Resource Allocation in Social Networks Mathijs de Weerdt Yingqian - - PowerPoint PPT Presentation

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Resource Allocation in Social Networks Mathijs de Weerdt Yingqian Zhang, Tomas Klos June 4, 2008 1 Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology CWI, Amsterdam, SEN-4 Ideas in this

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June 4, 2008

1

Resource Allocation in Social Networks

Mathijs de Weerdt

Yingqian Zhang, Tomas Klos

Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology CWI, Amsterdam, SEN-4

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June 4, 2008 2

Ideas in this talk

  • 1. Variant of resource allocation problem, i.e., in a social

network

  • 2. Dealing with resources as private information of

strategic agents

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June 4, 2008 3

Overview

  • Problem Definition
  • A Greedy Distributed Protocol
  • Algorithm
  • Run-time analysis
  • Experiments
  • Mechanism Design
  • Optimal + VCG
  • Greedy mechanism
  • Another Payment Function
  • Conclusions & Future Work
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June 4, 2008 4

Resource allocation

r1 r2 r1 r1 r1 r2 r2 r3 r2 r3

Agents value certain resource combinations (eg execute tasks) Resources initially reside with other agents

r1 r3 r2 r2 r1 r1 r2 €18 €8 r2 r1 r2 r3 €30

a8: t2 a10: t4 a9: t19

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June 4, 2008 5

Why social networks?

Social relations important in real- world task allocation:

  • Industrial procurement, eg

supply chain formation

  • Free-lancers networks

→ preferred partnerships instead

  • f plain markets
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June 4, 2008 6

Resources in Social Networks

agent a11∈A without tasks (contractor) agent a10∈A with three tasks∈T (manager) connections between two agents: allowed to allocate/cooperate

  • Each agent has:
  • resources
  • tasks with utility
  • connections
  • Each task t∈T
  • requires

resources rsc(t)

  • has a utility u(t)
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June 4, 2008 7

Problem Definition: Resource Allocation in a Social Network

  • Given
  • a network of potential partners, where
  • some agents have resources
  • other agents have tasks, and thus utilities for

combinations of resources,

  • determine a resource allocation (to neighbors) such

that sum of utilities (of fully satisfied tasks) is maximal.

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June 4, 2008 8

Greedy distributed protocol (GDAP)

Idea First allocate resources to tasks that have high utility and require few resources Definition The efficiency e(t) of a task t is:

r1 r1 r2 r2 r3 r1 r3 r2 r2 r1 r2 €30 €18 €8

e(t2)=6 e(t4)=9 e(t19)=2

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June 4, 2008 9

Greedy distributed protocol (GDAP)

m (manager): agent that has utility (task) for a combination of resources of different types c (contractor): agent that can provide a number of resources Repeat

  • 1. m: Send requests for resources for most efficient task to neighbors.
  • 2. c: Offers resources to request with highest efficiency.
  • 3. m: If task can be fully allocated, do so and remove it.
  • 4. m: Else, if all neighbors offered, remove it

Until no tasks are left

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June 4, 2008 10

Greedy distributed protocol (GDAP)

  • 1. m: Each manager agent calculates the efficiency e(t) for its

tasks Ta; sorts tasks in descending order of efficiency:

  • 1. e(t4)=9
  • 2. e(t0)=5
  • 3. e(t17)

=1

  • 1. e(t2)=6
  • 1. e(t19)=2
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June 4, 2008 11

Greedy distributed protocol (GDAP)

  • 1. m: Send requests for resources for most efficient task to

neighbors.

help me with t4, e(t4)=9 help me with t2, e(t2)=6 help me with t19, e(t19) =2

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June 4, 2008 12

Greedy distributed protocol (GDAP)

  • 2. c: Offers resources to request with highest efficiency.
  • 1. e(t4)=9
  • 2. e(t2)=6
  • 3. e(t19)=2

t4: t4: t19:

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June 4, 2008 13

Greedy distributed protocol (GDAP)

  • 3. m: If task can be fully allocated, do so and remove it.
  • 4. m: Else, if all neighbors offered, remove it.

a7: a11:

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June 4, 2008 14

Run-time analysis

For a social resource allocation problem with n tasks and m agents

  • O(n) iterations
  • per iteration: O(m) operations (in parallel)
  • so the run-time of GDAP is O(nm).
  • The number of communications messages is
  • per iteration (n), per task (n), O(m)
  • so number of communication messages is O(n2m).
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June 4, 2008 15

Experiments

  • Objective: study performance of the greedy distributed algorithm

GDAP in different problem settings:

  • Network topology / degree
  • Resource ratio: (# resources req’d)/(# resources available)

Measurements

  • Computation time
  • Solution quality (utility of tasks allocated)
  • for small problems: GDAP/OPT
  • for large problems: GDAP/Upper Bound
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June 4, 2008 16

Experiments (OPT)

  • OPT: by translation to ILP:
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June 4, 2008 17

Experiments (Upper bound)

  • Assume divisible goods
  • Represent as min-cost network flow problem:
  • node a for every agent-available-resourcetype > 0
  • edge from s to a with this as capacity
  • node b for every task-requested-resourcetype
  • edge to t with this as capacity and cost: -efficiency
  • edge from a to b if agents are neighbors
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Experimental settings

