Exploring Decentralized Systems: Lessons from Nature and StarLogo

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