1. Introduction to Computing
for Complex Systems
(Lab Session 5)
daniel martin katz
illinois institute of technology
chicago kent college of law
@computationaldanielmartinkatz.com computationallegalstudies.com

2. Simple Birth Rates

3. Simple Birth Rates

4. Simple Birth Rates

5. Simple Birth Rates
take a few minutes and play around with the model
consider the questions offered above

6. Thinking Conceptually:
Simple Birth Rates
What Does the Turtle Movement Add to the Model?
Are Turtles Added to the Model?
and If So How?
Are Turtles Removed from the Model?
and If So How?

7. Simple Birth Rates:
Exploring the Code

8. Simple
Birth
Rates

9. Experiment
Basic Setup
Simple Birth Rates
Death
Plots
Reproduction
Movement

10. Simple Birth Rates
“To Setup” Procedures

11. Simple
Birth
Rates
“To Go”
Procedures

12. Simple Birth Rates
Turtle Movement
Procedures

13. Simple Birth Rates
Please
Review
“ifelse”
How does
it work?

14. Simple Birth Rates
Take a Look
at the
Reproduction
Procedures

15. Simple Birth Rates
Death
Procedures
Plot
Procedures

16. Step 1: map the dependancies
Step 2: learn the syntax and
functionality for all
unknown primitives
Step 3: read each line of code and
determine what it doing
Simple Birth Rates
Step 4: sketch a procedures map
that follows the chronology
of your program
At this point it is more Important for you to go
though the models line by line on your own using
the above protocol

18. Wolf-Sheep Predation

19. The Lotka-Volterra Equation is Traditional
Approach to this Question is Differential
Equation
Classic Predator-Prey
Question to answer ... what do we learn through
the Agent Based Implementation that is not
captured the standard approach?

20. Wolf-Sheep Predation

21. Wolf-Sheep Predation
A Mini Eco-System Model
Sheep rely on Grass
Wolf rely upon Sheep
Implicitly Wolf rely upon grass

22. Wolf-Sheep Predation
Set Different Starting Values for Sheep
Return Rates for Food Can Differ
There are Birth Rates for Grass, Sheep, Wolves

23. Wolf-Sheep Predation
Lots of
Parameters
Grass
switcher
Shows how close an
agent is to death
Plots are
Useful for
observing
model
stability

24. Wolf-Sheep Predation

25. Wolf-Sheep Predation

26. Wolf-Sheep Predation
Relies upon a number of different rules
that we have seen in prior models
reproduction rule
death rule
different initial conditions
spatial movement around
the landscape
etc.

27. Wolf-Sheep Predation
This is default settings with grass on
What is happening in the model?

28. Wolf-Sheep Predation
What is happening in the model?
Changed 1 parameter “sheep gain from food”
(From 4 to 8)

29. Wolf-Sheep Predation
Notice the difference in the 4 model runs
Changed 1 extra parameter “wolf gain from food”
Still “sheep gain from food” (From 4 to 8)
Now also “wolf gain from food” (From 20 to 40)

30. Wolf-Sheep Predation
Wolf Sheep is more of an agent based model
remember in simple birth rates there
was a system level carrying capacity
Here we keep track of individual turtles and they can
die based upon individual values
And of course individual spatial interactions
Sheep
vs.
Grass
wolf
v.
sheep

31. Wolf-Sheep Predation
You can observe these interactions and the
declining energy counts
mr. wolf
better get
some food

32. Wolf-Sheep Predation
This energy count might
useful in a number of
models
Simulated Market where
“energy” could become
money, etc.
Agents could make various cost / benefit
calculations as they undertake a given action
Those agents need not make the “optimal” choice
(i.e. they could have cognitive biases, etc. and you
could write those into the model)

33. Novel Recombinations
of Code
We are trying to show a set of models
with useful features
to your substantive question(s) of interest
Then you can develop various novel
combinations of these and other models

34. Recycle & Reuse Code
You should re-use as much
code as possible
also, a code “scrapyard” from which you
might acquire parts to fix your model
Lots of Code Examples in existing models
Lots of Code Examples online

