building negotiation support systems to support on line dispute resolution john zeleznikow school of...

Building Negotiation Support Systems to support On Line

Dispute Resolution

John Zeleznikow

School of Information Systems

Victoria University

[email protected]

Negotiation Support Systems and On Line Dispute Resolution

The rising tide of pro se representation

• A recent American Bar Association study has shown

• 1) at least one of the parties were self-represented in over 88% of domestic relations cases; and

• 2) both parties were self-represented in 52% of the cases



• (Quatrevaux 1996) notes that there is a shortfall in legal systems for poor persons in the United States.

• (Branting 2001) claims that domestic abuse victims are particularly likely to have few resources and little opportunity to obtain the services of a lawyer.

• He states that the growth of the consumer movement has increased the trend for pro se litigation to organise their own litigation.



• The growing availability of books, document kits and computerised forms, together with the increasing availability of legal materials on the World Wide Web, has increased the opportunities for pro

• This swelling tide of pro se litigants constitutes a growing burden not only to the judiciary but the entire legal process.



• Typically, unrepresented litigants:

• 1) Extend the time taken for litigation – due to their lack of understanding of the process.

• 2) Place themselves at a disadvantage compared to their opponent(s).

• 3) Place the judicial decision-maker in the difficult position of deciding how much support and forbearance the decision-maker should offer to the pro se litigant.



• In this seminar we shall illustrate how web-based legal decision support systems can provide important support for pro-se litigants.

• Examples will be taken from: • Eligibility for Legal Aid• Property distribution in Family Law • Negotiation

• We shall also consider how NSS can help provide useful ODR advice.


Decision Support Systems

• When considering decision making as a knowledge-manufacturing process, the purpose of a decision support system is to help the user manage knowledge.

• A decision support system fulfils this purpose by enhancing the user's competence in representing and processing knowledge.

• It supplements human knowledge management skills with computer-based means for managing knowledge.


Decision Support Systems

• A decision support system accepts, stores, uses, receives and presents knowledge pertinent to the decisions being made.

• Its capabilities are defined by the types of knowledge with which it can work, the ways in which it can represent these various types of knowledge, and its capabilities for processing these representations.


Legal Decision Support Systems

• The development of our legal decision support systems has led to:

• (i) Consistency – by replicating the manner in which decisions are made, DSS are encouraging the spreading of consistency in legal decision-making.

• (ii) Transparency – by demonstrating how legal decisions are made, legal DSS are leading to a better community understanding of legal domains.



• (iii) Efficiency - One of the major benefits of decision support systems is to make firms more efficient.

For example, since VLA handles a large number of cases (and rejects even more) and unlike most private legal firms, it does not bill either by the hour, or even by each individual case, it is most concerned about being efficient.

About 60% of VLA’s labour costs are devoted to assessing whether applicants are eligible for legal aid



Clearly, it would be preferable if VLA lawyers spent their time providing clients with legal aid, rather than assessing the eligibility of clients for grants.

• (iv) Enhanced support for dispute resolution - Users of legal decision support systems are aware of the likely outcome of litigation and thus are encouraged to avoid the costs and emotional stress of legal proceedings


ODR in Australia

• The Victorian Department of Justice has comissioned a report on Online Alternative Dispute Resolution (www.justice.vic.gov.au).

• They classify the following forms of online ADR

• A) Facilitated negotiation e.g I-Courthouse;• B) Automated negotiation e.g. Cybersettle• C) Negotiation Support Systems e.g. Smartsettle

http://www.justice.vic.gov.au/


Types of Disputes Resolved

• Family Law

• Internet Name Disputes

• Consumer Transactions

• Insurance Disputes


Cases attracted and outcomes achieved

• Most successful sites were:• www.squaretrade.com• www.cybersettle.com

• Funding came from:• Philanthropic grants;• Government funding;• User fees;• Membership fees• Advertising revenue.

http://www.squaretrade.com/

http://www.cybersettle.com/


Limitations to access to ODR sites

• Access to a computer with minimum hardware and software support;

• Accessibility of sites to people with disabilities and slow on-line connection speeds;

• Language of services offered.


Australian ODR sites• www.notgoodenough.com.au and

www.complain.com.au are consumer complaints sites.

• www.adronline.com.au is a private venture offering automated negotiation, mediation and arbitration.

