2. AI Models of Negotiation For the Social Sciences: What Should Be in an AI-and-Law Model of Negotiation? Ronald P. Loui Computer Science and Engineering / Legal Studies Washington University in St. Louis USA

3. Loui December 06JURIX 2006 KeyNote 2 Life's To-Do List … Lecture at the Sorbonne in French … Become a President Obama appointee (was Obama really at ICAIL 2001?) …

4. Loui December 06JURIX 2006 KeyNote 3 What There Is in AI and Law on Negotiation: AI techniques for modelling legal negotiationAI techniques for modelling legal negotiation - E Bellucci, J Zeleznikow - … ICAIL, 1999 Family_Winner: integrating game theory and heuristics to provide negotiation support Family_Winner: integrating game theory and heuristics to provide negotiation support J Zeleznikow, E Bellucci - JURIX, 2003 …ODR Environment: Dialogue Tools and Negotiation Support Systems …ODR Environment: Dialogue Tools and Negotiation Support Systems … AR Lodder, J Zeleznikow - Harvard Negotiation Law Review, 2005 Integrating Artificial Intelligence, Argumentation and Game Theory to Develop an Online Dispute … Integrating Artificial Intelligence, Argumentation and Game Theory to Develop an Online Dispute … E Bellucci, AR Lodder, J Zeleznikow - Tools with Artificial Intelligence, 2004. ICTAI 2004. A framework for group decision support systems: Combining AI tools and OR techniques A framework for group decision support systems: Combining AI tools and OR techniques NI Karacapilidis, CP Pappis - European Journal of Operational Research, 1997 Mediation Systems Mediation Systems T Gordon, O Märker - Online-Mediation, 2002 A simple scheme to structure and process the information of parties in online forms of alternative ODR A simple scheme to structure and process the information of parties in online forms of alternative ODR GAW Vreeswijk - Proceedings of the First International ODR Workshop (2003) Model Checking Contractual Protocols Model Checking Contractual Protocols A Daskalopulu - Arxiv preprint cs.SE/0106009, 2001

5. Loui December 06JURIX 2006 KeyNote 4 Where I Start:

6. Loui December 06JURIX 2006 KeyNote 5 Where I Start: S ocSci 174. International Problem Solving. Roger Fisher (Law School). My first freshman lecture at Harvard, first A, … Tutorial: The Russian Army will get bogged down in Afghanistan Term Paper: The Pershing II's should be deployed in Europe

7. Loui December 06JURIX 2006 KeyNote 6 Principled Negotiation Appeals To reason or precedent Not merely to position of power

8. Loui December 06JURIX 2006 KeyNote 7 Principled Negotiation Appeals To reason or precedent PERSUADER, Sycara 89, Parsons-Jennings 96 Persuasive argumentation in negotiation Persuasive argumentation in negotiation KP Sycara - Theory and Decision, 1990 Collaborative plans for complex group action Collaborative plans for complex group action BJ Grosz, S Kraus - Artificial Intelligence, 1996 Negotiation through argumentation—a preliminary report Negotiation through argumentation—a preliminary report S Parsons, NR Jennings - ICMAS, 1996 Arguments, dialogue, and negotiation Arguments, dialogue, and negotiation L Amgoud, S Parsons, N Maudet - ECAI, 2000 Argument-based negotiation among BDI agents Argument-based negotiation among BDI agents SV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002

9. Loui December 06JURIX 2006 KeyNote 8 Principled Negotiation Appeals To reason or precedent PERSUADER, Sycara 89, Parsons-Jennings 96 Persuasive argumentation in negotiation Persuasive argumentation in negotiation KP Sycara - Theory and Decision, 1990 Arguing about plans: Plan representation and reasoning for mixed-initiative planning G Ferguson, J Allen - AIPS, 1994 Collaborative plans for complex group action Collaborative plans for complex group action BJ Grosz, S Kraus - Artificial Intelligence, 1996 Negotiation through argumentation—a preliminary report Negotiation through argumentation—a preliminary report S Parsons, NR Jennings – ICMAS, 1996 Arguments, dialogue, and negotiation Arguments, dialogue, and negotiation L Amgoud, S Parsons, N Maudet - ECAI 2000 Argument-based negotiation among BDI agents Argument-based negotiation among BDI agents SV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002

