Model Building Experiences using Garp3: Problems, Patterns and Debugging

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Model Building Experiences using Garp3 Problems,
Patterns and Debugging

• Jochem Liem, Floris E. Linnebank Bert Bredeweg
• Human-Computer Studies
• University of Amsterdam

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Motivation Usability to Formalization Bottleneck

• Availability of usable QR tools has led to
• More domain experts formalizing their conceptual
  knowledge
• The creation of more complex models
• Usability is no longer an issue
• However, knowledge formalization is the new
  bottleneck
• Modelers encounter similar representational
  issues, and reinvent solutions
• This paper categorizes representation issues and
  their solutions.

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Trained Groups in QR Modelling

• PhD-level researchers (NaturNet-Redime)
• 2,5 year working with Garp3
• Trainings each half year
• Support using Skype/Flashmeeting
• BSc. Students (Future Planet Studies)
• 4 weeks Concept maps, Ontologies
• 4 week QR modelling (Carbon Cycle)
• PhD-students (School for Information and
  Knowledge Systems)
• 1 hour lecture 2 hour practical Tree Shade
  model

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Entities or Quantities?

• Alternative representations
• Algae entity with Biomass quantity
• Algea concentration quantity
• Create a new entity when properties of this
  entity are important.
• E.g. photosynthesis quantity of the algae

• Model by Elena Nakova , Yordan Uzunov

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Configuration direction naming

• Prefer active voice over passive voice
• Shorter configuration names
• Improves texts based on QR models

• Model by Richard Noble, Floris E. Linnebank
  Bert Bredeweg

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Relation reification

• Configuration direction is arbitrary
• Long configuration name
• Where to model speed of symbiosis process?

• Model by Paulo Salles Bert Bredeweg

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Influences Proportionalities
I
Q1,-
Q2,?
I-
Q1,-
Q2,?
I
Q2,?
Q1-,-
I-
Q1-,0
Q2,?
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Causal Interactions
I
I
Q2,?
Q3-,-
Q1,-
I-
I
Q1,-
Q2,?
Q3-,-
I
I-
Q1-,-
Q2,?
Q3-,-
I-
I-
Q1-,0
Q2,?
Q3-,-
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Causal Chains

• Causal path Influence followed by
  proportionalities
• Rare Multiple influences in a single causal path
• Impossible Loop of proportionalities

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Multiple Competing Influences

• CO2 release I CO2 Concentration
• Burning I Co2 Concentration
• Inequality information does not help
• Burning CO2 concentration gt 0
• Introduce Photosynthesis
• Burning CO2 lt Photosynthesis

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Choosing Quantity Spaces

• Difficult task even for expert modelers
• Should be behaviorally significant
• Depends depends on the context
• Phytoplankton blocking sunlight of 1st producers
• Global warmings effect on phytoplankton

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Small, Medium, High considered harmful
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Actuators External actuator (1/3)

• Models (effect of) process outside the system
• Value assignment (MF)
• Exogenous behaviour (Scenario)

• Model by Eugenia Cioaca, Tim Nuttle, Bert Bredeweg

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Actuators Equilibrium Seeking Mechanism (2/3)

• Models equalizing flows due to potential
  difference

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Actuators Competing Processes Pattern (3/3)

• Models competing processes

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States in simulation

• Maximum number of states
• Cartesian product of all quantities
• E.g. Three Qs (3x3)3729 (excl. inequalities)
• Successor states without correspondences
• S-states 2q-1, q non-corresponding quantities

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Not all expected states

• Issue State is missing
• Create the state as a scenario
• Option 1 No states, state is inconsistent
• Option 2 State appears
• Next create a scenario that generates a previous
  state
• Continue to a state in the original simulation

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No States

• Considered difficult to debug
• Features
• Inconsistency
• gt 1 model fragment fired
• Clash between contents of
• Scenario MF
• Multiple MFs
• Troubleshooting
• Deactivate all MFs (at least 1 state from
  scenario)
• Activate MFs one by one

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Inconsistencies

• Magnitude or derivative value assignments (MF or
  scenario)
• Inequalities (MF or Scenario)
• Operators (plus or minus)
• Value assignments due to correspondences

• Value assignments due to influence resolution
• Exogenous behavior
• Engine rules
• Quantity constraints
• Continuity constraints

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Conclusions Future Work

• Catalog of
• Representational issues and their solutions
• Frequently occurring patterns
• Considerations when debugging
• Future work Support in Garp3/DynaLearn
• Premade patterns (QSs and actuators)
• Automatic model building
• Model diagnosis