1. A Popperian Platform for Programming and Teaching the Global Brain Karl Lieberherr Ahmed Abdelmeged Northeastern University, CCIS, PRL, Boston 11/20/20151

2. A claim is Meta information about one’s performance when interacting with another clever being. Meta information about the performance of one’s program. 4/24/20112Crowdsourcing

3. Outline Introduction Theory & hypotheses Methods Results & analysis Conclusion Theory Methods Results Conclusion Introduction Theoretical background Methods for playground design Results Conclusions 11/20/20153

4. Results Preview Explanation: SCG as a general pattern behind many different competitions: topcoder.com, kaggle.com, tunedit.org, … SCG usage for teaching – Innovation Success with Undergraduates using SCG on piazza.com: Qualitative Data Sources & Analysis – Avatar competitions are not for teaching (but for competitive innovation) Theoretical Properties of SCG 11/20/20154 Introduction Theory Methods Results Conclusion SCG = Scientific Community Game = Specker Challenge Game

5. Specker Claims: – Specker(X,Y(X),f,c): ForAll x in X Exists y in Y: f(x,y)≥c Example: – X = Conjunctive Normal Forms with restrictions – Y(X) = Assignments to CNFs – f(x,y) = fraction of satisfied clauses in x under y – c in [0,1] 11/20/20155 Introduction Theory Methods Results Conclusion

6. Popper That a claim is "falsifiable" does not mean it is false; rather, that if it is false, then some observation or experiment will produce a reproducible result that is in conflict with it. 11/20/20156 Introduction Theory Methods Results Conclusion

7. What SCG helps with How to identify experts? How to decide if an answer is worthwhile? –Use scholars to choose the winners How to organize egoistic scholars to produce social welfare: knowledge base and know-how how to defend it. –The scholars try to reverse engineer the solutions of winning scholars. 11/20/20157 Introduction Theory Methods Results Conclusion

8. Claims Protocol. Defines scientific discourse. Scholars make a prediction about their performance in protocol. Predicate that decides whether refutation is successful. Refutation protocol collects data for predicate. As a starter: Think of a claim as a mathematical statement: EA or AE. – all planar graphs have a 4 coloring. 11/20/20158

9. Claim involving Experiment Claim ExperimentalTechnique(X,Y,q,r) I claim, given raw materials x in X, I can produce product y in Y of quality q and using resources at most r. 9Crowdsourcing4/24/2011

10. Who are the scholars? Students in a class room – High school – University Members of the Gig Economy – Between 1995 and 2005, the number of self- employed independent workers grew by 27 percent. Potential employees (Facebook on kaggle.com) Anyone with web access; Intelligent crowd. 11/20/201510

11. Kaggle.com Competitions 2012 Facebook recruiting competitions – Task: Data scientist – Reward: Job – Teams: 197 Heritage Health Prize – Task: Hospital admissions – Reward: $ 3 million – Teams: 1118 Chess ratings – Elo versus the Rest of the World – Task: Predict outcome of chess games – Reward: $ 617 – Teams: 257 11/20/201511

12. Kaggle.com Competitions 2012 Eye Movements Verification and Identification – Task: Identify people – Reward: Kudos – Teams: 51 EMC Data Science Global Hackathon – Task: Air Quality Prediction – Reward $ 7030 – Teams: 114 11/20/201512

13. What Scholars think about! If I propose claim C, what is the probability that – C is successfully refuted – C is successfully strengthened If I try to refute claim C, what is the probability that I will fail. If I try to strengthen claim C, what is the probability that I will fail? 1311/20/2015 Introduction Theory Methods Results Conclusion

14. Degree of automation with SCG(X) 14 no automation human plays full automation avatar plays degree of automation used by scholar some automation human plays 0 1 more applications: test constructive knowledge transfer to reliable, efficient software avatar Bob scholar Alice 11/20/2015 Introduction Theory Methods Results Conclusion

15. Organizational Problem Solved How to design a happy scientific community that encourages its members to really contribute. Control of scientific community – tunable SCG rules – Specific domain, claim definition to narrow scope. 11/20/201515 happy = no scholar is ignored.

16. What is a loose collaboration? Scholars can work independently on an aspect of the same problem. Problem = decide which claims in playground to oppose or agree with. How is know-how combined? Using a protocol. – Alice claimed that for the input that Alice provides, Bob cannot find an output of quality q. But Bob finds such an output. Alice corrects. – Bug reports that need to be addressed and corrections. 11/20/201516 Playground = Instantiation of Platform Introduction Theory Methods Results Conclusion

17. Theory Extensive Form Representation of Game Community Property: All faulty actions can be exposed. 11/20/201517 Introduction Theory Methods Results Conclusion

18. Extensive-form representation 1.the players of a game: 1 and 2 2.for every player every opportunity they have to move 3.what each player can do at each of their moves 4.what each player knows for every move 5.the payoffs received by every player for every possible combination of moves 11/20/201518 Introduction Theory Methods Results Conclusion

