holistic preferences and prenegotiation preparation … · prenegotiation preparation –...

Holistic Preferences and PrenegotiationPreparation

Tomasz Wachowicz and Ewa Roszkowska

ContentsIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Prenegotiation Preparation – Negotiation Template and Its Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 5Multiple Criteria Decision Aiding and Two Ways of Preference Elicitation . . . . . . . . . . . . . . . . . . . . 8Formal Methods for Indirect Evaluation of Negotiation Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

UTASTAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12MARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Software Support of Prenegotiation Preference Elicitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18eNego System and Empirical Findings from Using Hybrid Holistic Prenegotiation Support . . . 20

The System and Its Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20The Module for a Hybrid Holistic Approach to Prenegotiation Preference Elicitation . . . . . 21The Use of the Evaluated Scoring System in the Bargaining Support in eNego . . . . . . . . . . . . 24The eNego Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Abstract

One of the activities within the prenegotiation preparation phase is to create ananalytical basis for decision support in negotiations. This is done by defining thestructure of the negotiation problem and eliciting the negotiators’ preferences.The negotiation analysis suggests using the simplest decision support methodshere, which are often based on preference aggregation paradigm, such as directrating. Some recent experimental works, however, indicate various cognitive and

T. Wachowicz (*)Department of Operations Research, University of Economics in Katowice, Katowice, Polande-mail: [email protected]

E. RoszkowskaFaculty of Economics and Finance, University of Białystok, Bialystok, Polande-mail: [email protected]

© Springer Nature Switzerland AG 2020D. M. Kilgour, C. Eden (eds.), Handbook of Group Decision and Negotiation,https://doi.org/10.1007/978-3-030-12051-1_64-1

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technical problems that may occur when such an approach is used. Therefore, inthis chapter, we discuss the issue of using an alternative approach that implementsa preference disaggregation paradigm and operates with holistic preference dec-larations. We analyze various options that may be used for designing the holisticprenegotiation preference elicitation protocol and present the results ofimplementing one hybrid holistic protocol in the bilateral negotiation supportsystem.

Introduction

Negotiations are complicated decision-making processes (Thompson 2015). In theseprocesses, the parties are involved (two or more) who usually appear to haveconflicting interests, and therefore they are jointly trying to find a solution thatwould satisfy – at least to some extent – their goals. This requires a mutualunderstanding of the counterpart’s needs and acknowledging the necessity of makingconcessions, which inherently makes a problem of developing an adequate negoti-ation approach. The false assumption regarding such an approach is that it may beeither soft or hard (Fisher et al. 2011). In fact, neither of these approaches isconsidered efficient, as they are focused on some social and psychological elementsof negotiation game and parties (people) themselves, rather than on the potentialvalue that may be created when the problem under consideration is solved. To knowwhat this value can be and how it may be divided among the parties, the detailedknowledge of the problem, its components, and context is required, which should beprocessed and analyzed to identify creative options for agreement. This involvesextensive analytical work, and cannot be effectively done during the negotiationprocess.

Therefore, the theory of negotiation recommends the parties to conduct a pre-negotiation preparation before getting involved in the actual bargaining (Zartman1989; Peterson and Lucas 2001; see also the chapter ▶ “Looking Back on Definingthe Right Problem in Group Decision and Negotiation”). From the behavioralperspective, it allows the parties to build the bridges between the conflict they faceand prospects of future cooperation, learn about the counterpart, reduce the uncer-tainty, assure that the concessions will be requited, and organize the internal support.From the formal perspective, which is more focused on decision making aspects ofthe process, it allows to operationalize and quantify the elements of the negotiationproblem, such as issues and options to be negotiated in the form of negotiationtemplate, as well as precisely elicit the negotiator’s preferences for all these ele-ments. The latter, however, requires some decision-making and cognitive skills. Toprevent negotiators from making errors or falling into certain metal biases during thepreference elicitation, it can be facilitated using various decision aiding methods(Young 1991; Raiffa et al. 2002) that preferably should be tailored to the decisionmaker’s (DM) cognitive style (see the chapter ▶ “Impact of Cognitive Style onGroup Decision and Negotiation”). As a result, the formal scoring systems can be

2 T. Wachowicz and E. Roszkowska

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built that represent the parties’ goals and needs, which may be used for the asym-metric and symmetric support of negotiators in their search for agreement.

Most of the prenegotiation protocols used for structuring and evaluating thenegotiation problem operate with the classic multiple criteria decision aiding(MCDA) techniques (see chapter ▶ “Multiple Criteria Decision Support”) that arebased on the preference aggregation approach (Raiffa 1982; Keeney and Raiffa1991; see also a broader discussion on the problem modeling in ▶ “NegotiationProcess Modelling: From Soft and Tacit to Deliberate”). They assume that thenegotiators, being skilled decision makers, can easily describe their preferences onthe atomic level (i.e., for the very basic elements of the negotiation problem) in aquantitative way (using numbers). Such preferences can be then aggregated todetermine the preferences over the negotiation offers, which are the packages ofselected options. One of the most frequently used methods to support negotiators intheir prenegotiation tasks is the direct rating (DR) method, which assumes thatnegotiators assign the numerical scores to the options that describe their preferencesin a cardinal way (see e.g., Edwards and Barron 1994). It is implemented in suchnegotiation support systems like Inspire (Kersten and Noronha 1999), Negoisst(Schoop et al. 2003; see also the chapter ▶ “A System to Support Complex Elec-tronic Negotiations”) or SmartSettle (Thiessen and Soberg 2003).

Despite DR’s simplicity, some electronic negotiation experiments revealed itsdrawbacks resulting from limited cognitive capabilities of negotiators. The negoti-ators found it challenging to operate with numbers of abstract interpretation (desir-ability scores, utilities, satisfaction levels) and were unable to map the predefinedpreference information about issues’ importance precisely and reliably into the directrating scorings systems (Roszkowska and Wachowicz 2014a, 2015a). Moreover,some in-class experiments on multiple criteria decision making proved that decision-makers were averse to use quantitative evaluations. Having the possibility of choos-ing, they very rarely describe their preferences in a purely quantitative way usingstrong scales (in 16% of situations only). Contrary, they are willing to define themqualitatively in the verbal, linguistic, or pictorial way (57%) (Roszkowska andWachowicz 2014b). Finally, specific technical nuances in the implementation ofthe DR approach in NSS may cause errors resulting from simple heuristics, such asunintentional blindness (Kersten et al. 2017).

Naturally, some other techniques could be used to prenegotiation preferenceelicitation support, which also makes use of the preference aggregation paradigm.The Analytic Hierarchy Process (AHP), for instance, releases the decision-makersfrom operating with numbers while declaring preferences (Satty 1980). It is based onthe series of pair-wise comparisons of the elements of negotiation problem using anine-point verbal scale. AHP relies on the supposition that humans are more capableof making relative judgments rather than the absolute ones. An example of usingAHP for multi-actor decision-making may be found in the chapter ▶ “Group Deci-sion Support Using the Analytic Hierarchy Process”. However, the main drawbackof AHP is that it may be used mainly in the cases of negotiation problems with alimited number of issues and possible options, and spanning the preference

Holistic Preferences and Prenegotiation Preparation 3

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elicitation results made on some salient examples of options on the whole continuousscale may be troublesome (Brzostowski et al. 2012).

In a series of papers (Wachowicz et al. 2012; Wachowicz and Błaszczyk 2013;Roszkowska and Wachowicz 2015b), TOPSIS method was also considered in theperspective of evaluation negotiation offers. In TOPSIS (Technique for OrderingPreferences by Similarity to Ideal Solution), options rates are replaced with thedistances measured to the reference aspiration and reservation packages, whichreduces the negotiator’s workload significantly. It allows additionally, to handlethe problem of evaluating new packages that can appear in the negotiations, thatfall behind the negotiation space defined in prenegotiation by the parties. However,using distance measures instead of subjective preferences requires making strongassumptions regarding the individual preferences of negotiators, so the risk occursthat the offers’ evaluations may not be precise.

The theory of multiple criteria decision aiding offers an alternative supportapproach that is based on preference disaggregation-aggregation paradigm(Jacquet-Lagreze and Siskos 2001; Matsatsinis and Grigoroudis 2018). In thisapproach, it is assumed that the DM’s preferences may be inferred from the prefer-ential information provided by them at the aggregated level (holistically). In thenegotiation context, this requires preferences to be declared for some examples ofnegotiation offers (full packages), which are then decomposed by some analyticprocedures to determine a system of scores for all atomic elements of the negotiationproblem (e.g., the system of value functions). Hence, the evaluation of any negoti-ation offer, not only those evaluated by the negotiators, is possible. The holisticapproach is considered to be less cognitively demanding as the decision-makers areasked to compare some alternative solutions that may really occur in the decisionproblem they face, which is more natural than comparing the single abstract optionswithout their broader context (Corrente et al. 2013; Kadziński and Tervonen 2013).

