Beyond the Centralized Mindset Mitchel Resnick Epistemology and

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

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 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

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

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)

Arti. Fishial Life Jude Battista Kendra Knudtzon

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