A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’

Digital watermarking evaluation and benchmarking are challenging tasks because of multiple evaluation and conflicting criteria. A few approaches have been presented to implement digital watermarking evaluation and benchmarking frameworks. However, these approaches still possess a number of limitations, such as fixing several attributes on the account of other attributes. Well‐known benchmarking approaches are limited to robust watermarking. Therefore, this paper presents a new methodology for digital watermarking evaluation and benchmarking based on large‐scale data by using external evaluators and a group decision making context. Two experiments are performed. In the first experiment, a noise gate‐based digital watermarking approach is developed, and the scheme for the noise gate digital watermarking approach is enhanced. Sixty audio samples from different audio styles are tested with two algorithms. A total of 120 samples were evaluated according to three different metrics, namely, quality, payload, and complexity, to generate a set of digital watermarking samples. In the second experiment, the situation in which digital watermarking evaluators have different preferences is discussed. Weight measurement with a decision making solution is required to solve this issue. The analytic hierarchy process is used to measure evaluator preference. In the decision making solution, the technique for order of preference by similarity to the ideal solution with different contexts (e.g., individual and group) is utilized. Therefore, selecting the proper context with different aggregation operators to benchmark the results of experiment 1 (i.e., digital watermarking approaches) is recommended. The findings of this research are as follows: (1) group and individual decision making provide the same result in this case study. However, in the case of selection where the priority weights are generated from the evaluators, group decision making is the recommended solution to solve the trade‐off reflected in the benchmarking process for digital watermarking approaches. (2) Internal and external aggregations show that the enhanced watermarking approach demonstrates better performance than the original watermarking approach. © 2016 The Authors. Software: Practice and Experience published by John Wiley & Sons Ltd.     

Z‐uncertain probabilistic linguistic variables and its application in emergency decision making for treatment of COVID‐19 patients

Probabilistic linguistic term sets (PLTSs) have many applications in the field of group decision making (GDM) because it includes both linguistic evaluation and probabilistic distribution when expressing preference information. However, the difference of information credibility in PLTSs is ignored, resulting in an inaccurate representation of decision information and unreasonable probability calculation. In this paper, we first consider the credibility of the information and propose the concept of Z‐uncertain probabilistic linguistic variables (Z‐UPLVs). Subsequently, the operational rules, normalization, distance and similarity measures, and comparison method of Z‐UPLVs are introduced. Then, a probability calculation method based on credibility, an extended TOPSIS method, and some operators are proposed, which can be applied to emergency decision making in the Z‐uncertain probabilistic linguistic (Z‐UPL) environment. Finally, an emergency decision‐making case of COVID‐19 patients and comparative analysis illustrate the necessity and effectiveness of this method.     

Two controlled experiments on model-based architectural decision making

ContextIn recent years, architectural design decisions are becoming more and more common for documenting software architectures. Rather than describing the structure of software systems, architectural decisions capture the design rationale and – often reusable – architectural knowledge. Many approaches and tools have been proposed in the literature to support architectural decision making and documentation (for instance, based on models, ontologies, or templates). In this context, the capturing, organization, and effective reuse of architectural knowledge has gained a lot of attention.ObjectiveHowever, there is little empirical evidence about the supportive effect of reusable architectural knowledge on the effectiveness and efficiency of architectural decision making.MethodTo investigate these aspects, we conducted two separate controlled experiments with software architecture students in which we tested the supportive effect of reusable decision models in decision making and documentation.ResultsOur results show that the use of reusable decision models can significantly increase both the efficiency and the effectiveness of novice architects.ConclusionWe can report, that our findings are in line with similar studies and support the claims regarding reusable architectural design decisions in principle.     