10. Loui December 06JURIX 2006 KeyNote 9 Principled Negotiation Appeals To reason or precedent PERSUADER, Sycara 89, Parsons-Jennings 96 Persuasive argumentation in negotiation Persuasive argumentation in negotiation KP Sycara - Theory and Decision, 1990 Understanding the Role of Negotiation in Distributed Search Among Heterogeneous Agents Understanding the Role of Negotiation in Distributed Search Among Heterogeneous Agents SE Lander, VR Lesser - IJCAI, 1993 Collaborative plans for complex group action Collaborative plans for complex group action BJ Grosz, S Kraus - Artificial Intelligence, 1996 Negotiation through argumentation—a preliminary report Negotiation through argumentation—a preliminary report S Parsons, NR Jennings - ICMAS, 1996 Arguments, dialogue, and negotiation Arguments, dialogue, and negotiation L Amgoud, S Parsons, N Maudet - ICMAS, 2000 Argument-based negotiation among BDI agents Argument-based negotiation among BDI agents SV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002

11. Loui December 06JURIX 2006 KeyNote 10 Principled Negotiation Appeals To reason or precedent Not To position of power

12. Loui December 06JURIX 2006 KeyNote 11 Un-Principled Negotiation Appeals Not To reason or precedent To position of power

13. Loui December 06JURIX 2006 KeyNote 12 Un-Principled Negotiation Appeals To position of power Enforceable agreements Unenforceable agreements No institutional context Game Theoretical Models of Negotiation x Solution Concept x Nash Equilibria x MultiAgent Ecommerce Systems

14. Loui December 06JURIX 2006 KeyNote 13 Un-Principled Negotiation Appeals To position of power Enforceable agreements Unenforceable agreements No institutional context Game Theoretical Models of Negotiation x Solution Concept x Nash Equilibria - A Beautiful Mind, shared Nobel Prize x MultiAgent Ecommerce Systems - Computers & Thought Winner 03

15. Loui December 06JURIX 2006 KeyNote 14 Un-Principled Negotiation Appeals To position of power Enforceable agreements Unenforceable agreements No institutional context Game Theoretical Models of Negotiation x Solution Concept x Nash Equilibria x MultiAgent Ecommerce Systems Badly mistaken path

16. Loui December 06JURIX 2006 KeyNote 15 Un-Principled Negotiation Appeals To position of power Enforceable agreements Newer "Institutional Economics" Nobel prizes Unenforceable agreements No institutional context Game Theoretical Models of Negotiation x Solution Concept x Nash Equilibria x MultiAgent Ecommerce Systems

17. Loui December 06JURIX 2006 KeyNote 16 AI Model of Negotiation: Venk Reddy (Harvard) 93, Mark Foltz (WU/MIT), 95 Kay Hashimoto (Harvard), 96 Diana Moore's (WU) B.Sc. Thesis, 95-97 Anne Jump (Harvard), 97-98 All undergrads But whom would you have model a social phenomenon? People who who have VERY good social skills OR Someone who thinks human interaction is like playing chess (von Neumann)

18. Loui December 06JURIX 2006 KeyNote 17 AI Model of Negotiation: Diana Moore's B.Sc. Thesis, Dialogue and Deliberation, 97 Agents that reason and negotiate by arguing Agents that reason and negotiate by arguing S Parsons, C Sierra, N Jennings - Journal of Logic and Computation, 1998 Cited by 328 Cited by 328

19. Loui December 06JURIX 2006 KeyNote 18 AI Model of Negotiation: Diana Moore's B.Sc. Thesis, 97 Search Dialogue/Protocol Persuasion/Argumentation Log-rolling/Problem Reformulation Process

20. Loui December 06JURIX 2006 KeyNote 19 AI Model of Negotiation: Diana Moore's B.Sc. Thesis, 97 Search Mixed-initiative planning/NLP-Pragmatics Heuristic valuation of payoffs Dialogue/Protocol This AI and Law community Persuasion/Argumentation Multiagent systems community Log-rolling/Problem Reformulation Mixed-initiative planning/NLP-Pragmatics Process Today's Talk

