Journal articles: 'Agent-based data simulation'

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Devia, Nelson, and Richard Weber. "Generating crime data using agent-based simulation." Computers, Environment and Urban Systems 42 (November 2013): 26–41. http://dx.doi.org/10.1016/j.compenvurbsys.2013.09.001.

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BERNASCHI, MASSIMO, and FILIPPO CASTIGLIONE. "COMPUTATIONAL FEATURES OF AGENT-BASED MODELS." International Journal of Computational Methods 02, no. 01 (March 2005): 33–48. http://dx.doi.org/10.1142/s0219876205000399.

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Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

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Zeng, Wen Ying, and Yue Long Zhao. "Mobile Data Replication Algorithms Based on Agent." Advanced Materials Research 225-226 (April 2011): 957–60. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.957.

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In mobile transaction process, there exists frequently data transferring and replication. By the bandwidth limitation, discontinuous characteristics, there are a lot of questions in mobile data loss, replication failure, etc. Aimed at the questions, the paper proposes several mobile data transfer and replication algorithms based on agent. Every proposed algorithm is analyzed and simulation is performed to compare their delay. Result shows that through collecting small data transactions by agent, data are batch processed to servers, which may reduce the total cost of replication and promote replication success ratio and reduce the delay of replications. Adaptive algorithm is also considered.

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Felbermair, Samuel, Florian Lammer, Eva Trausinger-Binder, and Cornelia Hebenstreit. "Generation of a synthetic population for agent-based transport modelling with small sample travel survey data using statistical raster census data." International Journal of Traffic and Transportation Management 02, no. 02 (October 10, 2020): 09–17. http://dx.doi.org/10.5383/jttm.02.02.002.

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This paper presents a step-by-step method to generate a synthetic population for agent-based transport modelling as input to MATSim software, which requires an activity chain for each agent. We make use of high spatial resolution statistical raster (250 m) census data, applying all calculations at this scale. Due to the small sample, size of travel survey data an Iterative Proportional Fitting method is not suitable. Therefore, we devise a method utilizing Bayesian networks, maximum likelihood and Markov Chain Monte Carlo simulation to reproduce attribute distribution and fit to raster margins. Stratified sampling along households is employed to generate activity chains for the synthetic population.

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Maeda, Iwao, David deGraw, Michiharu Kitano, Hiroyasu Matsushima, Hiroki Sakaji, Kiyoshi Izumi, and Atsuo Kato. "Deep Reinforcement Learning in Agent Based Financial Market Simulation." Journal of Risk and Financial Management 13, no. 4 (April 11, 2020): 71. http://dx.doi.org/10.3390/jrfm13040071.

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Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep reinforcement learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation. Artificial market simulations designed to reproduce realistic market features may be used to create unobserved market states, to model the impact of your own investment actions on the market itself, and train models with as much data as necessary. In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with market impact. Our simulations confirm that the proposed deep reinforcement learning model with unique task-specific reward function was able to learn a robust investment strategy with an attractive risk-return profile.

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Keller, Nicholas, and Xiaolin Hu. "Towards Data-Driven Simulation Modeling for Mobile Agent-Based Systems." ACM Transactions on Modeling and Computer Simulation 29, no. 1 (February 23, 2019): 1–26. http://dx.doi.org/10.1145/3289229.

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Singh, Karandeep, Chang-Won Ahn, Euihyun Paik, Jang Won Bae, and Chun-Hee Lee. "A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling." Artificial Life 24, no. 2 (May 2018): 128–48. http://dx.doi.org/10.1162/artl_a_00260.

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Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or “soft,” aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

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V. Jatti, Ashwini, and Dr V. J. K. Kishor Sonti. "Sinkhole Attack Detection and Prevention using Agent Based Algorithm." Journal of University of Shanghai for Science and Technology 23, no. 05 (May 24, 2021): 526–44. http://dx.doi.org/10.51201/jusst/21/05175.

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This study presents sinkhole attack detection and prevention using agent-based algorithm. In this algorithm, agents are used to provide information to all node from its reliable neighbors by negotiation in three steps, thus nodes may not be able to pay the attention to the traffic made by sinkhole attacker. In this work, network scale of 500×500 m2 square areas have been considered. Series of simulation are carried in each experiment. Every simulation run is being organized to work for 10mins. Network performance is evaluated in terms of throughput, packet delivery ratio, jitter, delay in packets delivery, data packets received, data packets drop using network simulations software. Network simulation results depicts that in proposed algorithm, throughput increases by 15 to 20 percent, packet delivery ratio increases by 30 to 40%, decrease in the jitter by 10 to 15 %, delay in packets delivery is decreased by 15 to 20 %, data packets received are increased by 15 to 20 % and number of the data packets drop are decreased by 5 to 15 %. Based on simulation results throughput, packet delivery ratio and data packets received increased in proposed agent-based algorithm. However, it is observed that, jitter, delay in packets delivery and data packets drop were decreased.

