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Simple Rewire Protocols for
Cooperation in Dynamic Networks
David Hales, Stefano Arteconi, Ozalp Babaoglu
University of Bologna, Italy
Bio-Inspired Workshop, Rome, 25th July 2006
Evolutionary Models
David Hales (University of Bologna)
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Recent evolutionary models demonstrate desirable properties of
cooperation and coordination
Based on ideas coming from evolutionary / bounded rationality
approaches (Simon, Arthur, Axelrod et al)
Such models relax assumptions of “ideal” rationality
Consider agents operate using simple heuristics
Often collective learning via a (cultural) evolutionary approach
The idea that (potentially random) innovations in agents are copied
by others (in some way) if they improve utility (defined in some
way)
www.davidhales.com
University of Bologna, Italy
Evolutionary Models
David Hales (University of Bologna)
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Such models capture self-organising and emergent processes
Argued: similar to those that occur in human or animal societies
Computational Social Science using agent-based simulation
Obviously controversial, rarely validated
Yet increasingly accepted as alternative to equilibrium analysis /
ideal rational approaches
More applicable to engineering applications - noise, incomplete
information, high dynamicity, heterogeneous agents etc.
Side-stepping controversy and validity of such models, can
we steal and adapt these ideas for “engineering” of desirable
properties in distributed systems?
www.davidhales.com
University of Bologna, Italy
Peer-to-Peer Systems
David Hales (University of Bologna)
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We have translated some of these models into protocols for use in
peer-to-peer (P2P) systems
P2P are generally open systems of client programs running on
user machines with no central authority or control
Electronically mediated and semi-automated social systems
Some general motivating questions are:
 How can such systems come to self-organise, cooperate and
coordinate to produce productive behaviour?
 How can the negative effects of free-riding and selfish
behaviour be avoided - promote social good?
 How can such systems scale well in a robust way?
 How can the effects of malicious behaviour be minimised?
www.davidhales.com
University of Bologna, Italy
tag systems
David Hales (University of Bologna)
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Previous “tag” models offer a simple mechanism by which some of
these questions can be addressed
Both cooperation and coordination (specialisation)
www.davidhales.com
University of Bologna, Italy
Evolution for Cooperation
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
• Algorithm based on social simulation models of “tags”
• Introduced by Holland early 1990’s
• Developed recently by Riolo; Hales; Edmonds.
• Tags are observable “markings”, labels or social cues, attached to agents
(e.g. hairstyle, dress, accent)
• In an evolutionary algorithm tags evolve just like any other artificial gene
in the “genotype”
• They are displayed directly in the “phenotype”
• When agents bias interactions towards those with similar tags,
even selfish evolution selects for cooperative and altruistic behaviour
www.davidhales.com
University of Bologna, Italy
Evolution for Cooperation
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
We translated the tag algorithm into a network
• nodes move to find “better” neighbors
• producing a kind of evolution in the network
• “bad guys” become isolated
Results in a “duplicate and re-wire” rule
• Producing a kind of “group selection” between clusters
• a functional reason for temporal structures found in the
“natural” networks?
www.davidhales.com
University of Bologna, Italy
SLAC Algorithm
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
Basic Algorithm
• Periodically do
• Each node compare “utility” with a random node
• if the other node has higher utility
• copy that node’s strategy and links (reproduction)
• mutate (with a small probability):
change strategy (behavior)
change neighborhood (links)
• fi
• od
www.davidhales.com
University of Bologna, Italy
SLAC algorithm
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
“Reproduction” = copying a more successful node
Before
After
E
E
C
C
B
B
A
D
D
A
G
G
Fu > Au
F
Where Au = average
utility of node A
www.davidhales.com
A copies F
neighbours &
strategy
F
In his case mutation has
not changed anything
University of Bologna, Italy
SLAC algorithm
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
“Mutation of the neighbourhood” = random movement in the net
Before
After
E
E
C
C
B
B
F
D
D
A
A
G
G
F
Mutation applied to F’s
neighbourhood
www.davidhales.com
F is wired to a randomly
selected node (B)
University of Bologna, Italy
SLAC Applied to the PD
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
Applied to a simulated Prisoner’s Dilemma Scenario:
• Where selfish behavior produces poor performance – Nash Eq.
• Nodes store a pure strategy, either cooperate or defect
• Play the single round PD with randomly selected neighbours
• Using their strategy
• We take average payoff as the node utility
• Mutation of strategy: flip strategy
• Nodes randomly selected to play a random neighbours some
number of times each period
www.davidhales.com
University of Bologna, Italy
David Hales (University of Bologna)
MF = 10
CyclesTag
to High
Cooperation
Cycles to 99% Coop
400
300
200
100
0
4000
8000
12000
16000
20000
Nodes
www.davidhales.com
University of Bologna, Italy
SLAC Applied to PD
David Hales (University of Bologna)
Cooperative nodes %
Neighbour
MF = 10Run
Typical
Individual
100
90
80
70
60
50
40
30
20
10
0
0
100
200
300
400
500
Cycles
www.davidhales.com
University of Bologna, Italy
How Does SLAC Work?
