Motivating Cooperation in Peer-to-Peer Communities

Helen Bretzke1, Julita Vassileva2

Computer Science Department1University of Toronto

2University of Saskatchewan

Canada

Outline• Peer-to-peer communities

– free-riding and participation

• User modelling– social type of user (w.r.t. cooperativeness)– user interests– user relationships

• Motivating Participation – rewards in terms of QoS– creating community awareness– promoting user reflection

What are Peer to Peer (P2P) systems?What are Peer to Peer (P2P) systems?

• Networked applications (“Networked applications (“servents”)servents”) that that act as both act as both servservers (producers) and cliers (producers) and clients ents (consumers) of shared resources.(consumers) of shared resources.

• Shared resources can be files, bandwidth, Shared resources can be files, bandwidth, computation cycles or human time and effort.computation cycles or human time and effort.

COMTELLA

• A P2P (Gnutella based) system for file sharing and service– users share academic papers, code

snippets, help

• non-centralized digital library for a research group / class

• motivation and community-building

Christopher CoxNSERC Summer2002 project

Helen BretzkeCRA-W and NSERC Summer’2002 project

Lingling Sunundergraduate project

Yamini UpadrashtaGraduate student

Resentful peers call them “Leeches”...

What is a free-rider?

(and sometimes far worse names.)

A proper definition:

A free-rider is a user who A free-rider is a user who consumes far more resources consumes far more resources

than s/he offers.than s/he offers.

“almost 70% of Gnutella users share no files, and nearly 50% of all responses are returned by the top 1% of sharing hosts” (Adar & Huberman, 2000)

What’s the problem?

Many call free-riding the “tragedy of the digital commons”

Others claim that this a poor analogy.Others claim that this a poor analogy.

In In anyany online community online community where there are where there are costscosts

associated with sharing, free-associated with sharing, free-riding can bring the system to riding can bring the system to

its knees.its knees.

……e.g. I-Helpe.g. I-Help

Beyond P2PBeyond P2P

I-Help deployment results

2 years, 2000+ users, all undergrad CS classes at the UofS, also in the UK, France and Colombia

Lessons learned: Usage / participation varies greatly Should be perceived as adding value

After reaching a “critical mass” becomes self-feeding

Encouraging students to participate is crucial

Greer J., McCalla G., Vassileva J., Deters R., Bull S., Kettel L. (2001) Lessons Learned in Deploying a Multi-Agent Learning Support System, Proceedings AIED'2001, IOS Press: Amsterdam 410-421.

Why do they do it?

Are users just greedy? Or Are users just greedy? Or is there more to it?is there more to it?

Users are lagging in a Users are lagging in a paradigm shift. In the paradigm shift. In the centralized, client-server centralized, client-server systems of yore, users systems of yore, users grew accustomed to being grew accustomed to being served.served. But in the decentralized But in the decentralized

world of P2P, they must world of P2P, they must also learn to contribute!also learn to contribute!

How can we convert piglets into peers?

• cultivate user understanding of her new cultivate user understanding of her new role in a new paradigm.role in a new paradigm.

• create a perception in the user of the create a perception in the user of the P2P network as a community of P2P network as a community of volunteers.volunteers.

• generate and promote a strong sense of generate and promote a strong sense of this community.this community.

Levels of participation

• Bring new files, give help

• Provide disk space / processor time

• Dispatch requests

• Stay on-line

• Use and quit

socially motivated

Why do people offer their time and resources? Different people have different motivations:

materialistic

Some are altruists

Some would help their friends and hope to make new friends through helping

Some seek glory

Some seek high marks

or money…

How to motivate participation?

altruistic

Know your user!

• User Type: Altruist? Socialist? Materialist?

• User Interests: What does she search / need?

• User Relationships and Community: Who shares interest with the user? Potential “friends” and “bozos”.

