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Cao, Sen. "Role-based and agent-oriented teamwork modeling." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2540.
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Teamwork has become increasingly important in many disciplines. To support
teamwork in dynamic and complex domains, a teamwork programming language and a
teamwork architecture are important for specifying the knowledge of teamwork and for
interpreting the knowledge of teamwork and then driving agents to interact with the
domains. Psychological studies on teamwork have also shown that team members in an
effective team often maintain shared mental models so that they can have mutual
expectation on each other. However, existing agent/teamwork programming languages
cannot explicitly express the mental states underlying teamwork, and existing
representation of the shared mental models are inefficient and further become an
obstacle to support effective teamwork. To address these issues, we have developed a
teamwork programming language called Role-Based MALLET (RoB-MALLET) which
has rich expressivity to explicitly specify the mental states underlying teamwork. By
using roles and role variables, the knowledge of team processes is specified in terms of
conceptual notions, instead of specific agents and agent variables, allowing joint
intentions to be formed and this knowledge to be reused by different teams of agents.
Further, based on roles and role variables, we have developed mechanisms of task
decomposition and task delegation, by which the knowledge of a team process is
decomposed into the knowledge of a team process for individuals and then delegate it to
agents. We have also developed an efficient representation of shared mental models
called Role-Based Shared Mental Model (RoB-SMM) by which agents only maintain
individual processes complementary with others?? individual process and a low level of
overlapping called team organizations. Based on RoB-SMMs, we have developed tworeasoning mechanisms to improve team performance, including Role-Based Proactive
Information Exchange (RoB-PIE) and Role-Based Proactive Helping Behaivors (RoBPHB).
Through RoB-PIE, agents can anticipate other agents?? information needs and
proactively exchange information with them. Through RoB-PHB, agents can identify
other agents?? help needs and proactively initialize actions to help them. Our experiments
have shown that RoB-MALLET is flexible in specifying reusable plans, RoB-SMMs is
efficient in supporting effective teamwork, and RoB-PHB improves team performance.
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NOYA, RICARDO CHOREN. "A MODELING LANGUAGE FOR AGENT BASED SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5304@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOA tecnologia de agentes tem ganho destaque, tanto na academia quanto na indústria, no que se refere ao desenvolvimento de sistemas distribuídos. Linguagens de modelagem e processos de desenvolvimento foram criados para documentar e formalizar o desenvolvimento de sistemas baseados em agentes. Entretanto, as linguagens de modelagem existentes ou se baseiam em noções de uma tecnologia de desenvolvimento anterior (orientação a objetos) ou não possuem artefatos (modelos) que denotam as características de agência existentes no sistema. Esta tese propõe uma linguagem de modelagem para sistemas baseados em agentes, a LM-SMA, que gera artefatos (modelos) que mostram a modelagem de aspectos de agência, tais como adaptação, aprendizado, interação e autonomia. A LM-SMA ainda possui artefatos que permitem a modelagem da parte do sistema que não é composta por agentes, por meio de ontologias.
The agent technology is gaining acceptance, both in academy and industry, with regards to distributed systems development. Modeling languages and development processes were created to formalize the development of agent based systems. Nevertheless, existing modeling languages are either based on previous development methods (object oriented) or they do not have artifacts (models) that show the agency characteristics that exist in a system. This thesis proposes a modeling language, for agent based systems, that generates artifacts that model agency aspects, such as adaptation, learning, interaction and autonomy. The language has artifacts that allow the modeling of the non- agent part of an agent based system, using ontology.
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Sapkota, Pratibha. "Modeling Diffusion Using an Agent-Based Approach." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1270659453.
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VALLURUPALLI, VAISHALI. "AGENT BASED MODELING OF A BIO-INVERTER." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172264092.
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Dickie, Alistair James. "Modeling robot swarms using agent-based simulation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FDickie.pdf.
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Hajhashemi, Elham. "Agent-based Modeling for Recovery Planning after Hurricane Sandy." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85012.
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Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. This research used survey data from 397 respondents in the NYC Metropolitan Area to develop an agent-based model for capturing commuter behavior and adaptation after the disruption. Six different recovery scenarios were tested to find which systems are more critical to recover first to promote a faster return to productivity. Important factors in the restoration timelines depends on the normal commuting pattern of people in that area. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. When the subway/rail system recovers earlier (with the associated power), more people are able to travel to work and be productive. The second important factor is school and daycare closure (with the associated power and water systems). Parents cannot travel unless they can find a caregiver for their children, even if the transportation system is functional. Therefore, policy makers should consider daycare and school condition as one of the important factors in recovery planning. The next most effective scenario is power restoration. Telework is a good substitute for the physical movement of people to work. By teleworking, people are productive while they skip using the disrupted transportation system. To telework, people need power and communication systems. Therefore, accelerating power restoration and encouraging companies to let their employees' telework can promote a faster return to productivity. Finally, the restoration of major crossings like bridges and tunnels is effective in the recovery process.Master of Science
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Zhou, Feng. "Application of agent based modeling to insurance cycles." Thesis, City University London, 2013. http://openaccess.city.ac.uk/12195/.
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Traditional models of analyzing the general insurance market often focus on the behavior of a single insurer in a competitive market. They assume that the major players in this market are homogeneous and have a common goal to achieve a same long-term business objective, such as solving profit (or utility) maximization. Therefore these individual players in the traditional models can be implemented as a single representative economic agent with full rationality to solve the utility optimization. To investigate insurance pricing (or underwriting) cycles, the existing literature attempts to model various isolated aspects of the market, keeping other factors exogenous. We and that a multi-agent system describing an insurance market affords a helpful understanding of the dynamic interactions of individual agents that is a complementary to the traditional models. Such agent-based models (ABM) try to capture the complexity of the real world. Thus, economic agents are heterogeneous and follow divergent behavioral rules depending on their current unique competitive situations or comparative advantages relating to, for example, their existing market shares, distribution channels, information processes and product differentiations. The real-world continually-evolving environment leads agents to follow common rules of thumb to implement their business strategies, rather than completely be utility-maximizer with perfect foresight in an idealized world. The agents are adaptively learning from their local competition over time. In fact, the insurance cycles are the results of these dynamic interactions of agents in such complex system.
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Vuorinen, T. (Tapio). "Simulating project network governance using agent based modeling." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201612023187.
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Project management literature has previously mainly focused on the technical aspects of project delivery. Less focus has been on the complex inter-connected relations between different organizational and individual actors in project networks. One of the newer approaches to project management research is project network governance. Its aim is to provide a framework for controlling, rewarding and enabling collaboration between organizational actors in project networks. Project management literature has shied away from using simulation as a research method for various reasons. However, at some point in the future problems can become increasingly complex and traditional research methods can be ineffective. Additionally, to research different phenomena regarding complex networks, be it between organizations, actors or project alliances, traditional research methods can fail altogether due to sheer scope. This is where simulation can come in. Discrete event, system dynamics and agent based simulation are the most used paradigms today. Three of the most prevalent are discrete event, system dynamics and agent based modeling. Agent based simulation is a powerful tool for discovering emergent behavior using autonomous agents. Agent based modeling relies on the different characteristics of agents — learning ability, number of connections to other agents and individual behavioral guidelines. This approach enables modeling of emergent behavior without knowledge of exact processes or structures — the agents can be made capable of creating all of these, even with simple algorithms. In project management context, these agents can be individual persons, organizations or projects. The levels and layers of detail are left for the modeler. An agent based model could be about a set of inter-connected organizations working on a project based on different inputs. The organizations could comprise different individual actors with their own volition. Project could consist of different inter-connected tasks with varying requirements and goals. The ensuing outcome of the project could be for example be influenced by different project network governance mechanisms. That is what this thesis is about. The goal of the thesis was to develop a simulation model which could be used to simulate project network governance. Following research questions were answered: RQ1: What are the most significant characteristics of project network and task network? RQ2: What are different mechanisms of project network governance? RQ3: What are different stages in the simulation model development? RQ4: What are the strengths and weaknesses of different modeling paradigms in simulating project networks? An agent based model was successfully developed using Anylogic software and following the simulation model development process. It was verified that agent based modeling can be used to simulate project networks and project network governance. Next step would be to validate the results using inputs gathered from real world dataset. The developed model could also be further enhanced by implementing more project network governance mechanisms or including multiple concurrent projects and resource constraints. Agent based modeling provides a powerful platform for experimenting and exploringProjektikirjallisuus on perinteisesti tarkastellut lähinnä projektien teknistä toteutusta. Vähemmän voimavaroja on kohdistettu kompleksisten projektiverkostojen sekä organisaatioden ja yksilöiden välillä olevien suhteiden tutkimukseen. Yksi projektimaailman uusimmista tutkimusalueista on projektiverkoston hallinta. Sen tarkoitus on tuottaa