GEOG3150 Semseter 2
Lecture 5
INTERACTION AND
BEHAVIOUR
Dr Nick Malleson
Dr Alison Heppenstall
Recap (last week)
Emergence
“The whole is greater than the
sum of its parts.” (Aristotle ?)
Complex structures emerge
from simple rules
Cellular Automata and the
Game of Life
3 Simple rules
Incredible, beautiful,
fascinating worlds
Attribution: Yewenyi at the English language Wikipedia
https://en.wikipedia.org/wiki/User:Yewenyi
Recap (last week)
Non-linearity (non-linear
systems)
System output is not directly
proportional to inputs
Small changes -> complex
outcomes
A bunnies tale
The logistic map
Flocks
Wolf / sheep predation
xn1  rxn (1  xn )
Recap (last week)
Chaos (chaotic systems)
“Deterministic systems whose trajectories
diverge exponentially over time” (url)
The butterfly effect
Usually a property of complex systems
Highly dependent on initial conditions
Very difficult to predict, in the long term
Not random!
Complexity (complex systems)
Many interaction components
“High dimensional chaos”
Complex systems exhibit emergence,
non-linearity and chaos
Photo attributed to Gianfranco Reppucci on Flickr
http://www.flickr.com/photos/giefferre/4446877066/
Today
Interaction
What is it? Why is it important in these models?
How do we simulate interactions?
Behaviour
How should we model behaviour?
How do we know which sorts of behaviour to
include?
Seminar
The Ethics of Individual-Level Modelling
Reading
Kennedy, W.G., 2012. Modelling Human Behaviour in Agent-Based Models.
in: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (Eds.),
Agent-Based Models of Geographical Systems. Springer Netherlands, pp. 167–179.
See the reading list (at the library)
Wooldridge, M (2000) Reasoning about rational agents MIT Press, Cambridge.
(There is also a review of the book in the JASSS).
Interaction and Behaviour
Interaction
What is it?
How should we model it?
Behaviour
What to include?
Modelling Behaviour
Cognitive architectures
Seminar - The Ethics of Individual-Level Modelling
Attribution: Yewenyi at the English language Wikipedia
https://en.wikipedia.org/wiki/User:Yewenyi
Interactions:
Global and Local
Interactions: a key part of agentbased models
One of the most powerful elements
for modelling complex systems
Global interactions
Macro scale of the model: these can
result in immediate massive changes
on all agents. E.g.:
A new policy on migration
Climate change
Limits on number of children couples
allowed to have.
Local interactions
Interactions that occur at the micro or meso level. These only
affect those in the local vicinity.
Question
Give me some examples of local and
global interactions in the real world, or in
your NetLogo models
Local interactions:
Global interactions:
Interactions
Interactions can be spatially constrained:
Neighbourhood feuds
Car crashes
Or a-spatially (e.g. across social networks):
Exchange of information between students about a cool new
Level 3 course…! Not explicitly spatially defined.
Twitter
From interactions, new information, new interactions etc
emerge.
Interactions add to the complexity of the system.
Getting the interactions correct is crucial.
As the experts say…
“In an agent-based model we can model
interactions explicitly as ways that individual agents
affect each other and their environment.
Consequently, the effects of interaction, and even
the kinds of interaction, can depend on the state of
the agents (including…their location) and on their
environment”
Railsback and Grimm p.169, (2012)
Types of Interactions
Direct interaction: agents directly
interacting with each other affect each other
e.g. exchanging information.
Mediated or indirect interaction: agents
interact indirectly via a mediating resource
e.g. competition for a shared resource.
Question
Give me some examples of direct and
mediated interactions
Direct interactions
Real world examples
Examples from agentbased models
Mediated interactions
Simple Example:
Direct Interaction
NetLogo > Models Library > Social Science > AIDS
Simulates the spread of HIV through an isolated population.
