virtual scientific-community-based foundations for popperian e-science

Virtual Scientific-Community-Based Foundations for Popperian e-Science

Karl LieberherrAhmed Abdelmeged

Northeastern University, CCIS, PRL, Boston

04/20/23 1

04/20/23 2

Ontology

Computer Science

Programming

Socio-Technical SystemThe Global Brain

Mathematics

Game Theory

ExtensiveFormMetaGaming

Mathematical Logic

Dialog Games IF Logic

inspired by ScienceWISE

Organization

Mechanism Design

A claim is …

• information about one’s performance when interacting with another clever being in a specific domain.

• information about the performance of one’s program.

4/24/2011 3Crowdsourcing

Outline

Theory

Methods

Results

Conclusion

Introduction

04/20/23 4

Introduction• Explanation: SCG as a general pattern behind many different

competitions: topcoder.com, kaggle.com, tunedit.org, Renaissance, …

• Make SCG a part of cyber-infrastructure (e-science) to support teaching and innovation in constructive domains.

• 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• Take Home: EASY USE WITH STANDARD TOOLS

• group research, teaching, (intelligent) crowd sourcing

04/20/23 5

IntroductionIntroduction TheoryTheory MethodsMethods ResultsResults ConclusionConclusion

SCG = Scientific Community Game = Specker Challenge Game

Popper

• One of the philosophers of science who has had a big impact.

• Popper’s solution: Falsification: A claim is falsifiable if you can imagine an observation that would cause you to reject the claim.

• 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.

04/20/23 6


What SCG helps with

• Build and maintain knowledge bases (sets of claims believed to be true).

• 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.

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Abstraction from 4 Examples

• From a CS journal paper• Insilico experiment• From kaggle.com: Facebook competition• From a calculus problem

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Example 1: From an Abstract of a 2005 Journal Paper

• An instance of a constraint satisfaction problem (CSP) is variable k-consistent if any subinstance with at most k variables has a solution.

• For a fixed constraint language L, r(k,L) is the largest ratio such that any variable k-consistent instance has a solution that satisfies at least a fraction of r(k,L) of the constraints.

04/20/23 9

Example 1

• From a 2005 TCS paper: Locally Consistent Constraint Satisfaction Problems by Manuel Bodirsky and Daniel Kral.

• Example– L = CNF– k = 1– What is r(1,CNF)?– Claims: r(1,CNF) = 0.6, r(1,CNF) = 0.7

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Example 1: Making a game to determine r(1,CNF)

• Observation: claims are falsifiable playing a two person game.

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Example 2: Claim involving Insilico Experiment

Claim InsilicoExperimental(X,Y,q,r)I claim, given raw materials x in X,I can produce product y in Y of quality qand using resources at most r.

12Crowdsourcing4/24/2011

Example 2: Making a game to determine InsilicoExperimental(X,Y,q,r)


04/20/23 13

Example 3: Data mining

• Facebook competition from Kaggle.com: – Given a social network graph x with deleted edges

and the original social network graph gs (secret, from a family X of social networks)

– guess the complete social network graph y– quality(x, gs, y) = mean average precision

(adapted from IR)– I claim I can achieve a mean average precision of q

• for social graphs in family X: DM1(X,q)• for a specific reduced social graph: DM2(x,q)

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Example 3: Making a game to determine the optimal claims

• Observation: claims DM1(X,q) and DM2(x,q) are falsifiable playing a two person game.

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Example 4: Specker• Claims:

– Specker(set X, set Y(X), function f(X,Y)->[0,1], constant c): ForAll x in X Exists y in Y(X): f(x,y)≥c

• Example 1– X = Conjunctive Normal Forms with various restrictions– Y(X) = Assignments to CNFs– f(x,y) = fraction of satisfied clauses in x under y– c in [0,1], e.g., c= 0.61

• Example 2 (a reduction of example 1)– X = [0,1]– Y(X) = [0,1]– f(x,y)=x*y+(1-x)(1-y^2))– c in [0,1], e.g., c=0.61

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Example 4: Specker

• Observation: claims Specker(X,Y,f,c) are falsifiable playing a two person game.

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What is the abstraction?

