Agents for spatial modelling - Department of Geography ... · PDF fileemotions, goal directed...
Transcript of Agents for spatial modelling - Department of Geography ... · PDF fileemotions, goal directed...
Agents for spatial modelling
Simulation “ a la Magritte”
On the Earth • Multiple interacting system types at different space and time scales
• Everything is connected to everything else!
Systems are generally • Heterogeneous • Spatially distributed • Many systems contain interacting discrete objects
Dynamics are generally complex (not just complicated!) • Sensitive to initial/boundary conditions • Path-dependent/contingent/adaptive
• Non-decomposable with multiple feedback loops • Far from equilibrium • Tipping points/Phase changes
Statistics are typically • Non gaussian - often with fat-tails • Non-stationary over time and/or space
Agent Based Modelling
Environmental Change typically displays complexity • Causes, processes and impacts of climate change • Ecosystems , their services, management and conservation • Environmental Hazards • Disease and pandemics • Urbanization and Land-use change • Economics, poverty, wealth distributions...
Agent Based Modelling
Environmental Change typically displays complexity
Explicitly Spatial Process-based understanding needed needed Environmental Models need to include the social process
•Dynamics of social, ecological and physical systems are coupled
•Decisions are not made on purely environmental but on economic, cultural and
political grounds also.
•Human and Ecological systems involve many interacting individuals in which global
structure emerges from and forms the framework for the small scale interactions
Large scale physical or economic simulations may seem remote, and may be hard to
translate into a form that relates to everyday experience.
•We need to make explicit the consequences of people's actions
•We need to give policy makers (and others) direct information about the
consequences of inaction.
Agent Based Modelling
Environmental Change typically displays complexity
Explicitly Spatial Process-based understanding needed needed Environmental Models need to include the social process
•Dynamics of social, ecological and physical systems are coupled
•Decisions are not made on purely environmental but on economic, cultural and
political grounds also.
•Human and Ecological systems involve many interacting individuals in which global
structure emerges from and forms the framework for the small scale interactions
Large scale physical or economic simulations may seem remote, and may be hard to
translate into a form that relates to everyday experience.
•We need to make explicit the consequences of people's actions
•We need to give policy makers (and others) direct information about the
consequences of inaction.
Agent Based Modelling
We typically lack explicit large-scale understanding
Environmental Change typically displays complexity
Explicitly Spatial Process-based understanding needed We typically lack explicit large-scale understanding needed
Agent Based Modelling
• Approach by simulation of every discrete object in the system • Use sets of rules for behaviour that are intuitively
reasonable to model the human aspects of the system
• Hope that large scale systematic behaviours emerge!
Agent Based Modelling
Agents are discrete actors capable of self-generated autonomous activity that effect change in their own
state and/or that of their surroundings
Agent Based Modelling
Agents are discrete actors capable of self-generated autonomous activity that effect change in their own
state and/or that of their surroundings
•A rock ?
• No
•A tree ?
• Yes
• reacts to light/water/CO2
• responds to attack with chemical signals
•An animal ?
• Yes
• adds possibility of free movement
• and reasoned behaviour – possibly even in humans...
Agent Based Modelling
Agents are discrete actors capable of self-generated autonomous activity that effect change in their own
state and/or that of their surroundings
Agent-based models represent some aspects of real-world agents using autonomous software components (objects).
•Originate in knowledge-based Artificial Intelligence
•Interlinked sets of rules determine agent behaviour
Agent Based Modelling
Agents are discrete actors capable of self-generated autonomous activity that effect change in their own
state and/or that of their surroundings
Agent-based models represent some aspects of real-world agents using autonomous software components (objects).
•Originate in knowledge-based Artificial Intelligence
•Interlinked sets of rules determine agent behaviour
Many types are possible in a single model Trees, people, households, businesses, institutions, NGOs, governments,
cities, countries... Many aspects possible, including qualitative information
emotions, goal directed behaviour, group formation, disease, crime...
