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AI Models of Negotiation For the Social Sciences: What Should Be in an AI-and-Law Model of Negotiation?

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. Life's To-Do List. … Lecture at the Sorbonne in French … …

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AI Models of Negotiation For the Social Sciences: What Should Be in an AI-and-Law Model of Negotiation?

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  1. 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

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

  3. What There Is in AI and Law on Negotiation: • AI techniques for modelling legal negotiation -E Bellucci, J Zeleznikow - … ICAIL, 1999 • Family_Winner: integrating game theory and heuristics to provide negotiation supportJ Zeleznikow, E Bellucci - JURIX, 2003 • …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 …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 techniquesNI Karacapilidis, CP Pappis - European Journal of Operational Research, 1997 • Mediation SystemsT Gordon, O Märker - Online-Mediation, 2002 • A simple scheme to structure and process the information of parties in online forms of alternative ODRGAW Vreeswijk - Proceedings of the First International ODR Workshop (2003) • Model Checking Contractual ProtocolsA Daskalopulu - Arxiv preprint cs.SE/0106009, 2001 JURIX 2006 KeyNote 3

  4. Where I Start: JURIX 2006 KeyNote 4

  5. Where I Start: SocSci 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 JURIX 2006 KeyNote 5

  6. Principled Negotiation • Appeals • To reason or precedent • Not merely to position of power JURIX 2006 KeyNote 6

  7. Principled Negotiation • Appeals • To reason or precedent • PERSUADER, Sycara 89, Parsons-Jennings 96 • Persuasive argumentation in negotiationKP Sycara - Theory and Decision, 1990 • Collaborative plans for complex group actionBJ Grosz, S Kraus - Artificial Intelligence, 1996 • Negotiation through argumentation—a preliminary reportS Parsons, NR Jennings - ICMAS, 1996 • Arguments, dialogue, and negotiationL Amgoud, S Parsons, N Maudet - ECAI, 2000 • Argument-based negotiation among BDI agentsSV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002 JURIX 2006 KeyNote 7

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

  9. Principled Negotiation • Appeals • To reason or precedent • PERSUADER, Sycara 89, Parsons-Jennings 96 • Persuasive argumentation in negotiationKP Sycara - Theory and Decision, 1990 • Understanding the Role of Negotiation in Distributed Search Among Heterogeneous AgentsSE Lander, VR Lesser - IJCAI, 1993 • Collaborative plans for complex group actionBJ Grosz, S Kraus - Artificial Intelligence, 1996 • Negotiation through argumentation—a preliminary reportS Parsons, NR Jennings - ICMAS, 1996 • Arguments, dialogue, and negotiationL Amgoud, S Parsons, N Maudet - ICMAS, 2000 • Argument-based negotiation among BDI agentsSV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002 JURIX 2006 KeyNote 9

  10. Principled Negotiation • Appeals • To reason or precedent • Not To position of power JURIX 2006 KeyNote 10

  11. Un-Principled Negotiation • Appeals • Not To reason or precedent • To position of power JURIX 2006 KeyNote 11

  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 JURIX 2006 KeyNote 12

  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 JURIX 2006 KeyNote 13

  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 JURIX 2006 KeyNote 14

  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 JURIX 2006 KeyNote 15

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

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

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

  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 JURIX 2006 KeyNote 19

  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 JURIX 2006 KeyNote 20

  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 JURIX 2006 KeyNote 21

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

  23. JURIX 2006 KeyNote 23

  24. Breakdown (BATNA) JURIX 2006 KeyNote 24

  25. Breakdown (BATNA) JURIX 2006 KeyNote 25

  26. JURIX 2006 KeyNote 26

  27. Party 1'saspiration Party 2'saspiration JURIX 2006 KeyNote 27

  28. Party 1'sproposals at t Party 2'sproposals at t JURIX 2006 KeyNote 28

  29. inadmissible(dominated)at t inadmissible(dominated)at t JURIX 2006 KeyNote 29

  30. In black: admissiblesettlementsat t(probabilityof agreement Is non-zero) JURIX 2006 KeyNote 30

  31. Breakdown column Breakdown row JURIX 2006 KeyNote 31

  32. Breakdownwould occurhere (BATNA) JURIX 2006 KeyNote 32

  33. 1's security level 1 would rather breakdown 2's security level 2 would rather breakdown JURIX 2006 KeyNote 33

  34. Prob(bd) = ? Eu1|s = 51 Eu2|s = 49α +54(1-α) JURIX 2006 KeyNote 34

  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) JURIX 2006 KeyNote 35

  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 pt(bd) JURIX 2006 KeyNote 36

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

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

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

  40. Agreement reached as Eu < u1 JURIX 2006 KeyNote 40

  41. offers reciprocated offers JURIX 2006 KeyNote 41

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

  43. Would you accept an 11-cent offer if yoursecurity were 10-cents? Best offer received security JURIX 2006 KeyNote 43

  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 JURIX 2006 KeyNote 44

  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 JURIX 2006 KeyNote 45

  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 JURIX 2006 KeyNote 46

  47. Pessimism-Punishment (PP) Agents Resentmentu(bd) = security + resentment(t) What is resentment?1. Dignity2. Pride3. Investment in society4. Protection against non-progressive manipulators 5. A GENUINE source of satisfaction: non-material, transactional, personal(?), transitory(?) JURIX 2006 KeyNote 47

  48. Pessimism-Punishment (PP) Agents Resentmentut(bd) = security + resentment(t) = u(bd) + r(t) for NP(t), non-progress for a period tWhat is resentment?6. Attached to a speech/dialogue act: BATNA through breaking down vs. BATNA through agreement7. A nonstandard utility (process utility)8. Specific or indifferent (I-bd-you vs. you-bd-me) JURIX 2006 KeyNote 48

  49. Eu never falls to u1 JURIX 2006 KeyNote 49

  50. Actually accepts becauseresentment resets with progress Nontrivial progess Resentment resets to zero each time there is progress JURIX 2006 KeyNote 50

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