21. Loui December 06JURIX 2006 KeyNote 20 AI-and-Law Model of Negotiation Offer/acceptance at the level of Scenarios Phrases Terms Uncertainty as to How claims might fare if pressed Whether the scenario might occur How the language might evolve How the case law (or standards) might evolve

22. Loui December 06JURIX 2006 KeyNote 21 AI-and-Law Model of Negotiation BATNA/security expressed as a RISK position Strong norms for Progress Explanation/ Questions and Answers Start with utility-payoffs To connect with social scientists To be precise & compact I already have a few stories to tell here

23. Loui December 06JURIX 2006 KeyNote 22 Pessimism-Punishment (PP) Agents Observation: parties to a negotiation (can) construct a probability distribution over potential settlements

24. Loui December 06JURIX 2006 KeyNote 23

25. Loui December 06JURIX 2006 KeyNote 24 Breakdown (BATNA)

26. Loui December 06JURIX 2006 KeyNote 25 Breakdown (BATNA)

27. Loui December 06JURIX 2006 KeyNote 26

28. Loui December 06JURIX 2006 KeyNote 27 Party 1's aspiration Party 2's aspiration

29. Loui December 06JURIX 2006 KeyNote 28 Party 2's proposals at t Party 1's proposals at t

30. Loui December 06JURIX 2006 KeyNote 29 inadmissible (dominated) at t

31. Loui December 06JURIX 2006 KeyNote 30 In black: admissible settlements at t (probability of agreement Is non-zero)

32. Loui December 06JURIX 2006 KeyNote 31 Breakdown row Breakdown column

33. Loui December 06JURIX 2006 KeyNote 32 Breakdown would occur here (BATNA)

34. Loui December 06JURIX 2006 KeyNote 33 1's security level 2's security level 2 would rather break down 1 would rather break down

35. Loui December 06JURIX 2006 KeyNote 34 Eu 1 |s = 51 Eu 2 |s = 49α +54(1-α) Prob(bd) = ?

36. Loui December 06JURIX 2006 KeyNote 35 Pessimism-Punishment (PP) Agents Observation: parties to a negotiation (can) construct a probability distribution over potential settlements Observation: from a probability distribution over potential settlements, there is an expected utility given settlement Observation: there is a probability of breakdown p(bd)

37. Loui December 06JURIX 2006 KeyNote 36 Pessimism-Punishment (PP) Agents Observation: from a probability distribution (at t) over potential settlements, there is an expected utility given settlement (at t) Observation: there is a probability of breakdown p t (bd)

38. Loui December 06JURIX 2006 KeyNote 37 Pessimism-Punishment (PP) Agents Definition: At t, calculate 1. An expected utility given settlement (Eu t |s) and 2. An expected utility given continued negotiation, Eu t = (Eu t |s) (1 - p t (bd)) + u(bd) p t (bd) Definition: Rationality requires the agent, at t, to: 1. Extend an offer, o, if Eu t < u(o) 2. Accept an offer, a, if Eu t < u(a), a  offers-to-you(t) 3. Break down unilaterally if Eu t < u(bd)

39. Loui December 06JURIX 2006 KeyNote 38 Pessimism-Punishment (PP) Agents Pessimism Empirical Observation: At sufficient granularity, p(bd) is decreasing in the time since last progress

40. Loui December 06JURIX 2006 KeyNote 39 Pessimism causes Eu to fall Next offer is made at this time Expectation starts to fall again

41. Loui December 06JURIX 2006 KeyNote 40 Agreement reached as Eu < u 1

42. Loui December 06JURIX 2006 KeyNote 41 reciprocated offers offers

43. Loui December 06JURIX 2006 KeyNote 42 security Best offer received Whenever u(acc) > security, acceptance occurs before breakdown!

44. Loui December 06JURIX 2006 KeyNote 43 security Best offer received Would you accept an 11-cent offer if your security were 10-cents?