19. 1 propose claim C from Claims 2 refute(C,1,2) p(C, …)?(1,-1):(-1,1) 1 scholar 2 scholar strengthen attempt C’ => C refute(C’,2,1) agree attempt C refute(C,2,1) p(C’, …)?(1,-1):(-1,1) p(C, …)?(1,-1):(-1,1) 11/20/2015 refute attempt C refute(C, proposer,other) p(…)?(proposer,other): (proposer,other) s: successful u: unsuccessful Introduction Theory Methods Results Conclusion p(C’, …)?(-1,1):(1,-1) u:1 2s:1 2 u:1 2 p(C, …)?(0,0):(1,-1) s:1 2u:1 2 19

20. Refutation Protocol Collects data given to predicate p. Alternates. refute(C,proposer,other) p(C, …)?(1,-1):(-1,1) claimpayoff for proposer if p true (defense) payoff for other if p true (defense) payoff for other if p false (refutation) payoff for proposer if p false (refutation) other tries to make p false while proposer tries to make p true. p false means successful refutation. p true means successful defense. 11/20/201520 Introduction Theory Methods Results Conclusion

21. Reinterpret Refutation Refutation leads to successful strengthening or successful agreement. 11/20/201521

22. Essence of Game Rules without Payoff scholars: 1, 2 LifeOfClaim(C) = propose(1,C) followed by (oppose(1,2,C)|agree(1,2,C)). oppose(1,2,C) = (refute(1,2,C)|strengthen(1,2,C,C’)), where stronger(C,C’). strengthen(1,2,C,C’) = !refute(2,1,C’). agree(1,2,C) = !refute(2,1,C) 11/20/201522 blamed decisions: propose(1,C) refute(1,2,C) strengthen(1,2,C,C’) agree(1,2,c)

23. Winning/Losing Scholar who first violates a game rule, loses. If none violate a game rule: the claim predicate c.p(1,2, …) decides. 11/20/201523 Introduction Theory Methods Results Conclusion

24. Game Rules for Playground All objects exchanged during protocol must be legal and valid. Each move must be within time-limit. 11/20/201524

25. Example: Independent Set Alice = proposer, Bob = other. Protocol / claim: AtLeastAsGood. Alice claims to be at least as good as Bob at IS. – Bob provides undirected graph G. – Bob computes independent set sB for G (secret). – Alice computes independent set sA for G. – Alice wins, if size(sA) >= size(sB) (= p(sA,sB)). 11/20/201525

26. More examples of Protocols Let f(x,y)=x*y+(1-x)(1-y^2)). Alice claims Math(0.61): Bob constructs an x in [0,1] and Alice construct a y in [0,1], and Alice guarantees that f(x,y)> 0.61. True claim but can be strengthened to 0.618. Alice claims Solar(RawMaterials,m,0.61). Bob constructs raw materials r in RawMaterials and Alice constructs a solar cell s in Solution from r using money m and so that efficiency(s)> 0.61. 11/20/201526 Introduction Theory Methods Results Conclusion

27. Community Property For every faulty decision action there exists an exposing reaction that blames the bad decision. – Reasons: We want the system to be egalitarian. – It is important that clever crowd members can shine and expose others who don’t promote the social welfare of the community. Faulty decisions must be exposable. It may take effort. 11/20/201527 Introduction Theory Methods Results Conclusion

28. Community Property Alternative formulation If all decisions by Alice are not faulty, there is no chance of Alice losing against Bob. – if Alice is perfect, there is no chance of losing. If there exists a faulty decision by Alice, there is a chance of Alice losing against Bob. – egalitarian game 11/20/201528

29. Summary: faulty decisions 1.propose(Alice,C),C=false 2.propose(Alice,C),C=not optimum, C=true 3.refute(Alice,Bob,C),C=true 4.strengthen(Alice,Bob,c,cs),c=optimum 5.strengthen(Alice,Bob,c,cs),c=false 6.agree(Alice,Bob,c),c=false 7.agree(Alice,Bob,c),c=not optimum, c=true 11/20/201529

30. SCG Equilibrium Reputations of scholars are stable. The science does not progress; bugs are not fixed, no new ideas are introduced. Extreme, desirable situation: All scholars are perfect: they propose optimal claims that can neither be strengthened nor refuted. 3011/20/2015 Introduction Theory Methods Results Conclusion

31. Claims: convergence to optimum 31 0 1 quality strengthening correct valuation over strengthening true claims (defendable) false claims (refutable) 11/20/2015

32. Convergence if every faulty action is exposed, convergence is guaranteed. 11/20/201532 Introduction Theory Methods Results Conclusion

33. Methods Developed Platform SCG Court = Generator of teaching/innovation playgrounds – http://sourceforge.net/p/generic-scg/code-0/11 http://sourceforge.net/p/generic-scg/code-0/11 0/tree/GenericSCG/ – Developed numerous playgrounds for avatars. Developed Algorithms Course using Piazza based on SCG Court experience – role of scholar played by humans – piazza.com: encourages students to answer each other’s questions. 11/20/201533 Introduction Theory Methods Results Conclusion