35. Getting to the
Code “Scrapyard”

36. Some Examples From
The Code “Scrapyard”

37. The Flocking Model

38. Scale-free correlations in starling flocks
Andrea Cavagnaa,b,1
, Alessio Cimarellib
, Irene Giardinaa,b,1
, Giorgio Parisib,c,d,1
, Raffaele Santagatib
, Fabio Stefaninib,2
,
and Massimiliano Vialea,b
a
Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy; b
Dipartimento di Fisica, Università di Roma “La Sapienza”, 00185 Rome,
Italy; c
Sezione Istituto Nazionale di Fisica Nucleare, Università di Roma “La Sapienza”, 00185 Rome, Italy; and d
Unità Organizzativa di Supporto di Roma,
Istituto per i Processi Chimico-Fisici, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
Contributed by Giorgio Parisi, May 11, 2010 (sent for review December 6, 2009)
From bird flocks to fish schools, animal groups often seem to react
to environmental perturbations as if of one mind. Most studies in
collective animal behavior have aimed to understand how a glob-
ally ordered state may emerge from simple behavioral rules. Less
effort has been devoted to understanding the origin of collective
response, namely the way the group as a whole reacts to its envi-
ronment. Yet, in the presence of strong predatory pressure on the
group, collective response may yield a significant adaptive advan-
tage. Here we suggest that collective response in animal groups
may be achieved through scale-free behavioral correlations. By
reconstructing the 3D position and velocity of individual birds in
large flocks of starlings, we measured to what extent the velocity
fluctuations of different birds are correlated to each other. We
found that the range of such spatial correlation does not have
a constant value, but it scales with the linear size of the flock. This
result indicates that behavioral correlations are scale free: The
change in the behavioral state of one animal affects and is affected
by that of all other animals in the group, no matter how large the
group is. Scale-free correlations provide each animal with an
effective perception range much larger than the direct interindivid-
ual interaction range, thus enhancing global response to perturba-
tions. Our results suggest that flocks behave as critical systems,
poised to respond maximally to environmental perturbations.
animal groups | collective behavior | flocking | self-organization |
emergent behavior
Of all distinctive traits of collective animal behavior the most
conspicuous is the emergence of global order, namely the
fact that all individuals within the group synchronize to some
extent their behavioral state (1–3). In many cases global ordering
amounts to an alignment of the individual directions of motion, as
in bird flocks, fish schools, mammal herds, and in some insect
swarms (4–6). Yet, global ordering can affect also other behav-
ioral states, as it happens with the synchronous flashing of tropical
fireflies (7) or the synchronous clapping in human crowds (8).
The presence of order within an animal group is easy to detect.
However, order may have radically different origins, and dis-
covering what is the underlying coordination mechanism is not
straightforward. Order can be the effect of a top–down central-
ized control mechanism (for example, due to the presence of one
or more leaders), or it can be a bottom–up self-organized feature
emerging from local behavioral rules (9). In reality, the lines are
often blurred and hierarchical and distributed control may
combine together (10). However, even in the two extreme cases,
discriminating between the two types of global ordering is not
trivial. In fact, the prominent difference between the centralized
and the self-organized paradigm is not order, but response.
Collective response is the way a group as a whole reacts to its
environment. It is often crucial for a group, or for subsets of it, to
respond coherently to perturbations. For gregarious animals
under strong predatory pressure, in particular, collective re-
sponse is vital (2, 11, 12). The remarkable thing about a flock of
birds is not merely the globally ordered motion of the group, but
the way the flock dodges a falcon’s attack. Collective response is
the trademark of self-organized order as opposed to a central-
ized one. Consider a group where all individuals follow a leader,
without interacting with one another. Such a system is strongly
ordered, as everyone moves in the same direction. Yet, there is
no passing of information from individual to individual and
hence behavioral fluctuations are independent: The change of
direction of one animal (different from the leader) has very little
influence on that of other animals, due to the centralized nature
of information transfer. As a consequence, collective response is
very poor: Unless detected directly by the leader, an external
perturbation does not elicit a global reaction by the group. Re-
sponse, unlike order, is the real signature of self-organization.
In self-organized groups the efficiency of collective response
depends on the way individual behavioral changes, typically
forced by localized environmental perturbations, succeed in
modifying the behavior of the whole group. This key process is
ruled by behavioral correlations. Correlation is the expression of
an indirect information transfer mediated by the direct in-
teraction between the individuals: Two animals that are outside
their range of direct interaction (be it visual, acoustic, hydrody-
namic, or any other) may still be correlated if information is
transferred from one to another through the intermediate
interacting animals. The turn of one bird attacked by a predator
has an influence not only over the neighbors directly interacting
with it, but also over all birds that are correlated to it. Correla-
tion measures how the behavioral changes of one animal in-
fluence those of other animals across the group. Behavioral
correlations are therefore ultimately responsible for the group’s
ability to respond collectively to its environment. In the same
way, correlations are likely to play a fundamental role in other
kinds of collective decision-making processes where informed
individuals (e.g., on food location or migration routes) can ex-
tend their influence over many other group members (10).
Of course, behavioral correlations are the product of in-
terindividual interaction. Yet interaction and correlation are dif-
ferent things and they may have a different spatial (and sometimes
temporal) span. Interaction is local in space and its range is typ-
ically quite short. A former study (13) shows that in bird flocks the
interaction range is of the order of few individuals. On the other
hand, the correlation length, namely the spatial span of the cor-
relation, can be significantly larger than the interaction range,
depending chiefly on the level of noise in the system. An ele-
mentary example is the game of telephone: A player whispers
a phrase into her neighbor’s ear. The neighbor passes on the
message to the next player and so on. The direct interaction range
is equal to one, whereas the correlation length, i.e., the number of
Author contributions: A. Cavagna, I.G., and G.P. designedresearch; A. Cavagna, A. Cimarelli,
I.G., R.S., F.S., and M.V. performed research; A. Cavagna, I.G., F.S., and M.V. contributed
new reagents/analytic tools; A. Cavagna, A.Cimarelli, I.G., G.P., F.S., and M.V. analyzed data;
and A. Cavagna wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
1
To whom correspondence may be addressed. E-mail: andrea.cavagna@roma1.infn.it,
irene.giardina@roma1.infn.it, or giorgio.parisi@roma1.infn.it.
2
Present address: Institut für Neuroinformatik, Universität Zürich, Winterthurerstrasse
190, CH-8057 Zurich, Switzerland.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1005766107/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1005766107 PNAS | June 29, 2010 | vol. 107 | no. 26 | 11865–11870
ECOLOGY
June 29, 2010 Issue
Flocking is still
an active area
of research