viitekehys, jolla projektiverkostoa voidaan esimerkiksi kontrolloida, palkita ja projektiverkoston osallistujien välistä yhteistyötä parantaa. Projektitutkimus on myös karttanut simulaatioiden käyttämistä tutkimusmetodina monestakin eri syystä. On kuitenkin todettava, että tulevaisuudessa ongelmat saattavat muuttua yhä monimutkaisimmiksi, jolloin perinteisemmät metodit voivat olla tehottomia. Kompleksisten verkostojen ja suhteiden tutkiminen perinteisin keinoin saattaa olla jopa mahdotonta silkan koon vuoksi. Simulaatiot ovat hyvä työkalu tilanteissa, joissa oikeaa systeemiä on hankala tutkia sellaisenaan. Nykyään prosessipohjainen, systeemidynaaminen ja agenttipohjainen mallinnus ovat kolme käytetyintä simulaatiometodia. Kolmesta vallitsevasta mallinnustavasta, agenttipohjainen mallinnus on uusin, mutta samalla myös joustavin. Agenttipohjainen mallinnus on tehokas työkalu emergentin käytöksen tutkimiseen autonomisten agenttien avulla. Agenttipohjaisen mallinnuksen taustalla ovat yksittäisten agenttien ominaisuudet — ne voivat oppia, niillä on erilaisia suhteita ympäristöönsä ja kullakin voi olla yksilölliset käyttäytymissäännöt. Täten emergenttiä käytöstä voidaan tutkia tuntematta järjestelmän rakennetta tai prosesseja — agentit kykenevät luomaan nämä itsenäisesti, jopa yksinkertaisten sääntöjen avulla. Projektikontekstissa agentit voivat olla yksittäisiä henkilöitä, organisaatioita tai vaikkapa projektialliansseja. Tarkkuus ja eri tasojen määrä jääköön mallintajan päätettäväksi. Esimerkiksi agenttipohjainen malli voisi olla useita organisaatioita työskentelemässä yhteisen projektin eteen erilaisista lähtökohdista. Organisaatiot voisivat koostua erilaisista yksilöistä, joilla on kyky toimia itsenäisesti. Projekti voisi koostua erilaisista tehtävistä, joilla on eriasteisia vaatimuksia ja päämääriä. Projektin lopputulos voisi määräytyä projektiverkoston hallinnan eri mekanismien vaikutuksesta. Tämän työn tavoitteena on tuottaa edellä kuvatun kaltainen malli. Työn tavoitteena on rakentaa simulaatiomalli projektiverkoston hallinnan simulointia varten. Työssä vastattiin seuraaviin tutkimuskysymyksiin: RQ1: Mitkä ovat projekti- ja tehtäväverkon tärkeimmät ominaisuudet? RQ2: Mitkä ovat projektiverkoston hallinnan eri mekanismit? RQ3: Mitkä ovat simulaatiomallin kehityksen eri vaiheet? RQ4: Mitkä ovat eri simulaatiometodien vahvuudet ja heikkoudet projektiverkoston hallinnan simuloinnissa? Kysymyksiin vastaamisen ohella, agenttipohjainen malli luotiin onnistuneesti käyttäen Anylogic-ohjelmistoa ja seuraten kirjallisuudesta perustuvaa simulaatiomallin kehitysprosessia. Agenttipohjainen malli verifioitiin toimivaksi projektiverkostojen ja projektiverkoston hallinnan simulointiin. Seuraava vaihe olisi validoida malli käyttäen oikeasta maailmasta saatua dataa. Lisäksi luotuun malliin voitaisiin tulevaisuudessa lisätä joko uusia projektiverkoston hallinnan mekanismeja tai samanaikaisia projekteja resurssirajoitteisilla tehtävillä. Agenttipohjainen mallinnus sopii tähän tarkoitukseen
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Waghray, Rasagnya. "EXPLORING REDUCED TRAFFIC SIGNALS USING AGENT BASED MODELING." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/372.
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The need for measures to reduce congestion in metropolitan traffic has been a pressing concern as citizens' cluster in larger cities with the immediate side effect of an increase in traffic demand. A functioning society depends on the mobility provided by the transportation network to enable its members to participate in essential activities such as production, consumption, communication, and recreation. However, it is necessary for a society to introduce congestion-relief measures for improved quality of life, the environment, and maintained safety of the citizens. The project has three components: 1. An interface, which models a road network and tools to describe data supplied to the network. 2. A simulation interface to observe the model run through time and produce suitable results for the naked streets and to find an improved traffic simulation for the cities. 3. Mathematical modeling for assessment of the pedestrian accident risk and their safety. The traffic is designed and implemented using agent-based modeling (ABM) techniques and I have used NetLogo as my testbed (Wilensky, 2003). Shared Space is not defined by the design or configuration of the environment. Design and detailing are important, but only as a catalyst for changing the way in which people interact within the public spaces. Design standards are not adequate. Even if you follow all design guidelines, it does not guarantee that the space will meet the requirements. Departing from established practice requires determination, careful thought and observation, and the courage to explore and refine novel solutions.
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Demianyk, Bryan C. P. "Development of agent-based models for healthcare: applications and critique." IEEE, 2010. http://hdl.handle.net/1993/31049.
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Agent-based modeling (ABM) is a modeling and simulation paradigm well-suited to social systems where agents interact and have some degree of autonomy. In their most basic sense, ABMs consist of agents (generally, individuals) interacting in an environment according to a set of behavioural rules. The foundational premise and the conceptual depth of ABM is that simple rules of individual behaviour will aggregate to illuminate complex and/or emergent group-level phenomena that are not specifically encoded by the modeler and that cannot be predicted or explained by the agent-level rules. In essence, ABM has the potential to reveal a whole that is greater than the sum of its parts. In this thesis, ABMs have been utilized as a modeling framework for three specific healthcare applications, including:
• the development of an ABM of an emergency department within a hospital allowing the modeling of contact-based infectious diseases such as influenza, and simulating various mitigation strategies;
• the development of an ABM to model the effectiveness of a real-time location system (RTLS) using radio frequency identification (RFID) in an emergency department, used for patient tracking as one measure of hospital efficiency; and,
• the development of an ABM to test strategies for disaster preparedness (high volume, high risk patients) using a fictitious case of zombies in an emergency department.
Although each ABM was purposeful and meaningful for its custom application, each ABM also represented an iteration toward the development of a generic ABM framework. Finally, a thorough critique of ABMs and the modifications required to create a more robust framework are provided.February 2016
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Unrath, Craig S. "Dynamic exploration of helicopter reconnaissance through agent-based modeling." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA383393.
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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, Sept. 2000.Thesis advisor(s): Gaver, Donald ; Hiles, John ; Jacobs, Patricia A. "September 2000." Includes bibliographical references (p. 109-110). Also available in print.
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Chen, Hong. "Modeling information quality in agent-based e-commerce systems." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28167.
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In agent-based e-commerce systems, most buying agents rely primarily on ranking algorithms to make purchase decisions. These algorithms are based on one or a combination of several features of goods information, such as price, seller's reputation, etc.; however, information quality is usually ignored. This leads to the problem that product information is retrieved without proper regard to their quality. Realizing the importance and necessity of information quality for buying agents, this thesis introduces an Information Quality (IQ)-based ranking approach. A model which combines information quality assessment with user expectation is proposed. An approach on representation and interpretation of information quality is also developed. Two sets of experiments are conducted to verify the applicability of the proposed model and explore the effectiveness of individual information quality dimensions. The experimental results show that information quality based ranking does lead to better user satisfaction.
Keywords: Information quality, agent system, e-commerce
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Ayub, Yasir, and Usman Faruki. "Container Terminal Operations Modeling through Multi agent based Simulation." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5549.
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This thesis aims to propose a multi-agent based hierarchical model for the operations of container terminals. We have divided our model into four key agents that are involved in each sub processes. The proposed agent allocation policies are recommended for different situations that may occur at a container terminal. A software prototype is developed which implements the hierarchical model. This web based application is used in order to simulate the various processes involved in the following operations on the marine side in a case study of a container terminal in Sweden by adopting a multi-agent based simulation technique. Due to the increase in usage of container transportation, container terminals are experiencing difficulties in the management of the operations. The software provides a decision support capability to terminal managers for scheduling and managing the operations effectively while also visually presenting the time it takes to complete the process and its associated cost. Terminal managers need to implement certain policies to improve the management and operations of the container terminal. The policies are evaluated and tested under various cases to provide a more comparative overview. The results of the simulation experiments indicate that the waiting time for arriving vessels is decreasing when in queue with more than three vessels arriving on same day.Simulation is a good technique that help analyst to take decision considering each factor of problem that is to be simulated. Simulation in addition with multi agent environment provides better understanding of modeling the entities. The complexity of CT environment and multiple involvement of agents simultaneously enables CT suitable domain for multi agents. We have modeled the four CT operations that are carried out at each CT. These operations are modeled in hierarchical sequence like berth allocation, QCs allocation, Transport vehicles allocation and YCs allocation. The most important factor of simulation is the measurement of dynamic time of each operation. We have simulated and compared different agents active time and service time compared with associated cost. The berth allocation is very important asset from all the operations that are carried out at the CTs. The effective utilization of FCFS berth allocation policy reduces the vessel waiting time in waiting queue. The developed terminal simulator tool allocates all resources dynamically while looking at the number of containers that will be loaded and unloaded at QS and yard storage area. The result of simulation tool presents the good dynamic allocation of transport vehicles. The dynamic resource allocation helps to minimize the congestion and bottlenecks that may occur at CTs. The result of three experiments depicts that the berth allocation and agent allocation is improved and vessel service time is reduced at berth side which automatically reduces vessels waiting time in queue. Besides this the transport allocation and YCs allocation is dynamically assigned by looking at number of containers in the vessels. The terminal simulator helps managers to analyze the simulated results and take better decision at hand.
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Visudhiphan, Poonsaeng 1973. "An agent-based approach to modeling electricity spot markets." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28270.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 323-327).
(cont.) The model could also be used to analyze market factors (such as new market rules) and their effects on market price dynamics and market participants' behaviors, as well as to identify the "best" response action of one participant against the opponents' actions.