• www.retailtenancy.nsw.gov.au permits an applicant to register for mediation services and lodge forms, pay fees and engage in a bulletin board discussion with either an advice officer or a mediator

http://www.notgoodenough.com.au/

http://www.complain.com.au/

http://www.adronline.com.au/

http://www.retailtenancy.nsw.gov.au/


UN Forum on ODR• Interested in Online Dispute Resolution?• Always wanted to visit Australia?• … Now here’s your excuse

The Third Annual Forum on Online Dispute Resolution will be held in Melbourne, Australia 5-6 July 2004 • hosted by the International Conflict Resolution

Centre at the University of Melbourne • in collaboration with the United Nations

Economic and Social Commission for Asia and the Pacific


Forum Aims

To report on online dispute resolution activities at the regional and global level

To take stock of accumulated knowledge and expertise

To increase understanding the current state of technology and its likely future directions

To discuss the major barriers to online dispute resolution and ways of expanding its benefits.


Plan early Participation is free but is limited to 150 participants worldwide. Participants are responsible for their own travel. However, discounted accommodation is available for Forum participants. There are a limited number of stipends covering travel and accommodation for residents of less developed countries. To register your interest in either participating or presenting a paper, please fill in the form attached and return to [email protected]. To ensure you don’t miss out, return your expression of interest today!


GETAID - a Web based decision support system for determining eligibility for legal aid

• After passing a financial test, applicants for legal aid must pass a merits tests.

• The merits test involves a prediction about the likely outcome of the case if it were to be decided by a Court.

• VLA grants officers, who have extensive experience in the practices of Victorian Courts, assess the merits test.


GetAid

• A decision tree is a directed graph in which the nodes represent domain concepts and possible values for each concept are captured in arcs emerging from each node.

• Leaf nodes represent conclusions. • Figure 1 depicts a decision tree that represents

reasoning used by VLA lawyers, to determine whether an applicant for legal aid, who is scheduled to appear in a minor (Magistrates) court, has met statutory guidelines.


Figure 1 Decision tree for eligibility for legal aid.

Acquittalprospects

Severity offine

Likelypenalty

2. not reasonable

Guidelines satisfied

1. is at least $750

2. is less than $750 1. actual or suspendedimprisonment

2. intensivecorrection order

3. a community basedorder for more than

200 hours

4. a community based orderand the defendent cannot

communicate

Guidelines not satisfied.

Specialcircumstan

ces

2. yes

1. reasonable

5. community basedorder for less than 200

hous

Refer to specialist

1. no


Sequenced Transition Networks

• Sequenced Transition Networks (STNs) extend Decision Trees with labeled nodes and arcs.

• The variation, illustrated in Figure 2, enables user prompts and explanations to be more easily generated directly from information in the tree and facilitates the maintenance of the system.


Theapplicant

The penalty

1. has reasonableprospects for acquital on

the primary matter

A convictionis likely to

2. does not havereasonable prospects

for acquital on theprimary matter

Guidelines satisfied

1. is at least assevere as a fine

of $750

2. is not as severe as a fineof $750

1. lead to an actual orsuspended term of

imprisonment2. lead to an

intensivecorrection order

3. lead to acommunity based

order for more than200 hours

4. lead to a communitybased order and the

defendent cannotcommunicate their

rehabilitation needs to theCourt

Guidelines not satisfied.

5. lead to a fine or community work order of less thn 200 hours

There

1. are no specialcircumstances

Refer to specialist

2. may be special circumstances

0121 R550

0251 R552

0

02

021,022,023,024,01101

012,025

01210251

01220252

Not sure :Acquittal prospects

diagram

Not sure :Penalty summaryoffences diagram

Not sure :Special circumstances

diagram

Not sure :Penalty summary

offences $750diagram


Sequenced Transition Networks

• The STN based approach can facilitate the development of knowledge based systems for knowledge that can readily be captured as a decision tree.

• However, not all knowledge is procedural.

• For example, the node in Figure 2 relates to the prospects for acquittal of an applicant for legal aid.

• Determining the possibility of a successful defence to a charge requires legal expertise.

• Such knowledge cannot readily be modeled using decision trees.


Toulmin Argument Structures

• Toulmin concluded that all arguments, regardless of the domain, have a structure, which consists of six basic invariants: claim, data, warrant and backing (as well as modality and rebuttal which we do not use).

• Every argument makes an assertion. The assertion of an argument stands as the claim of the argument.