Turtle = Human
People form sexual relationships
Stand still in a relationship
Otherwise walk around
Everyone tests for HIV occassionally
Infected status:
Green -> uninfected
Blue -> infected (unknown)
Red -> infected (known)
Known infected always use a condom
(and these never fail)
HIV Example (Direct Interaction)
Setup
Default settings
Test frequency 0.3
times/year
Results after ~1000 weeks
Proportion infected
stabilises at 19%
What will happen if we reduce the length of
commitment to 20 weeks? (more frequent sexual
relationships)
HIV Example (Direct Interaction)
Setup
Same as previous
Commitment increased
to 20 weeks
Results after ~1000
weeks
Proportion infected
83% (and growing)
Altering the rules of the interactions have a dramatically different
effect on the system.
Simple Example:
Indirect Interaction
NetLogo > Models Library > Social Science > Cooperation
Turtle = Cow
Patch = Amount of grass on each
patch variable. Short grass grows
more slowly.
Cows are competing for a resource.
Successful cows reproduce more
often
Two behaviours:
Cooperative: don’t eat short grass
Greedy: always eat
“Cooperative agents leave more food
for the overall population at a cost to
their individual well-being”
Greedy versus Cooperative
Default settings.
Cooperative probability = 0.50
(Half cows cooperative)
What happens if energy from grass reduces to from
51 to 20? (Cows will need to eat more grass).
Greedy versus Cooperative
Grass-energy = 20 (reduced
from 50)
Again, changing the nature of the interactions (cows and grass)
has a considerable impact on the outcomes
Interaction and Behaviour
Interaction
What is it?
How should we model it?
Behaviour
What to include?
Modelling Behaviour
Cognitive architectures
Seminar - The Ethics of Individual-Level Modelling
Implementing interactions in
NetLogo
Taking the Cow example (competition for a
resource).
Two entities:
Grass (represented as patches with variable
amounts on)
Cows (represented as turtles)
Model is spatial (grass belongs to a field).
Cows Initialisation
Cow variables:
Location
Metabolism (amount of energy lost per turn)
Cooperative (Red)
Greedy (Blue)
to setup-cows
set-default-shape turtles "cow"
;; applies to both breeds
create initial-cows [
setxy random-xcor random-ycor
set energy ( metabolism * 4 )
ifelse (random-float 1.0 < cooperative-probability) [
set breed cooperative-cows
set color red - 1.5
] [
set breed greedy-cows
set color sky - 2
]
]
end
The interaction: Cows eat Grass
to eat-cooperative ;; turtle procedure
if grass > low-high-threshold [
set grass grass – 1
set energy energy + grass-energy
]
end
to eat-greedy ;; turtle procedure
if grass > 0 [
set grass grass – 1
set energy energy + grass-energy
]
end
Interaction & Behaviour Example:
Animal Predation
Another example – direct
interaction
Wolf-sheep predation
ask wolves [
move
set energy energy - 1
let prey one-of sheep-here
if prey != nobody [
ask prey [ die ]
;; wolves lose energy as they move
;; grab a random sheep
;; did we get one? if so,
;; kill it
set energy energy + wolf-gain-from-food
;; get energy from eating
]
if energy < 0 [ die ]
reproduce-wolves
]
;; die if run out of energy
Design concepts for simulating
interactions (1)
Basic Principles:
This example looked at competition for a resource
that is limited.
Emergence:
What happens to the amount of grass and cow
population over time? These results emerge from
the interactions between the two types of cows.
Different patterns emerge at different time-scales.
Design concepts for simulating
interactions (2)
Adaptive Behaviour
At present this isn’t in here, but can you think of
what sort of behaviour you could include and
how to code it?
Prediction
Do the simulation results match what is
reported in the literature or a commonly
accepted hypothesis?
Design concepts for simulating
interactions (3)
Interaction:
Only one type of interaction here: between the cows and
grass. Could we code in interaction between the greedy and
cooperative cows? How might that change the final
outcomes? Would you want the effect of any new interaction
to affect all of the cows, or just some?
