AI Models of Negotiation For the Social Sciences: What Should Be in an AI-and-Law Model of...
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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
December 06 JURIX 2006 KeyNote 2 Loui
Life's To-Do List…Lecture at the Sorbonne in French…
…Become a President Obama appointee
(was Obama really at ICAIL 2001?)…
December 06 JURIX 2006 KeyNote 3 Loui
What There Is in AI and Law on Negotiation:
AI techniques for modelling legal negotiation -E Bellucci, J Zeleznikow - … ICAIL, 1999Family_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, 2005Integrating 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 NI Karacapilidis, CP Pappis - European Journal of Operational Research, 1997Mediation SystemsT Gordon, O Märker - Online-Mediation, 2002A 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 ProtocolsA Daskalopulu - Arxiv preprint cs.SE/0106009, 2001
December 06 JURIX 2006 KeyNote 5 Loui
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 AfghanistanTerm Paper: The Pershing II's should be deployed in Europe
December 06 JURIX 2006 KeyNote 6 Loui
Principled NegotiationAppeals
To reason or precedentNot merely to position of power
December 06 JURIX 2006 KeyNote 7 Loui
Principled NegotiationAppeals
To reason or precedentPERSUADER, Sycara 89, Parsons-Jennings 96Persuasive argumentation in negotiationKP Sycara - Theory and Decision, 1990Collaborative plans for complex group actionBJ Grosz, S Kraus - Artificial Intelligence, 1996Negotiation through argumentation—a preliminary reportS Parsons, NR Jennings - ICMAS, 1996 Arguments, dialogue, and negotiationL Amgoud, S Parsons, N Maudet - ECAI, 2000Argument-based negotiation among BDI agentsSV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002
December 06 JURIX 2006 KeyNote 8 Loui
Principled NegotiationAppeals
To reason or precedentPERSUADER, Sycara 89, Parsons-Jennings 96Persuasive argumentation in negotiationKP Sycara - Theory and Decision, 1990
Arguing about plans: Plan representation and reasoning for mixed-initiative planningG Ferguson, J Allen - AIPS, 1994Collaborative plans for complex group action BJ Grosz, S Kraus - Artificial Intelligence, 1996Negotiation through argumentation—a preliminary reportS Parsons, NR Jennings – ICMAS, 1996 Arguments, dialogue, and negotiationL Amgoud, S Parsons, N Maudet - ECAI 2000Argument-based negotiation among BDI agentsSV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002
December 06 JURIX 2006 KeyNote 9 Loui
Principled NegotiationAppeals
To reason or precedentPERSUADER, Sycara 89, Parsons-Jennings 96Persuasive argumentation in negotiationKP Sycara - Theory and Decision, 1990
Understanding the Role of Negotiation in Distributed Search Among Heterogeneous Agents SE Lander, VR Lesser - IJCAI, 1993Collaborative plans for complex group action BJ Grosz, S Kraus - Artificial Intelligence, 1996Negotiation through argumentation—a preliminary reportS Parsons, NR Jennings - ICMAS, 1996 Arguments, dialogue, and negotiationL Amgoud, S Parsons, N Maudet - ICMAS, 2000Argument-based negotiation among BDI agentsSV Rueda, AJ Garcıa, GR Simari - Journal of Computer Science and Technology, 2002
December 06 JURIX 2006 KeyNote 10 Loui
Principled NegotiationAppeals
To reason or precedentNot To position of power
December 06 JURIX 2006 KeyNote 11 Loui
Un-Principled NegotiationAppeals
Not To reason or precedentTo position of power
December 06 JURIX 2006 KeyNote 12 Loui
Un-Principled NegotiationAppeals
To position of powerEnforceable agreementsUnenforceable agreements
No institutional contextGame Theoretical Models of Negotiation
x Solution Conceptx Nash Equilibriax MultiAgent
Ecommerce Systems
December 06 JURIX 2006 KeyNote 13 Loui
Un-Principled NegotiationAppeals
To position of powerEnforceable agreementsUnenforceable agreements
No institutional contextGame Theoretical Models of Negotiationx Solution Conceptx Nash Equilibria - A Beautiful Mind, shared Nobel Prizex MultiAgent Ecommerce Systems - Computers & Thought Winner 03
December 06 JURIX 2006 KeyNote 14 Loui
Un-Principled NegotiationAppeals
To position of powerEnforceable agreementsUnenforceable agreements
No institutional contextGame Theoretical Models of Negotiation
x Solution Conceptx Nash Equilibriax MultiAgent
Ecommerce Systems
Badly mistaken path
December 06 JURIX 2006 KeyNote 15 Loui
Un-Principled NegotiationAppeals
