{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/15126"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/15126","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"A Network Approach to Scaling Agent-Based Model Outputs","abstract":"Abstraction of reality through agent-based modeling has enabled us to analyze and explore previously intractable problems. Yet, there is always a trade-off between the scope of the model and the constraints of time and computational resources. Various approaches have been developed to help overcome these constraints in large-scale agent-based models. This dissertation reviews the existing methods that help simulate agent-based models at large sizes and proposes a new methodology based on network analysis to predict the change in the model's outputs with the change in its size. Using the model outputs produced at a smaller scale, along with measurements of the changes in the underlying agent connections, this methodology is able to reproduce the outputs of a large-scale model without the need to run the model at full size. This approach is demonstrated using several well-known agent-based models, including models of segregation, disease spread, and traffic congestion. These examples demonstrate the ability of this methodology to predict model outputs at a large scale across changes to inputs, network topologies, and agent behavior. The use of the presented methodology can help address the growing complexity of agent-based models by providing researchers with a way to obtain large-scale model results using the streamlined process described in this dissertation. This dissertation further explores the nature of scaling in agent-based modeling, using insights gained from network analysis to provide guidance on scaling models to produce the most representative results.","abstract_html":"Abstraction of reality through agent-based modeling has enabled us to analyze and explore previously intractable problems. Yet, there is always a trade-off between the scope of the model and the constraints of time and computational resources. Various approaches have been developed to help overcome these constraints in large-scale agent-based models. This dissertation reviews the existing methods that help simulate agent-based models at large sizes and proposes a new methodology based on network analysis to predict the change in the model's outputs with the change in its size. Using the model outputs produced at a smaller scale, along with measurements of the changes in the underlying agent connections, this methodology is able to reproduce the outputs of a large-scale model without the need to run the model at full size. This approach is demonstrated using several well-known agent-based models, including models of segregation, disease spread, and traffic congestion. These examples demonstrate the ability of this methodology to predict model outputs at a large scale across changes to inputs, network topologies, and agent behavior. The use of the presented methodology can help address the growing complexity of agent-based models by providing researchers with a way to obtain large-scale model results using the streamlined process described in this dissertation. This dissertation further explores the nature of scaling in agent-based modeling, using insights gained from network analysis to provide guidance on scaling models to produce the most representative results.","abstract_has_math":false,"creators":["Malikov, Maxim A"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T19:51:50Z","subjects":["Agent-based models","Modeling","Networks","Scaling","Simulations"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/15126"],"render_values":[{"text":"hdl:1920/15126","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agent-based models","Modeling","Networks","Scaling","Simulations"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/15126"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Abstraction of reality through agent-based modeling has enabled us to analyze and explore previously intractable problems. Yet, there is always a trade-off between the scope of the model and the constraints of time and computational resources. Various approaches have been developed to help overcome these constraints in large-scale agent-based models. This dissertation reviews the existing methods that help simulate agent-based models at large sizes and proposes a new methodology based on network analysis to predict the change in the model's outputs with the change in its size. Using the model outputs produced at a smaller scale, along with measurements of the changes in the underlying agent connections, this methodology is able to reproduce the outputs of a large-scale model without the need to run the model at full size. This approach is demonstrated using several well-known agent-based models, including models of segregation, disease spread, and traffic congestion. These examples demonstrate the ability of this methodology to predict model outputs at a large scale across changes to inputs, network topologies, and agent behavior. The use of the presented methodology can help address the growing complexity of agent-based models by providing researchers with a way to obtain large-scale model results using the streamlined process described in this dissertation. This dissertation further explores the nature of scaling in agent-based modeling, using insights gained from network analysis to provide guidance on scaling models to produce the most representative results."]},{"key":"dc:title","label":"Title","values":["A Network Approach to Scaling Agent-Based Model Outputs"]}]}],"canonical_facts":{"dc:date.issued":["2025"],"dc:description.other":["Abstraction of reality through agent-based modeling has enabled us to analyze and explore previously intractable problems. Yet, there is always a trade-off between the scope of the model and the constraints of time and computational resources. Various approaches have been developed to help overcome these constraints in large-scale agent-based models. This dissertation reviews the existing methods that help simulate agent-based models at large sizes and proposes a new methodology based on network analysis to predict the change in the model's outputs with the change in its size. Using the model outputs produced at a smaller scale, along with measurements of the changes in the underlying agent connections, this methodology is able to reproduce the outputs of a large-scale model without the need to run the model at full size. This approach is demonstrated using several well-known agent-based models, including models of segregation, disease spread, and traffic congestion. These examples demonstrate the ability of this methodology to predict model outputs at a large scale across changes to inputs, network topologies, and agent behavior. The use of the presented methodology can help address the growing complexity of agent-based models by providing researchers with a way to obtain large-scale model results using the streamlined process described in this dissertation. This dissertation further explores the nature of scaling in agent-based modeling, using insights gained from network analysis to provide guidance on scaling models to produce the most representative results."],"dc:identifier":["hdl:1920/15126"],"dc:subject":["Agent-based models","Modeling","Networks","Scaling","Simulations"],"dc:title":["A Network Approach to Scaling Agent-Based Model Outputs"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:51:50Z"}