Current approaches used for modeling electricity spot markets are static oligopoly models that provide top-down analyses without considering dynamic interactions among market participants. This thesis presents an alternative model, an agent-based model, and uses it to analyze the markets under various conditions. These markets, in which the participants engage in sealed-bid auctions to sell and/or buy electricity regularly, are viewed as multiagent systems, or as repeated games, played by participants with incomplete information. To represent these market characteristics, the agent-based model is selected, consisting of several power-producing agents with non-uniform portfolios of generating units. These agents employ learning algorithms, including Auer et al. 's, softmax action selection, or Visudhiphan and IliC's model-based algorithms, in determining bid-supply functions from available information. The simulated outcomes from the agent-based model depend on the choice of non-uniform portfolios and on the learning algorithms that the agents employ. Model verifications against the actual markets are suggested; however, due to a lack of certain confidential information, numerical examples cannot be presented. Nevertheless, the model is used to analyze the effects of market structures and the effect of load-serving entities on the power-producer bidding behavior and market outcomes. This model could provide one of the main tools for regulators, system planners, and market participants to use scenario simulations to investigate market conditions that could lead to high electricity prices.
by Poonsaeng Visudhiphan.
Ph.D.
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Wang, Connie Hou-Ning. "Agent-Based Overlapping Generations Modeling for Educational Policy Analysis." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4112.
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Educational systems are complex adaptive systems (CAS). The macroeffects of an educational policy emerge from and depend on individual students' reactions to the policy. However, educational policymakers traditionally rely on equation-based models, which are deficient in reflecting the work of microbehaviors. Using inappropriate tools to make policies may be a reason why there were many unintended educational consequences in history. A proper methodology to design and analyze policies for complex educational systems is agent-based modeling (ABM). Grounded in the theories of CAS and computational irreducibility, ABM is capable of connecting microbehaviors with macropatterns. The purpose of this study was to contribute to the application of ABM in educational policy analysis by constructing an agent-based overlapping generations model with hypothesized inputs to qualitatively represent the environment of the Taipei School District. Four research questions explored the effects of Taipei's 2016 student-assignment mechanism and its free tuition policy on educational opportunity and school quality under different assumptions of students' school-choice strategies. The simulated outputs were analyzed using descriptive statistics and paired samples t tests. The findings, which could hardly be revealed by traditional models, showed that the effects were complex and depended on students' strategies along with the number of choices students were allowed to make; the assignment outcomes for elite students were robust to the mechanism, and the free tuition policy worsened school quality. Although exploratory, these findings can serve as hypotheses and a guide for Taipei's policymakers to collect empirical data in evaluating their 2016 mechanism and tuition policy.
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Simakova, Irina. "Agent-Based Modeling Exploring Sovereign CDS Spreads in Europe." Doctoral thesis, Università degli studi di Bergamo, 2018. http://hdl.handle.net/10446/105203.
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In this thesis, we systematize the studies of an agent-based modeling in finance. We introduce the application of the agent-based modeling to the detection of the linkage between two complex systems on the macro- and micro-levels – the European market of sovereign Credit Default Swaps (macro-level) and the Italian equity market (micro-level). To execute this, we develop a framework for the capacity to slave the simulated dynamics of the first market to the changes in the dynamic direction of the second market in a long term by the transformation of the $-game slaving algorithm in the part of the choice of the optimal strategy. Moreover, by means of the Tremor Price Dynamics, we explain the practicality of the global market dynamics for the predictions of the local market fluctuations. Then, we use the effect of decoupling to obtain the knowledge about the time of (negative) bubble occurrence in the European market of sovereign CDS, and on its base we divide analytically the time series of two markets into separate bubble-periods. As a result, we obtain the information confirming the lead-lag linkage between the European market of sovereign Credit Default Swaps and the Italian equity market which could not been caught by means of the standard approach. Finally, we demonstrate a simple example of how to make the predictions of the changes in the dynamic direction of the lagged local market on the base of the bubble-periods appearing on the leading global market.
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COCCO, LUISANNA. "Complex system simulation: agent-based modeling and system dynamics." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266241.
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This thesis deals with some simulation based approaches used to study software market and software development. Nowadays, the term Software as a Service is everywhere and is described as the future of software. SaaS, also called On-Demand Software, is a software application delivery
model that together with Commercial Open Source Software another pricing approach is slowly gaining ground. Indeed, in recent years, traditional software also called On-Premise software appears overpriced, user's willingness to buy it is decreased and therefore the purchase preferences
are moving from traditional pricing models to new pricing approaches. To study these new pricing tendencies, different models have been realized by using two of the most common numerical techniques: Agent based Modeling and System Dynamics. With agent based modeling two business models have been realized: a model to study the
competition among CRM On-Premise and On Demand vendors and another model to study the competition among CRM On-Demand vendors offering CRM products, with and without source
code availability. Our goals are to propose business models to analyze and study the CRM software market, and to propose a useful tool to forecast business winning strategy and investment and pricing business policies.
Instead, with system dynamics a tool for highlighting how a Global Software Development environment on the Cloud Platform may facilitate GSD with respect to an environment set up On Premise has been realized.
All these models are based on many insights from literature and market analysis. However, concerning the business models, this is the first time that the software market has been modeled using heterogeneous agent model and detailing investment and pricing policies of firms and purchase preferences of customers, and consequently building the model on existing scientific knowledge has not been simple. In addition, lack of experimental data to initialize or validate the models clearly limits the validity of the models, and for this reason the future main objective will be to validate the model using real enterprise data.
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Yereniuk, Michael A. "Global Approximations of Agent-Based Model State Changes." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-dissertations/614.
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How can we model global phenomenon based on local interactions? Agent-Based (AB) models are local rule-based discrete method that can be used to simulate complex interactions of many agents. Unfortunately, the relative ease of implementing the computational model is often counter-balanced by the difficulty of performing rigorous analysis to determine emergent behaviors. Calculating existence of fixed points and their stability is not tractable from an analytical perspective and can become computationally expensive, involving potentially millions of simulations. To construct meaningful analysis, we need to create a framework to approximate the emergent, global behavior. Our research has been devoted to developing a framework for approximating AB models that move via random walks and undergo state transitions. First, we developed a general method to estimate the density of agents in each state for AB models whose state transitions are caused by neighborhood interactions between agents. Second, we extended previous random walk models of instantaneous state changes by adding a cumulative memory effect. In this way, our research seeks to answer how memory properties can also be incorporated into continuum models, especially when the memory properties effect state changes on the agents. The state transitions in this type of AB model is primarily from the agents’ interaction with their environment. These modeling frameworks will be generally applicable to many areas and can be easily extended.
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WU, JIUN YAN. "Applications of Agent-Based Modeling and Simulation in Organization Management." Kyoto University, 2020. http://hdl.handle.net/2433/258985.
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GALLI, EMANUELE. "Agent based modeling and simulation for critical and interdependent systems." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/1324.
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Lo studio delle infrastrutture critiche è un campo relativamente nuovo nella ricerca scientifica e che trova solo recentemente una precisa collocazione nelle differenti aree di ricerca, ovvero la cosiddetta “Protezione delle infrastrutture critiche“ (CIP).
Considerando la classificazione di Lewis1, la CIP racchiude sette problematiche ancora da risolvere e che rappresentano le sfide attuali e future per gli scienziati interessati a questa area di ricerca. I problemi considerati riguardano: 1) la vastezza del problema, ovvero la grande dimensione del problema rende impossibile la protezione di tutte le infrastrutture; 2) chi deve prendere le decisioni finali per risolvere una eventuale “catastrofe”; 3) la condivisione dei dati ovvero l’assenza di un modello standard per lo scambio dei dati rende quest’ultimi completamente incompatibili tra di loro; 4) la conoscenza totale del problema, poiché è composto da domini e tecnologie completamente differenti tra di loro; 5) lo studio delle interdipendenze, in quanto ogni infrastruttura ha dipendenze dirette o indirette con le altre; 6) strumenti inadeguati, ovvero l’assenza di un approccio generale che si possa applicare a qualsiasi caso di studio; 7) conflitto asimmetrico, piccoli attacchi o danni possono causare grandi disastri.
Tali problematiche hanno attratto anche noi, in modo specifico nello studio delle interdipendenze umane e fisiche, nella loro valutazione e quantificazione. In particolare, obiettivo di questa tesi è stato quello di fornire un framework per la simulazione e analisi delle interdipendenze fisiche, geografiche, informatiche e temporali. A tale fine è stato definito un nuovo approccio che si basa sulla modellazione e simulazione ad agenti insieme alla teoria e architettura della simulazione distribuita, la quale permette il riutilizzo di simulatori già implementati come pure l’incremento delle performance e il bilancio del carico.
Inoltre abbiamo utilizzato un ulteriore approccio alternativo basato sulla micro-simulazione atto allo studio delle interdipendenze con maggiore dettaglio. Per raggiungere tale scopo, è stato anche implementato un simulatore del traffico cittadino basato sulla simulazione parallela e ad eventi discreti al fine di simulare milioni di utenze e valutare come e quali infrastrutture sono influenzate dalla rete dei trasporti.
In conclusione possiamo dire che i maggiori contributi di questa tesi sono stati i seguenti:
Dopo un attento studio delle metodologie già presenti in letteratura, abbiamo introdotto un nuovo modello per simulare e analizzare le infrastrutture critiche il quale utilizza la modellazione e simulazione ad agenti. Un agente è una entità che ha uno specifico comportamento il quale può interagire con l’ambiente in cui vive e con gli altri agenti al fine di raggiungere un obiettivo comune. La modellazione e simulazione ad agenti è stata utilizzata per definire le interdipendenze fisiche, logiche e geografiche, dirette e/o indirette.
Abbiamo utilizzato le tecniche di simulazione distribuita e parallela al fine di riutilizzare sia i simulatori dei domini specifici già implementati sia per distribuire il carico e incrementare allo stesso tempo le performance. Tali caratteristiche permettono di simulare grandi scenari composto da migliaia e migliaia di componenti e molteplici infrastrutture allo stesso tempo. A tale scopo, abbiamo utilizzato lo standard di simulazione distribuita High Level Architecture così che il framework possa essere flessibile nella introduzione di nuovi simulatori di domini specifici. Con tale framework è stato possibile simulare le interdipendenze informatiche e quelle fisiche.