David Hales (University of Bologna)
tio
no
Mu
ta
Game
Interactions
f ta
g
Clusters
Shared
tags
Copy tag and strategy
www.davidhales.com
University of Bologna, Italy
SLAC Applied to File Sharing P2P
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
Applied to a simulated P2P File Sharing Scenario:
• Simplified form of that given by Q. Sun & H. Garcia-Molina 2004
• Nodes control how much capacity devoted to generating or
answering queries based on P = [0..1]
• P =1.0 selfish (only generates queries)
• P =0.0 altruist (only answers queries)
• We take as node utility the number of hits
• Mutation of strategy: change P randomly
• Flood fill query method, TTL’s etc
www.davidhales.com
University of Bologna, Italy
SLAC Applied to P2P File Sharing
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
Some simulation results
queries (nq)
hits (nh)
average per node
60
Selfishness reduces
50
40
30
Average performance increases
20
10
0
0
20
40
60
80
100
cycles
A typical run for a 104 node network
www.davidhales.com
University of Bologna, Italy
SLAC Applied to P2P File Sharing
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
Some simulation results
average per node
queries (nq)
hits (nh)
40
35
30
25
20
15
10
5
0
100
1
1000
10
10000
100
100000
1000
nodes
Results showing number of queries (nq) and number of hits (nh)
(averaged over cycle 40..50) for different network sizes
with10 individual runs for each network size
www.davidhales.com
University of Bologna, Italy
SLAC to SLACER
David Hales (University of Bologna)
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SLAC is OK for some tasks – as we have seen
But produces disconnected components
This is no good when we want
An “Artificial Friendship Network” to span the network
• Connected – such that all nodes are linked with short path
• Chains of trust between all nodes – preferably short also
• To achieve this we modify SLAC and introduce SLACER
www.davidhales.com
University of Bologna, Italy
SLACER algorithm
David Hales (University of Bologna)
Basic Algorithm
• Periodically do
• Each node compare “utility” with a random node
• if the other node has higher utility
• copy that node’s strategy and links, probabilistically retaining
some existing links
• mutate (with a small probability):
change strategy (behavior)
change neighborhood (links), probabilistically retaining some
existing links
• fi
• od
www.davidhales.com
University of Bologna, Italy
SLAC to SLACER
David Hales (University of Bologna)
SLAC
www.davidhales.com
SLACER
University of Bologna, Italy
SLACER – Some Results
David Hales (University of Bologna)
www.davidhales.com
University of Bologna, Italy
SLACER – Some Results
David Hales (University of Bologna)
www.davidhales.com
University of Bologna, Italy
SLACER - Some Results
David Hales (University of Bologna)
Cycles
www.davidhales.com
University of Bologna, Italy
SLACER – Future Applications
David Hales (University of Bologna)
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By establishing a fully connected “Artificial Social Network” (ASN)
This can be used as input to existing P2P applications
Specifically those that assume or require trusted social networks
as input
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Currently harvested from e-mail contacts or “buddy lists” in chat
applications
Example: Collective spam filtering:
J. S. Kong, P. O. Boykin, B. Rezei, N. Sarshar, and V.
Roychowdhury, “Let you cyberalter ego share information and
manage spam,” 2005. Available as pre-print:
http://xxx.lanl.gov/abs/physics/0504026.
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www.davidhales.com
University of Bologna, Italy
Conclusion
David Hales (University of Bologna)
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Simple copy and rewire algorithm
No need for centralized trust or enforcement mechanism
No need for knowledge of past interactions
Process cooperative behavior even when nodes behave in an
egotistical way, locally and greedy optimizing
• Works through a kind of “group selection” – “tribal selection”
• Can produce trusted and cooperative Artificial Social Networks
• Could be applied to existing protocols with minor modification
• Available on open source P2P simulation platform Peersim.
www.davidhales.com
University of Bologna, Italy
Related Publications
David Hales (University of Bologna)
Self-Organising Cooperation in Peer-to-Peer Systems
References
• Hales & Edmonds (2005) “Applying a socially-inspired technique
(tags) to improve cooperation in P2P Networks”, IEEE
Transactions on Systems, Man, and Cybernetics, Part A
• Hales & Arteconi (2006) “SLACER: A Self-Organizing Protocol for
Coordination in P2P Networks”, IEEE Intelligent Systems,
21(2):29-35, March / April 2006.
www.davidhales.com
peersim.sourceforge.net
www.davidhales.com
University of Bologna, Italy
SLAC and SLACER
David Hales (University of Bologna)
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Fini
www.davidhales.com
University of Bologna, Italy
The End
David Hales (University of Bologna)
Thank you!
www.davidhales.com
University of Bologna, Italy