Latifa Ghalayini and Dana Deeb. Development of an Intelligent Win-Win Negotiation Model. (ICDEc 2021)

1. Development of an Intelligent Win-Win
Negotiation Model
Latifa Ghalayini and Dana Deeb
Lebanese University, Lebanon

2. Negotiation

3. Negotiation

4. Win-win Outcomes

5. Negotiation Approach

6. Integrative Negotiation Approach

7. Integrative Negotiation Approach
Objective Criteria Mutual Gain
Exchanging Information
Group Problem Solving

8. Win-win Outcome
Information
buyer supplier

9. Objective
Resolve optimality of negotiation outcomes in an incomplete
information negotiation context.

10. Previous Solutions

11. Source: Developed by the author

12. Solution

13. Information Technology
Artificial Intelligence
Machine Learning

14. Hypothesis
● H1: Automated negotiation agents that use Artificial Intelligence can intelligently assess offers
to reach optimal win-win negotiation outcomes
o H1-a: In incomplete information negotiation between suppliers and a buyer, modeling
suppliers’preference models is achievable through Artificial intelligence
o H1-b: Intelligent assessment of suppliers’ offers in procurement negotiation attains
optimal win-win procurement outcomes

15. What information is hidden?

16. Reservation Values

17. buyer
supplier

18. Win-win Outcome
Information
buyer supplier

19. Win-win Outcome
Information
buyer supplier
Previous Negotiation
Data

20. Model
3 foundations
An Automated
Negotiation
Process Model
1
A Utility
Measurement
Function
2 3

21. Source: Developed by the author

22. Mutual Gains Theory
Rational Choice Theory
Decision Theory

23. Model
3 foundations
An Automated
Negotiation
Process Model
1
A Utility
Measurement
Function
2 3

26. Brown & Curhan (2012)
Decision Utility
Experienced Utility
Diagnostic Utility

27. wantability likeability

29. wantability likeability

30. buyer supplier
Historical Negotiation Data
PUMs
Machine
Learning

31. Model
3 foundations
Automated
Negotiation
Process Model
1
Utility
Measurement
Function
2
Opponent
Predicted Utility
Model (PUM)
3

32. ZOPP
ZOPA
(Zone of Predicted Profitability)

33. buyer
supplier

34. Defining ZOPP
The ZOPP is the range of profitable solutions of an opponent
negotiator predicted from his historical negotiation information. Its
minimum coincides with the ZOPA’s minimum in which any value
under this minimum is considered a loss. However, its maximum is
minimized from the ZOPA’s maximum, in which any value
exceeding it, is considered an inordinate gain.
Source: Developed by the Author

35. Source: Developed by the Author

36. Experiment

37. MAORA
(Multi Attribute Online Reverse Auction)

38. Source: Prepared by the author

39. Source: Prepared by the author

40. Source: Prepared by the
author

41. Source: Prepared by
the author

42. Supplier Type Action
Supplier 1 Strong/Greedy (SG) HIP
Supplier 2 Strong/Credible (SC) RO
Supplier 3 Competitive/Credible (CC) MIC
Supplier 4 Competitive/Deceptive (CD) HIC
Supplier 5 Weak/Deceptive (WD) HIC
Supplier 6 Weak/Credible (WC) MIC
Types of Suppliers and Decision Points in the Negotiation Experiment
Source: Prepared by the author

44. SG CC CD WD WC
SC

45. Model
3 foundations
Automated
Negotiation
Process Model
1
Utility
Measurement
Function
2
Opponent
Predicted Utility
Model (PUM)
&
ZOPP
3

46. Hypothesis Proved
● H1: Automated negotiation agents that use Artificial Intelligence can intelligently assess offers
to reach optimal win-win negotiation outcomes
o H1-a: In incomplete information negotiation between suppliers and a buyer, modeling
suppliers’preference models is achievable through Artificial intelligence
o H1-b: Intelligent assessment of suppliers’ offers in procurement negotiation attains
optimal win-win procurement outcomes

47. Additional
Merits
Objectivity
Equal Treatment
Minimized Corruption
Lower Costs
Better Value of Money

48. All praise belongs to Allah,
Lord of all the worlds