In this chapter, we discuss the issue of using the holistic disaggregation-aggre-gation paradigm in the multi-issue prenegotiation preparation process. We describefirst the prenegotiation preparation phase and formalize its part related to problemstructuring (template design), preference elicitation, and determining the negotiationoffer scoring system (template evaluation). This also includes a brief description ofpreference elicitation by means of classic aggregation mechanism, that is, the directrating. Then, we consider other options that can be used for template evaluation witha particular focus on holistic methods. We describe two selected methods based onpreference disaggregation-aggregation approach in detail, that is, the most knownUTA (UTilities Additives) (Jacquet-Lagreze and Siskos 1982) and MARS (Measur-ing Alternatives near Reference Solutions) (Górecka et al. 2016). Then, we showhow these methods are used in the group decision and negotiation support systems.We describe in detail the eNego system (Wachowicz and Roszkowska 2020), itsconstruction and functionalities it offers to negotiators in prenegotiation preparationby integrating some concepts of UTA andMARS. Finally, we show the experimentalresults that investigate the accuracy of the scoring systems determined by thenegotiators using the hybridized holistic approach (implemented in eNego) withthose determined with the classic aggregation approach, that is, the direct rating


mechanism. They also examine the issues related to the ease of use and cognitivedemand of the holistic approach in prenegotiation. The results are interesting, as theyshow the holistic approach to be not as low cognitively demanding as it is suggestedin the literature. Quite contrary, the preference elicitation process conducted bymeans of a holistic approach may occur to be more time consuming and tiresomethan the one conducted with the use of classic approach, yet this is a trade-off for thehigher scoring system accuracy of the former.

Prenegotiation Preparation – Negotiation Template and ItsEvaluation

The broad definition of prenegotiation conceives it as the period in relations betweenthe parties, in which the negotiation (bargaining) is considered (and often adopted)as a means to achieve their goals (Tomlin 1989). It is often perceived as an initialdiagnostic phase of the negotiation process, the goal of which is to move parties fromconflicting to cooperative perceptions and behaviors and increase this way thechances for success in actual bargaining (Saunders 1985; Zartman 1989), that is,in solving their joint decision-making problem (Thompson 2015). This requirespreparation and gathering information about all important elements of the conflictingsituation, the negotiation problem, the stakeholders involved, their interests, and thesituational context. As prenegotiation shapes the negotiation strategy and approach,it is considered to have a crucial impact on the bargaining process and outcomes(Peterson and Lucas 2001; Peterson and Shepherd 2011). Therefore, it should beperformed with adequate diligence and adequacy.

There are many suggestions regarding how to conduct prenegotiation and orga-nize the preparation activities (Lewicki et al. 2003; Peterson and Shepherd 2010).The most general recommendation divides prenegotiation into three phases (Raiffaet al. 2002). In the first phase, the negotiator is supposed to prepare alone, to build abig picture of the forthcoming negotiations as privately seen from their own per-spective. In phase two, a joint meeting with the counterpart(s) is suggested to beorganized, which allows verifying the private opinion about the problem andcounterparts. In view of new knowledge gained in phase two, during phase three,the negotiator is supposed to think alone again and build their general strategy for theforthcoming negotiation. The issues for consideration in all three phases of theprenegotiation were summarized in the form of a prenegotiation checklist by Simonsand Tripp (2003). The items from their checklist grouped into four categories areshown in Fig. 1.

A quick reading of the checklist allows distinguishing two groups of blocks thatare concentrated on two separate elements of prenegotiation activities: recognizingthe negotiation problem (blocks: negotiator and counterpart) and elaborating pre-scriptions for negotiation behavior (blocks: situation and relationship). The firstgroup is of particular interest from the viewpoint of decision making and decisionsupport in negotiations and consists of two steps. Step 1 is focused on building adetailed structure of the negotiation problem by identifying the possible issues to be


negotiated, defining the feasible resolution levels (options) for these issues, andspecifying the possible alternatives for the negotiation agreement (see also thechapter ▶ “Looking Back on Defining the Right Problem in Group Decision andNegotiation”). To help negotiators in defining the negotiation problem and itsstructure various problem structuring methods may be used, for example, thecognitive mapping (Eden 2004; Mingers and Rosenhead 2004) or the elements ofPrOACT (Problem, Objectives, Alternatives, Consequences, Tradeoffs) approach(Hammond et al. 1998). The examples of the use of the cognitive mapping toadequate problem structuring and evaluation may be found in other chapters ofthis Handbook, for example, ▶ “Group Support Systems: Concepts to Practice”,▶ “Procedural Justice in Group Decision Support”.

Step 2 requires the analysis of such a problem from the viewpoint of thenegotiator’s interests, that is, setting their goals, aspiration and reservation levels,and identifying this way a structure of their preferences that may be used to evaluatethe alternatives (e.g., in the form of the scoring system). This two-step process iscalled by Howard Raiffa a designing and evaluating the negotiation template (Raiffaet al. 2002), and it resembles the process of structuring and analyzing the multiplecriteria decision-making problem of sorting or choice problematic for a singledecision-maker (Figuera et al. 2016). Note that similarly the group decision makingprocesses are structured and evaluated – see the chapter ▶ “A Group DecisionSupport System for Multiple Criteria Decisions: GRUS”.

To define the template formally, negotiators need to identify the set of negotiationissues as well as the sets of feasible resolution levels for these issues. Note, however,that some negotiation issues may be quantitative and have a continuous character,which makes it impossible to define finite and countable sets of their resolutionlevels. In such a situation, it is recommended to focus on the subset of selectedsalient options only, which discretizes the problem and makes it easier to analyze.Therefore, we define a negotiation template in the form of the following m + 1-tuple:

Prenegotiation preparation

Negotiator

� Problem � Negotiation issues� Alternatives� Issue importance

(preferences)� BATNA� Reservation levels

Counterpart

� Additional issuesthey may value

� Issue importance (preferences)

� BATNA� Reservation levels� Your target (when all

the above considered)

Situation

� Deadlines (who is not flexible with regard to time)

� Fairness norms used in argumentation

� Topics to avoid and the responsestrategy for them

Relationship

� The consequences of strategy undertaken (are our negotiation repetitive?)

� Trust issues� Strategies and tactics

your counterpartmay use

� Limits in authority� Agenda

Fig. 1 Prenegotiation checklist. (Source: based on Górecka et al. 2016)




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T ¼ G, Xif gi¼1,...,m

n o: ð1Þ

where: G ¼ {gi}i ¼ 1, . . ., m denotes a set m of issues and Xi ¼ x ji

n oj¼1,...,ni

is a set of

ni salient options for issue gi.The salient options from template T are considered as potential resolution levels

that comprise the negotiation offers. Therefore, without loss of generality, the set offeasible offers A may be defined as a Cartesian product of all m sets of feasibleoptions, that is,

A ¼Y

i¼1,...,m

Xi: ð2Þ

To compare the offers from A, the negotiator’s preferences for various resolutionlevels that comprise the sets Xi in the template T need to be declared. Additionally,some assumptions have to be made regarding how these preferences may beprocessed, that is, the preference model should be set up. Classically, the negotiationanalysis uses an additive preference model (Raiffa 1982), which implicitly assumesthat the preferences are independent among the issues (Keeney and Raiffa 1976).Consequently, the preferences are represented by marginal value functions v0i :Xi ! Vi that allow to represent the quality of an option x j

i in a form of numerical

rating v0i x ji

� �. If v0i functions are unbounded, they represent the differences in issue

importance only implicitly. Sometimes, however, the negotiator may wish to declarethe issue weights explicitly. In such a case, the weights wi � 0 are defined such as�i ¼ 1, . . ., mwi ¼ 1 and the marginal value functions are represented in a scaled form,

v00i x ji

� �� 0; 1½ �.

The global value function V, which can be used to evaluate negotiation offersa � A, is an additive aggregate of marginal value functions:

V að Þ ¼X

i¼1,...,m

Xj¼1,...,ni

z ji að Þ � vi x ji

� �, ð3Þ

where: z ji að Þ are binary switchers indicating if the option x ji comprises offer a (1) or

not (0); and vi ¼ v0i for unbounded preference declarations or vi ¼ wiv00i , if weights

are declared and scaled marginal value functions used.Note, however, that if randomization between options in Xi is possible, z

ji að Þ can

be a real number describing the fraction of option x ji that is used to describe the

performance of offer a.Thus, the additive scoring system can be represented in the simplified form:

S ¼ vi x ji

� �n oi¼1,...,m; j¼1,...,ni

� �: ð4Þ


Using additive scoring systems to score negotiation offers is very convenient andstraightforward. Therefore, even if the conditions for preference additivity andindependence are violated, in some situations, the use of the additive scoring systemmay be reinstated by, for instance, redefining the set of issues (Keeney and Raiffa1991). However, if the preferences have a more complicated structure, for example,are defined by means of pseudo-criteria or outranking relations, the scoring system,as defined in Eq. (3), cannot be determined. Nevertheless, there may still be possibleto use some analytic techniques to generate the global scores for offers, though, notin the form presented in Eq. (4).