• A mechanism is required to act as a justification for the claim, given the data— the warrant



The backing supports the warrant and in a legal argument is typically a reference to a statute or precedent case.

• Toulmin structures provide a representation, which allows for a separation of inferencing from explanation.

• An explanation is generated by reproducing the data, warrant or backing and is performed after, and independently from the claim inference.



• Since experts could not readily represent knowledge about an applicant's prospects for acquittal as a decision tree, we decided to model the process as a tree of Toulmin arguments.

• The first of these is illustrated in the following figure.

• During knowledge acquisition, the expert is prompted to articulate factors (data items) that may be relevant in determining a prospect for an acquittal claim, without any consideration about how the factors may combine to actually infer a claim value.


overall strength ofevidence on key

elements of charge

overwhelmingextremely strongstrongmoderately strongweak

overall strength ofcircumstantial

evidence


Evidence was obtainedillegaly

very likelylikelyarguablenot likely

Policeengaged in

illegal activitywithout

authorisation

Admissionobtained

under duress

Accused notinformed of

right tosilence

direct evidence existscompelling indirect evidence exists

no evidence exists

some indirect evidence exists


no evidence exists



no evidence exists


Propensity for theevidence to be unduly

prejudicial to theaccused


The applicant

does not have reasonableprospecst for acquital on the

primary matter

has reasonable prospecst foracquital on the primary matter

strength of crown case

client's instruction


full admissionpartial admissioncomplete denialpositive defense offerredno instructions

Liklihood that crucialcrown evidence will be

ruled inadmissable



Argument Tree for Acquittal Prospects

• It was difficult for the principal domain expert to articulate the ultimate argument (the argument on the extreme right of the previous figure).

• She could not express her heuristic as rules because the way in which the factors combine is rarely made explicit.

• Her expertise was primarily a result of the experience she had gained in the domain.


Argument Tree for Acquittal Prospects

• Although it is feasible to attempt to derive heuristics, the approach we used was to present a panel of experts with an exhaustive list of all combinations of data items as hypothetical cases and prompt the panel for a decision on acquittal prospects.

• Six experts and the knowledge engineer were able to record their decision in all of the exhaustive hypothetical cases (for that argument) in approximately 40 minutes. The decisions from each rater were merged to form a dataset of 600 records that were used to train neural networks.


GetAid

• The GetAid system is currently being used by Victorian solicitors on-line, in conjunction with web based lodgement of applications for legal aid (see http://www.vla.vic.gov.au/upload/briefcase_12-2002.pdf) .

• It can be viewed at http://203.143.70.182/getaid/login/

• Toulmin Argument Structures have also been used to build up Split-Up – a system which advises upon property distribution in Ausstralian Family Law.

http://www.vla.vic.gov.au/upload/briefcase_12-2002.pdf

http://203.143.70.182/getaid/login/


Alternative Dispute Resolution

• (Katsh and Rifkin 2001) state that compared to litigation, negotiation has the following benefits

• Lower cost

• Greater speed

• More flexibility in outcomes

• Less adversarial

• More informal

• Solution rather than blame-oriented

• Private


On Line Dispute Resolution

• On Line Dispute Resolution has additional benefits:

• Disputants do not have to meet face-to-face: an important factor if there has been a history of violence;

• Mediation can occur at any time, with participants located in different countries


Computer Tools for Supporting Legal Negotiation

• [Chung and Pak 1997] stress that although negotiation is a human problem, computers are already at the bargaining table to transform the negotiation process.

• The Harvard Negotiation Project of [Fisher and Ury 1981] claimed that the few generally accepted principles of successful negotiation require world knowledge beyond the scope of existing artificial intelligence methods.

• Principled negotiation advocates separating the problem from the people.

• It promotes a focus on interests of the party rather than allowing negotiation to deteriorate into a contest of 'who will back down first' as occurs in Positional Negotiation.


Computer Tools for Supporting Legal Negotiation

• [Fisher and Ury 1981] advocate the notion of Know your best alternative to a negotiated agreement (BATNA) — The reason you negotiate with someone is to produce better results than would otherwise occur.

• If you are unaware of what results you could obtain if the negotiations are unsuccessful, you run the risk of entering into an agreement that you would be better off rejecting; OR Rejecting an agreement you would be better off entering into.

