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Influence of Neighborhood and Reorganization in Networked Agent Simulation Udara C. Weerakoon and Vicki H. Allan Utah State University. Coalition formation in a network of agents Nodes - agents Edges - " can work with" relationships No centralized control

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Influence of Neighborhood and Reorganization in Networked Agent Simulation

Udara C. Weerakoon and Vicki H. Allan Utah State University

  • Coalition formation in a network of agents
  • Nodes - agents
  • Edges - "can work with" relationships
  • No centralized control
  • Agents propose or join a coalition
  • Agents reorganize their partners - utility

Fig. 3. Profit Vs. Agent Connections.

Initial profit is negative for all agents

Communication cost is a burden on the profit of hedging agents

Fig. 1.Uncommitted agents Task-executing agents

Committed agents

  • Methods
  • GOAL: determine strategies to improve success in coalition formation network
  • Only local information without backtracking
  • Strategic agentsselect
    • The coalition with the highest percent of committed agents
    • The coalition for which peers have the best match with needed skills
    • Strategic agentscan propose a new coalition
      • If its peers have a high chance of satisfying the criteria
      • Randomly as a last resort
    • Hedging environment
    • Agents commit to multiple coalitions
    • Multiple coalition s are associated with the same task
      • We study five types of reorganization (Abdallah and Lesser, 2007): performance (Gaston and Jardins, 2005), structural (Thadakamalla et al., 2004), egalitarian, inventory, and intelligent.


  • 1st set of experiments
  • The same No. Neighbors
  • No reorganization
  • Hedging, random, and strategic
  • 2nd set of experiments
  • The max. No. Neighbors is 15
  • With reorganization
  • Hedging only

Fig. 5. Performance-rate Vs. Time (with reorganization).

Reorganization increases performance initially

Egalitarian reorganization outperforms intelligent reorganization

The higher number of isolated agents in inventory and performance reorganization reduces the performance-rate of the agent society

  • Conclusions
  • The strategic agents are better than the lower bound (of the random agents) and competitive with an upper bound (of the hedging agent)
  • Success of local strategies (without hedging) depends heavily on sufficient neighbors
  • Egalitarian reorganization is the most beneficial topological type reorganization in the hedging environment
  • A simple reorganization strategy, if applied over time, creates undesirable structural features like isolated agents
  • A better plan would be to have a mixture of strategies: some which directly pursue goals and others which seek to rebuild and utilize agents which have been abandoned in the simulation

Fig. 2. Performance-rate Vs. Agent Connections.

The maximum performance-rate of random agents (MPRA) is 30% (30% of introduced tasks can be completed)

Strategic agents require ten connections to achieve MPRA

Hedging agents require five connections to achieve MPRA and ten connections to outperforms strategic agents


This material is based in part upon work supported by the National Science Foundation under grant number 0812039

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