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Agent-based Modeling and Simulation > Course > Flashcards

Course Flashcards

1
Q

What is a model? Describe it

A
  • Description of a phenomenon
  • Representation of a system
  • Simplification
  • Set of instructions for generating a behavior
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2
Q

What different types of behavior in a model is there?

A
  • Model behavior - what the model does

* Model structure - What makes it do what it does?

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3
Q

What kind of purpose does a model have?

A
  • Conceptual frame
  • Communication tool
  • Summarizing a large amount of data
  • Provide understandning
  • Identify missing knowledge
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4
Q

What is a system?

A

Collection of entities that act and interact together

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5
Q

What is a simulation?

A
  • Imitation
  • Developing an executable model
  • Experimenting with a model
  • Category of problem solvning
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6
Q

There are several ways of studying a system - what are they?

A
  • Experimenting with the actual system
  • Experimenting with model of a system
  • -> Physical
  • -> Mathematical (analytical, statistical, simulation)
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7
Q

Why would you like to do a model and not try out the actual (physical) system?

A
  • Not accessible (to small, large etc)
  • Not existing yet
  • Unknown structure
  • To expensive
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8
Q

What are the 7 dimensions for simulation models?

A
  1. Object behind the model
  2. Static vs. Dynamic model
  3. Domain of the simulation time
  4. Determinism vs. Stochastic
  5. How entities are represented
  6. Kind of dependency
  7. Domain/range of variables
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9
Q

What is the domain of the simulated time?

A
  • Continuous - change in small intervals

* Discrete - changes in particular points

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10
Q

Describe the deterministic approach vs stochastic

A
  • Deterministic - future events can be predicted from simulated history
  • Stochastic - probability that a particular event occurs
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11
Q

Describe how different entities can be represented

A
  • Macro models - describes a single object whichs’s properties are put into relation to each other
  • Micro models - several objects whichs properties and relation between are analyzed
  • Multi-level-models - objects of one model are aggregated into higher-level objectives and tacked as a model on the higher level. Properties and relations on the higher level may be derived from properties and relations on the lower level objectives.
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12
Q

Whats the characteristics of ABM?

A
  • Dynamic
  • Usually time-discrete
  • Mostly Stochastic
  • Non-linear
  • Micro models, also multi-level models
  • All kind of objectives
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13
Q

Whats the alternative microsimulation approaches?

A
  • Queueing systems (discrete event simulation)
  • Object-oriented simulation
  • Cellular automata
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14
Q

Describe discrete event simulation

A
  • Simulation state is only updated during event processing
  • Very efficient; easy to distribute
  • Event queue contains events that will be done –> advanced time, change state according to event type, generate events and put into a queue.
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15
Q

Describe object oriented simulation

A
  • Sequence of events as input, triggering state change
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16
Q

Describe cellular automata

A
  • Every cell is equal
  • State transition is local in time and space
  • Macro-level phenomena can be observed that cannot be derived from micro-level transition functions
  • Parallel update of all cells
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17
Q

What is a multi-agent model?

A
  • Representation of an original system of the metaphor of a multi-agent system
  • The active entities are “agents”
18
Q

What is a multi-agent simulation?

A

Is running a multi-agent model

19
Q

What is an agent?

A

An entity in an environment that is able to perform actions in the environment for fulfilling its goals

20
Q

What is the element of an agent-based simulation model?

A
  • Agents
  • Interactions
  • Environment
21
Q

What is the characteristics of an agent?

A
  • Autonomous
  • Bounded rationality
  • Heterogeneous
  • Adaptive
22
Q

Describe a soup?

A
  • No structure
  • No restriction
  • starting point before relationships evolve
23
Q

Describe a map

A
  • Explicit space with a coordinate system

* Discrete or continuous

24
Q

What is a good model?

A
  • Sufficient
  • Reproductible
  • Simple
  • Comprehensible
  • Feasible
  • Flexible
  • Maintainable/extandable
25
Q

Describe the modelling strategies

A
  • KISS - start simple, extend
  • KIDS - start descriptive, simplify
  • TAPAS - use existing model and adapt
  • Pattern-oriented - identify patterns, modify model untill you capture all data
26
Q

What 7 elemens are included in model design?

A
  1. Agent type
  2. Agent properties
  3. Environment
  4. Agent behaivour
  5. Design time step
  6. Choose parameters
  7. Choose measures
27
Q

What is agent-centered approach?

A

Observe. agents i real world, develop model, simulate

28
Q

What is interaction-centered approach?

A

take birds eye view and look for interactions, describe interactions and create agent that interact

29
Q

What is the process-centered approach?

A

start by describe overall process, identify actors ab distribute sub-process actors

30
Q

What is environmental-centred approach?

A

if objects react to different environemtn, figure out why

31
Q

What is an error?

A
  • Model does not comply with spec.
32
Q

What is an artefact?

A
  • Core assumptions and accessory assumptions making the model
  • There are significant and non-significant assumption
33
Q

What is validity?

A
  • Process of determining if the simulation is accurate

* Without data exclude artefact by testing/sensitivity analysis

34
Q

what different validity types are there?

A
  • Face validity
  • Empirical validity
  • Behavioral validity
  • Structural validity
35
Q

What is sensitivity analysis?

A

For testing the impact of parameter settings

* Factor screening/local sensitivity (small changes) / global sensitivity (entire range)

36
Q

What is calibration?

A
  • Optimization toward intended output
37
Q

What may cause bad calibration?

A
  • Too many degrees of freedom, model brittleness, dependencies
38
Q

What is the relation between sensitivity and calibration?

A
  • Sensitivity between the model output and parameters

* Calibration is about optimizing the output towards the intended output

39
Q

Whats the traditional do-not’s for the simulation?

A
  1. Not enough well-defined objective
  2. Inappropriate level of model detail
  3. Failure to get the appropriate data
  4. Inappropriate output data analysis
40
Q

What should you watch out for during a simulation?

A
  1. Model complexity and brittleness
  2. Scalability
  3. technical scalability
  4. Efficient calibration
  5. appropriate level of resolution

Agent-based Modeling and Simulation (2 decks)

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