A ranking function based on principal‐value Pythagorean fuzzy set in multicriteria decision making

Multicriteria decision making (MCDM) has been attracting attention in recent years. There are two essential directions in the research territory, one direction is the research of representation of evaluation information and another is the construction of ranking function. In this paper, we consider some nonstandard fuzzy sets, intuitionistic, and interval‐valued fuzzy sets. Especially, the Pythagorean membership grade and Pythagorean fuzzy set receive much attention. Then, to reflect the importance of principal value, we shall propose the principal‐value Pythagorean fuzzy number (p‐PFN) and principal‐value Pythagorean fuzzy set. Furthermore, a novel ranking function is constructed to select the ideal alternative(s) based on p‐PFNs in MCDM. Finally, an investment strategy decision‐making problem is proposed to reveal the availability and practicability of the function under the new environment.     

EFFICIENT ABSTRACTION SELECTION IN REINFORCEMENT LEARNING

This article addresses reinforcement learning problems based on factored Markov decision processes (MDPs) in which the agent must choose among a set of candidate abstractions, each build up from a different combination of state components. We present and evaluate a new approach that can perform effective abstraction selection that is more resource‐efficient and/or more general than existing approaches. The core of the approach is to make selection of an abstraction part of the learning agent's decision‐making process by augmenting the agent's action space with internal actions that select the abstraction it uses. We prove that under certain conditions this approach results in a derived MDP whose solution yields both the optimal abstraction for the original MDP and the optimal policy under that abstraction. We examine our approach in three domains of increasing complexity: contextual bandit problems, episodic MDPs, and general MDPs with context‐specific structure. © 2013 Wiley Periodicals, Inc.     

Game‐Based Simulation for Cross‐Cultural Decision Making Training

Increasing globalization has had a major impact on manufacturing and service industries as well as on coalition operations conducted by the military. What is common to both the commercial and military sectors is the recent surge in interest in cross‐cultural decision making (CCDM) training. Existing CCDM training approaches tend to employ either some form of multi‐agent simulation or some variant of classical game theory. Despite their manifest benefits, these approaches have specific limitations that need to be overcome to create an effective cross‐cultural training system. Multi‐agent simulations typically lack theoretical underpinnings while classical game theory‐based approaches take a limited view of strategic decision making. Specifically, by adopting a Western view of rationality, game‐theoretic approaches fail to accommodate considerations such as fairness, altruism and reciprocity. Empirical research in strategic economic games has shown that humans respond to more than merely monetary incentives. In particular, research has shown that cultural norms play a central role in human decision making behavior, especially in non‐Western cultures. This paper presents an innovative approach to game‐based simulation that combines findings from behavioral game theory with classical game theory and multi‐agent simulation to exploit the strengths of each approach while making learning enjoyable, memorable, and fun. An illustrative game‐based simulation for CCDM training is also presented. The simulation framework is equally applicable to teaching other soft skills as well as skills that are too hazardous or too expensive to teach in the realworld through live exercises. © 2012 Wiley Periodicals, Inc.     

NEGO — Group decision support system

There are two major frameworks for decision making: maximizing and satisficing. A combination of both may be used to describe group decision making (GDM). In the satisficing approach, decision makers (DMs) formulate aspiriation levels or demands which take the form of constraints. Choosing from among different decisions, DMs take into account their preferences or wants, which take the form of objective functions.GDM is divided into two stages: first, each DM makes a decision, and second, DMs negotiate so as to achieve a compromise decision. Negotiating is an iterative process. Negotiations are completed when all demands have been met.The group decision support system “NEGO” assists DMs in finding a compromise. It has been used for solving a GDM problem at the corporate level and is currently utilized in management courses.     

Making Decisions on Innovation: Meetings or Networks?

