achieving fairness and excellence in social software
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Achieving Fairness and Excellence in Social Software. Ruben R. Puentedura, Ph.D. A Definition of Social Software. Resnick (2004): Social computing is processing of social information that is distributed in social collectivities . - PowerPoint PPT PresentationTRANSCRIPT
Achieving Fairness and Excellence in Social
Software
Achieving Fairness and Excellence in Social
Software
Ruben R. Puentedura, Ph.D.Ruben R. Puentedura, Ph.D.
A Definition of Social Software
A Definition of Social Software
•Resnick (2004): Social computing is processing of social information that is distributed in social collectivities.
•Extending Resnick’s definition: social software allows selective interaction with different subgroups of social collectivities, and may provide the tools for social computing processes.
•Resnick (2004): Social computing is processing of social information that is distributed in social collectivities.
•Extending Resnick’s definition: social software allows selective interaction with different subgroups of social collectivities, and may provide the tools for social computing processes.
Some Assumptions Underlying This Talk…
Some Assumptions Underlying This Talk…
•A key goal of using social software in the classroom is to stimulate new forms of participation and collaborative creative processes.
•Colleges and universities should be safe places, but not bland places.
•Excellence should not be sacrificed for the sake of consensus.
•A key goal of using social software in the classroom is to stimulate new forms of participation and collaborative creative processes.
•Colleges and universities should be safe places, but not bland places.
•Excellence should not be sacrificed for the sake of consensus.
Part 1: Social Software and Negotiation
Part 1: Social Software and Negotiation
Although Social Software Enables New Possibilities, It Also Brings In
Some New Problems…
Although Social Software Enables New Possibilities, It Also Brings In
Some New Problems…• The lack of standard cues for social interaction
can lead to a rapid development of rifts in discussions, with prematurely hardened positions.
• The relative pseudo-anonymity and opacity of social software spaces spawns phenomena that have few (or no) physical world counterparts:
• On Usenet: Godwin’s Law
• In Online Games: Griefers
• InWikis: WikiVandals
• The lack of standard cues for social interaction can lead to a rapid development of rifts in discussions, with prematurely hardened positions.
• The relative pseudo-anonymity and opacity of social software spaces spawns phenomena that have few (or no) physical world counterparts:
• On Usenet: Godwin’s Law
• In Online Games: Griefers
• InWikis: WikiVandals
These Issues Are Analogous To Those Encountered By
Professional Negotiators…
These Issues Are Analogous To Those Encountered By
Professional Negotiators…
Techniques Drawn From Getting To Yes (Fisher & Ury
1981)
Techniques Drawn From Getting To Yes (Fisher & Ury
1981)• Separate people and issues;
• Focus on ultimate pedagogical goals, rather than individual student positions;
• Explicitly generate options for parameters connecting these positions to the goals before positions harden:
• Separate the invention stage from the evaluation stage.
• Maintain explicit objective criteria throughout.
• Separate people and issues;
• Focus on ultimate pedagogical goals, rather than individual student positions;
• Explicitly generate options for parameters connecting these positions to the goals before positions harden:
• Separate the invention stage from the evaluation stage.
• Maintain explicit objective criteria throughout.
Techniques Drawn From Getting Past No (Ury 1991)
Techniques Drawn From Getting Past No (Ury 1991)• Identify “dirty tricks” explicitly:
• logical fallacies;
• unfair tactics.
• Enforce “time outs” for cooling things down, but do not shut discussions down;
• Reframe viewpoints explicitly;
• Keep building bridges to consensus even after positions have hardened.
• Identify “dirty tricks” explicitly:
• logical fallacies;
• unfair tactics.
• Enforce “time outs” for cooling things down, but do not shut discussions down;
• Reframe viewpoints explicitly;
• Keep building bridges to consensus even after positions have hardened.
Part 2: Balancing Power in Creative Systems
Part 2: Balancing Power in Creative Systems
Building Fair Choice Systems
Building Fair Choice Systems
•We want to get the best possible results for a collaborative project;
•We also want to prevent “spoilers” from ruining collaborations;
•However, we would like to do this in as democratic a fashion as possible.
•Hence: look at building some sort of collective editing system (e.g., a wiki).
•We want to get the best possible results for a collaborative project;
•We also want to prevent “spoilers” from ruining collaborations;
•However, we would like to do this in as democratic a fashion as possible.
•Hence: look at building some sort of collective editing system (e.g., a wiki).
