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Agent-based Modeling and Restructurations
Uri Wilensky
http://ccl.northwestern.eduCenter for Connected Learning & Computer-
Based Modeling (CCL)
Northwestern Institute on Complex Systems (NICO)
Northwestern University
Indiana University
May 21, 2006
OverviewThe goal of this talk is to argue for a widespread
adoption of complex-systems perspectives and methods, and specifically agent-based modeling:
In particular, we argue for the use of agent-based modeling– To Reformulate school Content (K - 20)– As experimental methods for evaluating policy– As a method to build and assess theory
• Examples of each of these from current CCL work
A thought experiment
Imagine a country where everyone uses Roman numerals. The educators in this country were very concerned with problems of numeracy amongst the citizens.
• Some focused on numerical misconceptions- If CX is ten more than C, then CIX must be ten more than CI
• Some wrote computer-programs to enable students to practice Roman arithmetic
• Some construct wooden blocks with X, I, V, C
A thought experiment (cont)
Imagine the educators had invented
Hindu-Arabic numerals.
• Before: the learning gap in arithmetic was immense - only a small number of trained people could do multiplication.
• After: multiplication became part of what we can expect everyone to learn.
Restructurations
Structurations -- the encoding of the knowledge in a domain as a function of the representational infrastructure used to express the knowledge
Restructurations -- A change from one structuration of a domain to another resulting from a change in representational infrastructure (Wilensky, Papert, Sherin, diSessa, Kay, Turkle, Noss & Hoyles, 2005; Wilensky & Papert, 2006)
Complex Systems & Restructuration
With the invention of complex systems representations such as agent-based modeling, we are now poised to create restructurations of
• content for students and for research
Agent-based modelingCreating computer models in which individual
computational entities interact to create large-scale patterns
• The entities are the agents. Each agent has its own descriptive “state variables” (e.g., age, energy, wealth), graphical depiction and behaviors (simple computational rules e.g., move, eat, buy)
• Out of the interactions of the agents following their rules, emerges a large-scale pattern: the emergent phenomenon
Affordances of Agent-based modeling vs. equational modeling
• Agents represent individual elements of the model – The model is built with agents (wolves, molecules, indiv. customers) as
opposed to with aggregates (wolf populations, pressure, customer pop.s)
• Agent behaviors can leverage body knowledge• Local interactions, Proximate mechanisms.• Make use of spatial dimension• Model is runnable
– visualization of dynamics at multiple levels– Immediate feedback
• Model is incrementally changeable– Enabling what-if investigations– Enabling change to model for varying initial conditions
• Design Micro-rules that generate macro- pattern• Glass-box models
• Requires little formal mathematical machinery
Affordances of Agent-based modeling vs. equational modeling
• Non-linearity– Most worldly phenomena are non-linear – Move away from linearity and calculus– Computational reps are non-linear by default
• Discreteness– Increasingly discrete models are replacing continuous
models
4 areas for agent-based restructurationripe for the picking
• STEM and social science courses K-20• Policy• Theories of learning in social contexts• Theories of individual cognition
At the CCL, we have begun to work on all four of these. My hope is to encourage others to do so as well.