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Ivanova, Yoana. "Methods and means for agent-based visualization of digitized data." Yearbook Telecommunications 8 (October 3, 2021): 13–22. http://dx.doi.org/10.33919/ytelecomm.21.8.2.

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The research aims to present the possibility to visualize the processes simulated by agent-based modelling method, contributing to a better understanding the methods of digital transformations of physical objects and the means for visualization of digitized data. The theoretical framework is supported by examples related to practical realization of agent-based visualization in the simulation environment NetLogo or a cloud platform. The individual stages in the process of working with a personal online GIS-assistant Land Viewer for performing built-in analysis of satellite images, with data clustering capabilities, are described in detail. The scientific and applied contribution of the research is expressed in the analysis of the possibilities for creating an agent-based visualization to a suitable simulation model, selected by the library of the simulation software.

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Bell, David, and Chidozie Mgbemena. "Data-driven agent-based exploration of customer behavior." SIMULATION 94, no. 3 (December 8, 2017): 195–212. http://dx.doi.org/10.1177/0037549717743106.

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Customer retention is a critical concern for mobile network operators because of the increasing competition in the mobile services sector. Such unease has driven companies to exploit data as an avenue to better understand changing customer behavior. Data-mining techniques such as clustering and classification have been widely adopted in the mobile services sector to better understand customer retention. However, the effectiveness of these techniques is debatable due to the constant change and increasing complexity of the mobile market itself. This design study proposes an application of agent-based modeling and simulation (ABMS) as a novel approach to understanding customer behavior through the combination of market and social factors that emerge from data. External forces at play and possible company interventions can then be added to data-derived models. A dataset provided by a mobile network operator is utilized to automate decision-tree analysis and subsequent building of agent-based models. Popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore possible customer churn scenarios. ABMS is used to understand the behavior of customers and detect reasons why customers churned or stayed with their respective mobile network operators. A CART decision-tree method is presented that identifies agents, selects important attributes, and uncovers customer behavior – easily identifying tenure, location, and choice of mobile devices as determinants for the churn-or-stay decision. Word of mouth between customers is also explored as a possible influence factor. Importantly, methods for automating data-driven agent-based simulation model generation will support faster exploration and experimentation – including with those determinants from a wider market or social context.

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Popov, Konstantin, Mahmoud Rafea, Fredrik Holmgren, Per Brand, Vladimir Vlassov, and Seif Haridi. "PARALLEL AGENT-BASED SIMULATION ON A CLUSTER OF WORKSTATIONS." Parallel Processing Letters 13, no. 04 (December 2003): 629–41. http://dx.doi.org/10.1142/s0129626403001562.

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We discuss a parallel implementation of an agent-based simulation. Our approach allows to adapt a sequential simulator for large-scale simulation on a cluster of workstations. We target discrete-time simulation models that capture the behavior of Web users and Web sites. Web users are connected with each other in a graph resembling the social network. Web sites are also connected in a similar graph. Users are stateful entities. At each time step, they exhibit certain behaviour such as visiting bookmarked sites, exchanging information about Web sites in the "word-of-mouth" style, and updating bookmarks. The real-world phenomena of emerged aggregated behavior of the Internet population is studied. The system distributes data among workstations, which allows large-scale simulations infeasible on a stand-alone computer. The model properties cause traffic between workstations proportional to partition sizes. Network latency is hidden by concurrent simulation of multiple users. The system is implemented in Mozart that provides multithreading, dataflow variables, component-based software development, and network-transparency. Currently we can simulate up to 106 Web users on 104 Web sites using a cluster of 16 computers, which takes few seconds per simulation step, and for a problem of the same size, parallel simulation offers speedups between 11 and 14.

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Izumi, Kiyoshi, Yoshifumi Nishida, and Yoichi Motomura. "Risk Evaluation by Human Trajectory Simulation Based on Real Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 2 (March 20, 2011): 220–25. http://dx.doi.org/10.20965/jaciii.2011.p0220.

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This paper proposes a new approach integrating the modeling of moving persons from sensor data and agent-based simulation for indoor layout design viewed from preventing children’s accidents. Our model focuses on interaction between indoor objects and children to estimate the risk of indoor accidents. We discuss the agent-based simulation of multiple persons moving in public spaces and its application to evaluating information presentation for guidance.

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Borawska, Anna, and Malgorzata Latuszynska. "Incorporating Neuroscience Data into Agent-Based Simulation Models of Buyer Behavior." EUROPEAN RESEARCH STUDIES JOURNAL XXIII, Issue 4 (November 1, 2020): 1197–212. http://dx.doi.org/10.35808/ersj/1738.