Some Problems Rear Their Head…
Some Problems Rear Their Head…
•Not everyone is as knowledgeable on a given subject as someone else…
•Some of the least knowledgeable people are also the least likely to admit this…
•A first solution: create a voting system that rewards “good” contributions, giving the “good” contributors more say over what is retained in the project.
• e.g., Slashdot’s Karma system
•Not everyone is as knowledgeable on a given subject as someone else…
•Some of the least knowledgeable people are also the least likely to admit this…
•A first solution: create a voting system that rewards “good” contributions, giving the “good” contributors more say over what is retained in the project.
• e.g., Slashdot’s Karma system
We’re Not Home Free Yet, Though…
We’re Not Home Free Yet, Though…
• If we’re not careful, we can create a “dictator” over the editing process.
•For instance, assume:
• a team of three people where a simple majority of the votes suffices to accept or reject a post;
• all team members receive one vote to start, and get an additional vote each time a post of theirs is accepted;
• if a member has their first two posts accepted, they can now become “dictator–for–life” over the rest of the team…
• If we’re not careful, we can create a “dictator” over the editing process.
•For instance, assume:
• a team of three people where a simple majority of the votes suffices to accept or reject a post;
• all team members receive one vote to start, and get an additional vote each time a post of theirs is accepted;
• if a member has their first two posts accepted, they can now become “dictator–for–life” over the rest of the team…
We Need a Way to Calculate Political
Power
We Need a Way to Calculate Political
Power•A suitable political power index should
take into account the number of ways a participant’s vote can effectively decide an outcome.
•Once this index has been calculated, it can be used to modify the “voting power increase” procedure, so as to keep any individual’s power within desirable limits.
•A suitable political power index should take into account the number of ways a participant’s vote can effectively decide an outcome.
•Once this index has been calculated, it can be used to modify the “voting power increase” procedure, so as to keep any individual’s power within desirable limits.
The Banzhaf Index of Power
The Banzhaf Index of Power
• The Total Banzhaf Power of a voter p (TBP(p)) is the number of coalitions C satisfying the following three conditions:
• p is a member of C
• C is a winning coalition
• If p is deleted from C, the resulting coalition is not a winning one
• The Banzhaf Index for the ith voter is given by:
• The Total Banzhaf Power of a voter p (TBP(p)) is the number of coalitions C satisfying the following three conditions:
• p is a member of C
• C is a winning coalition
• If p is deleted from C, the resulting coalition is not a winning one
• The Banzhaf Index for the ith voter is given by:
The Banzhaf Index - An Example
The Banzhaf Index - An Example
• Assume three voters: p1 (has 50 votes), p2
(has 49 votes), p3 (has 1 vote)
• 51 votes are needed to accept a suggestion
• The winning coalitions are: C1={p1, p2,
p3}, C2={p1, p2}, C3={p1, p3}
• The Banzhaf Indices are BI(p1)=3/5,
BI(p2)=1/5, BI(p3)=1/5
• Assume three voters: p1 (has 50 votes), p2
(has 49 votes), p3 (has 1 vote)
• 51 votes are needed to accept a suggestion
• The winning coalitions are: C1={p1, p2,
p3}, C2={p1, p2}, C3={p1, p3}
• The Banzhaf Indices are BI(p1)=3/5,
BI(p2)=1/5, BI(p3)=1/5
How Should One Use The Banzhaf Index?
How Should One Use The Banzhaf Index?
• In our previous example:
• It could be used to determine a maximum for the number of votes added to any participant’s tally…
•…or it could be used to adjust the threshold number of votes needed to accept a post…
•…or some combination of the two.
• In our previous example:
• It could be used to determine a maximum for the number of votes added to any participant’s tally…
•…or it could be used to adjust the threshold number of votes needed to accept a post…
•…or some combination of the two.
Part 3: Voting and Recommendation
Systems
Part 3: Voting and Recommendation
Systems
Building Fair Recommendation Systems
Building Fair Recommendation Systems•Suppose that seven people are asked
to vote on three learning objects, so as to recommend the “best” one. Here’s how they voted:
•So, learning object C should be the chosen one, right? Not so fast…
•Suppose that seven people are asked to vote on three learning objects, so as to recommend the “best” one. Here’s how they voted:
•So, learning object C should be the chosen one, right? Not so fast…
Voter 1
A
Voter 2
A
Voter 3
B
Voter 4
B
Voter 5
C
Voter 6
C
Voter 7
C
Building Fair Recommendation Systems
(cont.)
