Large-Scale Agent-Based Modeling with Repast HPC: A Case Study in Parallelizing an Agent-Based Model

2015 ◽  
pp. 454-465 ◽  
Author(s):  
Nicholson Collier ◽  
Jonathan Ozik ◽  
Charles M. Macal
Keyword(s):  
Large Scale ◽  
Agent Based Modeling ◽  
Agent Based Model ◽  
Agent Based

Large-Scale Agent-Based Models for Transportation Network Management under Unplanned Events

2014 ◽  
pp. 206-231 ◽  
Author(s):  
Yunjie Zhao ◽  
Adel W. Sadek
Keyword(s):  
Large Scale ◽  
Transportation Network ◽  
Simulation System ◽  
Agent Based Modeling ◽  
Lessons Learned ◽  
Future Research ◽  
Agent Based ◽  
Transportation Modeling ◽  
Major Incidents

The focus of this chapter is on issues surrounding the development and applications of large-scale agent-based traffic models. Following a brief overview of Agent-Based Modeling and Simulation (ABMS) applications in transportation modeling, the chapter proceeds to describe the authors’ continued efforts and experiences with the development, calibration, validation, and application of a regional agent-based traffic model of the Buffalo-Niagara metropolitan area. The model is developed using the TRansportation ANalysis SIMulation System (TRANSIMS), an open-source, agent-based suite of transportation models. A unique feature of the chapter is its focus on unplanned or extreme events, such as severe snowstorms and major incidents on the freeways, and how the models may be calibrated and applied under such situations. The chapter concludes by summarizing the main lessons learned from the Buffalo case study and providing suggestions for future research.

Large-Scale Agent-Based Models for Transportation Network Management under Unplanned Events

2015 ◽  
pp. 1316-1341
Author(s):  
Yunjie Zhao ◽  
Adel W. Sadek
Keyword(s):  
Large Scale ◽  
Transportation Network ◽  
Simulation System ◽  
Agent Based Modeling ◽  
Lessons Learned ◽  
Future Research ◽  
Agent Based ◽  
Transportation Modeling ◽  
Major Incidents

The focus of this chapter is on issues surrounding the development and applications of large-scale agent-based traffic models. Following a brief overview of Agent-Based Modeling and Simulation (ABMS) applications in transportation modeling, the chapter proceeds to describe the authors' continued efforts and experiences with the development, calibration, validation, and application of a regional agent-based traffic model of the Buffalo-Niagara metropolitan area. The model is developed using the TRansportation ANalysis SIMulation System (TRANSIMS), an open-source, agent-based suite of transportation models. A unique feature of the chapter is its focus on unplanned or extreme events, such as severe snowstorms and major incidents on the freeways, and how the models may be calibrated and applied under such situations. The chapter concludes by summarizing the main lessons learned from the Buffalo case study and providing suggestions for future research.

scholarly journals Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

2021 ◽  
Author(s):  
Iris Lorscheid ◽  
Matthias Meyer
Keyword(s):  
Laboratory Experiment ◽  
Mixed Method ◽  
Agent Based Modeling ◽  
Decision Processes ◽  
Mixed Method Research ◽  
Team Cognition ◽  
Agent Based Model ◽  
Agent Based ◽  
Individual Level ◽  
Team Decision

AbstractDespite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.

Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques

2012 ◽  
pp. 301-326
Author(s):  
Mitchell Welch ◽  
Paul Kwan ◽  
A.S.M. Sajeev ◽  
Graeme Garner
Keyword(s):  
Large Scale ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Livestock Disease ◽  
Interacting Agents ◽  
National Model ◽  
Complex Phenomena ◽  
Modelling System

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

Analyzing the Influences of Passive Investment Strategies on Financial Markets via Agent-Based Modeling

2008 ◽  
pp. 224-238 ◽  
Author(s):  
Hiroshi Takahashi ◽  
Satoru Takahashi ◽  
Takao Terano
Keyword(s):  
Asset Pricing ◽  
Financial Markets ◽  
Financial Market ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Investment Strategies ◽  
Agent Based Model ◽  
Efficient Market ◽  
Agent Based ◽  
Price Fluctuations

This chapter develops an agent-based model to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. This analysis focuses on the effects of Passive Investment Strategy in a financial market. From the extensive analyses, we have found that (1) Passive Investment Strategy is valid in a realistic efficient market, however, it could have bad influences such as instability of market and inadequate asset pricing deviations, and (2) under certain assumptions, Passive Investment Strategy and Active Investment Strategy could coexist in a Financial Market.

scholarly journals Development of an agent-based model to assess the impact of substandard and falsified anti-malarials: Uganda case study

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Sachiko Ozawa ◽  
Daniel R. Evans ◽  
Colleen R. Higgins ◽  
Sarah K. Laing ◽  
Phyllis Awor
Keyword(s):  
Agent Based Model ◽  
Agent Based ◽  
The Impact
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