Download Computational Social Science

ETH D-GESS: 851-0585-37L
Social Modelling, Agent-Based
Simulation and Collective Intelligence
(Week 2)
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 1
Emergent Residential Segregation
using
Simple Social Behavior
ETH D-GESS: 851-0585-37L Week 2
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 2
This is a graduate seminar course in a field of scientific inquiry called
Computational Social Science, or CSS for short.
During the course we will read about, talk about, and consider
theoretic and practical instances of complex social processes
instantiated as computer code.
By the end of the course we will want to discuss our mental models of
what is Collective Intelligence and how we might code it.
But that is still a long, long way off ….
Let’s get started!
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 3
In this course we are going to study Complex Systems,
not merely Complicated Systems.
So, we may need to draw a practical distinction:
A Ferrari is a complicated system but its performance is predictable.
A complex system will “almost exclusively” be non-linear, possibly
computable, usually stochastic, will be sensitive to initial
conditions, will have path dependencies, and will demonstrate
probabilistic tendencies. They will often have instance distributions
that follow a Power Law and have regime (or phase) changes
near so called tipping points.
Social models typically involve the study of complex social systems.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 4
In CSS we begin by constructing mental models of the social
systems and social processes that interest us. Then we derive
hypotheses regarding the endogenous and exogenous forces
that we think may drive the system. Finally, we instantiate our
model (and those hypotheses) in computer code.
As our code executes, we compare the data produced by our
executing program(s) with data collected from the “real-world.”
Thus, the “real-world” provides us with empirical data for
inspiration and comparison, and the computer becomes our
laboratory. We often refer to instantiating social models in
software as working in-silico.
In Computational Social Science we usually test our models in-silico.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 5
Working this way, in-silico, allows us to examine social processes,
social models, and our assumptions about how the world, our local
environment, our social groups, and even we ourselves react to (and
within) the world around us.
That is what this course is about.
This course is here to help you start thinking about, constructing,
analyzing, and using social modelling and agent-based simulation as
a way to understand, to explain, and to improve your world.
Computational social models can give us insights not possible by other means.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 6
Course Overview
Procedure (Parts I & II):
1. Examine a selection of published, formal models of social processes
2. Learn how to analyze and extend simple models and to develop your own
social process models using existing computer-coded examples
By the end of the course you will be able to:
1. Recognize several well-known, “key,” social process models
2. Understand, analyze, & compare model-outcomes using empirical data
3. Influence the outcome of a coded positive or normative process model
Grading:
1. Reading, writing, class participation, running code, 2-exams, & extra-credit.
Social Modelling, Agent-Based
Simulation and Collective Intelligence
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 7
We will have lectures by:
 Professor Dr. Dirk Helbing
•
•
•
•
•
•
Pedestrian models
Game theoretic models
Traffic models
Internationally recognized scientist and author
A thought leader in Computational Social Science
A “future-thinker”
 Dr. Matthias Leiss
• Economic and market models
• ETH D-GESS program in Computational Social Science
Let me introduce myself.
Then, I want to meet you …
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 8
I am a technologist turned scientist …
Academics by degree, school (year)
BS Psychology, College of the Southwest (1996)
BS Computer Science, Eastern New Mexico University (1998)
Minors in mathematics & electronics technology
MS Electrical Engineering, New Mexico State University (2000)
Thesis: “Cybernetic intelligence: a return to complex qualitative
feedback theory”
(Daryl mouse in software)
(ZI prototypes)
PhD Computational Social Science, George Mason University (2015)
Thesis: “Towards Emergent Social Complexity”
So, who am I?
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 9
Engineering & Technology by company, location (function)
Honeywell, Phoenix (FPGA & EPLD hardware design engineer)
GMU, Fairfax (adjunct professor in Electrical Engineering)
SRS, Arlington (DARPA SETA)
I3, Arlington (JIEDDO & US Army REF SETA)
GMU, Fairfax (research project manager, agent-based modeler)
Camber, Centreville (agent-based modeler)
Harmonia, Blacksburg (machine intelligence MDA)
Vencore, Arlington (ONR SETA)
Misc., others, (hardware design, simulation technologist, site
management, etc.)
What are my engineering bona fides?
