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Agent Based Modelling
March 17th 2015 Land Surface Process Modelling (GEO4-4406) Judith Verstegen ([email protected])
Topics
• ABM theory • Tools • Examples
ABM theory
Definition
• In an Agent Based Model (ABM), a system is represented as a set of agents, interacting with each other and their environment
• Also called: – Multi Agent System – Multi Agent Simulation – Agent Based Simulation – Individual Based Model
Differential equations vs. agents
Changes in the densities of prey population (N1) and the predator population (N2): • dN1/dt = b1N1 - k1N1N2 (1) • dN2/dt = k2N1N2 - d2N2 (2) In these equations b1 is the birth rate of the prey, d2 is the death rate of the predators, and k1 and k2 are constants.
http://ccl.northwestern.edu/papers/bio/long/
Why do we use ABMs?
• To try to reproduce observed system scale patterns potentially caused by individuals
• And to try to understand the processes causing these patterns
• To predict the future system state for a certain scenario. However, data availability is usually a huge problem in ABM
What is an agent?
• An agent is an autonomous, uniquely identifiable individual
What is an agent?
• An agent is an autonomous, uniquely identifiable individual
• Situated in an environment
bare soil
grass
What is an agent?
• An agent is an autonomous, uniquely identifiable individual
• Situated in an environment • Having behaviour, connecting
percepts (of the environment and other agents) to actions
bare soil
grass
grass Æ eat
What is an agent?
• An agent is an autonomous, uniquely identifiable individual
• Situated in an environment • Having behaviour, connecting
percepts (of the environment and other agents) to actions
• Having a state, consisting of one or a set of attributes, which changes over time
bare soil
grass
grass Æ eat
energy = energy + 10
Case 1
• We want to model decision making in the parliament
1. What should be the agents? 2. What is the environment? 3. What could be the behaviour? 4. What is the state (atrributes) of the agents?
Conceptual model ABM
(Potential) properties of ABMs
• emergence
“simple, local, individual behaviour leads to complex and ‘surprising’ global patterns”
(Potential) properties of ABMs
• emergence • stochasticity and feedback loops Æ path-dependence
“Current choices determine future possibilities”
(Potential) properties of ABMs
• emergence • stochasticity Æ path-dependence • learning / adaptivity
“Agents use the results of current actions to adapt their future behaviour”
Educated agent example
When do we use field models and when ABMs? • Agents vs. fields (vector vs. raster)
field agent spatial variation continuous discrete objects attribute has value at all locations is linked to agent neighborhood adjacent cells /
Euclidean distance adjacent polygons / networks
processes behaviour of space as a whole
behaviour of a single agent
time environment changes agent’s attributes and/or location changes Æ macro-level patterns ‘emerge’
Coupling ABMs and fields
Coupling ABMs and fields
bare soil
grass
grass Æ eat
energy = energy + 10
growth = … grass = grass – grass_eaten + growth
Case 2
• When you want to model sediment transport in a river system, would you use an agent based model, field based model, or coupled model?
Tools
General or dedicated tools
• General tools: – General programming languages (Python, Java, C++) – Mathematics packages (MATLAB, Mathematica) – Spreadsheets with macros
• Dedicated ABM tools:
– NetLogo – Repast (Java or C++) – Mason (Java library) – Agent Analyst extension for ArcGIS
Model complexity vs. ease development
Examples
Learned before the break
• ABMs to reproduce, understand, predict
• Agent properties: uniquely identifiable, has an environment, has behaviour, has a state
• Potential ABM properties: emergence, path-dependence and learning
• General and dedicated tools
Usage
Agent Based models are used in, e.g.,: • Biology, ecology Æ animal migration, dispersion of plants • Sociology Æ crowd behaviour, segregation • Social geography Æ spatial planning, land use change • Economics Æ market models
Examples highlighted here: • traffic jams • ants • spatial planning
Traffic jams
Case 3
• Agent Based Model are not yet used widely in earth sciences. Discuss with your neighbour and find at least two potential earth science related systems/processes that can be represented with an ABM (5 mins)
Spatial planning
* LIGTENBERG, A. (2006) Exploring the use of Multi-Agent Systems for Interactive Multi-Actor Spatial Planning. Geo-Information and Remote Sensing. Wageningen, Wageningen University.
Conceptual Framework Agent Based Spatial Planning Model*
Reality
Spatial planning
• Three different groups have to agree on where to expand the city
• They have different initial desires considering the locations of expansion
Implemented learning: • Input = actions of the others • Output = changed desires
proposal
decisions
ideas about others updates
desire updates
Spatial planning With learning Without learning Average nr of proposals (-) 140 164 Average agreed area (ha) 52 45 Effectiveness (ha/proposal) 0.37 0.28
How often (% of time) a cell is selected for new urbanization with (left) and without (right) learning.
GIS
Summary
• In an Agent Based Model (ABM), a system is represented as a set of agents
• An agent is an autonomous entity, situated in an environment, having a state that can change by executing behaviour
• ABMs can have advantages over field based model (understanding, variability) but also disadvantages (data requirements)
• ABMs can be created in different general or dedicated toolsets
• ABMs can be used to simulate processes from very different domains at different scales
Practical
Install NetLogo from
http://ccl.northwestern.edu/netlogo/download.shtml at
C:\\temp
Practical
Carry out tutorial #1 and #3 on the NetLogo website: http://ccl.northwestern.edu/netlogo/faq.html If you have a Blackboard account: • Log in to Blackboard • Go to 2012 3 Land surface process modelling (GEO4-4406) • Go to Assessment Agent-based modeling • Answer the questions for tutorial #1 and #3
Assignment
Assignment 3: Agent-based modelling The tutorial on agent-based modelling by Macal & North (2010) in your reader explains what Agent-based models (ABM) are. It provides the structure of an ABM (agents, relationships & interactions and environment). Read the paper of Bennet and Tang (2006) (reading material for this topic). Write an essay that explains the relationships & interactions steering agent behaviour used in the Elk model, providing examples of rules used in the model. In addition, try to identify and explain some key ABM properties in the Elk model, like emergence and path-dependence. Write an approximately 2 page short paper or essay on this topic. Hand in before March 20th 17.00h by emailing Judith Verstegen, [email protected].
Questions?