Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation to tackle Complexity Hans Vangheluwe
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation 1 Modelling and Simulation for . . . The Modelling Relationship Causes of Complexity 2 Large Number of Components Diversity of Components Non-compositional/Emergent Behaviour Uncertainty Dealing with Complexity 3 Multiple Abstraction Levels Optimal Formalism Multi-Formalism Multiple Views/Aspects Multi-Paradigm Modelling 4
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . Simulation . . . when too costly/dangerous analysis ↔ design
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . Simulation . . . real experiment not ethical “physical” simulation, training
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . Simulation . . . evaluate alternatives
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . Simulation . . . “Do it Right the First Time”
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . essence: “shooting” problems
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . defining a “hit” 20 15 Height (m) 10 5 θ origin (0, 2) target (30, 1) 0 0 5 10 15 20 25 30 Distance (m)
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . optimizing a “performance metric” 30 25 20 15 10 5 0 0 10 20 30 40 50 60 70 80 90
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . optimal solution. . . s
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . Modelling/Simulation . . . and code/app Synthesis
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . The spectrum of uses of models Documentation Formal Verification (all models, all behaviours) Model Checking (one model, all behaviours) Test Generation Simulation (one model, one behaviour) . . . calibration, validation, optimization, . . . Application Synthesis
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . Design (“How?”) Requirements (“What?”) Detached or Semi-detached Style (classical, modern, . . . ) Number of Floors Number of rooms of different types (bedrooms, bathrooms, . . . ) Garage, Storage, . . . Cellar Energy-saving measures . . .
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . System Boundaries System to be built/studied Environment with which the system interacts
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Modelling and Simulation for . . . System vs. “Plant” www.mathworks.com/products/demos/simulink/PowerWindow/html/PowerWindow1.html
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling The Modelling Relationship REALITY MODEL GOALS Real-World Base entity Model only study behaviour in experimental context within context Model Base System S Model M a-priori knowledge experiment simulate within context = virtual experiment validation Experiment Simulation Results Modelling and Simulation Observed Data Process
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling The Modelling Relationship System Frame Output Frame Input (real or model) Variables Variables Experimental Frame generator acceptor transducer set of all “contexts” in which model is valid includes experiment descriptions: parameters, initial conditions ∼ re-use, testing
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Dealing with Complexity
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Large Number of Components Crowds www.3dm3.com
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Large Number of Components Number of Components – hierarchical (de-)composition
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Diversity of Components Diversity of Components: Power Window
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Diversity of Components Diversity of Components: Paper Mill www.gov.karelia.ru
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Diversity of Components Paper Mill Model PaperPulp mill Waste Water Treatment Plant System of WWTP and Stormwater tanks (DEVS) M,S M,S M,S WWTP (DESS) M,S Aeration Sedimentation Switch Mixing Q Effluent Influent Activated sludge unit M,S (DESS) Clarifier (DESS) Q Recycle (return) flow Output function M,S M,S M,S M,S Input function Stormwater tank 1 Input/Output function Stormwater tank 2 overflow fish EDRF + GE X X CFF GF CFA X + RRA algae Fish Farm
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Non-compositional/Emergent Behaviour Non-compositional/Emergent Behaviour
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Non-compositional/Emergent Behaviour Engineered Emergent Behaviour
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Uncertainty Often related to level of abstraction: for example continuous vs. discrete www.engr.utexas.edu/trafficSims/ uncertainty � = imprecise � = not rigorous
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Guiding principle ( ∼ physics: principle of minimal action) minimize accidental complexity , only essential complexity remains Fred P . Brooks. No Silver Bullet – Essence and Accident in Software Engineering. Proceedings of the IFIP Tenth World Computing Conference, pp. 1069–1076, 1986. http://www.lips.utexas.edu/ee382c-15005/Readings/Readings1/05-Broo87.pdf
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Solutions multiple abstraction levels optimal formalism multiple formalisms multiple views
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Multiple Abstraction Levels Different Abstraction Levels – properties preserved
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Multiple Abstraction Levels Levels of Abstraction/Views: Morphism model M_t M_d abstraction simulation trajectory traj_t traj_d detailed abstract (technical) level (decision) level
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Multiple Abstraction Levels Abstraction Relationship foundation : the information contained in a model M . Different questions (properties) P = I ( M ) which can be asked concerning the model. These questions either result in true or false. Abstraction and its opposite, refinement are relative to a non-empty set of questions (properties) P . If M 1 is an abstraction of M 2 with respect to P , for all p ∈ P : M 1 | = p ⇒ M 2 | = p . This is written M 1 ⊒ P M 2 . M 1 is said to be a refinement of M 2 iff M 1 is an abstraction of M 2 . This is written M 1 ⊑ P M 2 .
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Optimal Formalism Most Appropriate Formalism
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Optimal Formalism Forrester System Dynamics model of Predator-Prey interaction uptake_predator loss_prey Grazing_efficiency prey_surplus_BR predator_surplus_DR Predator Prey 2−species predator−prey system
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Optimal Formalism Causal Block Diagram model of Harmonic Oscillator 0.0 x0 K 0.0 1.0 x IC − OUT I y0 1.0 PLOT y IC
Modelling and Simulation Causes of Complexity Dealing with Complexity Multi-Paradigm Modelling Optimal Formalism Petri Net model of Producer – Consumer 1 1 P.Calculating Wait4Prod Rem.from buffer Produce 0 Buffer 0 0 C.Calculating Wait4Cons 1 Buffer−p Consume Put in Buffer
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