Relevant bibliographies by topics / Agent-based data simulation

Academic literature on the topic 'Agent-based data simulation'

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Published: 9 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Agent-based data simulation"

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Martignoni, Robert Antonio. "Evaluation of the business model for mobile data services : an agent-based simulation approach /." [S.l.] : [s.n.], 2009. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000293550.

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Tufail, M. "The extraction and usage of patterns from video data to support multi-agent based simulation." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3008120/.

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The research work presented in this thesis is directed at addressing the knowledge acquisition bottleneck frequently encountered in computer simulation. The central idea is to extract the required knowledge from video data and use this to drive a computer simulation instead of the more conventional approach of interviewing domain experts and somehow encapsulating this knowledge in a manner whereby it can be used in the context of computer simulation. More specifically the idea presented in this thesis is to extract object location information from video data and then to mine this information to identify Movement Patterns (MPs) and then to utalise these MPs in the context of computer simulation. To act as a focus for the work rodent behaviour simulation was considered. Partly because video data concerning rodent behaviour was relatively easy to obtain and partly because there is a genuine need to achieve a better understanding of rodent behaviour. This is especially the case in the context of crop damage. There are a variety of computer simulation frameworks. One that naturally lends itself to rodent simulation is Multi Agent Based Simulation (MABS) whereby the objects to be simulated (rodents) are encapsulated in terms of software agents. In more detail the work presented is directed at a number of research issues in the context of the above: (i) mechanisms to identify a moving object in video data and extracting associated location information, (ii) the mining of MPs from the extracted location information, (iii) the representation of MPs in such a way that they are compatible with computer simulation frameworks especially MABS frameworks and (iv) mechanisms where by MPs can be utilized and interacted with so as to drive a MABS. Overall two types of mechanisms are considered, Absolute and Relative. The operation of rodent MABSs, driven using the proposed MP concept, is fully illustrated in the context of different categories of scenarios. The evaluation of the proposed MP driven MABSs was conducted by comparing real world scenarios to parallel simulated scenarios. The results presented in the thesis demonstrated that the proposed mechanisms for extracting locations, and consequently mining MPs, from video data to drive a MABS provides a useful approach to effective computer simulation that will have wide ranging benefits.
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Zheng, Jiaqi. "Interactive Visual Analytics for Agent-Based simulation : Street-Crossing Behavior at Signalized Pedestrian Crossing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264991.

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To design a pedestrian crossing area reasonably can be a demanding task for traffic planners. There are several challenges, including determining the appropriate dimensions, and ensuring that pedestrians are exposed to the least risks. Pedestrian safety is especially obscure to analyze, given that many people in Stockholm cross the street illegally by running against the red light. To cope with these challenges, computational approaches of trajectory data visual analytics can be used to support the analytical reasoning process. However, it remains an unexplored field regarding how to visualize and communicate the street-crossing spatio-temporal data effectively. Moreover, the rendering also needs to deal with a growing data size for a more massive number of people. This thesis proposes a web-based interactive visual analytics tool for pedestrians' street-crossing behavior under various flow rates. The visualization methodology is also presented, which is then evaluated to have achieved satisfying communication and rendering effectiveness for maximal 180 agents over 100 seconds. In terms of the visualization scenario, pedestrians either wait for the red light or cross the street illegally; all people can choose to stop by a buffer island before they finish crossing. The visualization enables the analysis under multiple flow rates for 1) pedestrian movement, 2) space utilization, 3) crossing frequency in time-series, and 4) illegal frequency. Additionally, to acquire the initial trajectory data, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is engaged in the crowd simulation. Then different visualization techniques are utilized to comply with user demands, including map animation, data aggregation, and time-series graph.
Att konstruera ett gångvägsområde kan rimligen vara en krävande uppgift för trafikplanerare. Det finns flera utmaningar, bland annat att bestämma lämpliga dimensioner och se till att fotgängare utsätts för minst risker. Fotgängarnas säkerhet är särskilt obskyrlig att analysera, eftersom många människor i Stockholm korsar gatan olagligt genom att springa mot det röda ljuset. För att klara av dessa utmaningar kan beräkningsmetoder för bana data visuell analys användas för att stödja den analytiska resonemangsprocessen. Det är emellertid ett oexplorerat fält om hur man visualiserar och kommunicerar gataövergången spatio-temporal data effektivt. Dessutom måste rendering också hantera en växande datastorlek för ett mer massivt antal människor. Denna avhandling föreslår ett webbaserat interaktivt visuellt analysverktyg för fotgängares gatöverföring under olika flödeshastigheter. Visualiseringsmetoden presenteras också, som sedan utvärderas för att ha uppnått tillfredsställande kommunikation och effektivitet för maximal 180 agenter över 100 sekunder. Vad beträffar visualiseringsscenariot, väntar fotgängare antingen på det röda ljuset eller tvärs över gatan; alla människor kan välja att stanna vid en buffertö innan de slutar korsa. Visualiseringen möjliggör analysen under flera flödeshastigheter för 1) fotgängarrörelse, 2) rymdutnyttjande, 3) korsfrekvens i tidsserier och 4) olaglig frekvens. För att förvärva den ursprungliga bana-data är Optimal Reciprocal Collision Avoidance (ORCA) algoritmen förknippad med folkmassimuleringen. Därefter utnyttjas olika visualiseringstekniker för att uppfylla användarnas krav, inklusive kartanimering, dataaggregering och tidsserier.
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Kratz, Jakob, and Viktor Luthman. "Comparison of spatial partitioning data structures in crowd simulations." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302340.