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Ranjit, Saurav, Apichon Witayangkurn, Masahiko Nagai, and Ryosuke Shibasaki. "Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data." ISPRS International Journal of Geo-Information 7, no. 5 (May 8, 2018): 177. http://dx.doi.org/10.3390/ijgi7050177.

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Diallo, Azise Oumar, Arnaud Doniec, Guillaume Lozenguez, and René Mandiau. "Agent-based simulation from anonymized data: An application to Lille metropolis." Procedia Computer Science 184 (2021): 164–71. http://dx.doi.org/10.1016/j.procs.2021.03.027.

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Wang, Peng, Ge Li, Rusheng Ju, and Yong Peng. "Random Finite Set Based Data Assimilation for Dynamic Data Driven Simulation of Maritime Pirate Activity." Mathematical Problems in Engineering 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/5307219.

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Maritime piracy is posing a genuine threat to maritime transport. The main purpose of simulation is to predict the behaviors of many actual systems, and it has been successfully applied in many fields. But the application of simulation in the maritime domain is still scarce. The rapid development of network and measurement technologies brings about higher accuracy and better availability of online measurements. This makes the simulation paradigm named as dynamic data driven simulation increasingly popular. It can assimilate the online measurements into the running simulation models and ensure much more accurate prediction of the complex systems under study. In this paper, we study how to utilize the online measurements in the agent based simulation of the maritime pirate activity. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the conventional vectors based data assimilation algorithms. The random finite set based general data model, measurement model, and simulation model are introduced to support the proposed algorithm. The details of the proposed algorithm are presented in the context of agent based simulation of maritime pirate activity. Two groups of experiments are used to practically prove the effectiveness and superiority of the proposed algorithm.

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Zhang, Sheng, Hui Li Yang, and Hou Jin Xiong. "Hierarchical Data Fusion in WSN Based on Mobile Agent." Applied Mechanics and Materials 513-517 (February 2014): 1154–59. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1154.

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We put forward hierarchical data fusion in WSN, in which different fusion algorithm is used in each logic layer. The singular values are kicked out before data fusion with QCP (Quantization coding on precision) method in the cluster, while Difference comparison fusion algorithm are used between cluster head nodes. Mobile agents are used in above two layers. Simulation results demonstrate that the algorithm improves the accuracy of the data integration, lower power consumption and extend the life of the network.

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Zhu, Yong Bin, and Li Yan. "The Research on Financial Network Simulation Based on Agent." Applied Mechanics and Materials 513-517 (February 2014): 4454–57. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4454.

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During the research of the abnormal flowing of funds, the real financial transaction data warehouse can't be obtained for various reasons. In order to construct the data warehouse needed by this research platform, the paper provides an implementation approach using intelligent Agent software to act for the real roles to solve the problem. From the analysis of the simulation results, the data provided by the simulation method in this paper is feasible.

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Sun, Zhi Xun, and Jie Lin. "A System for Data Processing on Supply-Chain Distributed Simulation Platform Based on Agent." Applied Mechanics and Materials 26-28 (June 2010): 397–400. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.397.

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To deal with the result data of the supply-chain simulation, such as data storage, analysis and display, a data system based on agent has been developed. Utilize agent’s intelligence and interaction; realize the dynamic display of the real-time simulation status and statistics analysis. The simulation performance can then be viewed and analyzed to provide the decision making. The system, as an indispensible part of the simulation platform, supports the simulation system effectively.

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Klein, Marvin, Lars Lüpke, and Markus Günther. "Home charging and electric vehicle diffusion: Agent-based simulation using choice-based conjoint data." Transportation Research Part D: Transport and Environment 88 (November 2020): 102475. http://dx.doi.org/10.1016/j.trd.2020.102475.

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Wu, Hao, and Zheng Hong Peng. "Agent-Based Modeling and Simulation on Planning Analysis." Applied Mechanics and Materials 631-632 (September 2014): 1312–19. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.1312.

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In contrast to China’s rapid urbanization, the development of urban planning technologies remained relatively lagged behind. By taking quantitative analysis urban planning as a starting point, the research discusses the scope and content of planning at various levels as well as the possibility of data acquisition so as to choose the applicable planning analysis models. Agent-based modeling (ABM) has become an important subject recently in urban planning due to its top-down approach. This study discusses the building of a analysis Agent-based model for urban planning and design in a bid to provide direct and visual method for planning practices. We built a planning model with the software Repast S which was proved being useful in escaping green space planning. From running this Agent model the dynamic visualization of disaster evacuation process could be simulated.

