Towards a manipulative mediator Lecture for Statistical Methods (89-326)

1. Towards a manipulative mediatorLecture for Statistical Methods (89-326) Yehoshua (Yoshi) Gev yoshigev@gmail.com Joint work with:S. Kraus, M. Gelfand, J. Wilkenfeld & E. Salmon

2. Outline • Background on negotiation and mediation • Our goal • Agent design • Experiments and results • Conclusions

3. Background

4. Domain of negotiation • Human-to-human negotiation • Closed set of issues with discrete solution values • Privateutility function • For example, in our neighbors’ dispute – • noise issue with these solution values: • “Tyler will continue to be loud” • “Tyler will be quiet after 1am” • “Tyler will be quiet after 12am” • etc.

5. Mediation • Assistance from a third party • Mediation styles: • facilitation • organize logistics (e.g., communication channel) • formulation • propose new solutions • encourage to move towards agreement • manipulation • offer incentives or impose penalties [Wilkenfeld et al., 2005]

6. Previous work • Very few automated mediators: • PERSUADER [Sycara, 1991] • based on CBR (on an existing knowledge base) • AutoMed[Chalamish & Kraus, 2009] • a formulative mediator • rule based • monitors the negotiations and proposes possible solutions • qualitative model for preferences representation

7. Our Goal

8. Motivation • Design a manipulative mediator • Create a model that includes incentives and penalties • Decide how to make decisions • The big question: • Can the authority be utilized to assist the parties?

9. Social science aspect • The research is a collaborative work withpolitical science and psychology groups • We had to negotiate over the settings • Should we allow the participants to speak freely?Or, restrict them to a closed-list of sentences? • Should our interface act as a DSS? • utilities calculator • history of actions • How can we force participants to use the interface?

10. Experiments setting • Natural negotiations: • participants negotiate via video-conferencing • a realistic scenario (neighbors’ dispute) • very simple computerized system • only an interface to exchange offers • no utility calculator • a mediator agent can participate as a third party • GENIUS environment [Koen et al., 2009]

12. Agent Design

13. Pilot • Tested the system and the scenario • Tested AutoMed in the new settings • Problems: • AutoMed sent very few suggestions • AutoMed’s suggestions were often not relevant

14. Modifications to AutoMed • Choose suggestions similar to last offers • Treat close ranks as same utility • Treat partial offers as 60% of their maximal score

15. Experiments

16. Experiment 1 • Two groups • Control/Baseline: without mediator • Treatment/Tested: with mediator • Comparison between groups • tested parameters: • dur – negotiation’s duration (seconds) • score – each parties’ score • diff – difference between parties’ scores • sat – parties’ satisfaction from result (questionnaire) • aid – measure of mediator’s assistance (questionnaire)

17. Experiment 1 – Results • Hypothesis: diff1 – diff2 = 0 • Unpaired two-tailed T-Test • t = 2.0904, df = 27 • p = 0.044 < 0.05 • Conclusion: diff1 != diff2

18. Experiment 1 – Results (cont.) • Only one significant advantage for the mediator • diff was lower with a mediator • Many participants disregarded the mediator • How can we make them consider the mediator?

20. Experiment 2 • We implemented an animated avatar • face appearing on the interface • text-to-speech capabilities • opening statement • accompanying text to suggestions • Intended to draw the participants’ attention • How did it affect the outcomes?

21. Experiment 2 – Results • Hypothesis: aid2 – aid3 = 0 • Unpaired two-tailed T-Test • p = 0.003 < 0.01 • Coclusion: aid2 != aid3

22. Experiment 2 – Results (cont.) Correlations: • Hypothesis: aid is uncorrelated with score (r = 0) • Pearson correlation: r = 0.43 • for N = 24: t = 2.234 • Using T-test: p = 0.035 < 0.05 • But • pairs of samples are dependant (really, N < 24) • besides, we cannot tell the direction of the influence • Maybe a different sig. test would work (Fisher trans.)

23. Experiment 2 – Results (cont.) • The participants paid more attention • aidwas higher with the avatar • Those who paid attention got higher scores • significant correlation between aid and score • however,aid and sat were not correlated • But, they still didn’t fully utilize the suggestions • average score didn’t improve significantly • What should be done next?

24. Conclusions

25. Difficulties • Current problems: • participants disregard the mediator offers • they are involved in the video discussion • they cannot see the high utility of the mediator’s offers • solution?: more persuading mediator / a utility calculator • almost all participants reach agreement • what would be the role of the manipulator? • experiments with human participants are expensive • solution?: use peer-designed agents (PDA) to test the mediator before experimenting with humans [Lin et al., 2010]

26. What’s next? • Search for a setting that can exploit the mediator • Model incentives and penalties • Design a manipulator in that model • More experiments…

27. Summary • Even generic agents are restricted by their model • Humans are not fully rational • don’t calculate their expected score • higher scores don’t mean higher satisfaction • The environment affect the mediator’s influence

28. Thank you…