Beyond the Centralized Mindset

Mitchel ResnickEpistemology and Learning Group

MIT Media Lab

Sciences of Complexity

• Complex phenomena arising from simple interactions among simple parts

• Research in:• Chaos• Self-organization• Adaptive systems• Nonlinear dynamics• Artificial Life

Decentralized Models

Flocks Of Birds• Traditionally, people assumed that their was a leader

bird at the front of the flock• Now, new theories view flocks as decentralized and

self-organizing• Each bird follows a certain set of rules, reacting to

the other birds and the flock patterns arise from these simple, local interactions.

Resnick’s Approach – Helping students understand decentralized

systems

• Probing student’s conceptions• Developing new conceptual tools• Developing new computational tools

Starlogo

• Goals:– To let students investigate the ways that

complex patterns can arise from interactions among individual creatures

– To enable students to build their own models

Starlogo, cont’d

• An extension of Logo with:• More turtles – can have thousands of creatures

working in parallel• Turtles have better “senses” – the senses allow the

turtles to interact with each other and the environment

• More complex turtle world – the environment has capabilities for interactions as well

Termite Example

Initial: Later:

Projects with Star Logo

• Traffic JamsRules:

» If there is a car close ahead, slow down» If there are not any cars close ahead, speed up (unless you

are at the speed limit)» If you detect a radar trap, slow down

What if there isn’t a radar trap? With just the first two rules what do you expect to happen? Why?

• Termites and Wood Chips• Ant Cemeteries

Decentralized Thinking

• Student’s work with Starlogo provided evidence of a strong centralized mindset

• Projects such as Starlogo may allow for a change in typical ways of thinking about projects

• Models allow for complex ideas to be presented to students of younger ages

Decentralized thinking

• Positive Feedback• Crucial role in decentralized phenomena• Example: Silicon Valley

• Randomness • “Seeds” aren’t necessary to initiate patterns and

structures• Self-organizing systems can create their own seeds,

and hence randomness plays an important role

Decentralized thinking, cont’d

• Idea of Levels is important• A flock isn’t a big bird – interactions among birds

give rise to a flock, interactions among cars make a traffic jam

• Objects on one level behave differently than objects on another level (cars move forward, traffic jams move back)

• Objects aren’t always a collection of parts• A traffic jam is an “emergent object,” emerging

from the interactions among lower-level objects

Decentralized thinking, cont’d

• Richer views of the environment• Need to think of the environment as something that

you can interact with• The path of an ant walking on a beach may be

complex, but that complexity isn’t a reflection on the ant, but of the environment. (Herbert Simon, Sciences of the Artificial)

Related Work

• Exploring Emergence– Online “Active Essay”– http://el.www.media.mit.edu/groups/el/projects/emergence/index.html

• The Virtual Fish Tank– The Computer Museum, Boston– http://www.tcm.org/html/fishtank/vft_walkthrough.html

Flocks, Herds and Schools:A Distributed Behavioral Model

Craig W. ReynoldsSymbolic Graphics Division

Display and Animation- Approaches

- Individual Scripting

- Simulation of individual birds

-Simulation

- Particle Systems

- Boid flocks

- Geometrical Object

- Visually Significant

- Orientation

- Complexity

- Interaction

Necessities for Flocking

-The geometric ability to fly- “dynamic, incremental, rigid, geometrical transformation of an object moving along and tangent to a 3-D curve”

- Or, as we like to call it, a flying Boid

- Local space and coordinates

- Translation, pitch, yaw

-Banking- The Roll

Natural Flocks

-Motivations-A desire to stay close to the flock

- Evolutionary pressures

- A desire to avoid collisions

-Complexity- No apparent overload function

- Constant time algorithm

Simulated Flocks

-Complexity- O(n^2)

-Limits size of flocks

-Simulation- Collision Avoidance

- Velocity matching

- Flock Centering- Localized perception

- Bifurcation

Simulated Flocks (cont’d)

-Decision making- Acceleration Requests

- Strengths

- To average or not to average?

- Expert Systems

- Prioritized acceleration allocation

Behavior- Motivations reach a steady state

- Flock is in harmony, each boid having balanced its desires

- Flock is also very boring

- Add obstacles- Complexity of natural flock determined by complexity of the natural environment

Environmental Obstacles

-Force Field- Angles

- Strength discrepancy and panic

-Steer-to-Avoid

Other Applications

- Schools

- Herds

- Traffic Patterns (Jams, in southern CA)

ArtiFishial Life

Jude BattistaKendra Knudtzon

ArtiFishial Life Project

• Fish schooling• Interactive Java applet exploring emergence,

self-adaptation, and artificial life • Graphical representation where physical

characteristics reflect behavior• Educational Focus