34. Avatar Interface AvatarI – public List propose(List forbiddenClaims); – public List oppose(List claimsToBeOpposed); – public InstanceI provide(Claim claimToBeProvided); – public SolutionI solve(SolveRequest solveRequest); Introduction Theory Methods Results Conclusion 11/20/201534

35. Instance Interface InstanceI – boolean valid(SolutionI solution, Config config); – double quality(SolutionI solution); Introduction Theory Methods Results Conclusion 11/20/201535

36. InstanceSet Interface InstanceSetI – Option belongsTo(InstanceI instance); – Option valid(Config config); }} 11/20/201536

37. Protocol Interface ProtocolI – double getResult(Claim claim, SolutionI[] solutions, InstanceI[] instances); – ProtocolSpec getProtocolSpec(); – boolean strengthenP(Claim oldClaim, Claim strengthenedClaim); 11/20/201537

38. Claim Class, for all playgrounds Claim – public Claim(InstanceSetI instanceSet, ProtocolI protocol, double quality, double confidence) 11/20/201538

39. Protocol Library AsGoodAsYou.java ExistsForAll.java ForAllExists.java Renaissance.java Survivor.java 11/20/201539 Introduction Theory Methods Results Conclusion

40. Claim Kinds in SCG Court Claim C(instance, q) Claim C(InstanceSet, q) Claim MaxResource(Algorithm,InstanceSet,n,ResExp) Claim MinResource(Algorithm,InstanceSet,n,ResExp) Claim IAmTheBest(), AtLeastAsGoodAsYou() Claims from predicate logic and some of its generalizations (IF Logic) 11/20/201540 Introduction Theory Methods Results Conclusion

41. Results Explanation: SCG as a general pattern behind many different competitions: topcoder.com, kaggle.com, Operations Research Competitions, tunedit.org, http://eterna.cmu.edu/ … SCG usage for teaching using forum – Innovation Success with Undergraduates using SCG on piazza.com: Qualitative Data Sources & Analysis Avatar competitions are not for teaching (but good for competitive innovation) Theoretical Properties of SCG 11/20/201541 SCG = Scientific Community Game = Specker Challenge Game Introduction Theory Methods Results Conclusion

42. 1 propose claim C from Claims 2 refute(C,1,2) p(C, …)?(1,-1):(-1,1) 1 scholar 2 scholar strengthen attempt C’ => C refute(C’,2,1) agree attempt C refute(C,2,1) 11/20/2015 refute attempt C refute(C, proposer,other) p(…)?(proposer,other): (proposer,other) s: successful u: unsuccessful p(C’, …)?(-1,1):(1,-1) u:1 2s:1 2 u:1 2 p(C, …)?(0,0):(1,-1) s:1 2u:1 2 High competition Introduction Theory Methods Results Conclusion 42

43. 1 propose claim C from Claims 2 refute(C,1,2) p(C, …)? (0,0) :(0,1) 1 scholar 2 scholar strengthen attempt C’ => C refute(C’,2,1) agree attempt C refute(C,2,1) 11/20/2015 refute attempt C refute(C, proposer,other) p(…)?(proposer,other): (proposer,other) s: successful u: unsuccessful p(C’, …)?(0,1): (0,0) u:1 2s:1 2 u:1 2 p(C, …)?(0,0): (1,0) s:1 2u:1 2 Low competition Introduction Theory Methods Results Conclusion 43

44. Competition Knob: minimum For each scholar – count claims that were successfully opposed (refuted or strengthened) encourages strong claims gather information from competitors for free – count claims that were not successfully agreed Good for teaching – students want minimum competition – good students want to build social capital and help weaker students 11/20/201544

45. Piazza Results Lower competition knob for teaching. For optimization claims got significant scientific discourse. Playgrounds cannot have too many scholars, otherwise they are overwhelmed. – about 5 is a good size – use hierarchical playgrounds: winning teams compete again 11/20/201545 Introduction Theory Methods Results Conclusion

46. Piazza Results Do not give hints at solutions. This significantly decreased the amount of discourse taking place. 11/20/201546

47. Gamification of Software Development and Teaching Want reliable software to solve a computational problem? Design a game where the winning team will create the software you want. Want to teach a STEM domain? Design a game where the winning students demonstrate superior domain knowledge. Crowdsourcing Doesn’t TopCoder already do this? STEM = Science, Technology, Engineering, and Mathematics 474/24/2011 Introduction Theory Methods Results Conclusion

48. Conclusions for Teaching Transition – refute: (1,-1):(-1,1) -> (0,0) :(0,1) – strengthen: (-1,1):(1,-1) -> (0,1): (0,0) – agree: (0,0):(1,-1) -> (0,0): (1,0) creates better playgrounds for learning by lowering competition and increasing teaching between scholars. 11/20/201548 Introduction Theory Methods Results Conclusion

49. Conclusions Flexible use of SCG using a forum environment with threads and replies using optimization playgrounds is productive: – teams took turns leapfrogging each other 11/20/201549