Designing and evaluating the template in prenegotiation by each party is cruciallyimportant from the viewpoint of decision making and decision support, both sym-metric and asymmetric, that can be provided to the parties during the bargainingphase (Raiffa 1982). First, each feasible offer that is put on the negotiation table maybe scored and compared to the others (including BATNA) while searching for anacceptable agreement. The trade-offs between issues may be easily captured, helpingto build the most efficient concessions strategy. The dynamics of the negotiationprocess can also be visualized for the parties in the form of negotiation historygraphs depicted in the evaluation spaces of each negotiator individually. They showthe scale and reciprocity of concessions made by the negotiator and their counterpart.If software support is considered, the scoring system may be used to suggest thenegotiator an offer to be submitted as a balanced response to the offers submitted andreceived earlier (Kersten and Lai 2007; see also the chapter ▶ “Automatic Negoti-ation”). The software support system, or a human third party, may also use theevaluated templates of both negotiators to support them symmetrically. The arbitra-tion may be offered to the parties who were unable to negotiate an agreementthemselves, for example, by suggesting some fair solutions externally (Brams2003). For those who achieve the compromise, the postsettlement improvementsof the negotiated agreement may also be suggested to assure the final agreement willbe efficient.

The extensive possibilities for negotiation support that may be offered based onthe scoring systems defined in prenegotiation require the process of designing andevaluating the negotiation template to be conducted diligently to assure an accuraterepresentation of the conflict situation. In the next section, we will consider variouspossibilities that are offered with this regard by the decision theory.

Multiple Criteria Decision Aiding and Two Ways of PreferenceElicitation

Multiple criteria decision analysis refers to the complex problems of choice, ranking,sorting, or description of the alternatives evaluated by means of multiple, usuallyconflicting criteria (Roy 1996; see the chapter ▶ “Multiple Criteria Decision Sup-port”). These problems need to be solved according to the decision maker’s judg-ments or preferences (Figuera et al. 2016). The goal of the choice problem is to selectthe single best alternative or reduce the group of alternatives to a subset of equivalent


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“good” ones. In the ranking problem, alternatives should be ordered from the best toworst. The order may be partial when incomparability is acceptable or complete. Insorting (classification) problems, the goal is to assign the alternatives into orderedand predefined performance groups, called categories. This approach is also used inranking problems with a multitude of alternatives. They are first grouped intocategories, which are then ordered according to specific ranking rules. Finally, thegoal of the description problem is to provide a detail description of alternatives andtheir consequences, to make the problem better understood by the decision-maker.

In general, multiple criteria decision analysis consists of three elementary steps:determining the structure of the decision problem, expressing and modeling deci-sion-maker preferences, and finally synthesizing the DM’s preferences and devel-oping recommendations regarding the alternatives.

On the first step of multiple criteria decision analysis, given a set of alternatives A¼{a1, . . ., an} and the set of criteria G ¼ {g1,. . ., gm} the performance values (options)aji are determined. They constitute a so-called decision matrix [aji]j ¼ 1, . . ., n; i ¼ 1, . . ., m.It is equivalent to what was described in the negotiation context as the process ofdesigning the negotiation template T with the associated set of feasible negotiationoffers A (see section “Prenegotiation Preparation – Negotiation Template and ItsEvaluation,” formulas 1 and 2).

Step 2 requires preference declarations and modeling. Three main models areusually used in this step (Greco et al. 2001; Słowiński et al. 2002): functional,relational, and rules-based.

The functional model is based on Multi-attribute Value Theory (MAVT) or Multi-attribute Utility Theory (MAUT), the latter when the uncertainties are considered.Various MAVT-based methods synthesize the preferences information in a globalvalue function (Keeney and Raiffa 1976), as described in section “PrenegotiationPreparation – Negotiation Template and Its Evaluation.” Such an approach has twomain advantages. Firstly, by assigning the global score to alternatives, the completeorder of alternatives (ranking) is obtained, which allows comparing alternatives fromthe set A univocally. Secondly, the MCDA methods based on MAVT are fullycompensatory. It means that a good score on one criterion can compensate for abad score on another one. From the viewpoint of building the negotiation offerscoring system, those properties are valuable and desirable (see section “Pre-negotiation Preparation – Negotiation Template and Its Evaluation”).

The most popular methods based on MAVT are SMART (The Simple Multi-Attribute Rating Technique) (Edwards and Barron 1994), SAW (Simple AdditiveWeighting) (Churchman and Ackoff 1954), TOPSIS (Technique for Order of Pref-erence by Similarity to Ideal Solution) (Hwang and Yoon 1981), MACBETH(Measuring Attractiveness by a Categorical Based Evaluation Technique) (Bana eCosta and Vansnick 1999), AHP (Saaty 2008), UTA (Siskos et al. 2005a).

The relational model has a representation in the form of an outranking relation(Roy 1996). The outranking methods are based on comparisons between pairs ofalternatives and verification whether one alternative is at least as good as the other.The global outranking relations synthesize the single-criterion preference relationsbetween each pair of alternatives. It allows considering one alternative to outperform


another but may leave some alternatives incomparable. The incomparability impliesthat a complete ranking is not always possible, and such ranking is referred to aspartial. Contrary to MAVT-based methods, the outranking ones are non-compensa-tory, that is, a good score on one on one criterion cannot compensate for a bad scoreon another one. The properties of outranking methods, as mentioned above, makethe application of them to the evaluation of negotiation template T limited, as notrade-offs and concessions among offers could be determined.

The most known of outranking methods are ELECTRE (Elimination and ChoiceExpressing Reality) (Roy and Bouyssou 1993; Figuera et al. 2016) and PRO-METHEE (Preference Ranking Organization Method for Enrichment Evaluations)(Brans 1982; Figuera et al. 2016). Various variants of their algorithms allow them tobe used both for choice (i.e., ELECTRE I, PROMETHEE I), ranking (i.e.,ELECTRE III, IV, PROMETHEE II with veto), and classification (i.e., ELECTRETRI, PROMETHEE TRI, and CLUSTER) problems.

The rule-based framework of DM’s preferences is a new approach to MCDA,being an alternative for MAVT and outranking approaches (Greco et al. 2001;Figuera et al. 2016). Decision-maker preference information is represented interms of exemplary decisions. Next, the decision rule preference model is builtimplementing preference information that is processed by the rough set mechanisms,which result in the series of “if. . ., then. . .” rules. This approach can be applied toany type of decision problem, that is, choice, sorting, or ranking.

Apart from preference model selection, the issue of preference informationprovided by DM is also of vital importance in step 2 of multiple criteria decisionanalysis. There are two paradigms used for processing preference information, thatis, the aggregation and disaggregation ones (Figuera et al. 2016).

The preference aggregation paradigm assumes that parameters of the preferenceaggregation model are known a priori, while the global preferences are not. There-fore, it requires of DM the direct and explicit declarations of all these parameters,such as the issue weights, options’ scores, preference, or indifference thresholds. Letus note that the preference aggregation approach assumes that the DMs are cogni-tively skilled and know the principles of preference elicitation and decision aidingquite well, and hence understand the true meaning of all the model parameters andthe consequences of their declaration on the performance of decision model. Themost popular methods based on direct preference information are DR, AHP,TOPSIS, or PROMETHEE methods.

The preference disaggregation paradigm, on the other hand, uses indirect orholistic preference information. It assumes that the parameters of the preferencemodel (unknown a priori) may be determined out of global preferences declared bythe DM for some examples of reference alternatives (AR) (Siskos et al. 2005b; Grecoet al. 2010; Matsatsinis et al. 2018). The indirect preference elicitation is consideredby some researchers (Corrente et al. 2013; Kadziński and Tervonen 2013) to becognitively easier and less demanding. However, it must be noted that in thedisaggregation approach, the problems with univocal identification of parametersof the preference model may occur. Usually, many various sets of such parametersexist that reproduce the holistic preferences correctly, which makes the problem of


choosing the most representative (best) one (Figueira et al. 2009; Greco et al. 2011).The examples of the methods based on the indirect preference elicitations are UTA,MARS, or GRIP methods (Figueira et al. 2009).