45. Loui December 06JURIX 2006 KeyNote 44 Pessimism-Punishment (PP) Agents Observation: You wouldn't accept 11¢ over 10 ¢ security, nor 51 ¢ over 50 ¢ security Observation: You wouldn't let your kid do it Observation: Your Mother wouldn't let you do it Observation: Your lawyer wouldn't let you do it Observation: Your accountant wouldn't let you do it Proposition: We shouldn't automate our agents to do it

46. Loui December 06JURIX 2006 KeyNote 45 Pessimism-Punishment (PP) Agents Question: Isn't this an issue of distributive justice Answer: Substantive fairness is trivial to model by transforming utilities Observation: There may (ALSO) be a procedural fairness issue

47. Loui December 06JURIX 2006 KeyNote 46 Pessimism-Punishment (PP) Agents Procedural fairness: the more the other party withholds progress, the more you will punish When the other party resumes cooperation, you are willing to forgo punishment

48. Loui December 06JURIX 2006 KeyNote 47 Pessimism-Punishment (PP) Agents Resentment u(bd) = security + resentment(t) What is resentment? 1. Dignity 2. Pride 3. Investment in society 4. Protection against non-progressive manipulators 5. A GENUINE source of satisfaction: non-material, transactional, personal(?), transitory(?)

49. Loui December 06JURIX 2006 KeyNote 48 Pessimism-Punishment (PP) Agents Resentment u t (bd) = security + resentment(t) = u(bd) + r(t) for NP(t), non-progress for a period t What is resentment? 6. Attached to a speech/dialogue act: BATNA through breaking down vs. BATNA through agreement 7. A nonstandard utility (process utility) 8. Specific or indifferent (I-bd-you vs. you-bd-me)

50. Loui December 06JURIX 2006 KeyNote 49 Eu never falls to u 1

51. Loui December 06JURIX 2006 KeyNote 50 Actually accepts because resentment resets with progress Resentment resets to zero each time there is progress Nontrivial progess

52. Loui December 06JURIX 2006 KeyNote 51 Resentment might not reset to zero if there is memory Agent breaks down before accepting

53. Loui December 06JURIX 2006 KeyNote 52 low-valued ρ high-valued ρ (Assumes no progress) Linear pess/linear specific pun

54. Loui December 06JURIX 2006 KeyNote 53 low-valued ρ high-valued ρ (Assumes no progress) Linear pess/linear indifferent pun

55. Loui December 06JURIX 2006 KeyNote 54 (Assumes no progress) low-valued ρ high-valued ρ Exponential pess/linear indifferent pun

56. Loui December 06JURIX 2006 KeyNote 55 rare alternation between breakdown and acceptance (Assumes no progress) Exponential pess/sigmoidal specific pun

57. Loui December 06JURIX 2006 KeyNote 56 Pessimism-Punishment (PP) Agents Variety of Plausible Behaviors Agent can make a series of offers, responds to offers Agent can wait, then offer, accept, or break down Agent can accept, offer, or break down immediately Agent can offer before accepting and vice versa Agent can breakdown before accepting and vice versa Agent can offer before breaking down and vice versa Agent can be on path to breakdown, then on path to acceptance because received offer changes Eu or resentment because extended offer changes Eu Concessions in time can be motivated Laissez-faire paths can be steered

58. Loui December 06JURIX 2006 KeyNote 57 Dominated by BATNA 1's offers in this round 2's offer in this round Eu 2 2's aspiration BATNA = 1's aspirationEu 1 What happens when two P&P agents interact?

59. Loui December 06JURIX 2006 KeyNote 58 What happens when two P&P agents interact? Eu 2 Eu 1 (t=2)Eu 1 (t=1)

60. Loui December 06JURIX 2006 KeyNote 59 What happens when two P&P agents interact? 1's security+ resentment 2's security+ resentment 1's offers in this round

61. Loui December 06JURIX 2006 KeyNote 60 What happens when two P&P agents interact?

62. Loui December 06JURIX 2006 KeyNote 61 What happens when two P&P agents interact?

63. Loui December 06JURIX 2006 KeyNote 62 What happens when two P&P agents interact? 1 breaks down Amount of (specific) resentment Laissez-faire path is through time