Stochasticity
Cows and patches of grass are randomly located. Order in
which the patches of grass are eaten (and by whom) is
random.
Question
What type of interactions have you been building
in your NetLogo models during the practicals?
Interaction and Behaviour
Interaction
What is it?
How should we model it?
Behaviour
What to include?
Modelling Behaviour
Cognitive architectures
Seminar - The Ethics of Individual-Level Modelling
Modelling Behaviour
Great advantage of
ABM!
But lots of important
decisions about what to
include in a model (and
how).
Photo attributed to Arts Electronica (CC BY-NC-ND 2.0)
Modelling Behaviour
Choose a number between 1 and 4
Submit your answers at: http://pollev.com/nickmalleson
What percentage of people will choose
1:?
2:?
3:?
4:?
https://www.polleverywhere.com/multiple_choice_polls/OLaZoLv2B2BQsFR
Humans are not random!
“Humans are not random. They (we) are
strange and wonderful” (Kennedy, 2012)
The previous example shows that even if we
want to be, human decisions are not
random.
So, do I model the entirety of an
organism’s behaviour?
Two camps of thinking in building these
models:
Keep it descriptive, stupid (KIDS)
Keep it simple, stupid (KISS)
No ‘correct’ way to model behaviour.
We recommend identifying the most important
behaviour and building that into your models.
Two contrasting views of KISS and KIDS approaches:
Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In Conte, R., Hegselmann, R., and Terna, P. (eds) Simulating Social Phenomena , pages 21–40.
Springer-Verlag, Berlin. (Note: this book is available in the library, the author has also made a draft of the chapter available online: http://wwwpersonal.umich.edu/~axe/research/AdvancingArtSim2005.pdf ).
Edmonds, B. and Moss, S. (2005). From KISS to KIDS: an ‘anti-simplistic’ modelling approach. In Davidsson, P., Logan, B., and Takadama, K., editors, Multi Agent Based
Simulation 2004, Lecture Notes in Artificial Intelligence , pages 130–144. Springer. Available online: http://cfpm.org/cpmrep132.html (also here).
How do I know what behaviour is
important?
Through the use of black magic!
Published literature
Talking to the experts
Numerical experimentation
Rigorous data analysis
It is important to carefully research all the
information there is about the system you
are interested in modelling.
Interaction and Behaviour
Interaction
What is it?
How should we model it?
Behaviour
What to include?
Modelling Behaviour
Cognitive architectures
Seminar - The Ethics of Individual-Level Modelling
(Human) Behaviour in AgentBased Models
Great advantage of
ABM!
But really, really difficult
Subjective choices
Complex psychology
(Seemingly) irrational
behaviour
Photo attributed to Arts Electronica (CC BY-NC-ND 2.0)
A number of different ways of implementing
behaviour have been proposed
Rule-based Systems
Simple rule-based systems
IF < hunger > is below < hungerThreshold1 > THEN agent-dies.
IF < hunger > is above < hungerThreshold2 > THEN address-another-goal.
IF < hunger > is between < hungerThreshold1 > and < hungerThreshold2> THEN search-for-food.
Kennedy (2012)
Node-based
Similar to decision trees
(Remember LOTR and MASSIVE ?)
History: Building Autonomous
Robots
Long history of endowing robots with autonomous behaviour
We can adapt this literature for use in agent-based modelling
Robots became progressively more ‘intelligent’
Sensing environments
Reflex actions
Goal-based
Plans
Individual world view
Learning / evolution
…
See Brooks, R. (1996) From Earwigs to Humans. Robotics and Autonomous Systems 20: 291--304
Early Behaviour: Reactive
Agents
Purely reactive – simply
respond to input
Behaviours layered
hierarchically
Behaviour at lower levels
takes priority over
abstract, higher level
behaviours
Early Behaviour: Reactive
Agents
Simple, robust, efficient, BUT:
Purely reactive, only take local-info into account
How to learn / improve performance?