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 10
Artificial Life:
Biologically detailed, autonomous, intelligent virtual agent models
Hypothesis: A pristine environment is necessary for the identification
of the fundamental principles of small-group social behavior.
Experiment: Each situated and embodied agent
has its own adaptive, self-organizing, cognitiveaffective artificial intelligence. In turn the entire
Virtual World, and its inhabitants, exist within a
networked computer cluster.
Turing, 1948
P-Type
Hypothesis: An environment having a sufficient absence of
cultural confounds but also one having a sufficient fullness of
socio-environmental stimuli is required for a complete
understanding of “emergent” sociality.
What do I research? ALife, emergent sociality, and collective intelligence
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 11
Now, who are you?
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 12
After a short break we will start on the models:
1.
2.
3.
4.
5.
6.
The Game of Life – (Conway, 1970)
Flocking– (Reynolds, 1986)
Swarming (Bonabeau, 2001 & 2002)
Residential Segregation – (Schelling, 1970 & 1971)
Social Segregation on a Realistic GIS Surface – (Crooks, 2011)
Pedestrians entering a large building when constrained by small-group
behaviors – (unpublished work for a government client Crooks, Hendrey,
Rouly, 2011)
... talk about software – the class has no coding requirements
... the weekly reading assignments – typically 1-2 short articles
and, of course, we will discuss the class deliverables again.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 13
break
5-6 minutes
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 14
"Things should be made as simple as possible - but no simpler."
Albert Einstein
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 15
Social Modelling, Agent-Based Simulation, and Collective
Intelligence reside within the interdisciplinary domain of
Computational Social Science (CSS)
Graphic from lecture notes Crooks, A. (2015). George Mason University.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 16
What is Agent-based Simulation?
Agent-based systems simulation and agent-based social modeling refers
generally to object-oriented software systems which, through the use of
computer code, instantiate models of living systems of social entities.
These systems, of software agents (objects), tend to have at least two
dominant characteristics
1. They are positive representations of the systems they model. Their
instantiations attempt to closely recreate or capture the abstract and or
detailed essence of the prototype system(s).
2. They can as well be normative, i.e., have control inputs that provide
static or runtime exogenous steering of internal feedback loops.
Derived from lecture notes Axtell, R. (2016). George Mason University
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 17
What Agent Systems Are *NOT*
“Computational” X:
when x refers to something from the social
sciences, it usually does not refer to agents.
(for example…)
 “Computational economics” refers to a numerical analysis of some
conventional (e.g., rational, equilibrium) system model.
 “Computational finance” involves finding a numerical solution to stochastic
partial differential equations (PDEs).
 “Computational game theory” considers the numerical determination of
equilibrium configurations (e.g., Nash, 1950)
 Finally, “systems dynamics” were once an important computational
approach in the social sciences. But, they too are not an agent-based
approach.
Text paraphrased from lecture notes Axtell, R. (2016). George Mason University.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 18
Speaking of agent-based simulation generally, and generative
agent-based models in particular:
There are at least, “three domains of practice wherein and whereupon
generative agent-based models are most useful or most usefully built. Those
domains of practice are: 1) models of historical systems that either existed or
are believed to have existed, but because of their antiquity cannot be revisited
for study by some other means, 2) models of "long-lived" systems that, for
reasons of test subject controllability cannot be studied, and by extension 3)
models of systems whose study involves unethical, illegal, unsafe, or unlikely
environmental settings or exogenous stimuli” (Rouly, 2016).
Social modeling can give us system insights not possible by any other means.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 19
The ABM Pedigree
• Biology:
– John von Neumann: self-reproducing automata (‘50s)
– John Conway: Game of Life (late ‘60s)
– Chris Langton: Artificial Life (late ‘80s)
• Social science:
– Simon, March and Cyert: the ‘behavioral school’ and
simulation of few agent systems (‘50s and ‘60s)
– Thomas Schelling: tipping model of segregation (late ‘60s)
• Computer science:
–
–
–
–
–
artificial intelligence (AI)
robotics
distributed AI (DAI)
multi-agent systems (MAS)
object-oriented programming (OOP)
One possible historic path to the ABM pedigree. (Axtell, 2016)
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 20
Broad CSS paradigms




Cellular automata
Big data – data-analytic, probabilistic, and actuarial models
Social networks
Generative models – Agent-Based models & Individual-Based Models
Simple tools




High-speed computers, parallel & sequential algorithms
High-level computer language(s)
Heuristics (qualitative and quantitative)
Spatial layouts (vector & raster)
Concepts
 Emergence
 Bottom-up computation
 “Micro-level rules lead to macro-level behaviors”
Some paradigms, tools, and concepts upon which practitioners of CSS rely.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 21
The Models
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 22
The Game of Life (Conway, circa 1970)
Life states: { dead, alive }
Rules:
Each cell checks the Life State of itself and those of the
cells in its local neighborhood at a Moore distance of 1.