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This report investigates how the construction and query time of multiple spatial partitioning data structures is impacted by spatial distribution of and number of agents in a crowd simulation. In addition a method is investigated for updating the data structures less frequently at the cost of increasing the radius queried, without affecting the correctness of the queries. The data structures are tested in a simulation using a Boids model and update and query times are measured. It is found that the performance of the grid is better than the quad tree and the kd- tree for low number of agents, but deteriorates more quickly when the number of agents increase. It is also found that this approach can decrease the sum of time spent updating and the time spent querying in the simulation. The effectiveness of this method is highly dependent on the update of the data structure.
Denna rapport undersöker hur konstruktion och grannsökning av flera datastrukturer för spatial partitionering påverkas av spatial fördelning av simuleringens agenter och antal agenter i simuleringen. Dessutom undersöks en metod för att uppdatera datastrukturerna mindre ofta, på bekostnad av att utöka grannsökningens radie, utan att påverka grannsökningens korrekthet. Datastrukturerna testas i en simulering baserad på Boids och uppdaterings- och frågetider för datastrukturerna mäts. Det visar sig att prestandan av grid är bättre än prestandan av quad tree och kd- tree för ett litet antal agenter, men att prestandan för grid försämras snabbare när antalet agenter ökar. Dessutom visar sig denna metod kunna ge en minskning i den totala tiden som går åt till att göra grannsökningar och uppdateringar av datastrukturen. Hur effektiv denna metod är beror i hög grad på hur lång uppdateringstiden är för den använda datastrukturen.
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Hassouna, Mohammed Bassam. "Agent based modelling and simulation : an examination of customer retention in the UK mobile market." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6344.

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Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.
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Zangeneh, L. "Investigating the challenges of data, pricing and modelling to enable agent based simulation of the Credit Default Swap market." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1435662/.

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The Global Financial Crisis of 2007-2008 is considered by three top economists the worst financial crisis since the Great Depression of the 1930s [Pendery, 2009]. The crisis played a major role in the failure of key businesses, declines in consumer wealth, and significant downturn in economic activities leading to the 2008-2012 global recession and contributing to the European sovereign-debt crisis [Baily and Elliott, 2009] [Williams, 2012]. More importantly, the serious limitation of existing conventional tools and models as well as a vital need for developing complementary tools to improve the robustness of existing overall framework immediately became apparent. This thesis details three proposed solutions drawn from three main subject areas: Statistic, Genetic Programming (GP), and Agent-Based Modeling (ABM) to help enable agent-based simulation of Credit Default Swap (CDS) market. This is accomplished by tackling three challenges of lack of sufficient data to support research, lack of efficient CDS pricing technique to be integrated into agent based model, and lack of practical CDS market experimental model, that are faced by designers of CDS investigation tools. In particular, a general data generative model is presented for simulating financial data, a novel price calculator is proposed for pricing CDS contracts, and a unique CDS agent-based model is designed to enable the investigation of market. The solutions presented can be seen as modular building blocks that can be applied to a variety of applications. Ultimately, a unified general framework is presented for integrating these three solutions. The motivation for the methods is to suggest viable tools that address these challenges and thus enable the future realistic simulation of the CDS market using the limited real data in hand. A series of experiments were carried out, and a comparative evaluation and discussion is provided. In particular, we presented the advantages of realistic artificial data to enable open ended simulation and to design various scenarios, the effectiveness of Cartesian Genetic Programming (CGP) as a bio-inspired evolutionary method for a complex real-world financial problem, and capability of Agent Based (AB) models for investigating CDS market. These experiments demonstrate the efficiency and viability of the proposed approaches and highlight interesting directions of future research.
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Elmir, Ahmad. "PaySim Financial Simulator : PaySim Financial Simulator." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14061.