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Izumi, Kiyoshi, Keiki Takadama, Hiromitsu Hattori, Nariaki Nishino, and Itsuki Noda. "Social and Group Simulation Based on Real Data Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 2 (March 20, 2011): 166–72. http://dx.doi.org/10.20965/jaciii.2011.p0166.

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Recently, social simulation research based on real data has appeared in various fields. This paper introduces studies of Agent-Based Simulation (ABSs) based on real data, focusing on introducing studies in the fields of financial marketing, traffic, pedestrians, and a sustainable society. We also introduce some approaches to establish a general method and/or theory about linking social simulation to real data. Finally, we categorize ABS research for understanding ABS research features.

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Hörl, Sebastian, and Milos Balac. "Introducing the eqasim pipeline: From raw data to agent-based transport simulation." Procedia Computer Science 184 (2021): 712–19. http://dx.doi.org/10.1016/j.procs.2021.03.089.

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Wang, Minghao, and Xiaolin Hu. "Data assimilation in agent based simulation of smart environments using particle filters." Simulation Modelling Practice and Theory 56 (August 2015): 36–54. http://dx.doi.org/10.1016/j.simpat.2015.05.001.

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Heywood, Peter, Steve Maddock, Jordi Casas, David Garcia, Mark Brackstone, and Paul Richmond. "Data-parallel agent-based microscopic road network simulation using graphics processing units." Simulation Modelling Practice and Theory 83 (April 2018): 188–200. http://dx.doi.org/10.1016/j.simpat.2017.11.002.

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Ternes, Patricia, Jonathan A. Ward, Alison Heppenstall, Vijay Kumar, Le-Minh Kieu, and Nick Malleson. "Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters." Open Research Europe 1 (July 20, 2022): 131. http://dx.doi.org/10.12688/openreseurope.14144.2.

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This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

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Ternes, Patricia, Jonathan A. Ward, Alison Heppenstall, Vijay Kumar, Le-Minh Kieu, and Nick Malleson. "Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters." Open Research Europe 1 (October 27, 2021): 131. http://dx.doi.org/10.12688/openreseurope.14144.1.

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This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

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van Heeswijk, W. J. A., M. R. K. Mes, J. M. J. Schutten, and W. H. M. Zijm. "Evaluating Urban Logistics Schemes Using Agent-based Simulation." Transportation Science 54, no. 3 (May 2020): 651–75. http://dx.doi.org/10.1287/trsc.2019.0971.

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The domain of urban freight transport is becoming increasingly complex. Many urban supply chains are composed of small and independent actors that cannot efficiently organize their highly fragmented supply chains, thereby negatively affecting the quality of life in urban areas. Both companies and local administrators try to improve transport efficiency and reduce external costs, but the effects of such interventions are difficult to predict, especially when applied in combination with each other (an urban logistics scheme). This paper presents an agent-based simulation model to quantify the effects of urban logistics schemes on multiple actors. We provide a detailed mathematical representation in the form of a Markov decision process. Based on an extensive literature study, we aggregate data to represent various actors in typical Western European cities. We perform numerical experiments to obtain insights into urban logistics schemes. The results show that most schemes yield significant environmental improvements but that achieving long-term financial viability is challenging for urban consolidation centers in particular. We also demonstrate that interventions, such as subsidies and access restrictions, do not always yield the intended effect. In a backcasting experiment, we identify conditions and schemes to achieve a financially viable urban consolidation center.

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Parente, Fabrizio, and Alfredo Colosimo. "Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study." Neural Plasticity 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6815040.

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Anticorrelations among brain areas observed in fMRI acquisitions under resting state are not endowed with a well-defined set of characters. Some evidence points to a possible physiological role for them, and simulation models showed that it is appropriate to explore such an issue. A large-scale brain representation was considered, implementing an agent-based brain-inspired model (ABBM) incorporating the SER (susceptible-excited-refractory) cyclic mechanism of state change. The experimental data used for validation included 30 selected functional images of healthy controls from the 1000 Functional Connectomes Classic collection. To study how different fractions of positive and negative connectivities could modulate the model efficiency, the correlation coefficient was systematically used to check the goodness-of-fit of empirical data by simulations under different combinations of parameters. The results show that a small fraction of positive connectivity is necessary to match at best the empirical data. Similarly, a goodness-of-fit improvement was observed upon addition of negative links to an initial pattern of only-positive connections, indicating a significant information intrinsic to negative links. As a general conclusion, anticorrelations showed that it is crucial to improve the performance of our simulation and, since these cannot be assimilated to noise, should be always considered in order to refine any brain functional model.

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Willenberg, D., M. Zuidgeest, and E. Beukes. "Quantifying MyCiTi supply usage using Big Data and Agent-Based Modelling." Journal of the South African Institution of Civil Engineering 64, no. 3 (September 14, 2022): 32–41. http://dx.doi.org/10.17159/2309-8775/2022/v64n3a4.