To position of powerEnforceable agreements
Newer "Institutional Economics" Nobel prizes
Unenforceable agreementsNo institutional context
Game Theoretical Models of Negotiationx Solution Conceptx Nash Equilibriax MultiAgent
Ecommerce Systems
December 06 JURIX 2006 KeyNote 16 Loui
AI Model of Negotiation:Venk Reddy (Harvard) 93, Mark Foltz (WU/MIT),
95Kay Hashimoto (Harvard), 96
Diana Moore's (WU) B.Sc. Thesis, 95-97Anne Jump (Harvard), 97-98
All undergradsBut 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)
December 06 JURIX 2006 KeyNote 17 Loui
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
December 06 JURIX 2006 KeyNote 18 Loui
AI Model of Negotiation:Diana Moore's B.Sc. Thesis, 97
SearchDialogue/ProtocolPersuasion/ArgumentationLog-rolling/Problem ReformulationProcess
December 06 JURIX 2006 KeyNote 19 Loui
AI Model of Negotiation:Diana Moore's B.Sc. Thesis, 97
SearchMixed-initiative planning/NLP-PragmaticsHeuristic valuation of payoffs
Dialogue/ProtocolThis AI and Law community
Persuasion/ArgumentationMultiagent systems community
Log-rolling/Problem ReformulationMixed-initiative planning/NLP-Pragmatics
ProcessToday's Talk
December 06 JURIX 2006 KeyNote 20 Loui
AI-and-Law Model of Negotiation
Offer/acceptance at the level ofScenariosPhrasesTerms
Uncertainty as to How claims might fare if pressedWhether the scenario might occurHow the language might evolveHow the case law (or standards) might evolve
December 06 JURIX 2006 KeyNote 21 Loui
AI-and-Law Model of Negotiation
BATNA/security expressed as a RISK positionStrong norms for
ProgressExplanation/ Questions and Answers
Start with utility-payoffs To connect with social scientistsTo be precise & compactI already have a few stories to tell here
December 06 JURIX 2006 KeyNote 22 Loui
Pessimism-Punishment (PP) Agents
Observation: parties to a negotiation (can) construct a probability distribution over potential settlements
December 06 JURIX 2006 KeyNote 30 Loui
In black:admissiblesettlementsat t
(probabilityof agreementIs non-zero)
December 06 JURIX 2006 KeyNote 33 Loui
1's securitylevel
2's securitylevel
2 would rather breakdown
1 would rather breakdown
December 06 JURIX 2006 KeyNote 35 Loui
Pessimism-Punishment (PP) Agents
Observation: parties to a negotiation (can) construct a probability distribution over potential settlementsObservation: from a probability distribution over potential settlements, there is an expected utility given settlementObservation: there is a probability of breakdown p(bd)
December 06 JURIX 2006 KeyNote 36 Loui
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)
December 06 JURIX 2006 KeyNote 37 Loui
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)
December 06 JURIX 2006 KeyNote 38 Loui
Pessimism-Punishment (PP) Agents
Pessimism
Empirical Observation: At sufficient granularity, p(bd) is decreasing in the time since last progress
December 06 JURIX 2006 KeyNote 39 Loui
Pessimism causes Eu to fall
Next offer is made at this time
Expectation starts to fall again
December 06 JURIX 2006 KeyNote 42 Loui
security
Best offer received
Whenever u(acc) > security, acceptance occurs before breakdown!
December 06 JURIX 2006 KeyNote 43 Loui
security
Best offer received
Would you accept an 11-cent offer if yoursecurity were 10-cents?
December 06 JURIX 2006 KeyNote 44 Loui
Pessimism-Punishment (PP) Agents
Observation: You wouldn't accept 11¢ over 10 ¢ security, nor 51 ¢ over 50 ¢ securityObservation: You wouldn't let your kid do itObservation: Your Mother wouldn't let you do itObservation: Your lawyer wouldn't let you do itObservation: Your accountant wouldn't let you do it
Proposition: We shouldn't automate our agents to do it
December 06 JURIX 2006 KeyNote 45 Loui
Pessimism-Punishment (PP) Agents
Question: Isn't this an issue of distributive justiceAnswer: Substantive fairness is trivial to model by transforming utilities
Observation: There may (ALSO) be a procedural fairness issue
December 06 JURIX 2006 KeyNote 46 Loui
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
December 06 JURIX 2006 KeyNote 47 Loui
Pessimism-Punishment (PP) Agents
Resentment
u(bd) = security + resentment(t)
What is resentment? 1. Dignity2. Pride3. Investment in society4. Protection against non-progressive manipulators5. A GENUINE source of satisfaction:
non-material, transactional, personal(?), transitory(?)