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The lack of legitimate datasets on mobile money transactions toperform research on in the domain of fraud detection is a big prob-lem today in the scientic community. Part of the problem is theintrinsic private nature of mobile transactions, not much infor-mation can be exploited. This will leave the researchers with theburden of rst harnessing the dataset before performing the actualresearch on it. The dataset corresponds to the set of data in whichthe research is to be performed on. This thesis discusses a solutionto such a problem, namely the Paysim simulator. Paysim is a -nancial simulator that simulates mobile money transactions basedon an original dataset. We present a solution to ultimately yieldthe possibility to simulate mobile money transactions in such a waythat they become similar to the original dataset. The similarity orthe congruity will be measured by calculating the error-rate betweenthe synthetic data set and the original data set. With technologyframeworks such as "Agent Based" simulation techniques, and theapplication of mathematical statistics, it can be demonstrated thatthe synthetic data is as prudent as the original data set. The aimof this thesis is to demonstrate with statistical models that PaySimcan be used as a tool for the intents of nancial simulations.
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Lopez-Rojas, Edgar Alonso. "Applying Simulation to the Problem of Detecting Financial Fraud." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

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This thesis introduces a financial simulation model covering two related financial domains: Mobile Payments and Retail Stores systems.   The problem we address in these domains is different types of fraud. We limit ourselves to isolated cases of relatively straightforward fraud. However, in this thesis the ultimate aim is to introduce our approach towards the use of computer simulation for fraud detection and its applications in financial domains. Fraud is an important problem that impact the whole economy. Currently, there is a lack of public research into the detection of fraud. One important reason is the lack of transaction data which is often sensitive. To address this problem we present a mobile money Payment Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data that contains both: normal customer behaviour and fraudulent behaviour.    These simulations are Multi Agent-Based Simulations (MABS) and were calibrated using real data from financial transactions. We developed agents that represent the clients and merchants in PaySim and customers and salesmen in RetSim. The normal behaviour was based on behaviour observed in data from the field, and is codified in the agents as rules of transactions and interaction between clients and merchants, or customers and salesmen. Some of these agents were intentionally designed to act fraudulently, based on observed patterns of real fraud. We introduced known signatures of fraud in our model and simulations to test and evaluate our fraud detection methods. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data or breaking any non-disclose agreements.   Using statistics and social network analysis (SNA) on real data we calibrated the relations between our agents and generate realistic synthetic data sets that were verified against the domain and validated statistically against the original source.   We then used the simulation tools to model common fraud scenarios to ascertain exactly how effective are fraud techniques such as the simplest form of statistical threshold detection, which is perhaps the most common in use. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level. This means that there seems to be little economic room for improved fraud detection techniques.   We also implemented other applications for the simulator tools such as the set up of a triage model and the measure of cost of fraud. This showed to be an important help for managers that aim to prioritise the fraud detection and want to know how much they should invest in fraud to keep the loses below a desired limit according to different experimented and expected scenarios of fraud.
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Alshammari, Sultanah. "A Data-Driven Computational Framework to Assess the Risk of Epidemics at Global Mass Gatherings." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505145/.

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This dissertation presents a data-driven computational epidemic framework to simulate disease epidemics at global mass gatherings. The annual Muslim pilgrimage to Makkah, Saudi Arabia is used to demonstrate the simulation and analysis of various disease transmission scenarios throughout the different stages of the event from the arrival to the departure of international participants. The proposed agent-based epidemic model efficiently captures the demographic, spatial, and temporal heterogeneity at each stage of the global event of Hajj. Experimental results indicate the substantial impact of the demographic and mobility patterns of the heterogeneous population of pilgrims on the progression of the disease spread in the different stages of Hajj. In addition, these simulations suggest that the differences in the spatial and temporal settings in each stage can significantly affect the dynamic of the disease. Finally, the epidemic simulations conducted at the different stages in this dissertation illustrate the impact of the differences between the duration of each stage in the event and the length of the infectious and latent periods. This research contributes to a better understanding of epidemic modeling in the context of global mass gatherings to predict the risk of disease pandemics caused by associated international travel. The computational modeling and disease spread simulations in global mass gatherings provide public health authorities with powerful tools to assess the implication of these events at a different scale and to evaluate the efficacy of control strategies to reduce their potential impacts.
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MERICO, DAVIDE. "Tracking with high-density, large-scale wireless sensor networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7785.