39. This is a really
interesting paper
Jing Han, Ming Li & Lei Guo
“soft control on collective
behavior of a group of
autonomous Agents by a
shill agent”
arXiv:1007.0803v1[cs.MA]6Jul2010
PUBLISHED IN JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY, 2006(19):54-62 1
Soft Control on Collective Behavior of a
Group of Autonomous Agents by a Shill Agent
Jing Han, Ming Li and Lei Guo
Abstract
This paper asks a new question: how can we control the collective behavior of self-organized multi-
agent systems? We try to answer the question by proposing a new notion called ‘Soft Control’, which
keeps the local rule of the existing agents in the system. We show the feasibility of soft control by
a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al.
for flocking of birds: each agent moves with the same speed but with different headings which are
updated using a local rule based on the average of its own heading and the headings of its neighbors.
Most studies of this model are about the self-organized collective behavior, such as synchronization of
headings. We want to intervene in the collective behavior (headings) of the group by soft control. A
specified method is to add a special agent, called a ‘Shill’, which can be controlled by us but is treated
as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize
the whole group to an objective heading. This control law is proved to be effective analytically and
numerically. Note that soft control is different from the approach of distributed control. It is a natural
way to intervene in the distributed systems. It may bring out many interesting issues and challenges on
the control of complex systems.
Index Terms
Collective Behavior, Multi-agent System, Soft Control, Boid Model, Shill Agent
This work was supported by the National Natural Science Foundation of China.
Jing Han and Lei Guo are with the Institute of Systems Science, AMSS, Chinese Academy of Sciences, Beijing,
100080, China. Ming Li is with the Institute of Theoretical Physics, Chinese Academy of Sciences. Corresponding author:
hanjing@amss.ac.cn.
Thanks to John Holland
For Suggesting it

40. The Flocking Model

41. The Flocking Model

42. The Flocking Model
Take a few minutes
and explore the
model including
these questions

43. The Flocking Model

44. The Flocking Model

45. The Flocking Model
What is “Population”?
It is a variable on the
slider
What is happening in
the “To Setup”?
create population
set turtles to
random shades
of yellow
set the size to 1.5
start with random x,y
heading coordinates
clear all

