I Love Algorithms

When I was small and young, I was close to the ground.  I collected rocks, sticks, glass, broken toys, pennies then dimes, whatever caught my eye.  Yes I am distracted by Sparkly Things.  To what was I supposed to be paying attention?

I like to play with categories and play games about categories.  I have one playmate with whom I share delightful, hilarious verbal exchanges in which we describe things, actions, ideas with two unrelating qualifiers.  Like, "this pen is elegant yet vulnerable."  Like, "that song is lulling and ironic."

To eavesdroppers, we are ridiculous.  To us, we know we are creating a game that disrupts usual categorical thinking and hearing what the effect is.  We haven't moved past two descriptions.  It needs to be quick and quippy for us.  It's so fun.

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I recently watched The Secret to Modern Living:  Algorithms.   Because it is a film, I was able to take in the context by having a visual pictures demonstrating the explanations.  

I wonder how much I might have comprehended if I just listened....the pictures and scenes were mesmerizing.

It has stuck with me.  Because we have been able to outsource the job of figuring algorithms to technology and computing,  

I wonder how much analytical and computational ability we are losing.

I wonder how the rush for manual  labor and thinking is diminishing our motivation to use categorical analyses of our own life's implicit biases.

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I am thinking about creating games and activities that enact the mathematical process of sorting and grouping towards a finite end.

I am thinking about a game that prompts children to "bubble sort" as well as "merge sort" or even "insert sort" themselves.  I'm finding games with themes along this line yet without a narrative that leads to an end....message, and ultimate meaning making.  

​If somehow this is intriguing you, I invite you into a play land of collaboration and research with me.

The following are said to be features of a good algorithm:

1. Precision: a good algorithm must have a certain outlined steps. The steps should be exact enough, and not varying. 

2. Uniqueness: each step taken in the algorithm should give a definite result as stated by the writer of the algorithm. The results should fluctuate by any means. 

3. Feasibility: the algorithm should be possible and practicable in real life. It should be abstract or imaginary. 

4. Input: a good algorithm must be able to accept a set of defined input. 

5. Output: a good algorithm should be able to produce results as output, preferably solutions. 

6. Finiteness: the algorithm should have a stop after a certain number of instructions. 

7. Generality: the algorithm must apply to a set of define inputs.

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How can we translate these into human ends? 

Off to the practice lab!