• Sets of claims• Claims are falsifiable• …

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RP1

PG1

claimsC11C12C13…

SC1SC2

SC1SC1

RP2

PG2

claimsC21C22C23…

SC3SC4

SC5SC1

SCG defines:• refutation protocol interface• generic rules for all playgrounds

Each playground defines:• domain• claims language• specific protocol • data exchanged• configuration data

Playgrounds

D1

D2


• Observation: claims are falsifiable playing a two person game. defendable = !refutable– propose r(1,CNF) = 0.7

• refutable

– propose r(1,CNF) = 0.6 can be strengthened to r(1,CNF) = 0.61 which is defendable (refutation attempts will be unsuccessful)

– propose r(1,CNF) = (sqrt(5)-1)/2 ~ 0.618 … optimum: defendable and cannot be strengthened

04/20/23 20

Who are the scholars?

• Scientists• 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.

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Kaggle.com Competitions2012

• 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

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Kaggle.com Competitions2012

• 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

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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?

• Scholars are free to invent; game rules don’t limit creativity!

2404/20/23


Degree of automation with SCG(X)

25

no automationhuman plays

full automationavatar plays

degree of automation used by scholar

some automationhuman plays

0 1

more applications:test constructive knowledge

transfer to reliable, efficient software

avatar Bobscholar Alice

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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.

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happy = can be creative, can thrive, have opportunity to learn, not ignored

Playground

• defines – what is wanted, e.g., an algorithm S in a particular

domain (inputs/outputs)– evaluation, e.g., how S is evaluated (quality)– claims, e.g., what kind of claims can be made

about S (expression with quantifiers)

• A playground defines WHAT is desired and the scholars/avatars define the HOW.

Theory

• Extensive Form Representation of Game• Properties

– Community Property: All faulty actions can be exposed.

– SCG Equilibrium– Convergence to optimum claim

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Extensive-form representation

1. the players of a game: 1 and 22. for every player every opportunity they have

to move3. what each player can do at each of their

moves4. what each player knows for every move5. the payoffs received by every player for

every possible combination of moves

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Large Action Spaces

• Thick arrows mean: select from a usually large number of choices

1

2

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Refutation Protocol

• Collects data given to predicate p. Alternates.

refute(C,proposer,other)

p(C, …)?(1,-1):(-1,1)

claim payoff for proposerif p true (defense)

payoff for otherif p true (defense)

payoff for otherif p false (refutation)

payoff for proposerif p false (refutation)

other tries to makep false while proposertries to make p true.

p false means successfulrefutation.

p true means successfuldefense.

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1

propose claim C from Claims

2

refute(C,1,2)

p(C, …)?(1,-1):(-1,1)

1 scholar2 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)

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refute attempt C

refute(C, proposer,other)p(…)?(proposer,other): (proposer,other)


p(C’, …)?(-1,1):(1,-1) p(C, …)?(0,0):(1,-1)

32

SCG Core

Game Rules for Playground

• All objects exchanged during protocol must be legal and valid.

• Each move must be within time-limit.• Scholar who first violates a playground rule,

loses.

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4/24/2011 Crowdsourcing 34

good bad

Logic with Soundness

claimssentences

not just true/false claims, but optimum/non-optimum claims:good: true/optimumbad: false/non-optimum


bad


good

Scientific Community Game Logicwith Community Principle

agreed by two scholars disagreed by two scholars

there exists a two-party certificateto expose misclassification

claimssentences


Comparison Logic and SCG

Logic• sentences

– true– false

• proof for being true– proof system, checkable– guaranteed defense

• proof for being false– proof system, checkable– guaranteed refutation

• Universal sentences

Scientific Community Game• sentences = claims

– good– bad

• evidence for goodness– defense, checkable– uncertainty of defense

• evidence for badness– refutation, checkable– uncertainty of refutation

• Personified sentences


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.

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Methods of Exploration

• Developed Platform SCG Court = Generator of teaching/innovation playgrounds– http://sourceforge.net/p/generic-scg/code-0/110/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.

