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Beyond the Centralized Mindset

Beyond the Centralized Mindset. Mitchel Resnick Epistemology 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.

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Beyond the Centralized Mindset

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  1. Beyond the Centralized Mindset Mitchel Resnick Epistemology and Learning Group MIT Media Lab

  2. Sciences of Complexity • Complex phenomena arising from simple interactions among simple parts • Research in: • Chaos • Self-organization • Adaptive systems • Nonlinear dynamics • Artificial Life

  3. 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.

  4. Resnick’s Approach – Helping students understand decentralized systems • Probing student’s conceptions • Developing new conceptual tools • Developing new computational tools

  5. 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

  6. 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

  7. Termite Example Initial: Later:

  8. Projects with Star Logo • Traffic Jams Rules: • 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

  9. 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

  10. 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

  11. 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

  12. 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)

  13. 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

  14. Flocks, Herds and Schools:A Distributed Behavioral Model

  15. Display and Animation • -Approaches • - Individual Scripting • - Simulation of individual birds • Simulation • - Particle Systems • - Boid flocks • - Geometrical Object • - Visually Significant • - Orientation • - Complexity • - Interaction

  16. 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

  17. 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

  18. Simulated Flocks • -Complexity • O(n^2) • Limits size of flocks • Simulation • Collision Avoidance • Velocity matching • Flock Centering • Localized perception • Bifurcation

  19. Simulated Flocks (cont’d) • Decision making • Acceleration Requests • Strengths • To average or not to average? • Expert Systems • Prioritized acceleration allocation

  20. 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

  21. Environmental Obstacles • Force Field • Angles • Strength discrepancy and panic • Steer-to-Avoid

  22. Other Applications • - Schools • Herds • Traffic Patterns (Jams, in southern CA)

  23. ArtiFishial Life Jude Battista Kendra Knudtzon

  24. ArtiFishial Life Project • Fish schooling • Interactive Java applet exploring emergence, self-adaptation, and artificial life • Graphical representation where physical characteristics reflect behavior • Educational Focus

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