If many rules, interactions are too complex to
understand. Conflicts between rules?
More Advanced: Layered
Behaviour
Different subsystems to deal with proactive /
reactive behaviours
Horizontal / Vertical structure
Horizontal: requires “mediator” function
Vertical: one pass / two pass
Behaviour Based Artificial
Intelligence (BBAI)
Example of layered
behaviour
Hierarchical layers of
behaviour
Alternative to “Good old
fashioned artificial
intelligence” (Brooks, 1996)
Upper layers take control
when they can
No explicit input-output
No overall goals
Robot can default to lowerlevel behaviour
Behavioural modules,
working concurrently
compete/contribute to
overall behaviour
See Brooks, R. (1996) From Earwigs to Humans. Robotics and Autonomous Systems 20: 291--304
Behaviour Based Artificial
Intelligence (BBAI)
Example uses
Crime simulation
Birks, D. J., Donkin, S., and Wellsmith, M. (2008).
Synthesis over analysis: Towards an ontology for volume
crime simulation. In Liu, L. and Eck, J., editors, Artificial
Crime Analysis Systems: Using Computer Simulations
and Geographic Information Systems, pages 160–192,
Hershey, PA. Information Science Reference.
An aside: this is geocomputation at its best! Adapting method from
other fields (computer science, artificial intelligence, psychology,
etc.) to build models and better understand the world.
Criticisms of Layered Behaviour
Very popular (?)
Natural decomposition of functionality
BUT
Lack clarity
Over-complicated with many layers
Conflict between layers?
Not similar to human cognitive process (?)
Interaction and Behaviour
Interaction
What is it?
How should we model it?
Behaviour
What to include?
Modelling Behaviour
Cognitive architectures
Seminar - The Ethics of Individual-Level Modelling
Beliefs Desires Intentions (BDI)
Probably the most common
/ popular architecture in
ABM
Rational model
All action requires some
deliberation
Good representation of
human cognition (?). But:
Core elements hard to
observe directly
Rational decisions only
useful to a limited degree
(e.g. pulling hand away
from fire requires no
rational thought)
Beliefs:
Internal knowledge about the
world
Memory of past experiences
Desires
Goals the agent is trying to
achieve
Can be short and long term
(e.g. ‘eat food’, ‘raise children’
Can be contradictory
Intentions
The most important
immediate goals
Can alter the rapidity with
which an agent changes
intentions (e.g. ‘caution’).
PECS
Physical conditions, Emotional states,
Cognitive capabilities and Social status
Internal states
Physical
Emotional
Cognitive
Social
Motives
Intensity functions
determine motive strength
Include level of need, personal preference, environment,
others..
Strongest motive becomes action guiding motive
Interaction & Behaviour Example:
Modelling Burglary
Personal
preference,
p
Drug level
Intensity of drugs motive
m=p/s
Social level
Personal
preference,
p
Time of
day, t
Intensity of social motive
Determine
Strongest
Motive
m = p f(t) / s
Sleep level
Personal
preference,
p
Time of
day, t
m = p f(t) / s
Intensity of sleep motive
Plan
Actions
Summary
Correctly simulating interactions is crucial to
the success of a model.
Need to think about:
Objective of the research
What type of interactions are needed?
turtle  turtle
turtle  patch
patch -> turtle??
What are the results of these interactions? Test
the model carefully; KISS then build up slowly.
Summary (2)
Interactions form an important part of the
behaviour.
Behaviour is programmed as a set of rules that
an agent will follow.
Lots of different ways of organising the rules
(e.g. using cognitive architectures, or
something simpler)
You must justify your choice of behaviour
We will want to see this in your projects
http://www.geog.leeds.ac.uk/courses/level3/geog3150/home/home/course_outline.php
Now: Seminar
Ethics of Individual-Level Modelling
http://www.geog.leeds.ac.uk/courses/level3/geog3150/seminars/
seminar2/
http://bobnational.net/record/203575