In step 3, the preference information is aggregated according to the preferencemodel. The MCDA methods differ in the mathematical algorithms utilized toaggregated preferences. There are even several guidelines that can be useful forchoosing the most appropriate method for the multiple criteria decision-makingproblem (Gershon 1981; Guitouni and Martel 1998; Saaty and Ergu 2015). Theirselection and use may also depend on the decision-makers’ cognitive abilities.

Figure 2 summarizes the process of the decision analysis of multiple criteriadecision problems.

Designing and evaluating the negotiation template by the negotiator is, in prin-ciple, equivalent to structuring and analyzing the multiple criteria decision-makingproblem in which the ranking is built based on cardinal scores. Therefore, the aboveschema may be simply considered as the prenegotiation preparation algorithm todetermine the negotiation offer scoring system.

One may note that apart from the classic approach recommended by the negoti-ation analysis, there is an alternative path that can be used in prenegotiationpreparation, which implements the indirect preference declarations by the negotiator.Therefore, in the following sections, we will describe the principles of this approachin detail as well as selected algorithms for indirect negotiation template evaluationand will show how it may be used to support the negotiators when implemented inthe negotiation support system.

Synthetizing theDM’s preferencesand developing recommendations

Expressing and modeling decision maker preferences

Determining the structure of the decision problem Decision problem

Direct preference elicitation

Indirect preference elicitation

Preference elicitation

Aggregation model(e.g. DR, AHP, TOPSIS)

Disaggregation (holis-tic) model (e.g. UTA, MARS, GRIP)

Decision recommendation

Fig. 2 Multiple criteria decision analysis process


Formal Methods for Indirect Evaluation of Negotiation Template

There are many methods based on holistic indirect preference declarations that canbe suggested for supporting the process of negotiation template evaluation(Matsatsinis and Grigoroudis 2018). Below, we present two of them, namelyUTASTAR (Siskos and Yannacopoulos 1985) and MARS (Górecka et al. 2016).The first one allows outlining the nuances of the general mechanism of the process ofscoring system construction and the potential problems one may encounter whileusing a preference disaggregation approach. MARS implements different disaggre-gation mechanisms but also shows how the major problem with defining the decisionexamples may be solved.

UTASTAR

Out of many methods that use the holistic preference declarations, UTA seems to bemost well-known and popular. This method was proposed by Jacquet-Lagreze andSiskos (1982) to aim at inferring one or more additive value functions from a givenranking on the reference set of alternatives. The early concept of UTA has beenimproved, extended, and adapted to many different decision-making situationsresulting in a formation of the whole family of UTA methods. Below we describethe algorithm of the UTASTARmethod (Siskos et al. 2005b; Matsatsinis et al. 2018),which conveys the basic principles of the UTA approach most comprehensively andstraightforwardly and is used later by us to build the hybrid holistic approach,implemented in eNego system. It consists of the following steps:

Step 1. Determining the problem structure.The problem structure is defined, as previously in section “Prenegotiation Prep-aration – Negotiation Template and Its Evaluation,” by the set of criteria G ¼{g1, . . ., gm}, the set of resolution levels (options) of criteria X ¼ x j

i

n oi¼1,...,n; j¼1,...,ni

and the set of alternatives A ¼ {a1, . . .ap}.Step 2. Defining the set of reference alternatives RS � A.

The set RS consists of those alternatives which make no difficulties in evaluationto DM.

Step 3. Providing the preference information on a set of reference alternatives RS inthe form of complete rank order.

Step 4. Building a set of additive value functions compatible with the DM’spreference declaration (preference disaggregation).The rank order of alternatives from RS declared by the DM is used to formulatethe following linear program (LP):


min zð Þ ¼X ARj j

k¼1σþ akð Þ � σ� akð Þ½ �,

subject to :

Δ ak, akþ1ð Þ � δ, if ak � akþ1:

Δ ak, akþ1ð Þ ¼ 0, if ak � akþ1Xm

i¼1

Xαi�1

j¼1wij ¼ 1,

wij � 0, σþ akð Þ � 0, σ� akð Þ � 0,

ð5Þ

where: σ+(ak)/σ�(ak) – are the overestimation and underestimation errors for the

global rating of offer ak, Δ(ak, ak + 1) is a difference in ratings for offers ak and

ak + 1, and wij ¼ v x jþ1i

� �� v x j

i

� �is a difference in ratings for two subsequent

resolution levels of issue i.

By solving the linear program, the ratings v ji of each option of each issue are

obtained. If an alternative solution occurs, some LP subproblems are defined andsolved to find the set of univocal ratings.

Step 5. Setting up the global value function V based on the results of the preferencedisaggregation process.In the vast majority of situations solving the linear problem (5) may result in twodifferent solutions: one with z ¼ 0, and another, where z > 0. In the former case,the nonempty set of value functions exists that is compatible with the DM’spreferences, and the global value function V is determined as an average of thosethat can be determined from series of LP subproblems that maximizing the valuesof options for subsequent criteria. If z> 0, there is no single set of value functionsthat fit the DM’s preference declarations, hence the latter need to be verified (steps3 and 4 repeated).

Step 6. Ranking alternatives from the set A using the global value function V.

The following example illustrates the application of the UTASTAR procedure forthe evaluation of the negotiation template and scoring the negotiation offers.

Example 1 Let us assume that seller and buyer negotiate the conditions of thepotential business contract, where the following negotiation issues and their resolu-tion levels are discussed:

• Unit price (in €): 70; 60; 50; 40; 30,• Time of payment (in days): 7;14; 21,• Returns conditions: “7% defects and 4% penalty”; “5% defects and 2% penalty”;

“4% defects and no penalty.”

We will build the scoring system for the negotiation template defined above forthe seller party using the UTASTAR method. From the seller’s point of view, thecriterion price is a benefit issue, while the time of payment is a cost issue. For thereturns conditions, the seller provides the following verbal evaluations: VP – “7%


defects and 4% penalty”; AV – “5% defects and 2% penalty” and VG – “4% defectsand no penalty,” where VG – very good, AV – average, VP – very poor. Thiscompletes step 1.

In step 2, the reference set has to be designed. It should be easy for DM toevaluate, but on the other hand, it should be informative enough, that is, all theoptions from the template should be represented in it. We will consider two alterna-tive reference sets in this example RS1 and RS2 (see Table 1). In our example, RS1 issimply a copy of RS2 with two additional offers added (shaded in Table 1).

In step 3, the seller builds the rank order of offers from RS1 and RS2, respectively(Table 1, columns 1–3). Note that building such an order requires the seller to make aseries of comparisons between pairs of offers and answer simple questions “do Iprefer alternative a or b?” No cardinal intensities of the preferences need to bespecified.

In step 4, the LP is built to disaggregate the ordinal preferences provided by theseller. For both reference sets, the LP models resulted in a nonempty set of valuefunctions (z ¼ 0). The global value functions determined out of the ordinal prefer-ences for exemplary offers are shown in Table 1, while the option ratings (theexplicit scoring systems S) – in Tables 2, 3, and 4.

As one can see, the results obtained (the scoring systems) differ for each referenceset. This difference is the effect of changing the informativeness of the reference set,which we mentioned while describing the UTASTAR procedure. Adding two moreoffers that the negotiator is willing (or recommended by the supportive facilitator) toevaluate change the system rather significantly. The weight of the issue of returns,for instance, is changed of almost ten rating points. The rating of the option of

Table 1 Rank order and rating of offers obtained by UTASTAR

Rankorder

RS1 RS2RS1 RS2Rating Rating

1 (70,21,AV) (70,21,AV) 69.7 71.92 (40,7,VG) (40,7,VG) 68.2 41.83 (50,21,VG) (50,21,VG) 66.6 31.24 (40,14,VG) (60,14,VP) 50.6 30.15 (40,7,AV) (30,21,VG) 40.2 28.56 (60,14,VP) (40,7,VP) 39.2 13.37 (30,21,VG) (30,21,AV) 38.2 11.78 (40,7,VP) 30.09 (30,21,AV) 19.1

Table 2 Marginal value function for issue price

Price 30 40 50 60 70

vi for RS1 0.0 18.8 28.4 36.9 50.6

vi for RS2 0.0 2.0 2.7 18.8 60.2


14 days of delivery also changes dramatically, from 2.3 to 11.3. Similarly, the shapeof marginal value functions of options of price differs significantly in both systems.

These differences show the need for careful design of the reference sets in theUTASTAR algorithm, as adding or removing a single offer may profoundly affectthe form of scoring system obtain. It also shows that some mechanisms for verifi-cation of the scoring system’s quality at the disaggregated level may be required tomake sure that such scoring system accurately represents the negotiator’spreferences.