• Split-Up uses KDD (Knowledge Discovery from Databases) to provide advice upon obtaining your BATNA


Australian Family Law• Family-Winner uses artificial intelligence and game theory

to advise in what order mediators should attempt to settle a dispute and how they should perform trade-offs

• Family law varies from other legal domains in that in general:

• There are no winners or losers — in most common law domains one party to a legal dispute wins a case whilst the other loses;

• There are a vast amount of litigated Family law cases each year

• Parties to a family law case often need to communicate after the litigation has concluded.


Australian Family Law – Property Distribution

• In determining the distribution of property under Australian divorce law a judge performs the following functions:

• She determines assets of the marriage the Court is empowered to distribute — common pool determination;

• She determines what percentage of the common pool each party to the marriage receives — percentage split determination; and

• She determines a final property order in line with decisions made in the percentage split and common pool determination.


Modeling the Common Pool Distribution

• Using a jurisprudential theory that we have developed, the task of common pool determination was classified as being suited to modeling using rule based reasoning

• We have modeled the task using Sequenced transition networks (STN).

• The next diagram is a STN which indicates whether a car used by one of the partners to the marriage, can be considered marital property.


Discretionary Reasoning in Split-Up

• The section of the Family Law Act dealing with the percentage of the common pool each partner receives is highly discretionary.

• Ascertaining knowledge about how a judge weighs and combine factors is difficult in that a guessed numerical weighting is unlikely to represent the weight of the factor in the context of a large number of interdependent factors.

• We wished to generate a descriptive model of the domain, and neural networks combined with a rule based reasoner was deemed appropriate



• Although the Act presents a flat list of relevant factors without specifying how these factors relate to each other, we believe domain specific knowledge is crucial in specifying relationships between factors and for the elicitation of the other factors which are relevant but not explicitly mentioned in the statute.

• The Split—Up system has a hierarchy of 99 factors which domain experts indicated were relevant for a percentage split prediction.



Ninety-four variables were identified as relevant for a determination in consultation with experts.

• The way the factors combine was not elicited from experts as rules or complex formulas

• Split Up is on line at http://www.ballarat.edu.au/~astranieri/splitup/splitup.php

http://www.ballarat.edu.au/~astranieri/splitup/splitup.php



• The choice of inferencing mechanism chosen depended upon jurisprudential concepts we developed.

• We use the argumentation theory of Stephen Toulmin to justify our decisions.

• The Family Law Act (1975) directs a decision maker to take into account the past contributions of each party to a failed marriage in addition to their resources for coping with life into the future.



• Values on the 94 variables were to be extracted from cases previously decided, so that a neural network could learn to mimic the way in which judges had combined variables.

• Rather than offering one definition for contributions and one for needs, the statute presents a 'shopping list' of factors to be taken into account in arriving at a property order.

• Although the statute presents a flat list of relevant factors without specifying how these factors relate to each other, we realised that the factors could be placed in a hierarchy.



• The development of the hierarchy required specific knowledge supplied by domain experts.

• A sophisticated hierarchy of ninety-four factors was elicited.

• The hierarchy provides a structure that was used to decompose the task of predicting an outcome into thirty-five sub-tasks.

• Outputs of sub-tasks further down the hierarchy are used as inputs into sub-tasks higher in the hierarchy.


Modeling Family Law Property Distribution

• The Split Up project applies Knowledge Discovery from Databases (KDD) to predict property division decisions made by judges of the Family Court of Australia following a divorce.

• We have noted that:• KDD is particularly suited to the discovery

of knowledge in discretionary domains



• KDD is particularly suited to the discovery of knowledge in discretionary domains.

• The discernment of tasks suited to KDD from those more appropriately suited to other methods relies heavily on the jurisprudential concept of open texture.

• The argumentation theory proposed by Toulmin can be used for the representation of domain knowledge in the data transformation phase.



• In the Split-Up project we wished to model how Australian Family Court judges exercise discretion in distributing marital property following divorce.

• Section 79(1) of the Family Law Act (1975) empowers the Family Court to make orders altering the property interests of parties to the marriage but does not lay down procedural guidelines for judicial decision makers.

• In practice, judges of the Family Court follow a five-step process in order to arrive at a property order:



• 1. Ascertain the property of the parties.• 2. Value all property of both parties.• 3. Determine which assets will be paramount

in property considerations. This is referred to as common pool property.

• 4. Determine a percentage of the property to be awarded to each party.

• 5. Create an order altering property interest to realise the percentage.