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Avatar Interface

• AvatarI – public List<Claim> propose(List<Claim> forbiddenClaims); – public List<OpposeAction> oppose(List<Claim>

claimsToBeOpposed); – public InstanceI provide(Claim claimToBeProvided); – public SolutionI solve(SolveRequest solveRequest);

• from http://sourceforge.net/p/generic-scg/code-0/110/tree/GenericSCG/src/scg/scg.beh


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Instance Interface (Domain)

• InstanceI – boolean valid(SolutionI solution, Config config); – double quality(SolutionI solution);


04/20/23 40

InstanceSet Interface (Domain)

• InstanceSetI – Option<String> belongsTo(InstanceI instance);– Option<String> valid(Config config); }}

04/20/23 41

Protocol Interface

• ProtocolI – double getResult(Claim claim, SolutionI[]

solutions, InstanceI[] instances); – ProtocolSpec getProtocolSpec(); – boolean strengthenP(Claim oldClaim, Claim

strengthenedClaim);

04/20/23 42

Claim Class, for all playgrounds

• Claim – public Claim(InstanceSetI instanceSet, ProtocolI

protocol, double quality, double confidence)

04/20/23 43

Protocol Library

• ExistsForAll.java• ForAllExists.java• Renaissance.java• AsGoodAsYou.java• Survivor.java

04/20/23 44


Second Method: Piazza ExperienceGale-Shapley

• We propose that, for all integers n > 0, the maximum iterations the Gale-Shapely algorithm with n men and n women can produce is n(n-1)+1.

Note: Thus far, the inputs used for all other claims arrives at only (n(n+1))/2.

04/20/23 45

Piazza Experience

• Leaf Covering: Improved running time from quadratic to constant time.

04/20/23 46

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

04/20/23 47

SCG = Scientific Community Game = Specker Challenge Game


Competition tuning: 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

04/20/23 48


Piazza Results

• Do not give hints at solutions. This significantly decreased the amount of discourse taking place.

04/20/23 49


Conclusions and Future Work

• We propose a systematic gamification of teaching STEM domains:– Design an SCG playground where the winning

students demonstrate superior domain knowledge.

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STEM = Science, Technology, Engineering, and Mathematics


Gamification of Software Development for Computational Problems

• Want reliable software to solve a computational problem? Design an SCG playground where the winning team will create the software you want.

• playground design = requirements– Programming the Global Brain

• socio-technical system (playground) will produce solution to requirements.

Crowdsourcing 514/24/2011


Conclusions

• Flexible use of SCG using a forum environment with threads and replies using optimization playgrounds is productive:– teams took turns leapfrogging each other– reached state-of-the-art and even improved it

• SCG has desirable theoretical properties.– faulty decision –> exposing reaction– equilibria– convergence to optimum claim

04/20/23 52


Future Work

• Make SCG part of cyber-infrastructure (e-science) both for avatars and human scholars.

• Polish SCG Court – The administrator software needs to be very reliable (to avoid

cheating by avatars).– Playground development and testing needs tool support.• Further develop SCG with forum software– Playground design defines requirements for know-how.– Hierarchical playgrounds: partitioning into balanced groups.– Restart playground after publishing all current ideas in

playground (if optimum is not yet reached).

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Links / Questions• SCG Home

– http://www.ccs.neu.edu/home/lieber/evergreen/specker/scg-home.html

• Piazza page for Algorithms– http://piazza.com/class#winter2012/cs4800/0

• Algorithms Home– http://www.ccs.neu.edu/home/lieber/courses/algorithms/cs4800/

sp12/course-description.html • Algorithms Feedback

– http://www.ccs.neu.edu/home/lieber/courses/algorithms/cs4800/sp12/feedback/

• SCG Court Source– http://sourceforge.net/p/generic-scg/code-0/110/tree/GenericSCG/

04/20/23 54

The End

• More Questions?

04/20/23 55

Extra slides

04/20/23 56

Essence of Game Ruleswithout 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)

04/20/23 57

blamed decisions:propose(1,C)refute(1,2,C)strengthen(1,2,C,C’)agree(1,2,c)

1


2

refute(C,1,2)

p(C, …)?(1,-1):(-1,1)

1 scholar2 scholar


refute(C’,2,1)

agree attempt C

refute(C,2,1)

p(C’, …)?(1,-1):(-1,1) p(C, …)?(1,-1):(-1,1)

04/20/23

refute attempt C

refute(C, proposer,other)p(…)?(proposer,other): (proposer,other)s: successfulu: unsuccessful


p(C’, …)?(-1,1):(1,-1)

u:1 2 s:1 2

s:1 2 u:1 2

p(C, …)?(0,0):(1,-1)s:1 2 u:1 2

58

1


2

refute(C,1,2)

p(C, …)? (0,0) :(0,1)

1 scholar2 scholar


refute(C’,2,1)

agree attempt C

refute(C,2,1)

04/20/23

refute attempt C

refute(C, proposer,other)p(…)?(proposer,other): (proposer,other)s: successfulu: unsuccessful

p(C’, …)?(0,1): (0,0)

u:1 2 s:1 2

s:1 2 u:1 2

p(C, …)?(0,0): (1,0)s:1 2 u:1 2

Low competition


59

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.