Agent-based modeling environments
• The examples are all implemented in NetLogo, an agent-based modeling environment developed at the Northwestern CCL
Other restructurated education work (particularly of content) has been done with:
Agentsheets, StarLogo, Molecular Workbench (mostly pre-collegiate content)
Swarm, Repast, Mason, Ascape (collegiate content and social science research)
Restructurated Content
Content Area Agent Emergent Phenomenon User Collaborator(s)
School content “Micro” behavior “Macro” behavior Students, teacher CCL (post) grads, staff
ProbLab (Prob/stats)
Random outcomes Statistical
distribution Middle school (elem.) Dor Abrahamson
Connected Chemistry
Molecules Pressure, heat, force Mid/High school Sharona Levy,
Mike Steiff, Mike Novak
EvoLab (evolution)
Organisms Adaptation, Speciation
Mid/High school Bill Rand,
Michael Novak
NEILS (electrostatics) Electrons, ions Current, charge, electrical field
High School Pratim Sengupta
EconLab (economics)
Humans Market, prices High school/Undergrad Spiro Maroulis
MaterialSim Atoms Crystals,
Grain growth Undergraduates Paulo Blikstein
Cities (urban studies)
Humans, land features
City development, sprawl
Undergrads/grads Ben Watson, Bill Rand
Connected Chemistry: agent-based molecular chemistry
(Levy, Novak & Wilensky)• Example: KMT & Gas Laws
– Learned through the exploration of agent-based models
– Focus on dynamics of change in addition to traditional curriculum goals, through a complex systems lens
• Agents;– Gas particles, operating according to KMT
assumptions
• Emergent properties– Pressure, speed distribution, temperature– Gas laws– Randomness & stability– Time lags between perturbation and
equilibration
Connected Chemistry: agent-based molecular chemistry
(Levy, Stieff & Novak)
• Agents: – Particles (molecules)
[in gas or solid]
• Emergent patterns: – Ideal gas law– Chemical kinetics
EconLab: Agent-based economics(Maroulis)
• Example: Oil Cartel– The exploration of the
economics of a market with imperfect competition.
– Participants experience why cartels are:
• difficult to sustain• harmful to consumers
• Agents:– Oil producers– Oil consumers
• Emergent Properties:– Market price and quantity– Deadweight loss
ProbLab (Abrahamson):Agent-Based Prob. & Stats
• Agents are computational procedures that make use of a “random” primitive
• Emergent pattern is a statistical distribution• Constructing probability by connecting
“micro” and “macro” views of randomness• Constructing outcome distribution as a
stochastic and multiplicative “transformation” on combinatorial analysis
MaterialSim: Agent-based Materials Science (Blikstein)
• Conventional focus: many-to-one (95 variables/18 equations in 30 minutes)
• Our focus: one-to-many (simple behaviors that explain a wide variety of phenomena)
• Agents: – Atoms
• Rule:– atoms “prefer” to be amongst equal
neighbors• Phenomena explained:
– Grain growth– Diffusion– Phase transformation– Solidification– Fusion– Etc.
EvoLab: Agent-based biological evolution (Rand & Novak)
How can we facilitate learners understanding of processes that take thousands of lifetimes to occur?
By enabling learners to experiment with rules for individual animals or for evolutionary mechanisms and artificially speeding up time, it is much easier to explore “evolutionary space”.
Agents: Moths, Wolf, Sheep, DNA, and any Individuals in Ecosystems
Emergent Patterns: Camouflage, Natural Selection, Neutral Mutation, Mimicry, Phenotypic Plasticity, Baldwin Effect, Coevolution, and many more
EvoLab: Agent-based biological evolution (Rand & Novak)
• Agents:– Competitors– Prey– Mates– Resources
• Emergent Patterns:– Selective pressures – Camouflaging– Adaptation of Motion– Genetic Drift– Bottleneck Effect– Baldwin Effect
Cities: Procedural Modeling of Urban Development (Watson & Rand)
How do cities grow? Can we use agent-rules to produce quasi-realistic city development patterns? Can we introduce some ability to control the outcome?
Represent developers, home buyers, and civic government as agents that move around and make decisions. Represent parcels of land as having value dependent on geography and development.
Allow learners to paint “honey” and “poison” on to the landscape to influence the decisions of these agents.
Speed up the time-scale of the system to allow quick realization of the processes.