• The Split-Up system implements steps 3 and 4 above, the common pool determination and the prediction of a percentage split.

• According to domain experts, the common pool determination task (Step 3) does not greatly involve the exercise of discretion, in stark contrast to the percentage split task (Step 4).



• Consequently, Split-Up implements the common pool determination by eliciting heuristics as directed graphs from domain experts using a methodology we have called sequenced transition networks.

• The following represents knowledge about whether a vehicle is considered marital property.


1. acquiredduring themarriage 2. a party outside

the marriage

2. evidence of asham

2. a gift orinheritance

1. neither agift nor

inheritance ?

2. acquiredbefore or afterthe marriage

2. maintained orimproved during

the marriage

2. both parties orthe family

1. the husband orwife

1. noevidence of

a sham

1. not maintained orimproved during the

marriage

1. only therecipient

Initial Node

There is

The vehicle will beexcluded from the pool

The vehiclewas

The vehicle will beincluded in the pool butthe Court cannot order

a transfer

VEHICLES

The vehicle isregistered to

The vehicle will be included inthe pool and the Court can

order a transfer

The giftbenefited

It was

The vehiclewas



• Domain expertise in family law is represented in the Split-Up system as Toulmin arguments.

• The Toulmin Argument Structure we use facilitates the development of hybrid systems and the generation of explanations.

• Ninety-four variables were identified as relevant for a determination in consultation with experts.

• The way the factors combine was not elicited from experts as rules or complex formulas



• Values on the 94 variables were to be extracted from cases previously decided, so that a neural network could learn to mimic the way in which judges had combined variables.

• Toulmin concluded that all arguments, regardless of the domain, have a structure that consists of six basic invariants: claim, data, modality, rebuttal, warrant and backing.

• Every argument makes an assertion based on some data.


Toulmin Arguments

• The assertion of an argument stands as the claim of the argument.

• Knowing the data and the claim does not necessarily convince us that the claim follows from the data.

• A mechanism is required to act as a justification for the claim.

• This justification is known as the warrant.


Toulmin Arguments

• The backing supports the warrant and in a legal argument is typically a reference to a statute or a precedent case.

• The rebuttal component specifies an exception or condition that obviates the claim.

• The Family Law Act (1975) directs a decision maker to take into account the past contributions of each party to a failed marriage in addition to their resources for coping with life into the future.


Split-Up

• Rather than offering one definition for contributions and one for needs, the statute presents a 'shopping list' of factors to be taken into account in arriving at a property order.

• Although the statute presents a flat list of relevant factors without specifying how these factors relate to each other, we realised that the factors could be placed in a hierarchy.

• The development of the hierarchy required specific knowledge supplied by domain experts.


Split-Up• A sophisticated hierarchy of ninety-four factors was

elicited.

• The hierarchy provides a structure that was used to decompose the task of predicting an outcome into thirty-five sub-tasks.

• Outputs of sub-tasks further down the hierarchy are used as inputs into sub-tasks higher in the hierarchy.

• Solid arcs in the following figure represent inferences performed with the use of rule sets

• Whereas dashed arcs depict inferences performed using neural networks (or indeed any other KDD technique).


Toulmin Arguments

• The classification scheme has been used to classify tasks in the domain of

• family law (35 arguments),

• refugee law (200 arguments),

• copyright law (50 arguments),

• eligibility for legal aid (8 arguments) and

• the evaluation of eye-witness evidence


DEUS – a template-based NSS• DEUS calculates the agreement and disagreement

between the litigants’ goals at any given time

• It helps mediators understand what issues are in dispute and the extent of the dispute over these issues.

• Split-Up is a hybrid rule-based/neural network systems that advises upon property distribution following divorce in Australia


Split-Up. Whilst Split-Up is not a NSS, it can be used

to determine one’s BATNA for a negotiation and hence provides an important starting point for negotiations.

• Split-Up first shows both litigants what they would be expected to be awarded by a court if their relative claims were accepted.

• It gives them relevant advice as to what would happen if some or all of their claims were rejected.


Split-Up as a Negotiation Support System

Users are then able to have dialogues with the system to explore hypothetical situations to establish clear ideas about the strengths and weaknesses of their claims.

• Suppose the disputants' goals are entered into the system to determine the asset distributions for both parties.