December 06 JURIX 2006 KeyNote 48 Loui
Pessimism-Punishment (PP) Agents
Resentment
ut(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)
December 06 JURIX 2006 KeyNote 50 Loui
Actually accepts becauseresentment resets with progress
Resentment resets to zero each time there is progress
Nontrivial progess
December 06 JURIX 2006 KeyNote 51 Loui
Resentment might not reset to zero if there is memory
Agent breaks down before accepting
December 06 JURIX 2006 KeyNote 52 Loui
low-valued ρ high-valued ρ
(Assumes no progress)
Linear pess/linear specific pun
December 06 JURIX 2006 KeyNote 53 Loui
low-valued ρ high-valued ρ
(Assumes no progress)
Linear pess/linear indifferent pun
December 06 JURIX 2006 KeyNote 54 Loui
(Assumes no progress)
low-valued ρ high-valued ρ
Exponential pess/linear indifferent pun
December 06 JURIX 2006 KeyNote 55 Loui
rare alternation betweenbreakdown and acceptance
(Assumes no progress)
Exponential pess/sigmoidal specific pun
December 06 JURIX 2006 KeyNote 56 Loui
Pessimism-Punishment (PP) Agents
Variety of Plausible BehaviorsAgent can make a series of offers, responds to offersAgent can wait, then offer, accept, or break downAgent can accept, offer, or break down immediatelyAgent can offer before accepting and vice versaAgent can breakdown before accepting and vice versaAgent can offer before breaking down and vice versaAgent can be on path to breakdown, then on path to acceptance
because received offer changes Eu or resentmentbecause extended offer changes Eu
Concessions in time can be motivatedLaissez-faire paths can be steered
December 06 JURIX 2006 KeyNote 57 Loui
Dominatedby BATNA
1's offers inthis round
2's offer inthis roundEu2
2's aspiration
BATNA =<u1(bd),u2(bd)>
1's aspiration Eu1
What happens when two P&P agents interact?
December 06 JURIX 2006 KeyNote 58 Loui
What happens when two P&P agents interact?
Eu2
Eu1(t=2)Eu1(t=1)
December 06 JURIX 2006 KeyNote 59 Loui
What happens when two P&P agents interact?
1'ssecurity+resentment
2'ssecurity+resentment
1's offersin this round
December 06 JURIX 2006 KeyNote 62 Loui
What happens when two P&P agents interact?
1 breaks down
Amount of(specific)resentment
Laissez-faire path is
<Eu1,Eu2>through time
December 06 JURIX 2006 KeyNote 63 Loui
Does the starting offer affect the laissez-faire path?
Both aregenerousat the start
1 isgenerousat start,2 is not
2 isgenerousat start,1 is not
December 06 JURIX 2006 KeyNote 68 Loui
In a different negotiation,some paths lead to acceptance, some to breakdown
Fixedagentcharacteristics
Variedaccelerationof offers
December 06 JURIX 2006 KeyNote 69 Loui
A third example where player 1 can guaranteean acceptance outcome with the right initial offers
December 06 JURIX 2006 KeyNote 70 Loui
An Envelope of NormalcyCan you keep the pathin a narrow envelope?
the axis passes through< uA(bd), uB(bd) >
If so, then agreement isPossible.
December 06 JURIX 2006 KeyNote 71 Loui
Where are the laissez-faire states, in terms of agents' relative power?
power = (ut(bd) – u1)/(Eut – u1)
When any partydoes not have power,Negotiation ends
December 06 JURIX 2006 KeyNote 73 Loui
Pessimism-Punishment (PP) Agents
An AI model of negotiationProcessEnforcement of agreementProcedural fairness / Negotiating normsNonstandard utility attached to speech actObjective probabilityConstructivism (rationality is if, not iff)Purely probabilistic dynamics
December 06 JURIX 2006 KeyNote 74 Loui
Pessimism-Punishment (PP) Agents
An AI model of negotiationProcessEnforcement of agreementProcedural fairness / Negotiating normsNonstandard utility attached to speech actObjective probabilityConstructivism (rationality is if, not iff)Purely probabilistic dynamics
December 06 JURIX 2006 KeyNote 75 Loui
Pessimism-Punishment (PP) Agents
An AI model of negotiationImplementable / Plausible / Simple / MemorableIconoclast (but better)
un-Nashnon-vonNeumannanti-GameTheory
Luce/Raiffa simplicity but requires some modern ideasBrings one main Legal Idea (procedural fairness) into familiar economic settingVictor Lesser: computational value of emotion
December 06 JURIX 2006 KeyNote 76 Loui
Pessimism-Punishment (PP) Agents
An AI model of negotiationImplementable / Plausible / Simple / MemorableIconoclast (but better)
un-Nashnon-vonNeumannanti-GameTheory
Luce/Raiffa simplicity but requires some modern ideasBrings one main Legal Idea (procedural fairness) into familiar economic settingVictor Lesser: computational value of emotion
December 06 JURIX 2006 KeyNote 78 Loui
AI Model of Negotiation:Diana Moore's B.Sc. Thesis, 97
SearchAnother 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/ProtocolPersuasion/ArgumentationLog-rolling/Problem ReformulationProcess
December 06 JURIX 2006 KeyNote 79 Loui
AI Model of Negotiation:Diana Moore's B.Sc. Thesis, 97
SearchDialogue/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/ArgumentationLog-rolling/Problem ReformulationProcess
December 06 JURIX 2006 KeyNote 80 Loui
AI-and-Law Model of Negotiation
What beautiful stories will we soon be able to tell here?