31
Agent-based Modeling and Restructurations Uri Wilensky http:// ccl.northwestern.edu Center for Connected Learning & Computer-Based Modeling (CCL) Northwestern Institute on Complex Systems (NICO) Northwestern University Indiana University

Agent-based Modeling and Restructurations

  • Upload
    gada

  • View
    41

  • Download
    0

Embed Size (px)

DESCRIPTION

Agent-based Modeling and Restructurations. Uri Wilensky http://ccl.northwestern.edu Center for Connected Learning & Computer-Based Modeling (CCL) Northwestern Institute on Complex Systems (NICO) Northwestern University Indiana University May 21, 2006. Overview. - PowerPoint PPT Presentation

Citation preview

Page 1: Agent-based Modeling and Restructurations

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

Page 2: Agent-based Modeling and Restructurations

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

Page 3: Agent-based Modeling and Restructurations

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

Page 4: Agent-based Modeling and Restructurations

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.

Page 5: Agent-based Modeling and Restructurations

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)

Page 6: Agent-based Modeling and Restructurations

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

Page 7: Agent-based Modeling and Restructurations

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

Page 8: Agent-based Modeling and Restructurations

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

Page 9: Agent-based Modeling and Restructurations

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

Page 10: Agent-based Modeling and Restructurations

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.

Page 11: Agent-based Modeling and Restructurations

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)

Page 12: Agent-based Modeling and Restructurations

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

Page 13: Agent-based Modeling and Restructurations

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

Page 14: Agent-based Modeling and Restructurations

Connected Chemistry: agent-based molecular chemistry

(Levy, Stieff & Novak)

• Agents: – Particles (molecules)

[in gas or solid]

• Emergent patterns: – Ideal gas law– Chemical kinetics

Page 15: Agent-based Modeling and Restructurations

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

Page 16: Agent-based Modeling and Restructurations

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

Page 17: Agent-based Modeling and Restructurations

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.

Page 18: Agent-based Modeling and Restructurations

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

Page 19: Agent-based Modeling and Restructurations

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

Page 20: Agent-based Modeling and Restructurations

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

Page 21: Agent-based Modeling and Restructurations

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

Page 22: Agent-based Modeling and Restructurations

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

Page 23: Agent-based Modeling and Restructurations

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

Page 24: Agent-based Modeling and Restructurations

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

Page 25: Agent-based Modeling and Restructurations

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

Page 26: Agent-based Modeling and Restructurations

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

Page 27: Agent-based Modeling and Restructurations

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

Page 28: Agent-based Modeling and Restructurations

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

Page 29: Agent-based Modeling and Restructurations

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

Page 30: Agent-based Modeling and Restructurations

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

Page 31: Agent-based Modeling and Restructurations

Center for Connected Learningccl.northwestern.edu

Papers, software, models and curricular units can be downloaded from the CCL web site