• The Split-Up system provided the following answers as to the percentages of the marital assets received by each party in a hypothetical example:


W’s % H’s %

Given one accept’s W’s beliefs 65 35

Given one accept’s H’s beliefs 42 58

Given one accept’s H’s beliefs but gives W primary residence of the children

60 40


Split-Up as a Negotiation Support System

• Clearly custody of the children is very significant in determining the husband’s property distribution.

• If H is unlikely to win custody of the children, the husband would be well advised to accept 40% of the common pool (otherwise he would also risk paying large legal fees and having on-going conflict).

• AdjustedWinner is a point allocation procedure that distributes items or issues to people on the premise of whoever values the item or issue more.


Adjusted Winner• The two players are required to distribute 100

points across the range of issues in dispute.

• The Adjusted Winner paradigm is a fair and equitable procedure. At the end of allocation of assets, each party accrues the same number of points.

• Although the system suggests a suitable allocation of items or issues, it is up to the human mediators to finalise the agreement acceptable to both parties.


Adjusted Winner• Arising from our work on the AdjustedWinner

algorithm, we noted that

• 1) The more issues and sub-issues in dispute, the easier it is to form trade-offs and hence reach a negotiated agreement;

• 2) We should choose as the first issue to resolve the issue on which the disputants are furthest apart - one wants it greatly, the other considerably less so.


Family_Winner

• Family_Winner supports the process of negotiation by introducing importance values to indicate the degree to which each party desires to be awarded the issue being considered.

• The system uses this information to form trade-off rules.

• As an allocation enables trade-offs to be executed, the details of an allocation will have some input into the resulting changes.

• It was necessary to obtain some negotiation specific data to provide an insight into what change, and what amount of change was expected as a result of trade-off execution.


Family_Winner• 650 negotiated and litigated cases, provided by the

Australian Institute of Family Studies, were analysed.

• It was hence discovered the shift in most issues appeared to be similar in number.

• We analysed the data and set numbers to each issue and position pair, and developed trade-off rules.

• The trade-off rules are used to allocate issues according to the logrolling strategy.

The system makes this analysis by transforming user input into trade-off values, which show the effect of an issue’s allocation on all unallocated issues.


Family_Winner

• To use Family_Winner we must assume

• A) The dispute can be modeled using principled negotiation;

• B) That weights can be assigned to each of the issues in dispute; and

• C) That sufficient issues are in contention to allow each side to be compensated for losing an issue.


Family_Winner• Users of the Family_Winner system enter

information such as the issues in dispute, indications of each issue’s importance to the respective parties and how the issues relate to each other.

• An analysis of the information is compiled, which is then translated into graphical trade-off maps.

• The maps illustrate the relevant issues, their importance to each party and trade-off capabilities of each issue.


Family_Winner• The user is asked if the issues can be resolved in

its current form. • If this is the case, the system then proceeds to

allocate the issue as desired by the parties.

• Otherwise, the user is asked to decompose an issue chosen by the system as the least contentious.

• The issue on which there is the least disagreement is chosen to be the issue first considered.


Family_Winner• Users are asked to enter sub-issues.

• As issues are decomposed, they are stored in a decomposition hierarchy, with all links intact.

• This process of decomposition continues through the one issue, until the users decide the current level is the lowest decomposition possible.

• At this point, the system calculates which issue to allocate to each party, then removes this issue from the each of the party’s respective trade-off maps, and makes appropriate numerical adjustments to remaining issues linked to the issue just allocated.


Law and Negotiation• Alexander has illustrated that women tend to be more

reluctant than men to continue conflict and are more likely to wave their legal rights in a mediation session.

• If their major goal is to be the primary care giver for their children, they may reach a negotiated settlement, which whilst acceptable to them is patently unjust.

• The wife may for example, give the husband the bulk of the property, in return for her being granted the primary care of the children.

• Whilst such an arrangement may meet the goals of both parents, it does not meet the paramount interests of the children, who will be deprived of subsequent financial resources.


Law and NegotiationFamily Law is one domain where mediation conflicts with notions of justice.

Bargaining theory has its limitation in providing decision support for family law negotiation. However, we have noticed that various negotiation domains are far more suitable than family law, for modeling using integrated game theory and knowledge based systems to advise upon trade-offs.

Thus, we decided to evaluate the use of Family_Winner in other negotiation domains.


The Extension of Family_Winner to non Family Law negotiation domains

• Since Family_Winner has been designed with a view to being utilised in many negotiation domains, there are no domain-specific requirements that prospective cases need to exhibit.