46. ask turtles [flock]
The Flocking Model
(We will get to [flock]
in just a moment)
to understand how
“display” helps the
interface
remove it from code
and then re-run
the model interface
notice it is giving
the model the
smooth movement

47. The Flocking Model
What is [ask turtles [FD 0.2]?
Turtles are moving FD .2
Immediately Updated using
the “display” command
.2 x (repeat 5) = 1
this is the same as [ fd 1]
then the model “ticks”
forward and does not stop
until button is turned off

48. The Flocking Model
back to ask turtles [flock]
which is used in the “to go”
“to flock” gateway to
balance of the model

49. The Flocking Model
flockmates = turtles-own agentset of nearby turtles
We use the “Set” Command to assign it a value
“Set” to an agentset of “other turtles in-radius vision”

50. What is
“other turtles
in-radius vision”?
Other = All Turtles
in Radius Except for
the Calling Turtle
Radius = Allows for
an agentset that is
defined by distance
from a calling agent
Vision = parameter
value that was set
on the slider

51. It is going to use an “if” springing condition
If no flockmates than it is
going to tick the model
forward (rinse and repeat)
If it does find a flockmate than notice there is
also an “ifelse” within the “if”
“To Flock” Procedure

52. “find-nearest-neighbor”
First how does it do the “find-nearest-neighbor”?
it has to “set” a value for this
looks within the “flockmates” and selects
“min one of” “flockmates” relative to distance
from myself
“min-one-of” handles
ties by selecting at
random

53. remember “ifelse” sets up two possible conditions ...
the “ifelse” split in the road
Take a look at how it is split up
Notice the brackets
If Condition is
satisfied than
[ separate ]
If Condition is not
satisfied (i.e. else)
[ align cohere]

54. Cohere, Align & Separate
If Condition is
satisfied than
[ separate ]
If Condition is not
satisfied (i.e. else)
[ align cohere]
Please Review the Cohere,
align & Separate
Procedures on your own

55. Cohere, Align & Separate
relies upon other
procedures as
shown above
= slider variable
= nested procedure

56. The Hawk/Dove Model
http://ccl.northwestern.edu/netlogo/models/community/

57. The Hawk/Dove Model
In the Community Models it is called “game theory”
http://ccl.northwestern.edu/netlogo/models/community/
Download the “gametheory.nlogo” file and save it to
the desktop or to a folder of your choosing

58. The Hawk/Dove Model
Easiest Thing is to simultaneously
install 3.1.5 along with Netlogo 4.1
Current Version of “gametheory”
is Implemented in Netlogo 3.1.5
you should be able run netlogo
3.1.5 and 4.1 on the same machine
If you do not already have netlogo 3.1.5 as
well as 4.1 --- please install it on your machine

59. How to Acquire
Netlogo 3.1.5
http://ccl.northwestern.edu/netlogo/download.shtml

60. The Hawk/Dove Model
After Installing, Open Version 3.1.5 on your desktop
From within 3.1.5 File ---> Open
Find the “gametheory.nlogo”
file and open it from within
netlogo version 3.1.5
(If necessary close
any open version of
netlogo 4.1)

61. The Hawk/Dove Model
the interface is slightly different and
some of the syntax is slightly different

62. The Hawk/Dove Model

63. The Hawk/Dove Model

64. The Hawk/Dove Model
Can you identify instances where the “retaliator”
behavioral strategy does not win out?

65. Parameter Sweeps?
Thinking about “parameter sweeps”
We would like to be able to evaluate all possible
parameter values with all possible parameter values
100 doves
100 hawks
100 retaliators
10 values
10 costs
100 reproduce-thresholds
100 init-energies
100 energy-time-thresholds
x
at least say 50 values
per parameter
configuration to
get some sort of
a statistical
distribution
100,000,000,000,000
Even with some of Netlogo’s Parallelization, this is
going to be hard -- here is why

66. Parameter Sweeps?
Perhaps we do not have to search the full space
perhaps we can grid the analysis and
interpolate between the spaces
Even for a limited incursion into the space,
we need to think about form of automation