Stock & Flows 3 & Overview of the Modeling Process

34 Pages·2012·0.94 MB·English

by  Nathaniel Osgood

Overview of the ABM Process &
ODD Protocol
Nathaniel Osgood
MIT 15.879

February 29, 2012
Model Specification Mechanisms
Stock & Flow Models: Agent-Based Modeling: “Fox
“Hedgehog Knowledge” Knowledge”
• Small modeling vocabulary • Large modeling vocabulary
• Power lies in combination of • Different subsets of
a few elements vocabulary used for
different models
• Analysis conducted
predominantly in terms of • Power in flexibility &
elements of model combination of elements
vocabulary
• Variety in analysis focus
ABMs: Larger Model Vocabulary & Needs
• Events • Subtyping
• Multiple mechanisms for • Mobility & movement
describing dynamics
• Graphical interfaces
– State diagrams
• Data output mechanisms
– Stock and flow
• Stochastics complicated
– Custom update code
– Scenario result interpretation
• Inter-Agent communication
– Calibration
(sending & receiving)
– Sensitivity analysis
• Multiple types of transitions
• Synchronous &
• Diverse types of agents
asynchronous distinction,
• Spatial & topological concurrency
connectivity & patterning
The Overview, Design concepts, and
Details (ODD) Protocol for ABM Design
• Consensus protocol derived from panel fo ABM
modelers
• Primary focus: Specification protocol
– To help understand, communicate & reproduce ABMs
• Secondary benefit: Process for ABM design
ODD: 3 Broad Components
• Overview: model goals & high level scope &
design
• Design concepts: Different aspects of design
being considered
• Remaining elements
Overview of Modeling Process
• Typically conducted with an interdisciplinary
team
• An ongoing process of refinement
• Best: Iteration with modeling, intervention
implementation, data collection
• Observation:
– Traditionally, the focus in ABM has been on insights
gained from the model delivered
– Often it is the modeling process itself – rather than
the models created – that offers the greatest value
ABM Modeling Process Overview
ODD:
A Key Deliverable!
ODD: Overview
Design
& high-level design components

& details
components
Learning
Reference mode Specification &
Parameter sensitivity
environm
Specification of reproduction investigation of
analysis
Problem/research State charts ents (e.g.
• Parameters intervention scenarios
question articulation Parameter & state Matching of DISimS)
• Quantitative causal Cross-validation Investigation of
variables intermediate time
Patterns for explanation relations Formal hypothetical external Visualizat
Qualitative State charts series
• Decision/behavior w/Discovered conditions ions
Model scope/boundary Influence & Causal loop
rules Matching of Patterns
selection (endogeous, diagrams Cross-scenario
Transitions observed data points
exogenous,ignored) Multi-agent interaction Robustness tests comparisons (e.g. CEA)
• Interactions
diagrams Constrain to sensible
Extreme value tests
Messaging
Key entities & their Process flow structure bounds
&handlers
relationships Key events Structural sensitivity Unit checking
Resources
Agents (&collectives)R ough model time & analysis
• Relationship Problem domain tests
Environment spatial extent
dynamics
Nesting hierarchy orO bservers to measure
• Mobility
network diagrams outputs
dynamics

Output of interest • Initial conditions
Recall: Coevolution
Observations/
Evaluation
External World
Mental Model
Actions & Choice of
Observations
Simulated Dynamics
Formal Modeling Artifacts
ABM Modeling Process Overview
ODD:
ODD: Overview
Design
& high-level design components

& details
components
Learning
Reference mode Specification &
Parameter sensitivity
environm
Specification of reproduction investigation of
analysis
Problem/research State charts ents (e.g.
• Parameters intervention scenarios
question articulation Parameter & state Matching of DISimS)
• Quantitative causal Cross-validation Investigation of
variables intermediate time
Patterns for explanation relations Formal hypothetical external Visualizat
Qualitative State charts series
• Decision/behavior w/Discovered conditions ions
Model scope/boundary Influence & Causal loop
rules Matching of Patterns
selection (endogeous, diagrams Cross-scenario
Transitions observed data points
exogenous,ignored) Multi-agent interaction Robustness tests comparisons (e.g. CEA)
• Interactions
diagrams Constrain to sensible
Extreme value tests
Messaging
Key entities & their Process flow structure bounds
&handlers
relationships Key events Structural sensitivity Unit checking
Resources
Agents (&collectives)R ough model time & analysis
• Relationship Problem domain tests
Environment spatial extent
dynamics
Nesting hierarchy orO bservers to measure
• Mobility
network diagrams outputs
dynamics

Output of interest • Initial conditions
ODD Overview: model goals & high
level scope & design
• Purpose
• Definition of key elements during operation
– Entities
– States
– Scales
• Process overview and scheduling