Abbiamo utilizzato la rappresentazione standard per la geoferenzazione degli oggetti al fine di creare scenari realistici e riutilizzare i dati delle differenti compagnie che offrono i servizi definiti critici.
Abbiamo anche considerato il workload generato dalle persone durante le loro regolari attività giornaliere sulla rete delle infrastrutture. La creazione del workload durante eventi catastrofici è ancora un problema aperto.
Abbiamo sviluppato un micro-simulatore parallelo e scalabile per la rete dei trasporti che utilizza un modello basato sulla rete di code ad eventi discreti, il quale utilizza il workload generato dal simulatore delle attività giornaliere.
Abbiamo definito alcune nuove metriche per misurare le interdipendenze dirette e indirette applicandole ai dati collezionati dai simulatori sviluppati. Tali metriche possono essere di grandi aiuto per coloro che devono gestire e prendere importanti decisioni al fine di prevenire catastrofi, ridurre il rischio di minacce e limitare i danni nel minor tempo possibile.The research community has been just recently attracted by the study of critical infrastructure. All related topics can be grouped in the so called "critical infrastructure protection" (CIP). Lewis1 defines the study of CIP as "the study of challenges to be met and solutions to be found". He also divides the challenges of CIP in seven possible categories, which are: 1. Vastness: related to the vastness of problem, which renders impractical to protect all infrastructures; 2. Command: associated to the problem to define who takes the last decision; 3. Information Sharing: the absence of a clear way to share and distribute information among different infrastructures made data completely incompatible; 4. Knowledge: every infrastructure has its own domain and technology, so it is very hard to have a whole knowledge of a so vast complex system; 5. Interdependencies: every infrastructure depends on many other ones directly or indirectly. Dependencies are caused by human organizational structures as well as physical linkage between components; 6. Inadequate Tools: there is not yet a general approach or tool to study critical infrastructure; 7. Asymmetric Conflict: small attack can produce big damages. Such scenario has also attracted us specifically in the study of human and physical interdependencies, their own valuation and quantification. In particular, the goal of this thesis has been to provide a framework for the simulation and analysis of physical, geographical, informational and temporal interdependencies using the agent based modeling and simulation approach with the theory and architecture of distributed simulation in order to allow the reuse of already implemented simulators as well as to increase performances and to scale the problem. We have also used the micro-simulation as an alternative approach to the study of critical infrastructure. For such aim, we have implemented a simulator of road-traffic using the parallel and discrete events approach in order to simulate daily traffic in big cities and to evaluate how other infrastructures and individuals depend on transportation system. The major contributions are as in the following: - We introduce a new model to simulate and analyze critical infrastructures and their interdependencies using the agent based modeling and simulation. An agent is an entity which has a specific behavior that can be influenced by the environment, the memory and experience of the agent, can interact with the environment and other agents (heterogeneous and homogenous) to reach the same goal, has a specific geographical position. The agent based modeling and simulation has been used to simulate and to define physical and geographical interdependencies. - We have used the parallel and distributed simulation to reuse already implemented and well-tested specific sector simulators as well as to distribute the load and increase both performances and scalability. Such characteristics allow to simulate a big scenario composed of thousands and thousands of components and multiple infrastructures at the same time. Moreover we have used a standard as the High Level Architecture so that the framework can be easily extended with new sector simulators. Information interdependency as well as physical one is simulated directly by the sector simulators. - We have used the standard representation to geo-reference objects in order to create realistic scenario and reuse real stakeholders data. - We have also considered the workload generated by the people on the infrastructure networks during their regular activities while is still a big challenge to provide the workload during catastrophic events. - We have developed a parallel and scalable micro-simulator for transportation network which uses the discrete-event queue model which uses the workload generated by the daily activities simulator. - We have introduced some new metrics to measure direct and indirect interdependencies using collected data from sector simulators. Such metrics are really helpful for managers who have to take important decisions to prevent catastrophic events and to reduce the risk of threats.
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Oremland, Matthew Scott. "Techniques for mathematical analysis and optimization of agent-based models." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/25138.
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Agent-based models are computer simulations in which entities (agents) interact with each other and their environment according to local update rules. Local interactions give rise to global dynamics. These models can be thought of as in silico laboratories that can be used to investigate the system being modeled. Optimization problems for agent-based models are problems concerning the optimal way of steering a particular model to a desired state. Given that agent-based models have no rigorous mathematical formulation, standard analysis is difficult, and traditional mathematical approaches are often intractable.
This work presents techniques for the analysis of agent-based models and for solving optimization problems with such models. Techniques include model reduction, simulation optimization, conversion to systems of discrete difference equations, and a variety of heuristic methods. The proposed strategies are novel in their application; results show that for a large class of models, these strategies are more effective than existing methods.Ph. D.
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Šalamon, Tomáš. "Development of Agent-based Models for Economic Simulation." Doctoral thesis, Vysoká škola ekonomická v Praze, 2005. http://www.nusl.cz/ntk/nusl-77101.
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This thesis is about the development of agent-based models that are a method of simulation of economic processes and environments using multi-agent systems. Agent-based modeling seems to be an unappreciated approach that is expected and has a potential for a much wider application than it actually has. The purpose of thiswork is to evaluate the reasons for such situation and to offer solutions. The following were identified among the reasons for a low utilization of the method: a wide gap between theory and practice in the field, doubtful reliability of the method, lowconfidence in its results, complexity, missing methodologies, problems with suitable development frameworks, limitations of computational performance, a lack of awareness among the public and certain other problems. Agentology; (i.e. a methodology for the development of agent-based models) was proposed in this thesis in order to address issues regarding the development of agent-based models. There are six defined roles of project participants in the methodology: expert, analyst, modeler, platform specialist, programmer and tester. The design and development process consists of four phases and nine steps beginning with task formulation, conceptual modeling, and platformspecific modeling to the development of the system. For the design phases, agent modeling language for agent-based models was derived.
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Rajendran, Balakumar. "3D Agent Based Model of Cell Growth." Cincinnati, Ohio : University of Cincinnati, 2009. http://www.ohiolink.edu/etd/view.cgi?acc_num=ucin1231358178.
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Thesis (M.S.)--University of Cincinnati, 2009.Advisors: Carla Purdy PhD (Committee Chair), Daria Narmoneva PhD (Committee Member), Ali Minai PhD (Committee Member). Title from electronic thesis title page (viewed April 30, 2009). Includes abstract. Keywords: Agent based modeling; cell growth; three dimensional. Includes bibliographical references.
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Murphy, John Todd. "Exploring Complexity in the Past: The Hohokam Water Management Simulation." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/194156.
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The Hohokam Water Management Simulation (HWM) is a computer simulation for exploring the operation of the Hohokam irrigation systems in southern Arizona. The simulation takes a middle road between two common kinds of archaeological simulation: large-scale, detailed landscape and environmental reconstructions and highly abstract hypothesis-testing simulations. Given the apparent absence in the Hohokam context of a central authority, the specific aim of the HWM is approaching the Hohokam as a complex system, using principles such as resilience, robustness, and self-organization. The Hohokam case is reviewed, and general questions concerning how the irrigation systems operated are shown to subsume multiple crosscutting and unresolved issues. Existing proposals about the relevant aspects of Hohokam society and of its larger long-term trajectory are based on widely varying short- and long-term processes that invoke different elements, draw different boundaries, and operate at different spatial and temporal scales, and many rely on information that is only incompletely available. A framework for pproaching problems of this kind is put forward. A definition of modeling is offered that specifies its epistemological foundations, permissible patterns of inference, and its role in our larger scientific process. Invoking Logical Positivism, a syntactic rather than semantic view of modeling is proposed: modeling is the construction of sets of assertions about the world and deductions that can be drawn from them. This permits a general model structure to be offered that admits hypothetical or provisional assertions and the flexible interchange of model components of varying scope and resolution. Novel goals for archaeological inquiry fall from this flexible approach; these move from specific reconstruction to a search for more universal and general dynamics. A software toolkit that embodies these principles is introduced: the Assertion-Based Computer Modeling toolkit (ABCM), which integrates simulation with the logical architecture of a relational database, and further provides an easy means for linking models of natural and social processes (including agent-based modeling). The application of this to the Hohokam context is described, and an extended example is presented that demonstrates the flexibility, utility and challenges of the approach. An attached file provides sample output.
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Qiu, Fasheng. "A Framework for Group Modeling in Agent-Based Pedestrian Crowd Simulations." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/cs_diss/56.
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Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations.
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Jansen, Luca Lena. "Energy Policy Instrument Modeling in the Agent-Based Energy Model DiDo." Thesis, KTH, Energiteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-245054.
