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Secrets of the Celtics: Winning Strategies for SDG Success

Explore the winning strategies used by the Celtics in the SDG competition to achieve success, including knowing the best prices for derivatives, creating tough raw materials, and leveraging the concept of Break-Even. Learn how to maximize profits and make strategic decisions based on real-world data and scenarios to secure victory in the game.

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Secrets of the Celtics: Winning Strategies for SDG Success

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  1. Secrets of the Celtics How to win at SDG without cheating Christopher Chalifour Duc Tri Le Thomas Pappas

  2. Inspiration:  The Super Robot From an email sent by Professor Lieberherr on 02/07/09 • "That robot will soon be the only one alive because it saps much life energy from the other robots." •  Guaranteed win? • "Even if the other robots don't buy from the super robot because they are "afraid" of her, the super robot will spot all good food in the market and will have the life energy to get it. " •  Perfect buying decisions • "In addition, ...  super robot can accumulate life energy with lots of small profits." •  Small, constant profits

  3. Performance of the Celtics • 47 deliveries of RawMaterials • Made a profit on all of them • 35 purchases of Derivatives • Only 2 ended up as a loss • Small loss of 0.021 & 0.043 • 58.185 seconds spent entire game • 6 rounds • 2 overtime rounds Source: http://www.ccs.neu.edu/home/lieber/courses/csu670/sp09/alex/competitions/mar10/4/history.html

  4. Our Success     What gave us the winning edge? • Knowing the best price to buy derivatives • Knowing the best price to sell derivatives • Knowing how to create tough raw materials • Knowing the best price to re-offer derivatives

  5. Pricing A Derivative Halving the Min-Decrement - Bender's Concept The Perfect Price = (Break-even + ½ MinDec)  • Forces the selling robot to price the derivative with this price as anything higher allows another robot to price their derivative at the "perfect price" resulting in our robot losing it's potential to make a sale and thus, make a profit • Forces an opposing robot to re-offer the derivative with a lower price of at most P = (Break-even - ½ MinDec) • Finishing this derivative at a break-even price causes the selling robot to still lose½ MinDec • Forces an opposing robot buying this derivative to lose ½ MinDec by finishing at a quality of at most break-even

  6. Creating a Tough RawMaterial Method #1 • Each Constraint will have a weight of 1 • Number of Constraints for each RelationNr = Weighted fraction of RelationNr x Maximum number of Constraints • A good RawMaterial but not truly symmetric

  7. Creating a Tough RawMaterial Method #2 • Remove RelationNrs with a weighted fraction of 0 • Divide the number of Constraints evenly among the remaing RelationNrs • Assign appropriate weights to Constraints so that weighted fraction will be satisfied • Better most of the time but not all • There is really no such thing as a perfect symmetric RawMaterial!

  8. Why "Break-Even" is broken (Credit to Xueyi Yu) As taught in class... Mentioned over email... • Break-Even is the highest possible finishing Quality given the worst possible raw material. • Calculated through using a Look-Ahead Polynomial • "Because from what I observed from the competition, a Robot can finsih a raw material with a quality > breakeven" • Xueyi Yu (02/19/09)

  9. A Real-World look at Break-Even Unused / Outdated Documentation Finished Quality =  0.4679802... for 10 constraints 0.4466790... for 100 constraints Break-Even =  .444444... • This data for this example is taken from the class website, shows how the more constraints, the closer the max Quality is to the Break-Even. • Look-ahead polynomials assume infinite constraints.

  10. Why focus on the delivery of RawMaterial? • We used actual qualities to make decisions rather than break even prices • The tougher the RawMaterial, the better we know of the worse case scenario • So we know that the chance of someone finishing better than what we priced is slim • When buying, if the worst case is higher than the Derivative's price, we know for sure we can make a profit

  11. Summary The Perfect Robot • Makes all decisions based on profit • Wont create derivatives that sell for a loss • Will almost never buy derivatives for a loss • Will never re-offer derivatives for a loss • Only guaranteed for a 1-on-1 scenario

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