Beyond the Centralized Mindset
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Transcript of Beyond the Centralized Mindset
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