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This work is investigating how energy policy instruments can be depicted explicitly in DiDo, the re-gional agent-based energy transition model for the Netherlands designed by the TNO. Within DiDo the transition of the energy system is considered a result of individual transformations of the different stake-holders in the system. The focus of this work lies on household agents, their investment decision making and the effects of policy instruments on this microeconomic level. Bounded rationality, heuristics and individual attitudes and perceptions are identified to play a relevant role when investing in energy assets and thus an explicit parameterization of these particularities of human behavior is proposed. The model-ing of the investment decision is following a rational approach originating from finance that features the Markowitz portfolio optimization, which is relying on the return of investments (ROI) in assets. In order to incorporate behavioral aspects and perceptions into this rational frame, the concept of subjective ROI is introduced. Arising from recent developments in behavioral economics, this concept is in line with the observations made on financial markets that challenge the long maintained assumption of normally dis-tributed ROI distributions. Finally, surveying individual willingness to pay for energy assets and testing the correlation with behavioral and policy instrument aspects using a regression analysis is proposed as the subsequent methodological step.Det här arbetet undersöker hur energipolitiska instrument kan avbildas uttryckligen i DiDo, den regionala agentbaserade energitransitionsmodellen för Nederländerna, designad av TNO. Inom DiDo anses skiftet av energisystemet vara ett resultat av individuella omvandlingar av de olika intressenterna i systemet. Inriktningen för detta arbete låg på hushållsmedlemmar, deras investeringsbeslutsfattande och effekterna av politiska instrument på denna mikroekonomiska nivå. Bundet rationalitet, heuristik och individuella attityder och uppfattningar identifierades för att spela en relevant roll när man investerar i energitillgångar och därmed föreslagits en explicit parametresering av dessa särdrag av mänskligt beteende. Modelleringen av investeringsbeslutet följer en rationell strategi som härrör från finansiering som använder Markowitz-portfölje optimering, som är beroende av avkastning av investeringar (ROI) i tillgångar. För att integrera beteendeaspekter och uppfattningar i denna rationella ram introduceras koncept av subjektivt ROI. På grund av den senaste utvecklingen inom beteendeekonomin ligger detta koncept i linje med observationerna på de finansiella marknaderna som utmanar det gammalt antagna-det om normalt fördelade avkastnings fördelningar. Slutligen föreslås undersökning av individuell vilja att betala för energitillgångar och testa korrelationen med beteendemässiga och politiska instrumentaspekter med hjälp av en regressionsanalys som det efterföljande metodologiska steget.
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Luca, Lena Jansen. "Energy Policy Instrument Modeling in the Agent-Based Energy Model DiDo." Thesis, KTH, Energiteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235814.
Full textAbstract:
This work is investigating how energy policy instruments can be depicted explicitly in DiDo, the regionalagent-based energy transition model for the Netherlands designed by the TNO. Within DiDo the transition of the energy system is considered a result of individual transformations of the different stakeholders in the system. The focus of this work lies on household agents, their investment decision making and the effects of policy instruments on this microeconomic level. Bounded rationality, heuristics and individual attitudes and perceptions are identified to play a relevant role when investing in energy assets and thus an explicit parameterization of these particularities of human behavior is proposed. The modeling of the investment decision is following a rational approach originating from finance that features the Markowitz portfolio optimization, which is relying on the return of investments (ROI) in assets. In order to incorporate behavioral aspects and perceptions into this rational frame, the concept of subjective ROI is introduced. Arising from recent developments in behavioral economics, this concept is in line with the observations made on financial markets that challenge the long maintained assumption of normally distributed ROI distributions. Finally, surveying individual willingness to pay for energy assets and testing the correlation with behavioral and policy instrument aspects using a regression analysis is proposed as the subsequent methodological step.Det här arbetet undersöker hur energipolitiska instrument kan avbildas uttryckligen i DiDo, den regionala agentbaserade energitransitionsmodellen för Nederländerna, designad av TNO. Inom DiDo anses skiftet av energisystemet vara ett resultat av individuella omvandlingar av de olika intressenterna i systemet. Inriktningen för detta arbete låg på hushållsmedlemmar, deras investeringsbeslutsfattande och effekterna av politiska instrument på denna mikroekonomiska nivå. Bundet rationalitet, heuristik och individuella attityder och uppfattningar identifierades för att spela en relevant roll när man investerar i energitillåangar och därmed föreslagits en explicit parametresering av dessa särdrag av mänskligt beteende. Modelleringen av investeringsbeslutet följer en rationell strategi som härrör från finansiering som använder Markowitz-portföljeoptimering, som är beroende av avkastning av investeringar (ROI) i tillgångar. För att integrera beteendeaspekter och uppfattningar i denna rationella ram introduceras koncept av subjektivt ROI. På grund av den senaste utvecklingen inom beteendeekonomin ligger detta koncept i linje med observationerna på de finansiella marknaderna som utmanar det gammalt antagandet om normalt fördelade avkastningsfördelningar. Slutligen freslås undersökning av individuell vilja att betala för energitillgångar och testa korrelationen med beteendemässiga och politiska instrumentaspekter med hjälp av en regressionsanalys som det efterföljande metodologiska steget.
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Foroutan, Pirouz. "Agent-based modeling of raccoon rabies epidemic and its economic consequences." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1070483886.
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Korukonda, Harika. "A Generic Agent Based Modeling Tool for Simulating Bio-Molecular Systems." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1296594324.
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Bartleson, Richard J. "Simple multi-agent cooperation an approach based on predator-prey modeling /." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000639.
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Foroutan, Pirouz. "Agent-based modeling of raccoon rabies epidemic and its economic consequences." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5fnum=osu1070483886.
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Thesis (Ph. D.)--Ohio State University, 2003.Title from first page of PDF file. Document formatted into pages; contains xiii, 130 p.; also contains graphics (some col.). Includes abstract and vita. Advisor: Mario Miranda, Dept. of Agricultural, Environmental & Development Economics. Includes bibliographical references (p. 122-126).
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Göhring, Daniel. "Constraint based world modeling for multi agent systems in dynamic environments." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2009. http://dx.doi.org/10.18452/16040.
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Die mobile Robotik stellt ein sehr junges und komplexes Forschungsfelder unserer Zeit dar. Innerhalb der letzten Jahrzehnte wurde es Robotern möglich, sich innerhalb ihrer Umgebung zu bewegen, zu navigieren und mit ihrer Umwelt zu interagieren. Aufgrund der Tatsache, dass die Welt von Unsicherheit geprägt ist und ein Roboter immer nur partielle Information über sie erhalten kann, wurden probabilistische Navigationsverfahren entwickelt, mit denen sich Roboter lokalisieren und Objekte ihrer Umgebung modellieren können. Weiterhin wurden in letzter Zeit Verfahren untersucht, die die kooperative Exploration der Umgebung durch eine Gruppe von Robotern zum Ziel haben. In der vorliegenden Arbeit wird ein neuartiges Konzept, welches sich Perzeptrelationen für die kooperative Umweltmodellierung zu Nutze macht, vorgestellt und evaluiert. Einen zweiten Beitrag der Arbeit stellen constraintbasierte Lokalisierungstechniken dar, die es einem oder mehreren Robotern auf effiziente Art und Weise ermöglichen, sich zu lokalisieren und ihre Umwelt zu modellieren.Mobile autonomous robotics is a very young and complex field of research. Only in recent decades have robots become able to explore, to move, navigate and to interact with their environment. Since the world is uncertain and since robots can only gain partial information about it, probabilistic navigation algorithms have become very popular whenever a robot has to localize itself or surrounding objects. Furthermore, cooperative exploration and localization approaches have become very relevant lately, as robots begin to act not just alone but in groups. Within this thesis a new approach using the concept of spatial percept-relations for cooperative environment modeling is presented and evaluated. As a second contribution, constraint based localization techniques will be introduced for having a robot or a group of robots efficiently localized and to model their environment.
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Tran, Trung-Minh. "Contributions to Agent-Based Modeling and Its Application in Financial Market." Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. http://www.theses.fr/2023UPSLP022.
