Agents for spatial modelling - Department of Geography ... · PDF fileemotions, goal directed behaviour, group formation, disease, crime ... Netlogo is treated in detail in “Agent

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...


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.



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.


We typically lack explicit large-scale understanding


Explicitly Spatial Process-based understanding needed We typically lack explicit large-scale understanding needed


• 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!


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 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 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...


•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


•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


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


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


Increasing Numbers


•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


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


Forest Model – a specific example •Bore Khola Valley

•Nepal Middle Hills

•27.5º50’N 85º20’E

•20km N of Kathmandu


Forest Model – a specific example •Bore Khola Valley

•Nepal Middle Hills

•27.5º50’N 85º20’E

•20km N of Kathmandu


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



N

4 km

Year 0 Year 600 Year 1200

•Tree density accumulates over time



N

4 km



•Now we send people out into the forest


but memory gives a huge benefit in gathering efficiency

with memory random

Foraging



N

4 km




•Then people clear the forest for farming



N

4 km




•Then people clear the forest for farming



•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



•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



Increasing Numbers




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




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




The island can flood in a few hours Evacuation may be necessary

Traffic simulations can help to understand the evacuation process


. Ozioma Uzoegwu 2013 Mphil. Thesis

Policy options can be tested to see what might improve evacuation times.


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


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!


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?


Data Gathering How to understand behaviour How to generalise case studies How deal with large and small scales

Documents

Agents for spatial modelling - Department of Geography ... · PDF fileemotions, goal directed behaviour, group formation, disease, crime ... Netlogo is treated in detail in “Agent