Social network structures

  • Small-world network (Watts, Strogatz,

1998): average shortest path length

scales O(log n), even with few long links

  • Scale-free network (Barabasi, Albert,

1999): few agents have many

neighbors; many have only a small number of neighbors

  • Random network (uniform): agents

are randomly connected

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Degree histogram

6/4/08 19

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June 4, 2008 20

Experiments

  • Objective: study performance of the greedy distributed algorithm

GDAP in different problem settings:

  • Network topology / degree
  • Resource ratio: (# resources req’d)/(# resources available)

Measurements

  • Computation time
  • Solution quality (utility of tasks allocated)
  • for small problems: GDAP/OPT
  • for large problems: GDAP/Upper Bound
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Setting 1a: 40 agents, 20 tasks, average network degree 6, uniform task utilities, varying resource ratio (total available resource / total required resource)

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Setting 1b: 40 agents, 20 tasks, uniform task utilities, resource ratio 1.2, varying degree

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Setting 1 overall: 40 agents, 20 tasks, uniform task utilities, varying both resource ratio and degree

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Setting 3: resource ratio 1.2, degree 6, size ratio of agents and tasks 5/3, varying number of agents from 100 to 2000.

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Summary of results

  • GDAP performs well (around 90%) when there are

sufficient resource available

  • high resource ratio,
  • and/or high degree
  • performs around 70% when resources are scarce
  • slightly better on small-world networks
  • very fast (computation time less than 2s for 2000

agents)

6/4/08 25

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June 4, 2008 26

Mechanism Design

  • Two different agents
  • Contractor agents are self-interested, maximizing utility ui(o); in this

setting basically the payment

  • Task manager agents are cooperative
  • Public information:
  • social network
  • task information: location; utility
  • Private information:
  • contractor agents’ available resources
  • Goal: a mechanism that is
  • incentive compatible for contractor agents
  • efficiently computable
  • as good as possible
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June 4, 2008 27

Exact mechanism with VCG payment

  • Exact mechanism OPT by transformation to ILP
  • VCG payment: marginal utility to social welfare

pi=vi(o) + W(o) - W(o-i) Properties

  • incentive compatible with respect to under-reporting
  • over-reporting may lead to infeasible outcomes
  • exponential algorithm
  • optimal outcome
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June 4, 2008 28

Greedy mechanism with VCG payment

  • rder tasks on efficiency (value/#resources)
  • T = ∅
  • for each task t
  • check using network flow if adding t to T is feasible
  • if so add t to T, otherwise delete t

Properties

  • polynomial algorithm, #resources-approximation
  • VCG payments cannot make Greedy incentive compatible (with

respect to under-reporting)…

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VCG and approximations

Theorem: VCG payments cannot make Greedy incentive compatible (with respect to under-reporting)

  • a1 is better off reporting r4 and r5 (payment 16) than

reporting also r1 (payment 15)

  • in line with Nisan & Ronen (00/07) result on

combinatorial auctions (reasonable & not optimal -> VCG not truthful)

t1: {r1,r2,r3} 15 t2: {r2,r4} 8 t3: {r3,r5} 8 a2: {r2,r3} a1: {r1,r4,r5}

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June 4, 2008 30

Greedy mechanism with alternative payment

  • Greedy payment:
  • order all tasks on efficiency (value/#resources)
  • for each task t
  • pay all agents that sell essential resources (to t)
  • delete those resources

Properties

  • Greedy mechanism is incentive compatible wrt under-reporting
  • because payment monotonically increasing in declared resources
  • W(o) ≤ total payment to contractors ≤ U(T)
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June 4, 2008 31

Preventing over-reporting

  • Deposit mechanisms:
  • first ask each agent to pay sum of task utilities as

deposit

  • calculate solution
  • if agent delivers promised resources, return deposit
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June 4, 2008 32

Contributions

  • Problem: resource allocation in social network setting
  • efficient distributed protocol
  • VCG cannot prevent over-reporting (leading to

infeasible outcomes) even with OPT

  • VCG does not prevent under-reporting with a Greedy

(non-optimal) algorithm either, while

  • a “Greedy” payment can prevent under-reporting

(budget-imbalance depends on social network setting)

  • over-reporting can be prevented by asking a deposit
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June 4, 2008 33

Future Work

  • mechanism where manager agents may also strategize
  • budget balance:
  • search for (weakly) budget balanced payment, or
  • prove non-existence and analyze experimentally
  • give also better bound on deposit
  • online mechanism: tasks and resources arrive over

time

  • distributed mechanism: only local payments
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June 4, 2008 34

References

  • Mathijs M. de Weerdt and Yingqian Zhang and Tomas B. Klos (2007).

Distributed Task Allocation in Social Networks. In Michael Huhns and Onn Shehory (Eds.). Proceedings of the 6th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-07), pp. 488--495. Research Publishing Services.

  • Yingqian Zhang and Mathijs M. de Weerdt (2007). VCG-based Truthful

Mechanisms for Social Task Allocation. In Proceedings of the Fifth European Workshop on Multi-Agent Systems (EUMAS-07), pp. 378--394.

  • Mathijs M. de Weerdt and Yingqian Zhang (2008). Preventing Under-

Reporting in Social Task Allocation. In Han La Poutre and Onn Shehory (Eds.). Proceedings of the 10th workshop on Agent-Mediated Electronic Commerce (AMEC-X).

  • Roman van der Krogt and Mathijs M. de Weerdt and Yingqian Zhang

(2008). Of Mechanism Design and Multiagent Planning. In Proceedings of the European Conference on Artificial Intelligence (ECAI-08).