Agent Based Modelling
•Agents are embedded in an environment • Agents may be able to change the environment • The environment may have its own dynamical processes that drive
change
• Environment may be continuous (e.g. topography) or discrete (rocks) or some mixture of both
• Spatial agents additionally have spatial co-ordinates and may have the power to change location within the environment
•An agent model typically has many agents of different types
– Often called Multi-Agent Systems (MAS)
– An agent may interact with and modify other agents, either directly or indirectly through the environment
•In either case interaction can
– Imply complex feedback loops across time and space scales
– Lead to the emergence of structure not explicitly represented in any single agent
Software agents are discrete entities with individually modifiable properties and behaviours
Agent Based Modelling
•Agents may be
“strong” with a rich cognitive structure, goal oriented plans, learning, norm creation and internal representations of other entities – able to reason about inputs and own internal state – can represent and model their own surroundings, and learn new responses
“weak” or “reactive” with simple fixed reactions to other agents and the
environment, possibly in differing ways in similar circumstances depending on history
Software agents are discrete entities with individually modifiable properties and behaviours
Agent Models and the environment
•Advantages – We can deal with heterogeneous, non equilibrium systems with
non-stationary time evolution
– Do “what-if” experiments in a way that may be impossible (or unethical) in a real system
– Easier to communicate results to policy makers or to the wider public
•Account for detailed population structure and behaviour – Age, sex, social class, health, wealth
– Modify behaviour based on perceptions and memory
– Bounded knowledge, erroneous beliefs, deception and deceit
– Generate histories and examine path dependence
•Deal with “Space and place” – semantic content
– movement, clustering, real geographies
Agent Based Modelling
Discrete Element Models Avalanches and debris flows Cliff-scree systems Individual-Based Models Forest simulation Herds and flocking Foraging Predator-prey models Agent-Based Models Epidemics Traffic simulation Crowds and escape from disaster Urban populations Social-Ecological Systems Land-use Change
Agent Based Modelling
Discrete Element Models Avalanches and debris flows Cliff-scree systems Individual-Based Models Forest simulation Herds and flocking Foraging Predator-prey models Agent-Based Models Epidemics Traffic simulation Crowds and escape from disaster Urban populations Social-Ecological Systems Land-use Change
Increasing Numbers
Increasing Complexity
Agent Based Modelling
Cliff –scree systems
Discrete Element Modelling
Cliff-scree systems – distribution of avalanches Early evolution – left-skewed, short tail, characteristic size Later evolution – long tail, power law – self-organized criticality Results largely independent of model parameters
Late period distributions - long tail, no typical size, distribution is close to a power law
Early period statistics – a short tail and a modal value
Discrete Element Modelling
Discrete Element Models Avalanches and debris flows Cliff-scree systems Individual-Based Models Forest simulation Herds and flocking Foraging Predator-prey models Agent-Based Models Epidemics Traffic simulation Crowds and escape from disaster Urban populations Social-Ecological Systems Land-use Change
Increasing Numbers
Increasing Complexity
•Individual-based model representing each tree
•Allometric rules for tree growth
•Competition primarily through shading
•Different functional types with varying shade tolerance and growth parameters
•Examples
• SORTIE (Pacala et al 1996)
• TROLL(Chave 1999)
Forest Model
Individual Based Modelling
Low growth trees in shade have a high probability of dying
Forest Model
Individual Based Modelling
Forest Model Number by area distribution is exponential
Most of the wood is in large trees Shade tolerant species crowd out the pioneers
Huge number of seedlings – but they don’t make it to maturity
Individual Based Modelling
Forest Model – a specific example •Bore Khola Valley
•Nepal Middle Hills
•27.5º50’N 85º20’E
•20km N of Kathmandu
Individual Based Modelling
Forest Model – a specific example •Bore Khola Valley
•Nepal Middle Hills
•27.5º50’N 85º20’E
•20km N of Kathmandu
Individual Based Modelling
Forest Model – a specific example
N
4 km
Topography
•4km square catchment
•Height data at 20m horizontal resolution, 10m in vertical
•Overall relief approx. 1000m
Individual Based Modelling
Forest Model – a specific example
N
4 km
Year 0 Year 600 Year 1200
•Tree density accumulates over time
Individual Based Modelling
Forest Model – a specific example
N
4 km
Year 0 Year 600 Year 1200
•Tree density accumulates over time
•Now we send people out into the forest
Individual Based Modelling
but memory gives a huge benefit in gathering efficiency
with memory random
Foraging
Agent Based Modelling
Forest Model – a specific example
N
4 km
Year 0 Year 600 Year 1200
•Tree density accumulates over time
•Now we send people out into the forest
•Then people clear the forest for farming
Agent Based Modelling
Forest Model – a specific example
N
4 km
Year 0 Year 600 Year 1200
•Tree density accumulates over time
•Now we send people out into the forest
•Then people clear the forest for farming
Agent Based Modelling
Year 600 Year 660 Year 720
•Fields Highlighted in green, degraded forest in yellow
•Farmers Exploit the lower part of the catchment first
•Trees are removed much faster then they can recover
Forest with people
Agent Based Modelling
Year 600 Year 660 Year 720
•Forested areas are good at attenuating water
•Soil compaction in farmed areas increases soil saturation
•As farming increases, flash floods become more likely
Bithell and Brasington 2008
Forest with people
Agent Based Modelling
Discrete Element Models Avalanches and debris flows Cliff-scree systems Individual-Based Models Forest simulation Herds and flocking Foraging Predator-prey models Agent-Based Models Epidemics Traffic simulation Crowds and escape from disaster Urban populations Social-Ecological Systems Land-use Change
Increasing Numbers
Increasing Complexity
Agent Based Modelling
Agent Based Modelling
Map data: Crown Copyright/ Database right 2013 – An Ordnance Survey/EDINA supplied service. Flood modelling by James Brown
Brown, J.D, Spencer,T. And Moeller,(2007) Water Resources Research 43.
