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The Game of Hangman, Decision Theory and Delivering Software - Redsnake Philly 2014

The Game of Hangman, Decision Theory and Delivering Software - Redsnake Philly 2014

Information entropy and utility functions. Bitmaps, makefiles and python generators. Who knew the seemingly simple game of hangman could be so much fun? And what does it all have to do with delivering software?

During the talk, we’ll solve the game starting with our naive childhood strategy then improve the strategy with some simple decision theory tricks. Along the way, we’ll build a model for feedback, information gain and failure that we can apply directly to the way we deliver software.

Chris Conley is a software engineer lead at Monetate. Prior to Monetate he co-founded an accelerator-backed startup, Houdini and a mobile train app, iSepta.org.

Chris Conley

April 05, 2014
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  1.  sre i scrabbler scrambler scratcher scrounger straddler straggler strangler

    struggler next letter = max(chance of success) a scrabbler scrambler scratcher scrounger straddler straggler strangler struggler scrabbler scrambler scratcher scrounger straddler straggler strangler struggler g t
  2. Feedback Strategy Avg Guesses Avg Body Parts n/a random 22.17

    15.43 n/a success 16.36 9.28 √ success 5.97 1.41
  3. s-r----er i scrabbler scrambler scratcher scrounger straddler straggler strangler struggler

    next letter = max(information gain) a scrabbler scrambler scratcher scrounger straddler straggler strangler struggler scrabbler scrambler scratcher scrounger straddler straggler strangler struggler g t 
  4. Feedback Strategy Avg Guesses Avg Body Parts n/a random 22.17

    15.43 n/a success 16.36 9.28 √ success 5.97 1.41 √ information 5.09 1.66
  5. s-r----er i scrabbler scrambler scratcher scrounger straddler straggler strangler struggler

    next letter = max(information gain × chance of success) a scrabbler scrambler scratcher scrounger straddler straggler strangler struggler scrabbler scrambler scratcher scrounger straddler straggler strangler struggler g t
  6. Feedback Strategy Avg Guesses Avg Body Parts n/a random 22.17

    15.43 n/a success 16.36 9.28 √ success 5.97 1.41 √ information 5.09 1.66 √ information & success 5.38 1.38