There are also other issues, more technical ones, that should be of interest whenimplementing the UTASTAR method. For instance, the value of the calibrationparameter δ that describes the minimal difference between the ranked alternativesor the αi, describing the number of sections into which the marginal value function(assuming to be partially linear) is divided for each issue. More extensive discussionover these technical problems may be found in Wachowicz and Roszkowska (2020).

MARS

The MARS method is also a multiple criteria preference elicitation approach that canbe used to evaluate the negotiation template. However, it differs from UTASTAR inthe scope of preferential information that can be provided by the negotiator, as wellas predefines for them the reference set of offers for evaluation (Górecka et al. 2016).It consists of five following steps:

Step 1. Determining the problem structure.As in the case of UTASTAR, the problem needs to be defined first for the MARSapproach in the form of the sets G, X, and A.

Step 2. Defining the reference set of alternatives for evaluation YnIRS.The set YnIRS is precisely predefined for the DM and consists of: (1) IdealReference Solution (IRS), which is an alternative comprised of the best optionsfor all the criteria and (2) alternatives near to ideal reference solution (nIRS),which consist of the best options for all the criteria except one. The reference setYnIRS is built based on the recommendations of the ZAPROS procedure

Table 3 Marginal value function for issue time of payment

Time of payment 7 14 21

vi for RS1 11.2 2.3 0.0

vi for RS2 11.3 11.3 0.0

Table 4 Marginal value function for issue returns

Returns VG AV VP

vi for RS1 38.2 19.1 0.0

vi for RS2 28.5 11.7 0.0


(Moshkovich et al. 2016) and consists of alternatives that are easy to compare,since they differ at most in two criteria only and require of the DM to compare asingle tradeoff and choose the one that cost them less.

Step 3. Pairwise comparison of alternatives from YnIRS.In MARS, the MACBETH-like semantic categories are used for alternativescomparisons. The ordinal scale may be applied, with judgments like {“morepreferable,” “less preferable,” “equally preferable”} or verbal scale describingthe strength of preference between the alternatives under consideration {“no,”“very weak” (d1), “weak” (d2), “moderate” (d3), “strong” (d4), “very strong”(d5), “extreme” (d6)}. It is also possible to use additional intermediate levelsdescribing the DM’s hesitation between the major categories in verbal scale, forexample, “between very strong and extremely strong” (d5–d6) (see Bana e Costaet al. 2016), similarly to AHP.

Step 4. Solving the linear program corresponding to the comparisons performedusing the MACBETH algorithm.The obtained scores form the Joint Cardinal Scale (JCS) with the alternativesfrom YnIRS scored on the 0–100 scale.

Step 5. Determining the option ratings out of JCS.The procedure requires to assign the scores to all options that form IRS equal to100 (each). Each non-ideal option receives the score equal to the score of thenIRS alternative, which it comprises.

Step 6. Determining the alternatives’ global values V.The global scores of alternatives can be determined from the following formula:

VðakÞ ¼Xmi¼1

ð100� JCSkiÞ ð6Þ

where JCSki is the rating of the option that comprises an offer ak for ith criterion(determined in step 5).

Let us note that in the MARS procedure, the scores reflect the distances to theIdeal Solution. Thus, the smaller the score, the better the alternative is. The scoresobtained from the MARS algorithm may be easily transformed into the range [0; 1] byapplying one of the normalization formulas.

Example 2 We consider the same negotiation problem, as described in Example 1.The seller is building the negotiation offer scoring system using the MARS algo-rithm. In step 2 the reference set YnIRS was determined for the negotiator. The IdealReference Solution IRS ¼ (70, 7, VG) and the set YnIRS consists of nine offersYnIRS ¼ {a1 ¼ IRS, a2, . . ., a9}, which are presented in Table 5 in column 2.

In step 3, the seller compares the ideal offer IRS and the offers from the setYnIRS using a predefined semantic scale. The results are presented in Table 6.

As described in the algorithm, the comparison of offers is made verbally, in termsof the tradeoffs, for example, comparing a2 ¼ (70, 14, VG) with a6 ¼ (50, 7, VG)requires of the seller answering a question: “do I prefer to concede of 20 for price or


of 7 days for the time of payment, and of how much?” In our example, the sellerconsider the concession of 7 days (a2) to be moderately better (d3) than a concessionof 20 EUR (a6).

In step 4, using the MACBETH algorithm, we obtain the cardinal scores JCS foreach offer (see column 3 in Table 5). These scores are assigned to the optionscomprising the offer (step 5), see columns 4 and 5 in Table 5.

In step 6, the global values of the offers are determined, which shows theconcessions necessary to make when moving from the ideal offer IRS to another.For the best offer V(IRS) ¼ 0, while for the worst one:V(30, 21, VP) ¼ (100 � 0) + (100 � 85.19) + (100 � 37.04) ¼ 177.77. For otheroffers from ak � A, we have: 177.77 V(ak) 300. For instance, the value of offer(50, 14, AV) is calculated based on its options’ ratings from Table 5: V(50, 14, AV)¼(100 � 51.85) + (100 � 92.59) + (100 � 77.78) ¼ 77.78. Consequently, concedingfrom the offer (50, 14, AV) to (30, 21, VP) will cost the seller 100 points, which isquite a significant amount taking into account the rating scale range from 0 to177.77.

Table 5 YnIRS and the ratings of its elements

Number offers Offers JCS Options Option rating

a1 ¼ IRS (70,7,VG) 100.00 70 100.0

7 100.0

VG 100.0

a2 (70,14,VG) 92.59 14 92.59

a3 70,21,VG) 85.19 21 85.19

a4 (70,7,AV) 77.78 AV 77.78

a5 (60,7,VG) 74.07 60 74.07

a6 (50,7,VG) 51.85 50 51.85

a7 (40,7,VG) 40.74 40 40.74

a8 (70,7,VP) 37.04 VP 37.04

a9 (30,7,VG) 0.00 30 0.00

Table 6 MACBETH-like comparisons in MARS approach

Offers a1 a2 a3 a4 a5 a6 a7 a8 a9a1 No d2 d2 d3 d3 d3 d4 d4 d5

a2 No d2 d2–d3 d2 d3 d4 d4 d5

a3 No d2 d2 d3 d3 d3 d5

a4 No d1 d3 d3 d3 d5

a5 No d3 d3–d4 d3 d5

a6 No d2–d3 d1–d2 d4

a7 No d1 d3

a8 No d3

a9 No


Comparing both methods based on the holistic approach, that is, UTASTAR andMARS, we can conclude that they do not enforce the negotiators to operate withabstract and interpretatively unclear scores that should be assigned directly to theelements of the negotiation template. However, MARS allows negotiators to declaremore detailed information about their preferences than UTASTAR, as the 12-pointverbal scale may be used to declare the differences in offers quality. This may allowobtaining more precise scoring systems than when rank order is only defined (as inUTASTAR). MARS advantage is also a predefined reference set, which releases thenegotiator from defining it themselves. Additionally, the alternatives in reference setare constructed to cover all the options from the template. It assures that the optionratings will be determined by the method directly, and not from the linear interpo-lation of neighboring options (or by copying the score of neighboring option, as itoften happens in UTASTAR). However, there is one drawback of such an approach.The number of reference offers in big negotiation problems (consisting of manyissues and feasible options) may be too big for the DM to evaluate them reliably,without any cognitive errors and inconsistencies.

An interesting solution might be fusing some elements of these methods and,additionally, to hybridize them with some other concepts of preference declarationsfor tuning the results and obtaining more precise scoring systems. However, it seemsclear that for the reasons related to the technical complexity of the algorithm,practical use of such approaches is only possible, if the prenegotiation protocolsthat use holistic (or hybrid holistic) approaches are implemented in software supportsystems.

Software Support of Prenegotiation Preference Elicitation

Defining the template and sketching out the general preferences for its elements maynot seem a challenging task for the negotiators. However, from the technicalviewpoint, determining the scoring system may require some analytics and compu-tations that are related to the specificity of the preference model, especially when thepreferences are declared holistically, and the parameters of preference models needto be inferred. Therefore, starting from the late 1980s, the software negotiationsupport systems are designed to support this part of prenegotiation preparation(Kersten and Lai 2007). These systems, mostly used in training and teachingnegotiations, implement the prenegotiation protocols that are adequately tailored tothe preference models P and the types of preference information S. There are manyexamples of negotiation support systems that use either direct or holistic preferencedeclarations.

For instance, systems such as SmartSettle (Thiessen and Soberg 2003) orNegoCalc (Wachowicz 2008) are based on the models of additive preferences andimplement the even swaps method (Hammond et al. 1998) to elicit the structure ofnegotiators’ preferences. The users define their preferences directly for thedecomposed elements of the negotiation template. The protocols are based on theseries of comparisons of tradeoffs between subsequent issues and derive this way the


cardinal scores v ji for all salient options x j

i under consideration. Web-Hipre(Mustajoki and Hamalainen 2000), which offers a group decision support facilities,also elicits the parties preferences using a disaggregated template and combinesMAVT and AHP.

