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

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 752
Author(s):  
Marzena Filipowicz-Chomko ◽  
Rafał Mierzwiak ◽  
Marcin Nowak ◽  
Ewa Roszkowska ◽  
Tomasz Wachowicz
Keyword(s):  
Information Needs ◽  
Efficient Frontier ◽  
Fair Division ◽  
Scoring Systems ◽  
Cognitive Effort ◽  
Fuzzy Decision ◽  
New Approach ◽  
Decision Aiding ◽  
Clustering Model ◽  
Fuzzy Decision Model

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.

scholarly journals A General Framework for Portfolio Theory. Part III: Multi-Period Markets and Modular Approach

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 60
Author(s):  
Stanislaus Maier-Paape ◽  
Andreas Platen ◽  
Qiji Jim Zhu
Keyword(s):  
General Framework ◽  
Optimization Problem ◽  
Risk Measure ◽  
Efficient Frontier ◽  
Portfolio Theory ◽  
Trading Strategies ◽  
Performance Criteria ◽  
Market Model ◽  
Constant Weight ◽  
New Approach

This is Part III of a series of papers which focus on a general framework for portfolio theory. Here, we extend a general framework for portfolio theory in a one-period financial market as introduced in Part I [Maier-Paape and Zhu, Risks 2018, 6(2), 53] to multi-period markets. This extension is reasonable for applications. More importantly, we take a new approach, the “modular portfolio theory”, which is built from the interaction among four related modules: (a) multi period market model; (b) trading strategies; (c) risk and utility functions (performance criteria); and (d) the optimization problem (efficient frontier and efficient portfolio). An important concept that allows dealing with the more general framework discussed here is a trading strategy generating function. This concept limits the discussion to a special class of manageable trading strategies, which is still wide enough to cover many frequently used trading strategies, for instance “constant weight” (fixed fraction). As application, we discuss the utility function of compounded return and the risk measure of relative log drawdowns.

scholarly journals Leveraging Domain Context for Question Answering Over Knowledge Graph

2019 ◽  
Vol 4 (4) ◽  
pp. 323-335 ◽  
Author(s):  
Peihao Tong ◽  
Qifan Zhang ◽  
Junjie Yao
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Knowledge Graph ◽  
New Approach ◽  
Rich Domain ◽  
Complex Information ◽  
Question And Answer ◽  
Knowledge Graphs ◽  
The Rich ◽  
Matching Models

Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

A fuzzy decision model for the design of rural natural gas networks

1993 ◽  
Vol 53 (3) ◽  
pp. 241-252 ◽  
Author(s):  
J. Davidson ◽  
W. Pedrycz ◽  
I. Goulter
Keyword(s):  
Natural Gas ◽  
Decision Model ◽  
Fuzzy Decision ◽  
Gas Networks ◽  
Fuzzy Decision Model

scholarly journals Bi-Objective Bilevel Programming Problem: A Fuzzy Approach

2015 ◽  
Vol 11 (2) ◽  
pp. 97-115 ◽  
Author(s):  
S. Haseen ◽  
A. Bari
Keyword(s):  
Programming Problem ◽  
Bilevel Programming ◽  
Decision Model ◽  
Stackelberg Game ◽  
Fuzzy Approach ◽  
Fuzzy Decision ◽  
Bilevel Programming Problem ◽  
Decision Variables ◽  
Fuzzy Decision Model ◽  
Fuzzy Cost

Abstract In this paper, a likely situation of a set of decision maker’s with bi-objectives in case of fuzzy multi-choice goal programming is considered. The problem is then carefully formulated as a bi-objective bilevel programming problem (BOBPP) with multiple fuzzy aspiration goals, fuzzy cost coefficients and fuzzy decision variables. Using Ranking method the fuzzy bi-objective bilevel programming problem (FBOBPP) is converted into a crisp model. The transformed problem is further solved by adopting a two level Stackelberg game theory and fuzzy decision model of Sakawa. A numerical with hypothetical values is also used to illustrate the problem.

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