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

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

3. 3 Background

4. 4 Domain of negotiation Human-to-human negotiation Closed set of issues with discrete solution values Private utility 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. 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. 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. 7 Our Goal

8. 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. 9 Social science aspect The research is a collaborative work with political 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. 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]

11. 11

12. 12 Agent Design

13. 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. 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. 15 Experiments

16. 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. 17 Experiment 1 – Results NdursumdiffsatAaidAsatBaidB 1. No mediator1509:3312241884.2 2. Simple mediator1410:5711927441.74.01.5 Hypothesis: diff1 – diff2 = 0 Unpaired two-tailed T-Test  t = 2.0904, df = 27  p = 0.044 < 0.05 Conclusion: diff1 != diff2

18. 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?

19. 19

20. 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. 21 Experiment 2 – Results Ndursumdiffsataid 1. No mediator1509:3312241884.23 2. Simple mediator1410:571193744.041.62 3. Animated mediator1213:3112451034.172.70 Hypothesis: aid2 – aid3 = 0 Unpaired two-tailed T-Test  p = 0.003 < 0.01 Coclusion: aid2 != aid3

22. 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.) score age sat aid score1 age-0.221 sat0.300.161 aid0.43-0.240.011

23. 23 Experiment 2 – Results (cont.) The participants paid more attention  aid was 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. 24 Conclusions

25. 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. 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. 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. 28 Thank you…