The Mediator system (Jarke et al. 1987) is an example of the group decisionsupport tool that implements the notion of evolutionary system design. There is anindividual single-user support module implemented in the Mediator system that isused in prenegotiation to elicit the parties’ preferences. This module operates with amixed aggregation-disaggregation approach embedded in MAVT philosophyaccording to the algorithm proposed earlier in the PREFCALC system (Jacquet-Lagrèze 1990). Within the aggregation step, the negotiators define the issue weightsexplicitly (wi) and then the system automatically generates the option values using apredefined quadratic scoring function. In disaggregation step, a subset of offers ischosen and ranked by the negotiator, for which the UTA algorithm is implemented toderive the option and issue values. Then, the compromise preference model is set upby the negotiator, who may manually tune both weights and option scores, as well asgo back to step 2 and reset the rank order to compute new modified UTA-basedscores (Jacquet-Lagreze and Shakun 1984).

Inspire (Kersten and Noronha 1999), one of the first and the most well-knownweb-based negotiation support systems, operates with a preference elicitation pro-tocol designed in the opposite way to the Mediator’s one. Its prenegotiation protocolis based on the hybrid conjoint measurement (Angur et al. 1996). This is a two-stepprocedure that starts with a direct declaration of preference for issues (wi) andoptions (v j

i ) by means of SMART-like scores assignments (Edwards and Barron1994). Then, in step two, the list of exemplary offers AR � A is presented tonegotiator together with their global ratings v(AR) resulting from v j

i values declaredby the negotiator. If negotiator does not agree with such aggregated values, they maybe changed to reflect the global scores of offers more accurately ev ARð Þ. Using thesenew scores Inspire performs the preferences disaggregation process that uses theprincipals of conjoint analysis. This way, the new adjusted option and issue ratingsare determined (ev j

i ) and used for further bargaining and postsettlement support. Asimilar solution is implemented in the Negoisst system (Schoop 2010).

NegoManage (Brzostowski and Wachowicz 2014) also makes use of the holisticapproach in preference declarations. However, it hybridizes it with the idea ofclustering and kernel density estimation (Parzen 1962). The system starts from thenegotiator’s declarations of the indifference surfaces, which represent some selectedclasses of offers of the same quality (categories). They are defined using an advancedlinguistic scale without any numerical declarations. Then, the negotiator is asked togive examples of offers from each surface, but NegoManage supports them byproviding an algorithm for the automated generation of such offers. Finally, tobuild the scoring system, the probability distributions of belonging to the surfaceare determined out of the examples. These distributions, combined with the surface’sscores derived from linguistic declarations, allow evaluating any feasible negotiationoffer from within the template.


As we can see, the holistic judgments are quite often used in the negotiationsupport systems for prenegotiation evaluation of the negotiation template. They are,however, often hybridized with other concepts and notions of preference declara-tions, as if the former requires additional support to be efficient. In the next section,we will describe in detail the recently designed negotiation support system calledeNego and provide some empirical findings on the use and usefulness of the hybridholistic preference elicitation mechanism that is implemented in it.

eNego System and Empirical Findings from Using Hybrid HolisticPrenegotiation Support

The System and Its Organization

eNego is an online negotiation support system used for negotiation teaching andresearch.1 It is designed to support bilateral synchronous or asynchronous multi-issue negotiations and helps negotiators in their prenegotiation and bargainingactivities. eNego is controlled by the administrator, who may easily set up the systemaccording to the experimental or teaching requirements. For every single negotiationor the whole negotiation experiment (series of negotiation instances), the negotiationprotocol needs to be settled that consists of the series of steps and activities that aregoing to be performed by the system’s users during the negotiation process. It allowsto include all the important support elements in the negotiation process as well asschedule some additional tasks, for example, the pre- and postnegotiation question-naires that allow gathering some additional information about the system use and itsevaluation.

eNego operates with a database of negotiation cases with explicitly predefinednegotiation templates, which are assigned to the experiments by the administrator.The system does not offer negotiators any support tools for joint template design andmodifications. Consequently, the prenegotiation preparation in eNego is limited tothe facilitation of the individual evaluation of the negotiation template. Variousmethods and their hybrids may be used in eNego to support the negotiators intemplate evaluation. They are coded as separate modules and included in thenegotiation protocol by the administrator. During the bargaining phase, eNegohelps negotiators in building and exchanging negotiation offers and messages andvisualizes the negotiation history. No postsettlement analysis and support have beenimplemented in the system so far, but the system is module-built, and any designedpostsettlement mechanism may also be included in the eNego negotiation protocolby the administrator when coded.

The system also provides an administrator with certain support mechanisms thatallow them to manage the users and experiments, for example, tools for automatedusers registration for groups, setting up the negotiation dyads within and between

1https://web.ue.katowice.pl/enego/


https://web.ue.katowice.pl/enego/

groups, or messaging with participants. A simple tool for designing new question-naires is also offered. Finally, the data extraction module is designed to help theadministrator to retrieve the specific data regarding the negotiation process andusers.

The general organizational schema of the eNego system is presented in Fig. 3.

The Module for a Hybrid Holistic Approach to PrenegotiationPreference Elicitation

Out of various preference elicitation algorithms implemented in eNego, one uses thenotion of holistic preference declarations. It is an algorithm similar to the one used inthe PREFCAL system for Mediator group decision support and hybridizes disag-gregation and aggregation approaches. For its disaggregation part, the UTASTARalgorithm was used, yet it includes the MARS-based way for determining the set ofreference alternatives. Some additional elements of preference elicitation are alsoimplemented in the prenegotiation protocol to handle the potential nonmonotonouspreferences. The whole algorithm consists of four steps (Wachowicz andRoszkowska 2020):

• Step 1. Calibrating the preference monotonicity for negotiation issuesTo determine the correct scoring system out of holistic declarations, the negoti-ators need to define the monotonicity of their preferences. To avoid false assump-tions, in eNego, the negotiator declares the best and worst options for each issue

Fig. 3 eNego – organizational schema


in the template. If not extreme options are selected as best, the marginal valuefunctions are considered to be unimodal, and the corresponding constraints areadded to UTA model to assure maximal value for the best option with incrementspossible on the slope between worst and best, and decrements – after movingfrom best option to the edge of the feasible range. The interface of declaring worstand best options in eNego for simple four-issue contracting negotiations is shownin Fig. 4.

• Step 2. Rank ordering the predefined MARS-based reference offers (disag-gregation step)Based on the idea of MARS, the set of reference alternatives is built using theprevious definition of the best options for each issue from step 1. The negotiator isasked to rank these alternatives according to the descending order. The compar-ison should not be cognitively too demanding, as the alternatives differ in twooptions of two issues at most. In eNego, the list of alternatives is presented to theuser, which are the active drag-and-drop elements. The user may easily build therank order reflecting their preferences by reorganizing the list using the mouse(Fig. 5). As the disaggregation engine implemented in eNego is based onUTASTAR, no intensities of preferences are required to be declared.

• Step 3. Tuning the ratings of template elements (decomposed level)eNego runs the linear program built according to the modified nonmonotonousUTASTAR model (for details see Wachowicz and Roszkowska 2020).2 When nosingle set of marginal value functions compatible with the preferences declared in

Fig. 4 Prenegotiation protocol in eNego – step 1

2The changes are technical, and were focused on determining a standardized scoring system for thenegotiation template, therefore we do not discuss them in detail in this chapter.


steps 1 and 2 can be found by the optimization algorithm (the goal functionsdescribing the scale of over and under-estimations of the offers is greater thanzero), eNego displays the warning to the user. It suggests the negotiator goes backto the previous steps of preference declarations and adjust them to eliminatepotential inconsistencies, as the rating system determined and displayed to theuser does not preserve the rank order of offers. The negotiator may also do amanual tuning of the scoring system displayed using direct rating approach and

change the option values v ji disaggregated in previous step (Fig. 6).

The process of resetting the rank orders and direct tuning the cardinal values isiterative and may be repeated until the negotiator feels satisfied with the scoringsystems.

• Step 4. Analyzing the global scores of selected alternatives (aggregation step)When the rating system is finally set by the negotiator in the iteratively repeatedsteps 1–3, its results are presented at the aggregated level to show the conse-quences of its use. Some selected offers from the negotiation space A are listedaccompanied by their global scores. The negotiator is asked to compare the offersand verify if their ratings reflect the differences in their quality (strength of thepreferences) adequately (Fig. 7). If they feel unsatisfied with the global rankingand ratings, they may go back to any of the previous steps and modify theirpreference declarations, both at an aggregated or disaggregated level.