Building Fair Recommendation Systems
(cont.)•Had you asked voters about their full
preference ranking, this would have been the result:
• In other words, B acted as a “spoiler” for A, allowing the (overall inferior) C to be ranked first!
•Had you asked voters about their full preference ranking, this would have been the result:
• In other words, B acted as a “spoiler” for A, allowing the (overall inferior) C to be ranked first!
Voter 1
A B C
Voter 2
A B C
Voter 3
B A C
Voter 4
B A C
Voter 5
C A B
Voter 6
C A B
Voter 7
C A B
What Can We Do About This?
What Can We Do About This?
•This scenario (and others like it) can never be completely avoided (Arrow’s Impossibility Theorem);
•However, simple majority/plurality systems are worse than other voting schemes in terms of the problems they cause.
•A scheme that is significantly better than these two: Condorcet Voting.
•This scenario (and others like it) can never be completely avoided (Arrow’s Impossibility Theorem);
•However, simple majority/plurality systems are worse than other voting schemes in terms of the problems they cause.
•A scheme that is significantly better than these two: Condorcet Voting.
Condorcet VotingCondorcet Voting
•Voters rank the options in order of preference;
•For each pair of options, the number of times one is preferred to the other is counted;
• If an option is preferred over all others, it is the winner.
•Ties and ambiguities can be handled separately by a range of methods, should they arise.
•Voters rank the options in order of preference;
•For each pair of options, the number of times one is preferred to the other is counted;
• If an option is preferred over all others, it is the winner.
•Ties and ambiguities can be handled separately by a range of methods, should they arise.
Condorcet Voting - An Example
Condorcet Voting - An Example
• Using our results from before:
• In pairwise contests:A is preferred to B 5 times; B is preferred to A 2 timesA is preferred to C 4 times; C is preferred to A 3 timesB is preferred to C 4 times; C is preferred to B 3 times
• So A is the winner.
• Using our results from before:
• In pairwise contests:A is preferred to B 5 times; B is preferred to A 2 timesA is preferred to C 4 times; C is preferred to A 3 timesB is preferred to C 4 times; C is preferred to B 3 times
• So A is the winner.
Voter 1
A B C
Voter 2
A B C
Voter 3
B A C
Voter 4
B A C
Voter 5
C A B
Voter 6
C A B
Voter 7
C A B
BibliographyBibliography
• Resnick, P. (2004). Paul Resnick’s Sabbatical Musings. Available online at: http://presnick.livejournal.com/
• Puentedura, R.R. (2003). A Matrix Model for Designing and Assessing Network-Enhanced Courses. Available online at: http://www.hippasus.com/resources/matrixmodel/puentedura_model.pdf
• Terra Nova - a collaborative weblog, focusing on the social science of virtual worlds. Available online at: http://terranova.blogs.com/
• Fisher, R. and W. Ury (1981). Getting to Yes: Negotiating Agreement Without Giving In. New York, New York: Penguin Books.
• Ury, W. (1991). Getting Past No: Negotiating Your Way from Confrontation to Cooperation. New York, New York: Bantam Books.
• Taylor, A.D. (1995). Mathematics and Politics: Strategy, Voting, Power and Proof. New York, New York: Springer.
• Sample software for computing the Banzhaf Index and implementing Condorcet Voting is available at: http://www.hippasus.com/resources/socialsoftware/
• Resnick, P. (2004). Paul Resnick’s Sabbatical Musings. Available online at: http://presnick.livejournal.com/
• Puentedura, R.R. (2003). A Matrix Model for Designing and Assessing Network-Enhanced Courses. Available online at: http://www.hippasus.com/resources/matrixmodel/puentedura_model.pdf
• Terra Nova - a collaborative weblog, focusing on the social science of virtual worlds. Available online at: http://terranova.blogs.com/
• Fisher, R. and W. Ury (1981). Getting to Yes: Negotiating Agreement Without Giving In. New York, New York: Penguin Books.
• Ury, W. (1991). Getting Past No: Negotiating Your Way from Confrontation to Cooperation. New York, New York: Bantam Books.
• Taylor, A.D. (1995). Mathematics and Politics: Strategy, Voting, Power and Proof. New York, New York: Springer.
• Sample software for computing the Banzhaf Index and implementing Condorcet Voting is available at: http://www.hippasus.com/resources/socialsoftware/
•http://www.hippasus.com• [email protected]•http://www.hippasus.com• [email protected]
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