64. Loui December 06JURIX 2006 KeyNote 63 Does the starting offer affect the laissez-faire path? Both are generous at the start 1 is generous at start, 2 is not 2 is generous at start, 1 is not

65. Loui December 06JURIX 2006 KeyNote 64 Breakdown at t=2 (pure pessimism)

66. Loui December 06JURIX 2006 KeyNote 65 Different laissez-faire paths

67. Loui December 06JURIX 2006 KeyNote 66 Breakdown at t=5 with resentment

68. Loui December 06JURIX 2006 KeyNote 67 All paths lead to breakdown

69. Loui December 06JURIX 2006 KeyNote 68 In a different negotiation, some paths lead to acceptance, some to breakdown Fixed agent characteristics Varied acceleration of offers

70. Loui December 06JURIX 2006 KeyNote 69 A third example where player 1 can guarantee an acceptance outcome with the right initial offers

71. Loui December 06JURIX 2006 KeyNote 70 An Envelope of Normalcy Can you keep the path in a narrow envelope? the axis passes through If so, then agreement is Possible.

72. Loui December 06JURIX 2006 KeyNote 71 Where are the laissez-faire states, in terms of agents' relative power? power = (u t (bd) – u 1 )/(Eu t – u 1 ) When any party does not have power, Negotiation ends

73. Loui December 06JURIX 2006 KeyNote 72

74. Loui December 06JURIX 2006 KeyNote 73 Pessimism-Punishment (PP) Agents An AI model of negotiation Process Enforcement of agreement Procedural fairness / Negotiating norms Nonstandard utility attached to speech act Objective probability Constructivism (rationality is if, not iff) Purely probabilistic dynamics

75. Loui December 06JURIX 2006 KeyNote 74 Pessimism-Punishment (PP) Agents An AI model of negotiation Process Enforcement of agreement Procedural fairness / Negotiating norms Nonstandard utility attached to speech act Objective probability Constructivism (rationality is if, not iff) Purely probabilistic dynamics

76. Loui December 06JURIX 2006 KeyNote 75 Pessimism-Punishment (PP) Agents An AI model of negotiation Implementable / Plausible / Simple / Memorable Iconoclast (but better) un-Nash non-vonNeumann anti-GameTheory Luce/Raiffa simplicity but requires some modern ideas Brings one main Legal Idea (procedural fairness) into familiar economic setting Victor Lesser: computational value of emotion

77. Loui December 06JURIX 2006 KeyNote 76 Pessimism-Punishment (PP) Agents An AI model of negotiation Implementable / Plausible / Simple / Memorable Iconoclast (but better) un-Nash non-vonNeumann anti-GameTheory Luce/Raiffa simplicity but requires some modern ideas Brings one main Legal Idea (procedural fairness) into familiar economic setting Victor Lesser: computational value of emotion

78. Loui December 06JURIX 2006 KeyNote 77

79. Loui December 06JURIX 2006 KeyNote 78 AI Model of Negotiation: Diana Moore's B.Sc. Thesis, 97 Search Another beautiful story: how making a proposal in a negotiation dialogue focuses heuristic search which causes utility estimates to build in the more probable settlement areas Dialogue/Protocol Persuasion/Argumentation Log-rolling/Problem Reformulation Process

80. Loui December 06JURIX 2006 KeyNote 79 AI Model of Negotiation: Diana Moore's B.Sc. Thesis, 97 Search Dialogue/Protocol Another beautiful story: How agents can ask each other "WHY NOT?" questions and respond with the specific constraints that cause their objective functions to fall below aspiration Persuasion/Argumentation Log-rolling/Problem Reformulation Process

81. Loui December 06JURIX 2006 KeyNote 80 AI-and-Law Model of Negotiation What beautiful stories will we soon be able to tell here?

82. Loui December 06JURIX 2006 KeyNote 81 What Is At Stake? A Personal View Intellectual History AI (w/AI and Law) will rewrite the mathematical foundations of the social sciences Actual Negotiation Practice What electronic world do you want to live in? Can agreement be found in the Middle East?