The Use of the Evaluated Scoring System in the Bargaining Supportin eNego

As described in section “Prenegotiation Preparation – Negotiation Template and ItsEvaluation,” the scoring systems are used by the parties not only to set up theirpriorities and quantify their goals, which helps the party to understand the negotia-tion better. It may also be utilized by any third party to support the negotiators duringthe bargaining phase. So does the eNego, offering the negotiators the user interfacedivided into four segments (Fig. 8).

The first segment (left-top corner) displays the negotiation process dynamics inthe form of the negotiation history graph. Offers sent by both parties are depictedhere by two separate series on the timeline with the rating values they assure to thesupported negotiator (asymmetric perspective of one party). This allows the nego-tiator to compare the concession strategies as well as to measure their reciprocity.

The top-right segment can be used to select new offers, as responses to the currentnegotiation status. When the target value of the new offer is typed to the text-box leftto the negotiation history graph, or a slider below it is used to change this value,eNego prints the list of offers near to this target value in the top-right box (“Exem-plary offers”).

The negotiator may use one of the eNego suggestions for new offers or composeone themselves using the bottom-left segment. It consists of a list of drop-downboxes that present the list of options for each issue. As negotiator manipulates withthese options, they may observe how the value of the offer changes to themselves



Fig. 8 eNego – UI for the bargaining phase



(“Total” line shows the value of the scoring function for the offer underconsideration).

Finally, the bottom-right section provides the users with a classic text-box, whichcan be used to send the messages to the counterpart. The system tracks the status ofeach negotiation and informs the parties about offers sent by their counterparts. Thestandard notification is sent via email. However, if both negotiators are active in thesystem, the recipient receives a short message in a pop-up window, which preventsthem from sending their own offer (which may be being composed at that time)without reviewing the one that has been just sent by the counterpart.

The history of the negotiation process can always be recalled from the drop-downmenu “Show schedule,” which consists of all the tasks scheduled by the eNegoadministrator as well as some fixed elements of the support process.

The eNego Experiments

The negotiation experiments are organized in the eNego system as the elements ofthe academic courses each semester starting from 2018. Four runs of experimentshave been organized so far, in which the participants were undergraduate, graduate,and postgraduate students from three Polish universities. Apart from didactic pur-poses, there are also the research goals of these experiments. They are focused onstudying the use and usefulness of decision support mechanisms in softwaresupported negotiations. Below, we describe two experiments organized in eNego,in which a hybrid holistic disaggregation-aggregation prenegotiation preferenceelicitation approach was implemented, and the quality of the results obtained (i.e.,the scoring system accuracy) was compared to those determined by means of theclassic approach that utilizes the direct rating method.

In both eNego experiments, the same bilateral multi-issue negotiation problemwas used, which was based on the original Cypress-Itex negotiations onceimplemented in the Inspire system (Kersten and Noronha 1999; Vetschera 2007).There were two parties in eNego negotiation, the bicycle producer and the partssupplier, negotiating a new contract for the delivery of rear-wheel gears. Four issueswere to be negotiated: price, time of delivery, time of payment, and returns. For eachissue, sets of feasible options were defined that made the following negotiationtemplate (Table 7).

The students played the roles of agents negotiating on behalf of principals. Inprenegotiation, the case description and confidential preferential information weredisplayed to each agent. The latter described in detail the goals and priorities of theprincipals. This information was also visualized by means of pie charts. Part of suchinformation for bicycle producer is shown in Fig. 9.

The eNego negotiators were asked to read the case description and then follow theprenegotiation preference elicitation process (as described in subsection “The Mod-ule for a Hybrid Holistic Approach to Prenegotiation Preference Elicitation”). Whenbuilding the scoring systems, they were asked to represent their principal prioritiesmost accurately. After completing their prenegotiation activities, the parties were


moved to the bargaining phase. They were asked to negotiate following the princi-pals’ instructions and settle with a satisfying agreement.

In such a context of representative negotiations, the agency theory identifiesseveral personal incentives that may influence the agents and cause them to act notfollowing their principals’ goals, such as their individual aspirations, objectives, andself-interest (Jensen and Zimmerman 1985; Lee and Thompson 2011). However,these nonessential issues can be reduced or eliminated by increasing the relationalattachment between the principal and the agent, for example, by rewarding theagent’s effort and engagement (Jacobides and Croson 2001). Since the participantsof the eNego experiment were students, who were taking part in the academiccourses on negotiation and decision making, we decided to link the rewardingsystem with their final course performance. For all the students in our study, theexperiment was the only evaluated activity that made the final course grade, and 30%of this grade came from the quality of prenegotiation preference elicitation.

In both eNego experiments (studies 1 and 2), the students were given handoutsand introductory lectures regarding the essence of prenegotiation preparation,

Table 7 Negotiation template for bicycle negotiations in eNego

Issues to negotiate Options

Price (in US$) $3.45; $3.75; $4.05; $4.35; $5.00

Delivery time (indays)

20; 30; 45; 60

Payment (days afterdelivery)

Upon delivery; 14; 30; 60

Returns policy Any defects, no penalty; 3% defects, no penalty; 5% defects, 2%penalty; 7% defects, 4% penalty

Fig. 9 Description of principals’ preferences in eNego negotiations


preference elicitation, and the use of the eNego system. However, in study 2, beforethe experiments, the students participated in series of in-class laboratories, duringwhich the process of determining the scoring system by means of the holisticapproach was additionally explained and practiced using Excel and purposelydesigned add-in. The purpose was to show the students the machinery of thepreference elicitation process and the nuances related to the influence of the prefer-ence declarations on the scoring system quality.

As the goal of our study was to analyze the effects of the prenegotiation support,we focused only on measuring the accuracy and similarity of the scoring systems theeNego users built to the preferential information provided in advance. Thebargaining results were of no interest for this study.

Technically, the reference scoring systems in eNego were build based on thecircle sizes and were considered to represent the principal’s preferences in an ultra-precise way. Then the scoring system of each agent was compared to the referencescoring system of the corresponding principal using the notions of ordinal andcardinal accuracy (for details see Wachowicz et al. 2019; Wachowicz andRoszkowska 2020):

• Ordinal accuracy is an index that measures to what extent the rank order of thetemplate elements in an agent’s scoring system is similar to the rank order in theprincipal’s scoring system. The measure is scaled to the range [0;1], where 1reflects full concordance of the agents scoring systems (all ranks are the same asin principal’s one), while 0 reflects the perfect discordance. Conceptually, themeasure is based on the Kendall rank correlation and matching index.

• Cardinal accuracy measures the discrepancies in the strength of preferencesbetween the agent’s and the principal’s scoring systems. It uses the notion ofManhattan distance. The absolute values of differences in ratings of all options inprincipal’s and agent’s scoring systems are determined and added. Such anaggregate measure depends on the template size. Therefore, it is standardizedusing an average accuracy simulated for randomly defined scoring systems. Asthe results, the agent’s scoring system that perfectly reproduces the agent’s onehas a cardinal accuracy equal to one, while the scoring system similar to averagerandom one – accuracy equal to zero.

Apart from the objective results measured by means of the quality of scoringsystems obtained, we also tried to verify the subjective users’ evaluation of thepreference elicitation process offered in the eNego system. Therefore, we usedpostnegotiation questionnaires in which such an opinion was gathered using bothopen and close questions (the latter, with a predefined five-point Likert scale).

As a reference point to the objective results obtained, we used the data fromsimilar bilateral negotiation experiments conducted earlier in the Inspire system –study 3 (see Roszkowska et al. 2017). In study 3, the contract negotiations wereconducted, the template of which was similar in size and structure of the principal’spreferences. Four issues were analyzed, for two of which the principal’s preferenceswere monotonically increasing, while nonmonotonous unimodal preference


functions represented two others. For study 3, we have selected the participants forwhich the same rewarding system was used, as in eNego studies 1 and 2. Note that inInspire, a hybrid conjoint measurement is used to determine the scoring systems,which consists – conversely to eNego – of aggregation-disaggregation approach.However, analyzing the written reports of study 3 participants, we did not find theinformation about the usage of the second step of the approach. Consequently, weassume that they did not change the global values of offers determined on the basisof the option values provided by them directly, nor they run the conjoint analysis tochange the initial scoring system. Therefore, we will consider the scoring systemsfrom study 3 as determined by means of a classic direct rating approach (fordecomposed negotiation template directly).

We had 69 students in study 1, and 112 in study 2. In study 3, there were 165students selected from the Inspire dataset that fulfilled the requirement describedabove. As a consequence, in all the studies, the students came from one universityand classes taught by one teacher. Hence, we eliminated the potential influence of theorganizational culture and differences in teacher characteristics and styles on theexperimental results (Wachowicz et al. 2018).