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Cape Town's Bus Rapid Transit (BRT) system, MyCiTi, uses an Automated Fare Collection (AFC) system that generates large volumes of transactional data on a daily basis. This data can be considered Big Data. The AFC data in its raw format, however, is incapable of supporting supply and demand analysis (e.g. studying bus occupancy rates). Agent-Based Modelling (ABM) can be used to analyse such data for that purpose. This paper discusses the development and calibration of a MATSim-based ABM to analyse AFC data for Cape Town's BRT system. It is shown that data-formatting algorithms are critical in the preparation of data for modelling activities. Furthermore, the development of appropriate ABM calibration parameters requires careful consideration in terms of appropriate data collection, simulation testing, and justification, which are discussed. The paper furthermore shows that the calibrated ABM can generate outputs such as bus on-board volumes, a system-demand overview, and even individual commuter path choice behaviour. Finally, a validation exercise shows that the model developed for this study is able to provide good estimates of on-board bus volumes (R2 = 0.85). It is, however, recommended that further research be conducted into studying agent path choices through simulation.

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Rosés, Raquel, Cristina Kadar, Charlotte Gerritsen, and Chris Rouly. "Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data." Journal of Artificial Intelligence Research 68 (July 9, 2020): 541–70. http://dx.doi.org/10.1613/jair.1.11831.

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In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

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Nikoohemat, S., P. Godoy, N. Valkhoff, M. Wouters - van Leeuwen, R. Voûte, and V. V. Lehtola. "POINT CLOUD BASED 3D MODELS FOR AGENT BASED SIMULATIONS IN SOCIAL DISTANCING AND EVACUATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2021 (June 17, 2021): 113–20. http://dx.doi.org/10.5194/isprs-annals-v-4-2021-113-2021.

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Abstract. Point clouds serve as the raw material for various models, such as Building Information Models (BIM). In this work, we investigate the reconstruction steps needed to create models that can be utilized directly for agent-based simulations. The input data for the reconstruction is captured with an indoor mobile mapping system. To show the prominence of this idea, we run social distancing and evacuation simulations on the reconstructed models. The simulations are run with multiple agents using a vision-based pedestrian model and A*-based path finding algorithm. The limitations of this approach are discussed. The video of the simulation is shared with the audience.Link to the video: https://youtu.be/r2D3IxXt7Ls

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Benoudina, Lazhar, and Mohammed RedjimiRedjimi. "Multi Agent System Based Approach for Industrial Process Simulation." Journal Européen des Systèmes Automatisés 54, no. 2 (April 27, 2021): 209–17. http://dx.doi.org/10.18280/jesa.540202.

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Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.

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Zare, Mohammad, Omid Naghshineh A., Erfan Salavati, and Adel Mohammadpour. "An agent-based model and detect price manipulation based on intraday transaction data with simulation." Applied Economics 53, no. 43 (August 2, 2021): 4931–49. http://dx.doi.org/10.1080/00036846.2021.1912282.

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Qu, You Miao, Wei Hua Li, and Hua Sheng Chen. "An Agent-Based Battlefield Simulation Framework for Decision Support." Applied Mechanics and Materials 336-338 (July 2013): 774–78. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.774.

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With the continuous development of information technology, as well as sophisticated reconnaissance technology and developed communications network technology, massive data has been brought into battlefield simulation system. Effective management and use of such massive, distributed information in battlefield simulation, thus turn information superiority into decision superiority, has increasingly become a hot research field. To provide decision support for commanders at all levels in the complex battlefield environment, we need to establish a hierarchical battlefield environment model, and to consider the demand of intelligent decision support during the modeling of battlefield. This paper refines the common characteristic of battlefield simulation systems, proposes a battlefield simulation framework based on multi-agent. Intelligent agent was used to model battlefield troops, which brings in decision support into battlefield simulation system. The framework can be implemented with mainstream programming languages. This framework has advantage in simplicity, versatility and scalability.

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Takadama, Keiki, and Kiyoshi Izumi. "Special Issue on New Trends in Agent-Based Simulation." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 2 (March 20, 2011): 165. http://dx.doi.org/10.20965/jaciii.2011.p0165.