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L'analyse de modèles complexes tels que les marchés financiers aide les gestionnaires à élaborer des politiques raisonnables et les commerçants à choisir des stratégies de négociation efficaces. La modélisation basée sur les agents est une méthodologie de calcul pour modéliser des systèmes complexes et analyser l'influence de différentes hypothèses sur les comportements des agents. Dans le cadre de cette thèse, nous considérons un modèle de marché financier qui comprend 3 types d'agents : les agents techniques, les agents fondamentaux et les agents de bruit. Nous commençons par l'agent technique avec le défi d'optimiser une stratégie de trading basée sur l'analyse technique à travers un système de trading automatisé. Ensuite, les méthodes d'optimisation proposées sont appliquées avec des fonctions objectives appropriées pour optimiser les paramètres du modèle ABM. L'étude a été menée avec un modèle ABM simple incluant uniquement des agents de bruit, puis le modèle a été étendu pour inclure différents types d'agents. La première partie de la thèse étudie le comportement commercial des agents techniques. Différentes approches sont introduites telles que : l'algorithme génétique, l'optimisation bayésienne et l'apprentissage par renforcement profond. Les stratégies de trading sont construites sur la base d'un indicateur avancé, Relative Strength Index, et de deux indicateurs retardés, Bollinger Band et Moving Average Convergence-Divergence. De multiples expériences sont réalisées sur différents marchés, notamment : le marché des crypto-monnaies, le marché boursier et le marché des contrats à terme cryptographiques. Les résultats montrent que les stratégies optimisées à partir des approches proposées peuvent générer des rendements plus élevés que leur forme typique et la stratégie Buy and Hold. En utilisant les résultats de l'optimisation des stratégies de trading, nous proposons une nouvelle approche pour optimiser les paramètres du modèle à base d'agents. La deuxième partie de la thèse présente une application de la modélisation multiagents au marché boursier. En conséquence, nous avons montré que les modèles ABM peuvent être optimisés en utilisant la méthode d'optimisation bayésienne avec plusieurs fonctions objectives. Les faits stylisés du marché réel peuvent être reproduits en construisant soigneusement les fonctions objectives de l'agent. Notre travail comprend le développement d'un environnement, les comportements des différents agents et leurs interactions. La méthode d'optimisation bayésienne avec le test de Kolmogorov-Smirnov comme fonction objective a montré des avantages et un potentiel dans l'estimation d'un ensemble optimal de paramètres pour un modèle de marché financier artificiel. Le modèle que nous proposons est capable de reproduire les faits stylisés du marché réel. En outre, un nouveau fait stylisé sur la proportion de commerçants sur le marché est présenté. Avec les données empiriques de l'indice Dow Jones Industrial Average, nous avons constaté que les traders fondamentaux représentent 9%-11% de tous les traders du marché boursier. À l'avenir, davantage de recherches seront menées pour améliorer le modèle et les méthodes d'optimisation, telles que l'application de modèles d'apprentissage automatique, l'apprentissage par renforcement multiagents ou l'examen de l'application sur différents marchés et instruments négociésThe analysis of complex models such as financial markets helps managers to make reasonable policies and traders to choose effective trading strategies. Agent-based modeling is a computational methodology to model complex systems and analyze the influence of different assumptions on the behaviors of agents. In the scope of this thesis, we consider a financial market model that includes 3 types of agent: technical agents, fundamental agents and noise agents. We start with the technical agent with the challenge of optimizing a trading strategy based on technical analysis through an automated trading system. Then, the proposed optimization methods are applied with suitable objective functions to optimize the parameters for the ABM model. The study was conducted with a simple ABM model including only noise agents, then the model was extended to include different types of agents. The first part of the thesis investigates the trading behavior of technical agents. Different approaches are introduced such as: Genetic Algorithm, Bayesian Optimization and Deep Reinforcement Learning. The trading strategies are built based on a leading indicator, Relative Strength Index, and two lagging indicators, Bollinger Band and Moving Average Convergence-Divergence. Multiple experiments are performed in different markets including: cryptocurrency market, stock market and crypto futures market. The results show that optimized strategies from proposed approaches can generate higher returns than their typical form and Buy and Hold strategy. Using the results from the optimization of trading strategies, we propose a new approach to optimize the parameters of the agent-based model. The second part of the thesis presents an application of agent-based modeling to the stock market. As a result, we have shown that ABM models can be optimized using the Bayesian Optimization method with multiple objective functions. The stylized facts of the actual market can be reproduced by carefully constructing the objective functions of the agent. Our work includes the development of an environment, the behaviors of different agents and their interactions. Bayesian optimization method with Kolmogorov-Smirnov test as objective function has shown advantages and potential in estimating an optimal set of parameters for an artificial financial market model. The model we propose is capable of reproducing the stylized facts of the real market. Furthermore, a new stylized fact about the proportion of traders in the market is presented. With empirical data of the Dow Jones Industrial Average index, we found that fundamental traders account for 9%-11% of all traders in the stock market. In the future, more research will be done to improve the model and optimization methods, such as applying machine learning models, multi-agent reinforcement learning or considering the application in different markets and traded instruments
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Gebre, Meseret Redae. "MUSE: A parallel Agent-based Simulation Environment." Oxford, Ohio : Miami University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1249053480.
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Mengistu, Dawit. "Multi-Agent Based Simulations in the Grid Environment." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00371.
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The computational Grid has become an important infrastructure as an execution environment for scientific applications that require large amount of computing resources. Applications which would otherwise be unmanageable or take a prohibitively longer execution time under previous computing paradigms can now be executed efficiently on the Grid within a reasonable time. Multi-agent based simulation (MABS) is a methodology used to study and understand the dynamics of real world phenomena in domains involving interaction and/or cooperative problem solving where the participants are characterized by entities having autonomous and social behaviour. For certain domains the size of the simulation is extremely large, intractable without employing adequate computing resources such as the Grid. Although the Grid has come with immense opportunities to resource demanding applications such as MABS, it has also brought with it a number of challenges related to performance. Performance problems may have their origins either on the side of the computing infrastructure or the application itself, or both. This thesis aims at improving the performance of MABS applications by overcoming problems inherent to the behaviour of MABS applications. It also studies the extent to which the MABS technologies have been exploited in the field of simulation and find ways to adapt existing technologies for the Grid. It investigates performance monitoring and prediction systems in the Grid environment and their implementation for MABS application with the purpose of identifying application related performance problems and their solutions. Our research shows that large-scale MABS applications have not been implemented despite the fact that many problem domains that cannot be studied properly with only partial simulation. We assume that this is due to the lack of appropriate tools such as MABS platforms for the Grid. Another important finding of this work is the improvement of application performance through the use of MABS specific middleware.
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Sopha, Bertha Maya. "Agent-Based Modeling and Simulation of Clean Heating System Adoption in Norway." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Program for industriell økologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12536.
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A sound climate policy encouraging clean energy investment is important to mitigate global warming. Previous research has demonstrated that consumer choice indeed plays an important role in adoption of sustainable technologies. This thesis strives to gain a better understanding of consumers’ decision-making on heating systems and to explore the potential application of agent-based modeling (ABM) in exploring mechanism underlying adoption in which heating system adoption by Norwegian households is taken up as a case study. An interdisciplinary approach, applying various established theories including those of psychology, is applied to create a model for consumer behavior and implement this behavior in an Agent-Based Model (ABM) to simulate heating technology diffusion. A mail-survey, carried out in autumn 2008, is a means to collect information for parameterizing the agent-based model, for gaining empirical facts, and for validating the developed model at micro-level. Survey sample consisted of 1500 Norwegian households drawn from population register and 1500 wood pellet users in Norway. The response rates were 10.3% and 34.6% for population sample and wood pellet sample respectively. This study is divided into two parts; empirical analysis and agent-based simulation. The empirical analysis aims at fully understanding the important aspects of adoptiondecision and their implications, in order to assist simulation. The analysis particularly contributes to the identification of differences/similarities between adopters and nonadopters of wood pellet heating with respects to some key points of adoption derived from different theories, psychological factors underlying the adoption-decision of wood pellet heating, and the rationales underlying Norwegian households’ decisions regarding their future heating system. The simulation study aims at exploring the mechanism of heterogeneous household decision-making giving rise to the diffusion of heating systems, and at revealing potential interventions toward wood pellet heating in Norway. A methodological approach of coupling ABM with empirical research is introduced to develop a conceptual model capturing households’ adoption-decision processes which is parameterized with empirical data. Simulation results demonstrate that the generated data from simulation is reasonably able to generate independent historical data at both macro- and micro-levels. It indicates that the proposed methodology is promising. As a whole, this thesis integrally addresses the study case using interdisciplinary perspective. The major contributions of the thesis lie in the inclusion of psychological factors, in addition to socio-demographic and technological factors, in adoption-decision, and the methodological proposal of coupling agent-based modeling (ABM) with empirical research and its application in the studied case.En virksom klimapolitikk for å stimulere til investeringer i fornybar energi er viktig for å redusere global oppvarming. Tidligere studier har vist at valg gjort av forbrukere spiller en viktig rolle for utbredelsen av bærekraftige teknologier. Denne studien søker å bedre forståelsen av hvordan forbrukere tar beslutninger angående oppvarmingssystemer og å identifisere potensielle anvendelse av agent-basert modellering (ABM) i anvendelse og diffusjon av oppvarmingssystemer. Anvendelse og diffusjon av oppvarmingssystemer blant norske husholdninger benyttes som en casestudie. Studien har en tverrfaglig tilnærming, og anvender bant annet teori fra psykologi. Agent-basert modellering benyttes for å etablere konkrete problemstillinger som skal undersøkes. En spørreundersøkelse, gjennomført høsten 2008, brukes for å samle informasjon til parameterisering av en agent-basert modell, for å etablere empiriske fakta, og for å validere den utviklede modellen på mikronivå. Undersøkelsens utvalg besto av 1500 norske husholdninger trukket fra Folkeregisteret og 1500 brukere av trepellets i Norge. Svarprosentene var henholdsvis 10,3 % og 34,6 % for utvalgene av den generelle befolkningen og trepelletsbrukerne. Denne studien er delt i to deler, empirisk analyse og agent-basert simulering. Hensikten med den empiriske analysen er å forstå viktige sider ved valg av oppvarmingssystem, og implikasjoner av disse sidene med hensyn til mulige tiltak. Den empiriske analysen understøtter slik simuleringen i andre del av studien. Et spesielt viktig bidrag fra den empiriske analysen er kunnskap om forskjeller mellom brukere og ikkebrukere av trepellets med hensyn til sentrale punkter i ulike teorier om anvendelse av teknologi, psykologiske faktorer som innvirker på utbredelsen av trepellets, og underliggende årsaker til norske husholdningenes valg av framtidig oppvarmingssystem. Simuleringen tar sikte på å utforske hvordan heterogen beslutningstaking i husholdninger henger sammen med diffusjon av oppvarmingssystemer, og avdekker mulige tiltak rettet mot trepellets i Norge. En nyskapende kobling mellom ABM og empirisk forskning muliggjør utviklingen av en konseptuel modell for husholdningers anvendelsebeslutningsprosesser. Resultater gir en demonstrasjon på at simuleringen er rimelig i stand til å reprodusere uavhengige historiske data på både makro- og mikronivå. Dette indikerer at den foreslåtte metoden for å koble ABM med empirisk forskning er lovende. Avhandlingen tar opp casestudien i et tverrfaglig perspektiv. De viktigste bidragene til avhandlingen er inkluderingen av psykologiske faktorer i anvendelsebeslutninger, i tillegg til sosio-demografiske og teknologiske faktorer, og den foreslåtte metoden for å koble agent-basert modellering (ABM) med empirisk forskning.
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Motamediyan, Farnaz. "Exploring Impacts of Project Overload on Creativity : An Agent-Based Modeling Approach." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4144.
