyze the GPS data in order to identify streets and places that the children frequented. Second, agent-based simulation modeling was developed to extend the proposed analysis method to school districts that had not participated in the GPS sub-study. In the process of developing a simulation algorithm, the validity of simulation modeling was checked by comparing the simulation results with the GPS tracking data. Third, victimization surveys with maps were conducted in six schools (including the school that participated in the GPS sub-study) that identified locations of crime incidents. Victimization survey results were overlaid with the analysis results of GPS data and agent-based simulation modeling in order to examine crime hot spots. The proposed technique of agent-based modeling produces an accurate denominator of crime rates at the micro-level which can then be used to analyze crime hot spots properly. In particular, this paper discusses how crime analysts can identify different types of hot spots, such as crime generators and crime attractors, by examining the number of crime incidents and the magnitude of crime rate. This paper argues intervention strategies need to be tailored for each type of crime hot spots, as these crime hot spots develop through different mechanisms. Finally, this paper discusses limitations of the proposed technique and the direction of future research.