Emergent patterns: Suburban Sprawl, Road Networks, Central Business District, Zipf’s Law of Urban Population, Clarke’s Rule of Radial Density
NIELS: agent-based electromagnetism (Sengupta)
Models depict phenomena in Electrostatics, Electricity, and Magnetism: an emergent perspective
Agents: Electrons, Atoms, Ions
Emergent Phenomena: Current, Voltage, Electric field
Educational Policy
Content Area Agent Emergent
Phenomenon User Collaborator(s)
Ed Policy Students, parents, teachers
School/District outcomes
Policymakers, researchers
CCL (post) grads, staff
School Choice Students, parents, teachers
School/District outcomes
Policymakers, researchers
Spiro Maroulis, Louis Gomez
Small Schools Students, teachers
Social capital Policymakers, researchers, principals
Spiro Maroulis, Louis Gomez
Curricular innovation
Students. Teachers,
curriculum specialists
Curricular adoption
Researchers, curriculum
writers, policymakers
Spiro Maroulis
School Choice (Maroulis)• Conventional focus: Does choice
“work”?• Our focus: Under what conditions
would it work or not work. E.g.:• When are “survivors” better
than “closers”?• Can we help the market
forces along?• Agents:
– students, households, schools• Emergent Properties:
– Enrollment patterns– Concentration of achievement
(Gini ratio) 0 20 40 60 80 100Pct Using Achievement Criteria
Survivors VA
Closers VA
Survivors vs Closers
School Change (Maroulis)• Conventional focus: Does a school adopt a
reform?• Our focus: What are the leverage points for
change?
• Agents: – students, teachers
• Agent-properties:– e.g., Closure or brokerage
• Emergent Properties: – School culture (academic press) – Adoption of innovation– Social capital
Theories of Social Learning
Content Area Agent Emergent
Phenomenon User Collaborator(s)
Social Learning
learners Group learning Education theorists
CCL (post) grads, staff
Piaget/Vygotsky learners Group learning Education theorists
Dor Abrahamson
Vygotskian ZPD
learners Group learning Education theorists
Jim Levin Michael Cole
Piaget/Vygotsky (Abrahamson)
• ABM for theory of learning– “Runnable” thought experiment– Flexible parametrization– Explicit (proceduralized)– Enables critique/compare
(accompanies paper)– Lingua franca for
intra/inter-disciplinary discourse
Agent: marbles player EP: group-learning patterns
To Vygotsky-adjust set best-max-moves best-max-moves-of neighbor End
Theories of individual cognition (Blikstein)
Content Area Agent Emergent Phenomenon User Collaborator(s)
Cognition Cognitive resources
individual learning/understanding
Education theorists
CCL (post) grads, staff
Conservation of volume
Cognitive agents such as “taller”
individual learning to conserve volume
Education theorists
Paulo Blikstein
Rock cycle
Cognitive agents such
as “connectors”
individual learning of rock cycle
Education theorists
Paulo Blikstein David Hammer
Conservation of Volume (Blikstein)
• Conventional focus: When/how do children “get” conservation?
• Our focus: Conservation as an emergent result of the behavior and interaction of non-intelligent agents
• Agents: – Perceptive elements (detect
“height”, “width”, “number”)– Administrative agents:
categories of perceptive agents (appearance, history)
• Emergent Properties: – Cognitive structures with good
performance evolve and survive– The agents are “dumb”, the
behavior is intelligent
Reasoning about the Rock cycle (Blikstein)
• Conventional focus: Learning as either a “blackboxed” cognitive activity or a brain science approach
• Our focus: Learning as an emergent behavior of simpler, easier to understand/model cognitive tasks
• Agents: – Knowledge retrievers– Knowledge connectors
• Emergent Properties: – Weak connectors are efficient for
short “sentence-sizes” but inefficient for long “sentence-sizes”
– Strong connectors are inefficient for short “sentence-sizes” but efficient for long “sentence-sizes”
weathering occursAn igneous
rock forms
lava going up
sediments are formed
settles at the bottom of the sea
Retriever
settles at the bottom of the sea
weathering occurs
connector
Summary Table
Unit, Content Domain Agent Emergent Phenomenon User
School content “Micro” behavior “Macro” behavior Students, teacher Education Policy individuals School/District outcomes Ed policy/researchers Social Learning individuals Group learning Ed theorists
Cognition Cognitive Resources Indiv. Learn/Understanding Ed researcher/ cog sci
Center for Connected Learningccl.northwestern.edu
Papers, software, models and curricular units can be downloaded from the CCL web site