agent-based models

AM-84 - Simulation Modeling

Advances in computational capacity have enabled dynamic simulation modeling to become increasingly widespread in scientific research. As opposed to conceptual or physical models, simulation models enable numerical experimentation with alternative parametric assumptions for a given model design. Numerous design choices are made in model development that involve continuous or discrete representations of time and space. Simulation modeling approaches include system dynamics, discrete event simulation, agent-based modeling, and multi-method modeling. The model development process involves a shift from qualitative design to quantitative analysis upon implementation of a model in a computer program or software platform. Upon implementation, model analysis is performed through rigorous experimentation to test how model structure produces simulated patterns of behavior over time and space. Validation of a model through correspondence of simulated results with observed behavior facilitates its use as an analytical tool for evaluating strategies and policies that would alter system behavior.

AM-82 - Microsimulation and calibration of agent activities
  • Describe a “bottom-up” simulation from an activity-perspective with changes in the locations and/or activities the individual person (and/or vehicle) in space and time, in the activity patterns and space-time trajectories created by these activity patterns, and in the consequent emergent phenomena, such as traffic jams and land-use patterns
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Describe how measurements on the output of a model can be used to describe model behavior
AM-79 - Agent-based Modeling

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.

AM-82 - Microsimulation and calibration of agent activities
  • Describe a “bottom-up” simulation from an activity-perspective with changes in the locations and/or activities the individual person (and/or vehicle) in space and time, in the activity patterns and space-time trajectories created by these activity patterns, and in the consequent emergent phenomena, such as traffic jams and land-use patterns
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Describe how measurements on the output of a model can be used to describe model behavior
AM-81 - Adaptive agents
  • Describe different approaches to represent the effects of agent adaptation in the context of a specific agent-based model
  • Explain the effects of agent adaptation in the context of a specific agent-based model 
AM-79 - Agent-based Modeling

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.

AM-79 - Agent-based Modeling

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.

AM-79 - Agent-based models
  • Compare and contrast agent-based models and cellular automata as approaches for modeling spatial processes
  • Describe how agent-based models use object-oriented programming constructs of inheritance and encapsulation to represent the behavior of heterogeneous and interactive and adaptive actors
  • Describe how multiple, different types of agents in a given system behave and interact with each other and their environment
  • Generate multiple, different types of agents in a given system
  • Describe how multiple parameters (e.g., number of agents, variability of agents, random number seeds for different series of random events or choices during each simulation) can be set within an agent-based model to change the model behavior
  • Explain how agent behaviors can be used to represent the effects actors have on each other and on their environment
  • Design simple experiments with an agent-based model
  • Design and implement a simple agent-based model using appropriate commercial or open source development tools
  • Conduct simple experiments with an agent-based model, analyze results, and evaluate their statistical significance with respect to degrees of freedom, sensitivity analyses, and uncertainty in the model
  • Describe how measurements on various inputs and outputs of a model can be used to describe model behavior and to relate model outcomes to various initial conditions
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Determine if an agent-based model has been run enough times with enough different random number seeds for rigorous inference of its results
AM-82 - Microsimulation and calibration of agent activities
  • Describe a “bottom-up” simulation from an activity-perspective with changes in the locations and/or activities the individual person (and/or vehicle) in space and time, in the activity patterns and space-time trajectories created by these activity patterns, and in the consequent emergent phenomena, such as traffic jams and land-use patterns
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Describe how measurements on the output of a model can be used to describe model behavior
AM-81 - Adaptive agents
  • Describe different approaches to represent the effects of agent adaptation in the context of a specific agent-based model
  • Explain the effects of agent adaptation in the context of a specific agent-based model 

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