• We hence used Family_Winner in:

• A) Panama Canal dispute of 1974;• B) Negotiation with terrorists;• C) Company Mergers.


Panama Canal Treaty• Family_Winner’s advice varied considerably from the settlement

obtained from negotiations held between the parties.

• As Family_Winner only has the ability to allocate issues to one of the parties, a comparison based upon creative settlements is not possible.

• Also, there needs to be a better understanding of exactly what issues are in contention, and how these issue definitions can be translated into issues ready for allocation by Family_Winner.

• The Panama Canal treaty was particularly difficult to resolve, since the disputants changed their goals during the dispute.

• Unfortunately, Family_Winner has no temporal aspects.


1988 hijacking of Kuwait Airlines flight 422

• Family_Winner advised upon negotiations between the Kuwaiti government and the hijackers.

• Issues covered were allowing the plane to land, fuel, food, access to media, release of hostages, release of convicted terrorists and the possible conviction of the terrorists.

• Family_Winner’s advice coincided with the eventual outcome of the siege.


A merger negotiation between companies

• Issues in dispute were, name, headquarters, chairman, chief executive officer and layoffs.

• The settlement reached by Brams and Taylor was identical to that achieved using Family_Winner.

• The results obtained from this example demonstrate the effectiveness of point assignments to show the importance value of an issue to a party, coupled with trade-off equations to assist in the allocation of issues valued closely by the respective parties.


Evaluating Family_Winner• An investigation of our examples has shown the benefit of

Family_Winner for advising upon trade-offs, compensation and the sequencing of negotiations as long as the issues can be described and remain static and points can be allocated to issues.

• The system cannot develop creative solutions nor (currently) deal with temporal changes.

• Problems can occur in legal domains where a negotiated settlement conflicts with our notion of justice.


Future Work

• We are developing systems for • A) the UK building industry and • B) plea-bargaining in Victoria, Australia.

– We have commenced a project on the Development and testing of a United Kingdom web-based decision support system for use in improving the consistency and predictability of adjudicators’ decisions in building construction disputes.

– We will build a web-based decision support system by


Building Industry Advisory System

– 1. Combining the records of project partners; electronically publishing these records; creating a standard hub where stakeholders can record adjudication data; and data mining the records;

– 2. Decision modeling of the domain of building industry dispute resolution by developing a web-based model of legal reasoning in adjudication;

– 3. Commissioning a tool for predicting the course of building dispute adjudications.



– The project has as objectives:

• a. The data mining of adjudication records.

• b. The development of new web-based, analytical tools to identify the predictability of decisions involving many variables, some of which will depend on legal reasoning, and some on purely statistical analysis. Accordingly, a decision model will be created based on a legalistic approach (rule based and case based) to adjudication, as derived from decisions that have been made by the UK courts.



• C. Testing the decision model against new adjudications which are being handled by project partners, and the identification of issues where the model aids predictability, and which may be used to facilitate settlement and reduce conflict.

• D. The impact of the achieving of these objectives will be to have greater transparency as to the cause of disputes which go to adjudication and the likely result of such disputes.

• E. The project will also provide an easily accessed, one-stop decision support system with information on procedure and case law and is capable of being used by adjudicators to test reasoning before publishing a decision.



• The project will develop

• (1) Ability to test reasoning;

• (2) Ability to test likelihood of success;

• (3) Information and guidance on procedure, thereby reducing the danger of procedural error by adjudicators;

• (4) Up-to-date information and guidance on matters that frequently come to adjudication;

• (5) Up-to-date information and guidance on case law;

• (6) Training tool;

• (7) Reporter on trends


Future Work

• In conjunction with VLA and the Victorian Office of Public prosecution we are developing ODR systems to help with regard to plea bargaining about sentencing.

• In evaluating Family_Winner, we noted that disputant’s preferences might well conflict with notions of justice.

• This is particularly true in Family Law, where the interests of children are paramount.


Conclusion

• We hence realized certain limitations of bargaining-type negotiation support systems such as Family_Winner.

• An evaluation of Family_Winner in diverse domains such as enterprise bargaining, company mergers and international disputes has shown that it is useful for advising upon trade-offs, compensation and the sequencing of negotiations as long as the issues can be described and remain static and points can be allocated to issues.

• The system cannot develop creative solutions nor (currently) deal with temporal changes.

building negotiation support systems to support on line dispute resolution john zeleznikow school of...

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