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Agent-Based Simulation (ABS), an interdisciplinary area embracing both the computer science and the social science, has attracted much attention and aided the understanding of socially complex phenomena. A current important issue in this research area is how to improve ABS effectiveness and comprehension, which makes further mutual influence between the computer science and the social sciences indispensable - e.g., (1) agent modeling involving learning mechanisms in the computer science and (2) social dynamics analysis needed in the social science. Such integration of these two areas would help fulfill the great potential of ABS, first in solving complex engineering problems using agent-based technology and second in developing and testing new theories on socially complex systems. This special issue features ABS papers from both of these important areas exploring new trends in ABS. The 10 papers composing this special issue start with papers by Nobutada Fujii and Hiroyasu Inoue analyzing the relationship between the network structure and system dynamics. In these papers, an agent-based computational economics approach has been active in applying agent-based technologies to financial and economic systems. Papers by Biliana Alexandrova-Kabadjova, Isamu Okada, TomokoOhi, and Nariaki Nishino cover consumer and financial markets using agent-based models. They test economic theory and examine market phenomena for market design. Agent-based simulation is increasingly used in application fields in the social sciences. Papers by Kiyoshi Izumi, Hideki Fujii, Hiromitsu Hattori, and Shigeo Sagai propose solutions for actual social problems such as injury prevention, traffic, and electrical power. Models are created based on behavior data, and the integration of an agent-based model and real data is a hot topic in this area. As the beginning of these technical papers, this issue starts by a position paper to give an ABS overview for understanding important issues in ABS from an overall viewpoint and for understanding state-of-the-art ABS. The information presented is invaluable in helping readers grasp the important features of ABS.

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Karjus, Andres, and Martin Ehala. "Testing an agent-based model of language choice on sociolinguistic survey data." Language Dynamics and Change 8, no. 2 (October 1, 2018): 219–52. http://dx.doi.org/10.1163/22105832-00802004.

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Abstract The paper outlines an agent-based model for language choice in multilingual communities and tests its performance on samples of data drawn from a large-scale sociolinguistic survey carried out in Estonia. While previous research in the field of language competition has focused on diachronic applications, utilizing rather abstract models of uniform speakers, we aim to model synchronic language competition among more realistic, data-based agents. We hypothesized that a reasonably parametrized simulation of interactions between agents endowed with interaction principles grounded in sociolinguistic research would give rise to a network structure resembling real-world social networks, and that the distribution of languages used in the model would resemble their actual usage distribution. The simulation was reasonably successful in replicating the real-world scenarios, while further analysis revealed that the model parameters differ in importance between samples. We conclude that such variation should be considered in parametrizing future language choice and competition models.

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Rai, Sanish, and Xiaolin Hu. "Building occupancy simulation and data assimilation using a graph-based agent-oriented model." Physica A: Statistical Mechanics and its Applications 502 (July 2018): 270–87. http://dx.doi.org/10.1016/j.physa.2018.02.051.

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Song, Niu, and Li. "Individual Behavior Simulation Based on Grid Object and Agent Model." ISPRS International Journal of Geo-Information 8, no. 9 (September 3, 2019): 388. http://dx.doi.org/10.3390/ijgi8090388.

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For the simplicity of spatial modeling in Cellular Automaton (CA) and the complexity of vector spatial expression in the Multi-Agent System (MAS), the concept of grid object as the spatial model of individual behavior simulation was proposed with spatial information, semantic information, and connection relationship of geographic entity. Then, by incorporating the MAS, the method for individual behavior simulation with the Grid Object and Agent Model (GOAM) was demonstrated. Meanwhile, a prototype system including the three subsystems was developed based on the GOAM, and experiments were conducted for two cases in different spatial environments. The prototype system can be used to obtain grid object data with 3D model data, to compute and simulate the behavior of individuals, and to render individuals. The two cases involve goal-driven behavior in both indoor and outdoor environments as examples to evaluate the validity of the GOAM and to provide a reference for building individual behavior simulation with the GOAM in other scenarios.

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TROITZSCH, KLAUS G. "EXTORTION RACKET SYSTEMS AS TARGETS FOR AGENT-BASED SIMULATION MODELS. COMPARING COMPETING SIMULATION MODELS AND EMPRICAL DATA." Advances in Complex Systems 18, no. 05n06 (August 2015): 1550014. http://dx.doi.org/10.1142/s0219525915500149.

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Extortion racketeering is an industry not only practiced by mafia, but also in groups such as hells angels. It occurs in a complex setting of criminals, victims, police and society, and its framework is set up by legal norms as well as informal norms of the actor groups involved. The paper presents two agent-based simulation models which differ with respect to the decision making mode, which is either stochastical with fixed probabilities or deliberative where decisions depend on utility considerations and norms learned during the process. The central research questions of the paper — beside the question how extortion racket system can be appropriately modeled — concern the divergence of the results of the two model versions, the comparison of the input parameter combinations, the motivations of input parameters and the validation of the results by comparing them to available empirical data.

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Gao, Ming, Zhongyuan Wang, Kai Wang, Chenhui Liu, and Shiping Tang. "Forecasting elections with agent-based modeling: Two live experiments." PLOS ONE 17, no. 6 (June 30, 2022): e0270194. http://dx.doi.org/10.1371/journal.pone.0270194.

