Social collaboration scenarios, such as
sharing resources between friends, are becoming increasingly prevalent in recent years. An example of this new paradigm is Social Cloud Computing, which aims at leveraging existing digital relationships within social networks for the exchange of resources among users and user communities. Due to their complexity, such platforms and systems have to be carefully designed and engineered to suit their purpose. In this paper, we propose a general-purpose simulation tool to help in the design and analysis of Social Collaboration Platforms, and discuss potential use cases and the architecture of the simulator. To show the usefulness of the simulator, we present a simple use case in which we study the effects of an incentive scheme on the system and its user community.
2. Motivation: Study of Complex Social Systems
Collaboration
Platform
How can we study collaboration and cooperation in complex social systems?
Theory Simulation Empirical Studies
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3. Agenda
1 Purpose and Applications of the Simulator
2 Architecture and Implementation
3 Case Study
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4. A Simulation Tool for Complex Social Systems
Purpose of the Simulation Tool
Platform design: Analysis of different architectures, schemes, settings, policies, ...
Prediction: Study effects of changes in system rules on the platform
Components
Network
Users
Objective
Characteristics
Resources
Platform Goal
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5. Potential Applications of the Simulation Tool
Network Structure User Types
Random vs. Small‐World structures Utility function distributions
Selfish
Provisioning
Leontief
Convex
Perfect
Substitutes
Sample Consumption
Applications
Effect of incentives on
Selection of collaboration
collaboration activities
intensity
Learning Behavior
User Strategies Incentive Schemes
5 12.10.2012 Christian Haas – A Simulator for Social Exchanges and Collaborations - Karlsruhe Service Research Institute
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6. Architecture: Overview of Simulator Structure
Social Collaboration Simulator Monitoring
User Incentive Experiment
Applications SmallWorldNetwork … Application Sensor
Scheme Controller
Exchange
Mechanisms Mechanism Currency Artifact … Exchange Sensor
Runtime
User Sensor
Core User Resource Relationship TrustContext
Elements Resource Sensor
Hierarchical layered structure with interchangeable components
Design
Component functionality specified in interfaces (API)
Principles
Scenarios and use cases are specified on the highest level
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Architecture and Case Study www.ksri.kit.edu
7. Scenario: Resource sharing between connected
users
Setting
Users connected based on
social network
Users request and provide
(computational) resources
(similar to BOINC)
Requests advertised to
other users
Observations from similar systems:
Users are heterogeneous (availability, sharing behavior,…)
Active contribution of users is vital for the system
Percentage of ‘free‐riders’ can be significant1
Focus: Contribution scheme to increase user participation 1: See Adar 2000
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8. Contribution Scheme
Goals Main idea1: Trading Constraint
Increase active participation, i.e. Each user i has a contribution score
strengthen resource provisioning
User i can request resources from user j
Restrict ‘free riding’‐ behavior iff
1: Adapted from Ranganathan et al. 2004
Contribution Score
κ ,κ :
Quantity Scarcity
How scarce is the provided resource,
How many resources did the user
defined by the ratio demand vs.
provide?
supply?
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9. Modelling the Users
Modeled through a Utility Function Selfish
Provisioning
Consuming vs Provisioning Leontief
Behavior and (‘selfish’) (‘reciprocal/altruistic’)
Utility Types Perfect
Convex
Substitutes
6 types with different utility types2: Consumption
(weak) selfish (weak) Leontief (weak) perfect substitutes
2: based on Andreoni et al. 2002
s12
Trust s21
Trust score influences matching of offers with requests
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10. Evaluation(1)
Effect on Utility
What is the effect of the contribution scheme on the average utility of users?
1.2
1
Average Utility per User
0.8
0.6
NoTradingConstraint
TradingConstraint
0.4
0.2
0
20 50 100 200 500
Network Size
Total system‐wide utility decreases
Contribution scheme decreases set of potential sharing partners
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11. Evaluation (2)
Effect on User Types
To what degree are different user types affected by the contribution
scheme?
1.2
Relative Utility compared to No Trading
1 Type 1: Selfish
0.8 Type 2: Weak Selfish
Constraint
0.6 Type 3: Leontief
Type 4: Weak Leontief
0.4
Type 5: Perfect Substitutes
0.2
Type 6: Weak Perfect
0 Substitutes
20 50 100 200 500
Network Size
Selfish users incur highest utility loss
Contribution scheme punishes ‘free‐riders’
Relative utility loss of other user types is low
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12. Steps towards Simulator Calibration
Study the effect of a
Scenario contribution scheme on users
Model collaboration system
Approach Model user behavior
User types differently affected by
Results contribution scheme
Application Type Distributions
to new Network Structures
scenarios …
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13. Conclusion
Summary
Simulation tool for social
collaboration
Layered architecture with
interchangeable components
Case study to show usefulness of
such a simulator
Future Work
New functionality (new mechanisms, network structures,…)
Further case studies
Provide as open‐source Re‐* tool
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14. Thanks
Christian Haas: ch.haas@kit.edu
http://www.ksri.kit.edu/SocialCloud
http://www.facebook.com/SocialCloudComputing
KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
Social Collaboration Simulator Monitoring
User Incentive Experiment
SmallWorldNetwork ... Application Sensor
Applications Scheme Controller
Exchange Exchange Sensor
Mechanism Currency Artifact ...
Mechanisms
Runtime
User Sensor
Core User Resource Relationship TrustContext
Elements Resource Sensor
KIT – University of the State of Baden-Württemberg and
National Research Center of the Helmholtz Association www.kit.edu