1

Towards a manipulative mediatorLecture for Statistical Methods (89-326)

Yehoshua (Yoshi) Gevyoshigev@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

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

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 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

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

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 – ResultsN dur sum diff satA aidA satB aidB

1. No mediator 15 09:33 1224 188 4.2 4.2

2. Simple mediator 14 10:57 1192 74 4 1.7 4.0 1.5

Hypothesis: diff1 – diff2 = 0

Unpaired two-tailed T-Test t = 2.0904, df = 27 p = 0.044 < 0.05

Conclusion: diff1 != diff2

21

11

21

NNs

xxt

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

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 – ResultsN dur sum diff sat aid

1. No mediator 15 09:33 1224 188 4.23

2. Simple mediator 14 10:57 1193 74 4.04 1.62

3. Animated mediator 12 13:31 1245 103 4.17 2.70

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.)

  score age sat aid score 1 age -0.22 1 sat 0.30 0.16 1 aid 0.43 -0.24 0.01 1

21

2

r

Nrt

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

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…