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Empirical Studies in Computer-Mediated Interest-Based Negotiations

Empirical Studies in Computer-Mediated Interest-Based Negotiations. Sohan D’souza MSc IT, 2009 British University in Dubai. Study Focus. Bilateral repeated negotiation under incomplete information Strategic role of goal information Effect of goal revelation on human negotiation strategy.

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Empirical Studies in Computer-Mediated Interest-Based Negotiations

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  1. Empirical Studies inComputer-MediatedInterest-Based Negotiations Sohan D’souza MSc IT, 2009 British University in Dubai

  2. Study Focus • Bilateral repeated negotiation under incomplete information • Strategic role of goal information • Effect of goal revelation on human negotiation strategy

  3. Consider this …

  4. What if …

  5. Then maybe …

  6. Contributions • Design of interest-based protocol for incomplete goal information, goal inquiry and goal revelation • First study to investigate outcomes and human use of goal revelation in task settings • Comparison of human negotiation strategies between position- and interest-based negotiation

  7. Significance • For Multi-Agent Systems • For Decision Support Systems • For Cognitive Science • For Psychology

  8. Topics in Negotiation • Issues and Outcomes • Task Dependency • Distributive vs. Integrative • Position-based vs. Interest-based • Multi-Agent System vs. Human

  9. Related Work • Interest-based negotiation specific to domains (Loewenstein et al., 1989) • Pre-negotiation goal framing (Loewenstein and Brett, 2007) • Position-based agent vs. human negotiations (Lin et al. 2008; Bosse and Jonker, 2005) • Human caution with information privacy (Heiskanen et al., 2001)

  10. Colored Trails - Platform • Multiplayer client-server platform • Board of colored tiles • Traversal by expending colored chips • Icons representing players and goals • GUI and decision support tool for human players

  11. Colored Trails – Relevance • Tiles analogous to different tasks • Chips as different resources or abilities used to complete tasks • Traversal route represents candidate task sequence towards goal • Multiple paths to goal possible • Information on opponents can be hidden or revealed

  12. Proposer A Proposer B MAKE PROPOSAL MAKE PROPOSAL REJECT REJECT Responder B Responder A ACCEPT ACCEPT Protocol Design - PBN • Position-based Negotiation (PBN) Protocol • Turn-based play • Rejections switch roles

  13. REVEAL GOAL Proposer A Proposer B MAKE PROPOSAL MAKE PROPOSAL ASK FOR GOAL ASK FOR GOAL Responder B Responder A REJECT REJECT ACCEPT ACCEPT Protocol Design - IBN • Interest-based Negotiation (IBN) Protocol • Can ask for goal only in response to offer • Can reveal goal only when asked

  14. Game Setup • 5x5 tile layout, 7 chips assigned • At most one player can independently reach their goal • Chip assignment allows for at least one player to get to goal by trading • Only one shortest path possible • Scoring function favors resource conservation and gives partial credit

  15. Experimental Setup • Masks and GUI changes to control information visibility and actions • Single game settings pool shared across both negotiation protocols • 4 communication minutes per game, else default to existing assignment • Each player anonymously plays each other player once, in both initial roles

  16. Results - Performance Average benefit for players in IBN/PBN conditions for different number of goal revelations (significant difference in bold) Average benefit for players in IBN/PBN conditions for different player dependencies (significant difference in bold)

  17. Results - Agreement Pair-wise agreement ratio Agreement frequencies by task dependency

  18. Results – Goal Revelation Goal revelation distribution and frequency

  19. Discussion • Adapted platform robust and flexible • Humans show unexpected trust in volunteering goals when asked • Self-sacrifice observed on the part of independent players • Possible cautionary generosity on the part of dependent players

  20. Future Work • Comparing to games with initially complete information • Allowing players to reveal goals without being asked • Allowing more than one shortest path • Removing anonymity and having repeated games between players • Building classifiers using the data

  21. Conclusions • Asked, humans generally reveal goals • Both altruistic and satisficing outcomes observed in IBN • Agreement likelihood and social benefit higher in IBN • Strategic benefit of goal information varies with task dependency • Precedent for future work on goal revelation in mediated IBN scenarios

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