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Given the continuous technological advances in computing and communication, it seems that we are rapidly heading towards the realization of paradigms commonly described as ubiquitous computing, pervasive computing, ambient intelligence, or, more recently, "everyware". These paradigms envision living environments pervaded by a high number of invisible technological devices affecting and improving all aspects of our lives. Therefore, it is easy to justify the need of knowing the physical location of users. Outdoor location-aware applications are already widespread today, their growing popularity showing that location-awareness is indeed a very useful functionality. Less obvious is how the growing availability of these locations and tracks will be exploited for providing more intelligent "situation-understanding" services that help people. My work is motivated by the fact that, thanks to location-awareness systems, we are more and more aware of the exact positions of the users but unfortunately we are rarely capable of exactly understanding what they are doing. Location awareness should rapidly evolve and become "situation-awareness" otherwise the ubiquitous-computing vision will become impracticable. The goal of this thesis is devising alternative and innovative approaches to the problem of indoor position estimation/assessment and evaluating them in real environments. These approaches are be based on: (i) a low-cost and energy-aware localization infrastructure; (ii) multi-sensor, statistically-based, localization algorithms; (iii) logic-based situation assessment techniques. The algorithms and techniques that are the outcome of this thesis have all been tested by implementing them and measuring (both in a quantitative sense and in a qualitative sense) the performance in the field.
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Books on the topic "Agent-based data simulation"

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1950-, Arai K., Deguchi Hiroshi 1955-, Matsui H, and International Simulation and Gaming Association. International Conference, eds. Agent-based modeling meets gaming simulation. Tokyo: Springer, 2005.

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Interdisciplinary applications of agent-based social simulation and modeling. Hershey, PA: Information Science Reference, 2014.

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International Workshop on Agent-Based Approaches in Economic and Social Complex Systems (3rd 2004 Kyoto, Japan). Agent-based simulation: From modeling methodologies to real-world applications : post-proceedings of the Third International Workshop on Agent-Based Approaches in Economic and Social Complex Systems 2004. Tokyo: Springer, 2005.

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S, Sichman Jaime, Bousquet François 1963-, and Davidsson Paul 1964-, eds. Multi-agent-based simulation II: Third international workshop, MABS 2002, Bologna, Italy, July 2002 : revised papers. Berlin: Springer, 2003.

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David, Hales, ed. Multi-agent-based simulation III: 4th international workshop, MABS 2003, Melbourne, Australia, July 14, 2003 : revised papers. Berlin: Springer, 2003.

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Luis, Antunes, Norling Emma, Paolucci Mario, Siekmann Jörg H, and SpringerLink (Online service), eds. Multi-Agent-Based Simulation VIII: International Workshop, MABS 2007, Honolulu, HI, USA, May 15, 2007, Revised and Invited Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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David, Hutchison. Multi-Agent-Based Simulation IX: International Workshop, MAPS 2008, Estoril, Portugal, May 12-13, 2008, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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1959-, Chen Shu-Heng, Terano Takao 1952-, and Yamamoto Ryūichi, eds. Agent-based approaches in economic and social complex systems VI: Post-proceedings of the AESCS International Workshop 2009 / Shu-Heng Chen, Takao Terano, Ryuichi Yamamoto, editors. Tokyo: Springer, 2011.

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Wurzer, Gabriel, Kerstin Kowarik, and Hans Reschreiter. Agent-Based Modeling and Simulation in Archaeology. Springer, 2014.

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Wurzer, Gabriel, Kerstin Kowarik, and Hans Reschreiter. Agent-based Modeling and Simulation in Archaeology. Springer, 2016.

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Book chapters on the topic "Agent-based data simulation"

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Mocko, Martin, and Jakub Ševcech. "Simulation of Bank Transaction Data." In Multi-Agent-Based Simulation XIX, 99–114. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22270-3_8.

