Holistic Preferences and Prenegotiation Preparation

Wachowicz, Tomasz; Roszkowska, Ewa

doi:10.1007/978-3-030-12051-1_64-1

Cited by 4 publications

(6 citation statements)

References 55 publications

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“…The negotiators may use the individual scoring system to analyze the profitability of concessions, track the negotiation progress by analyzing the concession paths, visualize the negotiators' moves on the negotiation history graphs, etc. (see [14][15][16]). However, from the viewpoint of comprehensive negotiation support, the scoring systems of all the parties are recommended to be used jointly by the external helpers to provide the negotiators with suggestions regarding fair, balanced, and reciprocated agreements.…”

Section: Mutual Evaluation Of the Negotiation Spacementioning

confidence: 99%

“…It also offers some additional organizational support to declare the issue weights correctly by using the swing weights. SMARTS is also frequently implemented into the prenegotiation protocols of the negotiation support systems (see Inspire [14], ACNF [26], or eNego [15]). The direct rating approach seems quite straightforward; however, it implicitly assumes that parties can declare their preferences quantitatively and precisely without any problems or disturbances.…”

Section: Classic Multiple Criteria Decision Aiding Approachesmentioning

confidence: 99%

“…The former is considered to be easier and more natural in use for Decision-Makers (DMs) than classic approaches that use the direct rating mechanism or conjoint approach, which requires a precise declaration of rating scores [36,37]. Hence, the UTA-based approach was proposed to support decision analysis in prenegotiation early in the MEDIATOR system and recently in eNego [15,21]. Similar holistic solutions were also proposed that use some hybridized approaches to enhance the scoring system accuracy or reduce the cognitive requirements.…”

Section: Classic Multiple Criteria Decision Aiding Approachesmentioning

confidence: 99%

“…To verify the theoretical approach proposed in Section 5.2, the numerical example, based on the negotiation case found in the eNego system [15], will be presented. In Step 1 of the algorithm the negotiation template is defined.…”

Section: Numerical Examplementioning

confidence: 99%

“…In Step 2, the negotiators N1 and N2 determine the fuzzy weights of the importance of issues using the 9-point scale linguistic evaluation from Table 1. Tables 6 and 7 present the fuzzy weights as well as the fuzzy normalized weights for both negotiators using Formula (15). Table 6.…”

Section: Numerical Examplementioning

confidence: 99%

See 4 more Smart Citations

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Filipowicz-Chomko

Mierzwiak

Nowak

et al. 2021

Entropy

Self Cite

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Negotiation scoring systems are fundamental tools used in negotiation support to facilitate parties searching for negotiation agreement and analyzing its efficiency and fairness. Such a scoring system is obtained in prenegotiation by implementing selected multiple criteria decision-aiding methods to elicit the negotiator’s preferences precisely and ensure that the support is reliable. However, the methods classically used in the preference elicitation require much cognitive effort from the negotiators, and hence, do not prevent them from using heuristics and making simple errors that result in inaccurate scoring systems. This paper aims to develop an alternative tool that allows scoring the negotiation offers by implementing a sorting approach and the reference set of limiting profiles defined individually by the negotiators in the form of complete packages. These limiting profiles are evaluated holistically and verbally by the negotiator. Then the fuzzy decision model is built that uses the notion of increasing the preference granularity by introducing a series of limiting sub-profiles for corresponding sub-categories of offers. This process is performed automatically by the support algorithm and does not require any additional preferential information from the negotiator. A new method of generating reference fuzzy scores to allow a detailed assignment of any negotiation offer from feasible negotiation space to clusters and sub-clusters is proposed. Finally, the efficient frontier and Nash’s fair division are used to identify the recommended packages for negotiation in the bargaining phase. This new approach allows negotiators to obtain economically efficient, fair, balanced, and reciprocated agreements while minimizing information needs and effort.

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Section: Mutual Evaluation Of the Negotiation Spacementioning

confidence: 99%

Section: Classic Multiple Criteria Decision Aiding Approachesmentioning

confidence: 99%

Section: Classic Multiple Criteria Decision Aiding Approachesmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

See 3 more Smart Citations

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Filipowicz-Chomko

Mierzwiak

Nowak

et al. 2021

Entropy

Self Cite

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Identifying decision-making style: Do REI-20 and GDMS measure the same?

Wachowicz,

Roszkowska,

Filipowicz-Chomko

2023

Decision

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Conflict resolution in the era of cognitive multicriteria decision‐making: an AHP‐retributive approach

Vargas

Moreno-Loscertales

Moreno-Jiménez

2021

Int Trans Operational Res

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This paper puts forth the foundation for a different type of negotiation that reflects in a more realistic way the behavior of human beings when making complex decisions, aligned with the cognitive process involved. The positive and the negative aspects of decisions reflect two components in the human brain: the nucleus accumbens and the amygdala. The first component deals with behavior related to rewards or potential gains, and the other with behavior related to risk aversion or potential losses. Negotiations require communication, learning, accommodation of positions, and development of alternatives and modification of constraints. Negotiation support systems help and advice negotiators; structure and analyze the problem; elicit preferences to construct a preference function; visualize different aspects of the problem and the process; and facilitate communication and learning. The methodology used is based on the Analytic Hierarchy Process previously employed in a retributive conflict in which each party calculates the incremental benefits it gets and the costs to its opponent. The Israeli–Palestinian conflict is used to show the viability of the approach and the type of inputs we need to study conflicts with this approach. The negotiation platform that we describe here help the actors to start and engage a negotiation from noncooperative parties. It can also be used in human– negotiations to expedite reaching an agreement if one exists. However, we are not seeking automated negotiations. Advantages and risks of Artificial Intelligence contribution in negotiation support systems for cognitive and retributive conflict resolution based on AHP (CRCR‐AHP) are also discussed.

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Holistic Preferences and Prenegotiation Preparation

Cited by 4 publications

References 55 publications

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Identifying decision-making style: Do REI-20 and GDMS measure the same?

Conflict resolution in the era of cognitive multicriteria decision‐making: an AHP‐retributive approach

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