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Project overload is an unpleasant phenomenon which is happening for employees inside those organizations trying to make the most efficient use of their resources. AE project inside VolvoCE (VCE) is an Advanced Engineering project eager to be innovative suffering from project overload. This research aims to help VCE to move towards creative organizational climate. To do this, the author used the Agent-based modeling (ABM) approach to examine the current reality of VCE and AE projects, where the opportunities and challenges for reducing the risk of project overload and moving towards innovation were identified. The results of this research allowed the researcher to define the gaps inside AE project and create a list of some recommendations. From these results: project overload can damage the employees’ focus and bring psychological stress reactions; Creative actions are less likely to be the result of a team with high level of project. On the other hand, motivation on proper challenging goal is more likely to help individual to alleviate the negative aspects of low level of project overload.
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Nguyen, Christian T. "Developing a conceptual architecture for a generalized agent-based modeling environment (GAME)." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FNguyen.pdf.
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Thesis (M.S. in Information Management Technology)--Naval Postgraduate School, March 2008.Thesis Advisor(s): Dolk, Dan. "March 2008." Description based on title screen as viewed on May 6, 2008. Includes bibliographical references (p. 57-60). Also available in print.
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El-adaway, Islam Hassan. "Construction dispute mitigation through multi-agent based simulation and risk management modeling." [Ames, Iowa : Iowa State University], 2008.
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Johnson, Peter. "Visioning local futures: agent-based modeling as a tourism planning support system." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86884.
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Often operating within a complicated, poorly understood environment, the process of tourism planning can be a difficult and challenging task. Technological aides have often been applied to the problems of planning, with varying levels of success. One computer simulation method used to study complex human social systems, agent-based modeling (ABM), is an approach that is increasingly used as a way to explore planning-relevant problems. Despite this promise, little work has explored the use of ABM to represent tourism dynamics, or as a support for planning, as evaluated by planners themselves.This research addresses these gaps in three steps: 1) by developing the concept of tourism as a phenomenon that is fundamentally individual-based. 2) Formalizing this conceptual framework into an ABM of tourism dynamics set in the tourism-centric Canadian province of Nova Scotia. 3) Providing this ABM for evaluation by tourism planning professionals as a step to identify the specific planning tasks to which this model adds greatest value and individual, technical, and organizational constraints to adoption.
The results of this research indicate that the use of an ABM-based planning support system (PSS) is strongest as a scenario development tool, providing an environment for formulating 'what if' style questions, data analysis, and a way to communicate results to community members and decision-makers. Despite these benefits, limitations to the ability of modelers to develop highly detailed and validated models emphasize the position of ABM as an emerging technology. Users also reported a lack of transparency in the model. This was attributed to design choices that were intended to make the ABM user-friendly, but also hid the functioning of the model from users. The application of ABM in planning is still a novelty, and this inhibits the confidence planners may have in using an ABM. This serves to illuminate a disconnect between the needs of the planner and what can be delivered with an ABM approach. Reducing this gap is a task that can be completed through further joint development efforts between planners and ABM modelers to more actively build on identified strengths of the ABM approach within planning.
Souvent entrepris dans un environnement complexe et mal compris, le processus de planification touristique peut s'avérer difficile et exigeant. Pour solutionner les problèmes de planification, des outils technologiques sont souvent utilisés avec plus ou moins de succès. Une méthode de simulation informatique utilisée dans l'étude des systèmes sociaux humains complexes, la modélisation ABM (agent-based modeling), est de plus en plus souvent appliquée à l'exploration des problèmes de planification. Malgré cette approche prometteuse, la modélisation ABM utilisée pour représenter la dynamique du domaine touristique, ou comme aide à la planification, a été peu étudiée et rarement évaluée par les planificateurs eux-mêmes.
La présente recherche vise à remédier à cette lacune en trois étapes : 1) en élaborant le concept de tourisme comme phénomène fondamentalement individuel ; 2) en formalisant ce cadre conceptuel par une modélisation ABM représentant la dynamique du tourisme en Nouvelle-Écosse, une province canadienne dont l'économie est centrée sur l'activité touristique ; 3) en fournissant, pour évaluation, cette modélisation ABM aux spécialistes de la planification touristique afin de savoir si elle pourrait permettre d'identifier les tâches de planification auxquelles elle apporterait une valeur ajoutée, de même que les contraintes individuelles, techniques et organisationnelles qui nuiraient à son adoption.
Les résultats de la présente recherche indiquent que l'utilisation d'un système de soutien à la planification (SSP) fondé sur l'approche de modélisation ABM est un outil de développement de scénarios plus puissant, qui fournit un environnement permettant de poser des questions du type « que faire si », d'analyser les données et de communiquer les résultats aux membres des collectivités et aux décideurs. Malgré ces avantages, la capacité limitée des modélisateurs de développer des modèles très détaillés et validés confirme que l'approche de modélisation ABM est encore au stade de technologie émergente. Les utilisateurs ont également fait état du manque de transparence du modèle résultant. Cette lacune est attribuable à des choix de modélisation initiaux visant à rendre le modèle résultant plus convivial, en masquant toutefois le fonctionnement de base de la modélisation ABM aux utilisateurs. L'application de la modélisation ABM au domaine de la planification est encore récente, ce qui nuit à la confiance des planificateurs dans l'utilisation de l'approche. Ce manque de confiance permet d'illustrer l'écart qui existe entre les besoins des planificateurs et la capacité de la modélisation ABM à les satisfaire. La réduction de cet écart est une tâche qui pourrait faire l'objet d'une nouvelle collaboration entre planificateurs et modélisateurs ABM afin de miser sur les forces de la modélisation ABM identifiées dans le domaine de la planification.
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Menth, Megan. "An agent-based modeling approach to assess coordination among humanitarian relief providers." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/32589.
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Master of ScienceDepartment of Industrial & Manufacturing Systems Engineering
Jessica L. Heier Stamm
Coordination between humanitarian organizations is critical during the response effort to a disaster, as coordinating aid improves efficiency, reduces duplication of efforts, and ultimately leads to better outcomes for beneficiaries. One particular challenge arises when temporary facilities must be established post-disaster due to the destruction of buildings. For example, the 2015 Nepal earthquakes created a need for the placement of over 4,000 temporary learning facilities after several school buildings were damaged or destroyed. It is important that humanitarians coordinate well to fill these needs efficiently and effectively, while maintaining equity among beneficiaries in the affected areas. This means ensuring that enough facilities are provided in a timely manner, and are distributed fairly to all in need. The goals of this thesis are to study coordination strategies focusing primarily on the placement of temporary educational facilities for children following a disaster. This research also aims to gather useful data by surveying active humanitarians in order to better understand their decisions made in the field. This work uses the results of this survey, along with publicly available data published after the 2015 Nepal earthquakes to create an agent-based simulation model, and uses the Nepal case study to demonstrate the efficacy of the model framework. This research finds that organizations' initial location of operation can greatly impact the number of facilities they are collectively able to establish, the geographic disparity across the region, and the organizations' utilization. Specifically, while focusing efforts on the districts with the most need is most efficient and effective, a more uniform approach yields a more equitable response. This work also finds that there can be a trade-off between overall effectiveness and the number of partnerships established in the field. These findings show a need for further study into the intricacies of coordination between humanitarian workers. This author advocates for the use of information sharing mechanisms among practitioners, as well as further utilization of agent-based modeling as a means of studying the complex nature of disaster response. Specifically there is a need to further study educational needs as a logistical problem, and strategies for solving the post-disaster facility location problem.
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Masad, David P. "Agents in Conflict| Comparative Agent-Based Modeling of International Crises and Conflicts." Thesis, George Mason University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10131444.
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Inter-state conflicts are a key area of study in international relations, and have been approached with a variety of techniques, from case studies of individual conflicts, to formal analysis of abstract models and statistical investigations of all such conflicts. In particular, there are a variety of theories as to how states make decisions in the face of conflicts – such as when to threaten force, when to follow through, and when to capitulate to an opponent’s demand. Some scholars have argued that states may be viewed as rational decisionmakers, while others emphasize the role of psychological biases affecting individual leaders. Decisionmaking is challenging to study in part because of its complexity: the decisionmakers may not just be individuals but organizations, following internal procedures and reflecting institutional memory. Furthermore, the decisions are often believed to be strategic, reflecting the decisionmakers’ anticipation of multiple other actors’ potential responses to each possible decision.
In this dissertation, I demonstrate that agent-based models (ABMs) provide a powerful tool to address this complexity, and advance their use as a bridge between different methodologies. Agents in ABMs can be used to represent countries and endowed with a variety of internal decisionmaking models which can operationalize a variety of theories drawn from case studies, psychological experiments or game-theoretic analysis. The specific decision model agents utilize may be changed without altering the sub-models governing how the agents interact with one another. This allows us to simulate the same overall interactions utilizing different decisionmaking theories and observe how the outcomes differ. Furthermore, if these interactions correspond to real-world events, we may directly see how much explanatory or predictive power the outputs of the model variants provide. If one variant’s outputs correspond closer to the empirical data, it provides evidence supporting that variant’s underlying theory.
I implement two agent-based models, extending well-established prior models of international conflict: the International Interaction Game (Bueno de Mesquita and Lalman, 1992) and the Expected Utility Model (Bueno de Mesquita, 2002). For each, I start with their original agent decisionmaking models, and develop several variants grounded in relevant theories. I then instantiate the models with historic, empirically-derived data and run them forward to generate sets of simulated outcomes, which I compare to empirical data on the relevant time periods. I find that non-rational models of decisionmaking in the International Interaction Game provide similar explanatory power to the purely rational model, and yield rich satisficing behavior absent in the original model. I also find that the Expected Utility Model variant implementing a Schelling (1966)-inspired model of coercion yields richer dynamics and greater explanatory power than the original model.
In addition to providing evidence in support of particular theories and hypotheses, this work demonstrates the power of the comparative modeling methodology in studying international conflict. Future work will involve adding more statistical controls to the model output analysis, comparative analysis between the outputs of the two overall models, and extension of the decisionmaking models for each. The same methodology may also be expanded to other formal and computational models of international relations, and social science more broadly.