If alive then display a pixel if dead do not. If this cell has
less than two neighbors alive or more than three
neighbors alive then, set this cell dead. If there are
exactly three alive neighbors, set Life State alive.
Randomized activation of cells continues “forever.”
Wilensky, U. (1998)
Concepts:
CSS modeling paradigm – cellular automata
Neighborhood types – Moore and von Neumann
Distance-neighborhoods – Chebyshev and Manhattan determine set cardinality
“Moore neighbors are all-around but von Neumann neighbors are orthogonal.”
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 23
Flocking (Reynolds 1986)
Agent Properties: { flight speed, vision distance,
turning capacity, separation spacing }
Rules:
1. “Collision Avoidance: avoid collisions with nearby
flock mates
2. Velocity Matching: attempt to match velocity with
nearby flock mates
3. Flock Centering: attempt to stay close to nearby
flock mates”
Concepts:
Wilensky, U. (1998)
Simple tools – homogeneous agents, spatial ABM, few rules, torus grid
Comparison:
Ballerini, M., et al. (2008). Interaction ruling animal collective behavior depends on
topological rather than metric distance: Evidence from a field study. Proceedings of
the national academy of sciences, 105(4), pp. 1232-1237.
Emergent behavioral complexity: bottom-up computation
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 24
Swarming (Bonabeau 2001, 2002)
http://neoswarm.com/bonabeau.html
Concepts:
Simple tools – homogeneous agents, spatial ABM, few rules, torus grid
Example of complex, counter-intuitive, social behavior
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 25
Residential Segregation (Schelling 1969 & 1970)
Agent states: { color A, color B }
Rules:
Execution takes place on a raster surface of arbitrary
size but one having a granularity where all areas are
“habitable.” Each agent can “see” (has knowledge of) its
neighbors out to some distance. Each agent has a
variable intensity preference for “color.” And, according
to their preference for a “color” can choose to move to
be near other agents whose “color” they most prefer. No
two agents can occupy the same location.
Wilensky, U. (1998)
Concepts:
Simple tools – spatial layout (raster), torus grid, activation order
Social hypothesis – people create segregated neighborhoods by simple choice
Emergent social complexity: “micro-level rules lead to macro-level behaviors”
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 26
Residential Segregation Using Vector-based GIS
(Crooks 2010)
Agent states: { color A, color B }
Rules:
Execution takes place on a surface of arbitrary size but
having vector defined sub-areas; some habitable others
not. Each agent can “see” (has knowledge of) its
neighbors out to some distance. Each agent has a
variable intensity preference for “color.” And, according
to their preference for a “color” can choose to move to
be near other agents whose “color” they most prefer. No
two agents can occupy the same location.
Concepts:
Simple tools – spatial layout (vector), original Schelling (late ‘60s early ‘70s)
Practical hypothesis – Schelling neighborhoods will emerge on (vector) layouts
The formal Schelling model in vector space produced an unexpected result.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 27
(Movie)
A spatial-agent based modeling constructed to answer a government question
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 28
Coding Tools
High-level languages: Java, MASON, NetLogo, C/C++,
MatLab, Javascript, R, Lisp/Scheme, SWARM, Objective-C,
Ruby/Rails, BASIC, VBasic, C#, Mathematica, Julia, ... others.
Installing and running NetLogo is required
(https://ccl.northwestern.edu/netlogo/5.1.0/)
Extra-credit: Work can be written in any language if you can
prove that you wrote the code, the code operates, and that it
instantiates a social system process or model.