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Election forecasting has been traditionally dominated by subjective surveys and polls or methods centered upon them. We have developed a novel platform for forecasting elections based on agent-based modeling (ABM), which is entirely independent from surveys and polls. The platform uses statistical results from objective data along with simulation models to capture how voters have voted in past elections and how they are likely to vote in an upcoming election. We screen for models that can reproduce results that are very close to the actual results of historical elections and then deploy these selected models to forecast an upcoming election with simulations by combining extrapolated data from historical demographic record and more updated data on economic growth, employment, shock events, and other factors. Here, we report the results of two recent experiments of real-time election forecasting: the 2020 general election in Taiwan and six states in the 2020 general election in the United States. Our mostly objective method using ABM may transform how elections are forecasted and studied.

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Alhussein, Hasnaa, Lynn Shehab, and Farook Hamzeh. "Improvisation in Construction Planning: An Agent-Based Simulation Approach." Buildings 12, no. 10 (October 5, 2022): 1608. http://dx.doi.org/10.3390/buildings12101608.

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Improvisation is the decision-making process addressing unexpected obstacles in a spontaneous but rational manner. Although undesirable, as it indicates deviation from plans, improvisation is unavoidable in construction to address issues related to unforeseen uncertainties. An adaptive planning system employing improvisation to react rapidly to unplanned events may therefore boost the performance in construction projects. Accordingly, this research aims to predict the outcomes of construction planning processes from an improvisational perspective by better understanding the dynamics of improvisation. It seeks to identify how different variations of improvisational parameters influence the improvisational outcome. This objective is achieved through an agent-based model used to simulate the improvisation practices at the level of planners interacting together. Parameters relating to planners, projects, and problems influencing each planner’s improvisational means are illustrated in the model. The model’s inputs were validated through data from large-sized projects. Linear regression models that predict the results of the improvisational practices were then developed through simulation experiments. Findings regarding the impacts of different types of improvisors on the improvisational outcomes are presented. The contribution of this study lies in enhancing the overall improvisational performance in construction planning to ultimately guide decision makers and planners to better handle uncertainties in projects.

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Zhang, Haibo, Xiaoming Liu, Honghai Ji, Zhongsheng Hou, and Lingling Fan. "Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing." Energies 12, no. 7 (April 11, 2019): 1402. http://dx.doi.org/10.3390/en12071402.

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Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

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Sanchez-Segura, Maria-Isabel, German-Lenin Dugarte-Peña, Fuensanta Medina-Dominguez, and Cynthya García de Jesús. "System dynamics and agent-based modelling to represent intangible process assets characterization." Kybernetes 47, no. 2 (February 5, 2018): 289–306. http://dx.doi.org/10.1108/k-03-2017-0102.

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Purpose This paper aims to address the use of modelling and simulation tools to enhance intangible process assets management by simulating and automating their characterization depending on their quality and impact on an organizational business goal. Design/methodology/approach The authors conducted a study comparing two simulation-based approaches to characterize intangible assets: system dynamics and agent-based simulation. Findings Strategic business studies have not yet considered the use of simulation techniques to characterize the intangible assets at length. The proposed solution introduces significant improvements for strategic data visualization, providing company stakeholders with a practical and helpful prism through which to view an easily adaptable, cheap and meaningful source of information about their company’s process assets, and their behaviour based on operation indicators. Practical implications This research offers decision-makers in knowledge-intensive organizations alternatives for effective strategic decision-making and for leveraging prospective views based on the specification of the organization’s knowledge. To do this, stakeholders will be able to use very promising low-cost simulation-based tools to create practical scenarios and potential situations that generate inputs for debate and decision-making by senior and middle management. Originality/value This paper reports an unprecedented comparative study of system dynamics and agent-based simulation to speed-up the characterization of the intangible process assets based on their quality and impact on strategic goals. It stresses the benefits and implications of the use of these techniques for better strategic management.

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Millington, James D. A., and John Wainwright. "Mixed qualitative-simulation methods." Progress in Human Geography 41, no. 1 (July 10, 2016): 68–88. http://dx.doi.org/10.1177/0309132515627021.

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Across geography there has been variable engagement with the use of simulation and agent-based modelling. We argue that agent-based simulation provides a complementary method to investigate geographical issues which need not be used in ways that are epistemologically different in kind from some other approaches in contemporary geography. We propose mixed qualitative-simulation methods that iterate back-and-forth between ‘thick’ (qualitative) and ‘thin’ (simulation) approaches and between the theory and data they produce. These mixed methods accept simulation modelling as process and practice; a way of using computers with concepts and data to ensure social theory remains embedded in day-to-day geographical thinking.

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Liu, Zhengchun, Dolores Rexachs, Francisco Epelde, and Emilio Luque. "A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity." Computers & Industrial Engineering 103 (January 2017): 300–309. http://dx.doi.org/10.1016/j.cie.2016.11.036.