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Milton, Rebecca M. "Using agent-based modeling to examine the logistical chain of the seabase." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FMilton.pdf.
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Höhme, Stefan. "Agent-based modeling of growing cell populations and the regenerating liver based on image processing." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-39562.
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In the presented thesis we elaborated a general agent based model for multicellular populations. We used this model to shed light on the processes that determine the growth of avascular tumor spheroids and studied the key mechanisms of liver regeneration.
In order to make such analyses possible, we developed a comprehensive software tool that allowed us to effectively simulate, visualize and analyze the constructed computational model. We started with a minimal model for two-dimensional monolayers which are a common experimental technique for in vitro cell cultures.
We successively advanced our model in order to reflect an in vivo situation more closely for example by simulating complex three-dimensional tumor spheroids embedded in granular medium and host tissue.
We proposed a biomechanical form of contact inhibition that was able to explain the experimentally observed linear growth of the diameter in monolayer cultures [Bru et al., 1998] [Bru et al., 2003] and their specific proliferation pattern where cells mainly proliferate at the monolayer border. Furthermore, our model could mimic the growth dynamics of monolayer cultures very precisely.
Subsequently, we considered three-dimensional cell aggregates by studying substrate detachment whereby normally two-dimensional monolayers due to the failure of certain control mechanisms expand perpendicular to the monolayer plane. Failure of growth control mechanisms is known to play an important role in the development of
cancer [Hanahan & Weinberg, 2000]. By additionally introducing nutrient diffusion and consumption, we established a further extended model for three-dimensional tumor spheroids which are a common experimental model in therapeutically oriented cancer research. Surprisingly, we found that the proposed biomechanical form of contact inhibition also explains the growth of these tumor spheroids. Thereby, our model suggests in agreement with experimental data [Freyer & Sutherland, 1985]
[Freyer & Sutherland, 1986] that the nutrient concentration in the environment of a growing tumor, which is widely believed to control its growth, only determines the size of its necrotic core. Moreover, also in this three-dimensional situation our model precisely mimicked the growth dynamics and proliferation pattern of tumor spheroids in vitro where the necrotic core is enclosed by an intermediate layer of
quiescent cells and an outer layer of proliferating cells [Kunz-Schughart, 1999].
We further advanced our model for the growth of three dimensional cell populations even closer towards in vivo tumors by including aspects from the surrounding tissue.
We showed that the biomechanical properties of an embedding tissue have a major impact on the growth dynamics and morphology of growing cell populations by systematically varying the biophysical properties of the embedding tissue. Our model predicts Saffman-Taylor-like instabilities leading to fractal interfaces and an
increased ability of cells to invade harsh environments if the motility of the embedding cells is small. We additionally observed large wavelength instabilities as a consequence of decreased density, increased elasticity, strong adhesion or 5. Summary 160 increased cell size of the embedding tissue or granular medium. Interestingly, we found a nearly complete inhibition of tumor growth for specific properties of the embedding tissue which, if experimentally validated, could have direct therapeutical
implications.
Furthermore, we achieved a remarkable agreement with experimental data on tumor growth dynamics by [Helmlinger et al., 1997] and [Galle et al., 2006]. However, the large variety of complex influences predicted by our model strongly indicates that the widespread experimental technique of embedding growing tumor spheroids in agarose gels [Helmlinger et al., 1997] [Galle et al., 2006] [Cheng et al., 2009] may
not be sufficient to realistically capture all the biomechanical effects of an
embedding tissue. Effects due to the granularity of the surrounding tissue, for example, are missing in experiments like those performed in [Helmlinger et al., 1997].
In contrast to chapter three where we mainly compared our model to published in vitro data, in chapter four we investigated a particular in vivo situation and studied the fascinating process of liver regeneration after intoxication with CCl4, a prototypical substance for drugs inducing pericentral liver damage.
We established a procedure to use three-dimensional confocal laser scans to reconstruct in vivo tissues by image processing and image analysis. We then combined this very detailed and quantitative information with a further advanced version of our repeatedly experimentally validated model. We started with a minimal
two-dimensional model for the regenerating liver lobule that nevertheless led to first impressions of the specific impact of the various factors that influence liver regeneration. On that basis we extended our model and created the first threedimensional agent-based model of the regenerating liver lobule.
By capturing a 16 day regeneration process, our model underlined the importance of the complex columnar microarchitecture within the liver lobules, which is formed by hepatocytes and sinusoids. This microarchitecture ensures optimal exchange of metabolites between blood and hepatocytes. The model unambiguously predicted a
so far unrecognized mechanism, the alignment of daughter hepatocytes along the
orientation of the closest sinusoid, which we named hepatocyte-sinusoid alignment (HSA), as essential for liver regeneration. Only if HSA was included into the model the simulated tissue architecture was in agreement with the experimentally obtained data and no other likely mechanism could replace it. In order to experimentally validate the model prediction of HSA, we analyzed the orientation of daughter
hepatocytes in relation to the sinusoids in three-dimensions. The results of this analysis clearly confirmed the model prediction and thus verified HSA as a yet unknown key mechanism of liver regeneration.
During this analysis we introduced novel techniques that made currently
experimentally not accessible information available by image processing and analysis of volumetric datasets obtained by confocal laser scanning microscopy. In addition to the three-dimensional analysis of HSA, we used a similar approach to obtain further currently not experimentally available information on the average 5. Summary 161 contact area between hepatocytes and sinusoids. Surprisingly, we found this
parameter to allow for an automatic differentiation between normal liver tissue and hepatocellular carcinoma. The further pursuit of this finding will be interesting.
In summary, in this thesis we present an interdisciplinary approach to combine microscopic imaging, image processing and analysis and computational modeling - all in three dimensions. The integration of methods and results from different scientific fields like cell biology, physics and computer science enabled us to obtain new insights in cancer research and hepatology.
We therefore consider the presented interdisciplinary approach and the
corresponding procedures exemplary and widely applicable in the systems biology of tissues in general.
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Gutierrez, Louis Michael. "Agent-based simulation of disease spread aboard ship." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FGutierrez.pdf.
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Thesis (M.S. in Modeling, Virtual Environments and Simulations (MOVES))--Naval Postgraduate School, March 2005.Thesis Advisor(s): Chris Darken. Includes bibliographical references (p. 47-48). Also available online.
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Kim, Bumsik. "Modeling Automated Vehicles and Connected Automated Vehicles on Highways." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103012.
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The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.Doctor of Philosophy
The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.
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Bard, Joseph. "Finding High Ground: Simulating an Evacuation in a Lahar Risk Zone." Thesis, University of Oregon, 2016. http://hdl.handle.net/1794/20519.
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Large lahars threaten communities living near volcanoes all over the world. Evacuations are a critical strategy for reducing vulnerability and mitigating a disaster. Hazard perceptions, transportation infrastructure, and transportation mode choice are all important factors in determining the effectiveness of an evacuation. This research explores the effects of population, whether individuals drive or walk, response time, and exit closures on an evacuation in a community threatened by a large lahar originating on Mount Rainier, Washington. An agent-based model employing a co-evolutionary learning algorithm is used to simulate a vehicular evacuation. Clearance times increase when the population is larger and when exits are blocked. Clearance times are reduced when a larger proportion of agents opt out of driving, and as the model learns. Results indicate evacuation times vary greatly due to spatial differences in the transportation network, the initial population distribution, and individual behaviors during the evacuation.
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Bitterman, Patrick. "A coupled agent-based model of farmer adaptability and system-level outcomes in the context of climate change." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5715.
Full textAbstract:
Social-ecological systems (SES) may become “locked in” particular states or configurations due to various constraints on adaptability imposed by feedback mechanisms or by processes designed to incentivize certain behavior. While these locked-in states may be desirable and robust to disturbances over relatively short time periods, limits on system adaptations may diminish the longer-term resilience of these states, and potentially of the system itself. The agricultural SES in the Iowa-Cedar River Basin in eastern Iowa is one such system. While highly productive, culturally important, and essential to local economies, the system is facing significant economic and environmental challenges. This dissertation presents the results of a project designed to survey the adaptability of farmers in the ICRB, model their actions subject to constraints, and plot potential future states under scenarios of climate change, policy, and market conditions. We utilize a coupled agent-based model (ABM) to examine the specified resilience of the system to future climate, leveraging the ability of ABMs to integrate heterogeneous actors, dynamic couplings of natural and human systems, and processes across spatiotemporal scales. We find that farmer behavior is primarily constrained by economic factors, including federal crop insurance subsidies and the financial risk of implementing different crops or practices. Finally, we generate alternative system trajectories by modeling twenty-one scenarios, identifying actionable adaptations and pathways for transforming the system to alternative, more sustainable states.
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McLane, Kevin John. "Symbiosis of Ectomycorrhizae and Trees, an Agent-Based Model." Youngstown State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1620380794126532.
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Singh, Meghendra. "Human Behavior Modeling and Calibration in Epidemic Simulations." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87050.
Full textAbstract:
Human behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.Master of Science
In the real world, individuals can decide to adopt certain behaviors that reduce their chances of contracting a disease. For example, using hand sanitizers can reduce an individual‘s chances of getting infected by influenza. These behavioral decisions, when taken by many individuals in the population, can completely change the course of the disease. Such behavioral decision-making is generally not considered during in-silico simulations of infectious diseases. In this thesis, we address this problem by developing a methodology to create and calibrate a decision making model that can be used by agents (i.e., synthetic representations of humans in simulations) in a data driven way. Our method also finds a cost associated with such behaviors and matches the distribution of behavior observed in the real world with that observed in a survey. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.