04/20/23 60


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.

04/20/23 61

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 constructs 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.

04/20/23 62


Questions received

• In learning game, give credit to all contributors, not just final one (DARPA 10 ball challenge)

• Predicate logic -> SCG: make explicit• Playground design: involve competitors

04/20/23 63

Questions

• Credit– first time the best claim is made– linear order by time– linear order by strength (quality)

04/20/23 64

New insight

• Need to know very little about refutation protocol.– collect data, what is available when is not

important– evaluate predicate with collected data

04/20/23 65

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.

04/20/23 66Playground = Instantiation of Platform


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)).

04/20/23 67

Specker• Claims:




04/20/23 68


Kaggle.com

• Facebook competition: – X = Social Network Graph with deleted edges,

Original Social Network Graph (secret)– Y(X) = estimated complete Social Network Graph– quality(x,y) = mean average precision adapted

from IR

04/20/23 69


Simpler talk

• Introduction: parameterized models of scientific communities

• Theory

04/20/23 70

Abstraction from 4 Examples

• From a CS journal paper• Insilico experiment• From kaggle.com: Facebook competition• From a calculus problem

04/20/23 71

Example 1: From an Abstract of a 2005 Journal Paper

• An instance of a constraint satisfaction problem (CSP) is variable k-consistent if any subinstance with at most k variables has a solution.

• For a fixed constraint language L, r(k,L) is the largest ratio such that any variable k-consistent instance has a solution that satisfies at least a fraction of r(k,L) of the constraints.

04/20/23 72

Example 1

• From a 2005 TCS paper: Locally Consistent Constraint Satisfaction Problems by Manuel Bodirsky and Daniel Kral.

• Example– L = CNF– k = 1– What is r(1,CNF)?– Claims: r(1,CNF) = 0.6, r(1,CNF) = 0.7

04/20/23 73



04/20/23 74

Example 2: Claim involving Insilico Experiment

Claim InsilicoExperimental(X,Y,q,r)I claim, given raw materials x in X,I can produce product y in Y of quality qand using resources at most r.

75Crowdsourcing4/24/2011

Example 2: Making a game to determine InsilicoExperimental(X,Y,q,r)


04/20/23 76

Example 3: Data mining

• Facebook competition from Kaggle.com: – Given a social network graph x with deleted edges

and the original social network graph gs (secret, from a family X of social networks)

– guess the complete social network graph y– quality(x, gs, y) = mean average precision

(adapted from IR)– I claim I can achieve a mean average precision of q

• for social graphs in family X: DM1(X,q)• for a specific reduced social graph: DM2(x,q)

04/20/23 77


Example 3: Making a game to determine the optimal claims

• Observation: claims DM1(X,q) are falsifiable playing a two person game. Claim DM2(x,q) is falsifiable when the secret is revealed.

04/20/23 78

Example 4: Specker• Claims:




04/20/23 79


Example 4: Specker

• Observation: claims Specker(X,Y,f,c) are falsifiable playing a two person game.

04/20/23 80

What is the abstraction?

• Sets of claims• Claims are falsifiable• …

04/20/23 81


• Observation: claims are falsifiable playing a two person game. defendable = !refutable– propose r(1,CNF) = 0.7

• refutable

– propose r(1,CNF) = 0.6 can be strengthened to r(1,CNF) = 0.61 which is defendable (refutation attempts will be unsuccessful)

– propose r(1,CNF) = (sqrt(5)-1)/2 ~ 0.618 … optimum: defendable and cannot be strengthened

04/20/23 82

What we get

• Engaged software developers– let them produce software that models an

organism that fends for itself in a real virtual world while producing the software we want. Have fun. Focus them.

– let them propose claims about the software they produce. Reward them when they

• defend their claims successfully or • oppose the claims of others successfully.

Crowdsourcing 83

Clear Feedback Sense of Progress

Possibility of Success

Authenticity

4/24/2011

Reinterpret Refutation

• Refutation leads to successful strengthening or successful agreement.

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virtual scientific-community-based foundations for popperian e-science

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