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Sklar, Elizabeth, and Ilknur Icke. "Using Simulation to Evaluate Data-Driven Agents." In Multi-Agent-Based Simulation IX, 71–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01991-3_6.

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Parsons, Bridgette, José M. Vidal, Nathan Huynh, and Rita Snyder. "Automatic Generation of Agent Behavior Models from Raw Observational Data." In Multi-Agent-Based Simulation XV, 121–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14627-0_9.

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Drchal, Jan, Michal Čertický, and Michal Jakob. "Data Driven Validation Framework for Multi-agent Activity-Based Models." In Multi-Agent Based Simulation XVI, 55–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31447-1_4.

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Sibertin-Blanc, Christophe, and Nathalie Villa-Vialaneix. "Data Analysis of Social Simulations Outputs - Interpreting the Dispersion of Variables." In Multi-Agent-Based Simulation XV, 133–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14627-0_10.

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Swarup, Samarth, and Reza Rezazadegan. "Constructing an Agent Taxonomy from a Simulation Through Topological Data Analysis." In Multi-Agent-Based Simulation XX, 1–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60843-9_1.

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Tufail, Muhammad, Frans Coenen, and Tintin Mu. "Extracting Movement Patterns from Video Data to Drive Multi-Agent Based Simulations." In Multi-Agent Based Simulation XVII, 128–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67477-3_7.

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Hassan, Samer, Juan Pavón, Luis Antunes, and Nigel Gilbert. "Injecting Data into Agent-Based Simulation." In Simulating Interacting Agents and Social Phenomena, 177–91. Tokyo: Springer Japan, 2010. http://dx.doi.org/10.1007/978-4-431-99781-8_13.

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Hassan, Samer, Luis Antunes, and Millán Arroyo. "Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution." In Multi-Agent-Based Simulation IX, 167–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01991-3_13.

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Genç, Zülküf, Michel Oey, Hendrik van Antwerpen, and Frances Brazier. "Dynamic Data-Driven Experiments in the Smart Grid Domain with a Multi-agent Platform." In Multi-Agent Based Simulation XVI, 121–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31447-1_8.

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Conference papers on the topic "Agent-based data simulation"

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Champagne, Lance E., and Raymond R. Hill. "Agent-model validation based on historical data." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419725.

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"Data Driven Simulation Modeling for Mobile Agent-based Systems." In 2016 Spring Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2016. http://dx.doi.org/10.22360/springsim.2016.tmsdevs.053.

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"Data-Driven Customer Behaviour Model Generation For Agent Based Exploration." In 2016 Spring Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2016. http://dx.doi.org/10.22360/springsim.2016.anss.060.

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Ying, Shen, Lin Li, and Yurong Gao. "Pedestrian simulation and distribution in urban space based on visibility analysis and agent simulation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837842.

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"Big Data, Agents and Machine Learning: Towards a Data-Driven Agent-Based Modeling Approach." In 2018 Spring Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2018. http://dx.doi.org/10.22360/springsim.2018.anss.021.

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Baggio, Jacopo A., and Marco A. Janssen. "Comparing agent-based models on experimental data of irrigation games." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721555.

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Yamane, Shohei, Kotaro Ohori, Hiroaki Yamada, Hiroaki Yoshida, and Hirokazu Anai. "Automatic and dynamic grounding method based on sensor data for agent-based simulation." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248216.

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Scogings, Chris J., and Ken A. Hawick. "Optimal Data Structures for Spatially Localised Agent-based Automata and Hybrid Systems." In Applied Simulation and Modelling. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.777-055.

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Zehe, Daniel, Vaisagh Viswanathan, Wentong Cai, and Alois Knoll. "Online Data Extraction for Large-Scale Agent-Based Simulations." In SIGSIM-PADS '16: SIGSIM Principles of Advanced Discrete Simulation. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2901378.2901384.

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Wang, Minghao, and Xiaolin Hu. "Data assimilation in agent based simulation of smart environment." In the 2013 ACM SIGSIM conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2486092.2486145.

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Reports on the topic "Agent-based data simulation"

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Vecherin, Sergey, Derek Chang, Emily Wells, Benjamin Trump, Aaron Meyer, Jacob Desmond, Kyle Dunn, Maxim Kitsak, and Igor Linkov. Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43740.

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Abstract:
The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. This paper describes a novel model for COVID-19 infection risks and policy evaluations. The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific interagent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. The application of the model is demonstrated for a typical office environment and for a real-world case. The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.
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