Canvey Island In the great 1953 flood, sea defences failed. 58 people died, 11000 evacuated.
Thames Estuary
Now 38000 people behind 4.66m high wall. The illusion of safety provided by the sea wall may have encouraged settlement.
Only a single exit road
Agent Based Modelling
Map data: Crown Copyright/ Database right 2013 – An Ordnance Survey/EDINA supplied service. Flood modelling by James Brown
Brown, J.D, Spencer,T. And Moeller,(2007) Water Resources Research 43.
Canvey Island In the great 1953 flood, sea defences failed 58 people died, 11000 evacuated
As the climate changes, extreme events are predicted to become more likely. Storm surges may again over- top or breach the flood barrier
Simulated breach
Agent Based Modelling
Map data: Crown Copyright/ Database right 2013 – An Ordnance Survey/EDINA supplied service. Flood modelling by James Brown
Brown, J.D, Spencer,T. And Moeller,(2007) Water Resources Research 43.
The island can flood in a few hours Evacuation may be necessary
Traffic simulations can help to understand the evacuation process
Agent Based Modelling
. Ozioma Uzoegwu 2013 Mphil. Thesis
Policy options can be tested to see what might improve evacuation times.
Agent Based Modelling
Agent Based Modelling and Disease
Disease models
Agents allow us to disentangle behaviour from other effects
•Propagation of disease is directly modelled as transmission between infected and susceptible individuals •Contact processes
– Can include effects of social networks – Are constrained by the physical environment – Can change with agent perceptions of symptoms
Agent Based Modelling and Disease
Conclusions
We can directly model processes in systems of discrete objects Deal with situations where we lack analytic power Emergent properties arise from collective interactions Multiple coupled systems can be dealt with Test policy options where not possible to experiment Very visual – good for policy communication
Larger scale, more complete, more complex systems Social processes and networks in real-world situations Model the “Anthropocene” – current “Earth System Models” do not include people
Grand Unified Models!
Future
Agent Based Modelling
Agents programming systems and references Modelling Environments Netlogo RePast (Swarm) Mason Gama ...many more Netlogo is treated in detail in “Agent based and individual-based modelling: a practical introduction” (2012) Grimm and Railsback (Princeton) For a review of many others see “Design of Agent-based models” (2011) by Tomas Salamon (Academic Series)
Starting references •“Agent based models of Geographic Systems” 2012 Heppenstall et al eds. (springer) •“Simulating Social Complexity: A Handbook” 2013 Edmonds and Meyer (eds) (springer) •“An introduction to multi-agent systems” 2nd ed. 2009 Wooldridge (Wiley) •“Growing artificial societies: social science from the bottom up” 1996 Epstein and Axtell (brooking Institution Press) ... a classic!
Agent Based Modelling
Challenges
Vizualization System size Spatial extent Complex interacting dynamical systems
Model coupling Cross-disciplinarity Sharing and reproducing models/results Joining complex dynamical models
Validation Reflexivity Causality Data integrity Handling uncertainty
Complexity How intelligent do agents need to be? How much complexity is “enough”? What can be simulated?
Scaling System size Parameter space exploration Processes at different scales Micro-macro links
Model ownership Democratization of knowledge Policy assessment Risk and environmental change
Problem framing What should be modelled? Who for? What is relevant?
Agent Based Modelling
Data Gathering How to understand behaviour How to generalise case studies How deal with large and small scales