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Martin, Niels Lasse, Ann Kathrin Schomberg, Jan Henrik Finke, Tim Gyung-min Abraham, Arno Kwade, and Christoph Herrmann. "Process Modeling and Simulation of Tableting—An Agent-Based Simulation Methodology for Direct Compression." Pharmaceutics 13, no. 7 (June 30, 2021): 996. http://dx.doi.org/10.3390/pharmaceutics13070996.

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In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

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Chen, Xiu Sheng, Cheng Rui Zhang, Shu Kun Cao, Ri Liang Liu, Qiang Wang, and Hong Bo Lan. "Intelligent Control of CNC System Based on STEP-NC." Applied Mechanics and Materials 16-19 (October 2009): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.915.

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In this paper, Data model used in intelligent CNC system based on STEP-NC is presented. A multi-agent architecture of the intelligent CNC system based on STEP-NC is proposed. The architecture consists of fourteen functional agents, such as interpreter agent, process planning agent, simulation agent, tool path planning agent, monitoring agent, expert system agent, etc. Some key technologies to implement the intelligent control including simulation, online inspection and expert system are discussed detailed. As a result, the architecture and key technologies proposed in this paper will aid the development work of the intelligent STEP-NC system.

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Sakaki. "Equality in Income and Sustainability in Economic Growth: Agent-Based Simulations on OECD Data." Sustainability 11, no. 20 (October 18, 2019): 5803. http://dx.doi.org/10.3390/su11205803.

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In countries that have developed under the current market economy, inequalities in income distribution tend to increase with three different trends, i.e., high (United States, United Kingdom, Japan), low (North Europe countries), and medium Gini coefficient levels. On the other hand, the relationship between income distribution and social welfare is generally a difficult problem to solve in economics. So, this paper discusses the impact of income distribution on the macroeconomy, limiting the scope to consistency with long-term economic growth. We attempt to answer these economic policy issues by simulation using an agent-based model based on replicator dynamics. As a result of the simulation in this paper, in general, in countries with the high marginal propensity to consume, long-term growth can be maintained by inducing equality in income distribution. On the other hand, a mature country with a low marginal propensity to consume can sustain not so high but stable growth despite increasing inequality in income distribution. According to simulation results based on OECD (Organisation for Economic Co-operation and Development) data, in the former UK, US, and Japan, the lower the Gini coefficient is, the higher the growth potential is, while in the latter Norway and Luxembourg, relatively stable growth is maintained even if the Gini coefficient increases.

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Lempp, Frieder. "A new agent-based simulation model of bilateral negotiation." International Journal of Conflict Management 31, no. 1 (October 7, 2019): 115–48. http://dx.doi.org/10.1108/ijcma-07-2019-0118.

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Purpose The purpose of this paper is to introduce a new agent-based simulation model of bilateral negotiation based on a synthesis of established theories and empirical studies of negotiation research. The central units of the model are negotiators who pursue goals, have attributes (trust, assertiveness, cooperativeness, creativity, time, etc.) and perform actions (proposing and accepting offers, exchanging information, creating value, etc). Design/methodology/approach Methodologically, the model follows the agent-based approach to modeling. This approach is chosen because negotiations can be described as complex, non-linear systems involving autonomous agents (i.e. the negotiators), who interact with each other, pursue goals and perform actions aimed at achieving their goals. Findings This paper illustrates how the model can simulate experiments involving variables such as negotiation strategy, creativity, reservation value or time in negotiation. An example simulation is presented which investigates the main and interaction effects of negotiators’ reservation value and their time available for a negotiation. A software implementation of the model is freely accessible at https://tinyurl.com/y7oj6jo8. Research limitations/implications The model, as developed at this point, provides the basis for future research projects. One project could address the representation of emotions and their impact on the process and outcome of negotiations. Another project could extend the model by allowing negotiators to convey false information (i.e. to bluff). Yet another project could be aimed at refining the routines used for making and accepting offers with a view to allow parties to reach partial settlements during a negotiation. Practical implications Due to its broad scope and wide applicability, the model can be used by practitioners and researchers alike. As a decision-support system, the model allows users to simulate negotiation situations and estimate the likelihood of negotiation outcomes. As a research platform, it can generate simulation data in a cost- and time-effective way, allowing researchers to simulate complex, large-N studies at no cost or time. Originality/value The model presented in this paper synthesizes in a novel way a comprehensive range of concepts and theories of current negotiation research. It complements other computational models, in that it can simulate a more diverse range of negotiation strategies (distributive, integrative and compromise) and is applicable to a greater variety of negotiation scenarios.

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Journal articles on the topic 'Agent-based data simulation'

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