Results

The average ordinal and cardinal accuracy indexes for all three studies are presentedin Table 8.

It is clearly seen that the hybrid holistic approach to prenegotiation preferenceelicitation and determining the scoring system accurately reflecting the negotiator’spreferences gives better results than the classic direct rating that operates with adisaggregated template only. The average ordinal accuracy, which simply shows towhat extent the agent’s scoring system reflects the rank order of preferences definedby the principal, is higher in study 1 and 2 than in study 3. These differences aresignificant (the value distributions in study 1 vs. study 3 differ at p ¼ 0.003 and instudy 2 vs. study 3 at p ¼ 0.001 in the Mann-Whitney U test). The same tendencycan be observed for cardinal accuracy, which is the lowest in study 3 and the highestin study 1. Here, however, the differences are not significant ( p > 0.414).

We see then that by using the hybrid holistic approach, the higher accuracy ofscoring systems and – consequently – the more reliable bargaining support ispossible. The hybrid approach protocol, however, is more extensive and requiresmore time to complete. Therefore, we asked the participants in studies 1 and 2, how

Table 8 Average values of ordinal and cardinal accuracy indexes

Experiment

Average accuracy of agent’s scoring system

Ordinal Cardinal

Study 1 (hybrid holistic approach, no training) 0.941 0.724

Study 2 (hybrid holist approach + training) 0.954 0.736

Study 3 (classic direct rating approach) 0.901 0.715


do they evaluate the hybrid holistic approach and how they compare it to the directrating, which they know from handouts and lectures. The results are shown in Table9.

The subjective evaluation of the use of the hybrid holistic approach implementedin eNego is fairly average and quite similar in both studies. The most enthusiasticopinion we observe in both studies for the user interface used to organize preferencedeclarations at the holistic level (step 2 in the holistic disaggregation-aggregationapproach), but it is still only one point higher than average.

The most surprising is the final opinion on using the hybrid holistic approachversus the classic direct rating (as it was in study 3). Here, the users in study 1 areeven slightly more prone to use the classic approach than the alternative holisticapproach. However, when the awareness of this approach increases (study 2), theusers seem to accept its inconveniences more, presumably expecting better and morereliable results it will produce. For this question, the difference in evaluation issignificant (3.49 vs. 4.31, at p ¼ 0.02).

Similarly, significant is the difference in the opinion regarding the workload anddifficulty of this approach (question 1). Although in both studies, the prenegotiationprotocol was the same, the participants in study 2 evaluated it more optimistically asless tiresome and time-consuming, that their colleagues in study 1. Again, the betterknowledge of the method itself seems to ease the strictness of its evaluation.

The differences in evaluations for the remaining answers, though always higher inthe case of study 2, occurred not significant.

Summary

In this chapter, we discussed the issue of prenegotiation preparation and its facilita-tion by using the decision support mechanisms based on the holistic preferencedeclarations. Such an approach requires the negotiator to express a general opinionregarding the quality (value, desirability) of some selected negotiation offers, by

Table 9 The average answers for the postnegotiation evaluation questionnaire in eNegoexperiments

Question

Average answer

Study1

Study2

The whole preference elicitation process in eNego was cumbersome andtime-consuming

4.28 5.04

It was difficult for me to build a ranking using the predefined alternatives 4.72 4.77

If I had an option, I would use a different set of alternatives to compare 4.19 4.49

The drag-and-drop interface for boxes with offers examples was unintuitiveand inefficient

5.23 5.31

I would prefer to assign the issue and option ratings myself just from thevery beginning, without any preceding holistic declarations

3.49 4.31

Seven-point Likert scale used in the questionnaire: 1 – totally agree,. . ., 7 – totally disagree


comparing the complete packages (future contracts). No precise declaration ofpreferences on the disaggregated level, that is, for atomic template elements, isrequired. Holistic comparisons, however, seem to be a typical situation the negoti-ators face during the negotiation process. They need to compare the offers submittedin subsequent negotiation rounds to the negotiation table, evaluate the trade-offs theyrequire and consider, which of them is better. They also compare the offers withBATNAs and the aspiration and reservation packages, to check if their goals are met.Therefore, using the holistic comparisons in the phase of the prenegotiation prepa-ration seems to be a natural prequel to what is going to happen during futurebargaining. Thus, understanding the general philosophy and mechanisms related toholistic preference declarations seems to be crucial from the viewpoint of theirproper use in the process of determining the reliable and accurate scoring systemsfor the negotiators.

We have started the discussion with the description of the prenegotiation prepa-ration phase and its stages related to negotiation template definition and evaluation toshow how important they are for the future negotiation support, understanding theparties’ approach, measuring the concessions, and their reciprocity and searching forthe balanced and fair solutions. We outlined the classic negotiation support approachthat is based on direct rating and preference aggregation paradigm and potentialproblems with its use related to the cognitive limitations of the decision-makers.Therefore, we sketched out the broader perspective on how the prenegotiationpreparation may be organized alternatively. We focused mostly on the approachbased on preference disaggregation mechanism, which releases the negotiators fromcognitively challenging declarations through numbers or quantitative comparisons,often considered as unintuitive.

Out of many methods based on the holistic preference declarations, we showedtwo: UTASTAR and MARS. The former one is one of the first methods designed toimplement the preference disaggregation approach and uses the most straightforwardpreference statements in a form or rank order. However, its results heavily depend onmany technical issues, such as the size of the reference set and the mixes of thealternatives it is comprised of, or the quantitative tunning parameters describing thenumber of breakpoints of marginal value function or differences in values among thereference alternatives. Another problem is the multitude of compatible value func-tions the UTASTAR identifies based on the holistic declarations of DM, for whichdetermining the mean values may result in losing some important information thatwas not explicitly and precisely declared by the decision-maker due to operatingwith rank orders only.

For this reason, we have presented another approach, that is, MARS. First, itsalgorithm operates with a predefined set of reference alternatives to be evaluated bythe negotiators. The construction of such a set is precisely defined, and based on thedeclaration of best resolution levels for each negotiation issue. Second, the mecha-nism for offers’ comparisons, based on notions of MACBETH, allows defining thestrengths of preferences, not only their order. Therefore, more reach preferentialinformation is provided, and more reliable scoring systems may be determined out of it.


The advantages of both these methods motivated us to design the negotiationsupport system called eNego, in which a hybrid prenegotiation preference protocolwas implemented. This protocol derives not only form the notions of UTASTAR andMARS but also introduces some debiasing mechanisms to reduce the cognitiveeffort and help negotiators to obtain accurate and reliable scoring systems. Wepresented the general design of this system, as well as the functionalities it offersduring the prenegotiation phase.

Finally, we investigated whether such a hybrid holistic approach performs betterthan the classic one that uses the notion of direct rating. In two experiments, wecompared the performance of eNego negotiators with an earlier study conducted inthe Inspire system, where direct rating was used. It occurs that using hybriddisaggregation-aggregation approach results in the scoring systems better fit to thenegotiators’ preferences both on the ordinal and cardinal level. The results may notbe spectacular with respect to cardinal errors but differ significantly for ordinalaccuracy. However, the subjective evaluation of the hybrid holistic approach bythe eNego system users is not particularly enthusiastic. It is better for the users towhom the holistic idea was explained in detail, and the training of its use was offeredbefore eNego negotiation. However, generally, this still shows that the holisticapproach may not be so cognitively easy, as some researchers suggest it. Someadditional effort and time may be required to be spent on prenegotiation preparationto make such a holistic approach effective.

Cross-References

▶A Group Decision Support System for Multiple Criteria Decisions: GRUS▶A System to Support Complex Electronic Negotiations▶Automatic Negotiation▶Group Decision Support Using the Analytic Hierarchy Process▶Group Support Systems: Concepts to Practice▶ Impact of Cognitive Style on Group Decision and Negotiation▶Looking Back on Defining the Right Problem in Group Decision and Negotiation▶Multiple Criteria Decision Support▶Negotiation Process Modelling: From Soft and Tacit to Deliberate▶ Procedural Justice in Group Decision Support

Acknowledgments We thank Prof. Gregory Kersten from Concordia University for his inspiringcontribution and support in earlier research related to measuring the accuracy of negotiation offerscoring systems in the principal-agent negotiations. The research presented in this chapter waspartly supported by the grant from the Polish National Science Center (2016/21/B/HS4/01583).






http://link.springer.com/search?facet-eisbn=978-3-030-12051-1&facet-content-type=ReferenceWorkEntry&query=Group Support Systems: Concepts to Practice





http://link.springer.com/search?facet-eisbn=978-3-030-12051-1&facet-content-type=ReferenceWorkEntry&query=Procedural Justice in Group Decision Support

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