Minimum requirements: Run NetLogo code, write 1-2 page white papers.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 29
Deliverables this week
Reading assignments:
Chapter 9 – in Miller, J. H., & Page, S. E. (2009). Complex adaptive
systems: An introduction to computational models of social life. Princeton
university press.
Chapter 14 – in Helbing, D. (Ed.). (2012). Social self-organization: Agentbased simulations and experiments to study emergent social behavior.
Springer.
Writing/Coding assignment:
Write a 1-2 page White Paper arguing for (and or against) the validity of
the Schelling or Reynolds models to describe qualitatively (and or
quantitatively) the behavior of the “human” or “boid” objects, respectively.
Week 2 deliverables: Read, Write, install and run Schelling and Reynolds.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 30
REFERENCES
• Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., & Cisbani, E., et al. (2008). Interaction ruling
animal collective behavior depends on topological rather than metric distance: Evidence from a field
study. Proceedings of the national academy of sciences, 105(4), pp. 1232-1237.
• Bonabeau, E. and Myer, C. (May 2001). Swarm Intelligence, A Whole New Way to Think About
Business. Harvard Business Review. pp. 106-114.
• Bonabeau, Eric. (2002). Agent-based modeling: Methods and techniques for simulating human
systems. Proceedings of the National Academy of Sciences, 99 (supplement 3), pp. 7280-7287.
• Crooks, A. T. (2010). Constructing and implementing an agent-based model of residential
segregation through vector GIS. International Journal of Geographical Information Science, 24(5),
661-675.
• Crooks, A., Hendrey, M. & Rouly, O. C. (2011). Pedestrians entering a venue constrained by smallgroup social interaction. Unpublished work. George Mason University.
• Cyert, R. & March, J. (1992). A behavioral theory of the firm (2 ed.). Wiley-Blackwell.
• Gardner, M. (October 1970). Mathematical Games – The fantastic combinations of John Conway's
new solitaire game "life". Scientific American 223. pp. 120–123.
• Helbing, D. (Ed.). (2012). Social self-organization: Agent-based simulations and experiments to study
emergent social behavior. Springer.
• Miller, J. & Page, S. (2009). Complex adaptive systems: An introduction to computational models of
social life. Princeton university press.
• Nash, J. (1950) Equilibrium points in n-person games. Proceedings of the National Academy of
Sciences. 36(1):48-49.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 31
REFERENCES
• Reynolds, C. (1987). Flocks, herds and schools: A distributed behavioral model. SIGGRAPH '87:
Proceedings of the 14th annual conference on Computer graphics and interactive techniques
(Association for Computing Machinery). pp. 25–34.
• Rouly, O. (2016). Towards Emergent Social Complexity. Unpublished dissertation. George Mason
University, Fairfax, Virginia, USA.
• Schelling, T. (1969) Models of segregation. American Economic Review. 59(2). pp. 488–493.
• Schelling, T. (1971). Dynamic Models of Segregation. Journal of Mathematical Sociology. 1(2). pp.
143–186.
• Simon, H (February 1955). A behavioral model of rational choice. The Quarterly Journal of
Economics. 69 (1): 99–118.
• von Neumann, J. (1951).The general and logical theory of automata, in L.A. Jeffress, ed., Cerebral
Mechanisms in Behavior – The Hixon Symposium. John Wiley & Sons, New York. pp. 1–31.
• Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning
and Computer-Based Modeling, Northwestern University, Evanston, IL.
• Wilensky, U. (1998). NetLogo Flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking.
Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston,
IL.
• Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for
Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 32
In the weeks that follow we will:
 discuss the vocabulary of ABM and Social Systems Modeling
 learn about the software tools used by CSS practitioners
 consider more of the theory behind the model instantiations
 see more formal and informal models of Social Simulation
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 33
Contact information
ETH Zurich
D-GESS Computational Social Science
Clausiusstrasse 50
8006 Zürich, Switzerland
http://www.coss.ethz.ch/
Ovi Chris Rouly, PhD.
Email: [email protected]
Telephone: (41) 044-633-8380
© ETH Zurich, 27 February 2016
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 34
LAST SLIDE
Department of Hum anities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 27.02.2016
| 35