Modelling

Modelling user type

• Monitor user’s actions regarding file sharing, relative time spent on-line, acts of interrupting service, total balance of user’s giving / taking

• Update a number in [-1, 1] representing user’s cooperativeness

• Motivational actions in the interface triggered by passing certain thresholds

• Define a taxonomy of subject categories (e.g. ACM subject index)

• Keep track of the categories of queries ( user interests)

• Update user level of interest in each sub-category using reinforcement learning

• Keep track of resources or services offered by the user in each interest category

Modelling user interests

Modelling user relationships• Monitor who offers services in the user’s areas of interest,

whose services the user chooses, the quality of the service, and who uses services offered by the user

• Represent each user relationship:For which each area of interest– Strength – how often, how successful service

(reinforcement learning used similar to user interests)

– Balance – reciprocity of services used/ given

• Adapt P2P topology – form a neighborhood for search using the best relationships (“friends”) in the area of search

• Propagate further queries of “friends”

Motivating participationReinforcing / rewarding

relationships• Friends are treated differently

– Transfers not interrupted– Queries processed with priority– Queries are propagated further

• Clusters of friends sharing an area of interest • Queries sent to friends in the area

– Higher chance to have relevant files – Faster responses – Better quality of files

• Better Quality of Service!

Motivating participation Building a community

• Harness the UM to gather information about the user’s friends and the user’s cooperativeness.

• Provide feedback to stimulate awareness and reflection in the user

• The trick is to find the right metaphor and create appropriate visualization to convey this feedback.

Social awareness

In large cities, the sidewalks provide the right kinds and numbers of interactions from which neighbourhoods emerge.

Users suffer from a lack of perspective and a lack of feedback. In such isolation, it is no small wonder that they behave selfishly.

In COMTELLA’s P2P environment, we have chosen a different metaphor...

A matter of scale

An astronomical metaphor

• Provides visual feedback

• Addresses issue of scale

• Attractive & interesting

Views of the community

• connectivity (hop-graph of currently reachable peers)

• shared interests

• overall ranking of peers (based on strength of relationships)

Shared interests

Ranking of peers

Brightness = reciprocity

Size = strength of relationship

Position = overall rank

Visual semantics

Prompting reflection

• Highlight cause and effect relationship between user actions and QoS.

• Give real-time feedback– unobtrusive peripheral animations

– ‘whispered’ messages (soft text)

• if user is curious, she can read the text

• but it is okay for her to ignore (slips under the radar)

Evaluation results

• Difficult to evaluate the effects of many features at once – separate experiments – Exp1: to evaluate the impact of modelling user

relationships on the QoS– two methods for evaluation:

• through simulation • through experiment with human subjects

– first results presented in W5 paper

3 “take-home” messages• Creating community awareness

• Modelling social aspects of user behaviour

– has the potential to increase user understanding of her new role in the network and to stimulate more cooperative behavior.

– as an engine for generating personalized community views– for book-keeping needed to reward participation– to adapt the environment and assure better QoS

the end… Not

Computing user type

• The measure of user cooperativeness at time t C(wt, t) = i * C(w t-1, t-1) + (1 - i) * wt, w [-1,0) (0,1] represent the weight of evidence, where w < 0 is a

selfish act while w > 0 is an altruistic act.

overallBalance = (1/n)*Y (BXY)

userType = (cooperativeness + overallBalance) /2 If userType is in [-1, -0.5) then user is selfish, if it is in [-0.5) ( 0.5]

then user is reciprocal, and if it is in (0.5, 1] then user is altruistic.

Computing user interests

• Reinforcement learning / exponential smoothing• The user’s strength of interest S in an area a is calculated

based on how frequently and how recently the user has searched in this area.

Sa(et, t) = i * Sa(e t-1, t-1) + (1 - i) * et where et [0, 1] is calculated as et = 1/ d,

d = 1 + level_distance between the level of the sub-area of the query and the level of the area a in the ontology

hierarchy. Currently, the ontology hierarchy has only 2 levels, so et = 0.5

Computing the balance of a relationship

• BXY = (N XY N YX ) / (N XY N YX ) • BXY [-1, 1]

• N XY - number of times X took from Y

• N YX - number of times Y took from X