Within some streams of thinking in the management of innovation and product development, the crux is the manager's active engagement in the evaluation, selection and control of the various activities through gate and portfolio meetings in which information is presented and decisions are made that manage innovation projects at a distance. This traditional managerial perspective regards the meetings as (important) ‘obligatory passage points’ but cannot explain a number of observations that reveal few decisions being made at those meetings. A network process perspective on the management of innovation is derived as an alternative to the normative linear view. This alternative perspective makes it possible to explain how innovation projects actually consist of myriad actions, negotiations, and micro‐decisions in the effort to create strong networks, leaving few decisions for the official gate and portfolio meetings. Through the analysis of two cases, this paper demonstrates how project managers work to stabilize the network in order to involve numerous human and non‐human actors and to encourage more and more of them into joining the network. Successfully establishing stable networks and successfully filling the templates for their projects leaves little room – and requires little intervention – for decision makers at portfolio meetings, where approvals are sought rather than decisions made. This study explains how gate and portfolio management meetings are, in some instances, better viewed as checkpoints rather than as decision meetings, how decision making is displaced from the meetings, and how the use of gate and portfolio management systems have created a number of mandatory templates which must be dealt with by the project managers. These mandatory documents function as boundary objects between and among the different worlds of the actors involved and establish new obligatory passage points in the management process; thus boundary objects become transformed into obligatory passage points. Implications for managers and research are outlined, including methods of dealing with the management of product innovation projects when the focus shifts from planning, preparations and decision making toward the co‐creation of technology and markets and involves interessement of human and non‐human actors.     

A pseudo‐stochastic approach for optimal decision making under limited information: a case of an aggregate production system

In this study, which is both analytical and numerical, we compute the effective information horizon (EIH), i.e., the minimal time interval over which future information is relevant for optimal control and for measuring the performance of a single part‐type production system. Optimal control modeling and process solving, which consider aspects of decision making with limited forecast, are exemplified by a single part‐type production system. Specifically, the analysis reveals practical situations in which there is both a performance loss as well as feasibility violation when only information expected within the planning horizon is considered. The analysis is carried out by developing a pseudo‐stochastic model. We follow previous “pseudo‐stochastic” approaches that solve stochastic control problems by using deterministic, optimal control methods. However, we model the expected influences of all future events, including those that are beyond the planning horizon, as encapsulated by their density functions and not only by their mean values.     

Uniqueness of reconstruction for Yager's t‐norm combination of probabilistic and possibilistic knowledge

Often, about the same real‐life system, we have both measurement‐related probabilistic information expressed by a probability measure P(S) and expert‐related possibilistic information expressed by a possibility measure M(S). To get the most adequate idea about the system, we must combine these two pieces of information. For this combination, R. Yager—borrowing an idea from fuzzy logic—proposed to use a t‐norm f_&(a,b) such as the product f_&(a,b)=a· b, i.e., to consider a set function f(S)=f_&(P(S),M(S)). A natural question is: can we uniquely reconstruct the two parts of knowledge from this function f(S)? In our previous paper, we showed that such a unique reconstruction is possible for the product t‐norm; in this paper, we extend this result to a general class of t‐norms. © 2011 Wiley Periodicals, Inc.     

Parameter reductions in N‐soft sets and their applications in decision‐making

Parameter reduction is an important operation for improving the performance of decision‐making processes in various uncertainty theories. The theory of N‐soft sets is emerging as a powerful mathematical tool for dealing with uncertainties beyond the standard formulation of the soft set theory. In this research article, we extend the notion of parameter reduction to N‐soft set theory, and we also justify its practical calculation. To this purpose, we define related theoretical concepts (e.g. N‐soft subset, reduct N‐soft set and redundant parameter) and examine some of their fundamental properties. Then, we argue that the idea of attributes reduction from the rough set theory cannot be employed in the N‐soft set theory in order to reduce the number of parameters. Consequently, we take an original position in order to adequately define and compute parameter reductions in N‐soft sets. Finally, we develop an application of parameter reduction of N‐soft sets.     

Unanimously acceptable agreements for negotiation teams in unpredictable domains

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences.     

A multigranular hierarchical linguistic model for design evaluation based on safety and cost analysis

Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy linguistic approach to model vagueness and the Dempster‐Shafter theory of evidence for modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi‐Expert Multi‐Criteria decision model that will be able to deal with multigranular linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular linguistic model based on the Linguistic Hierarchies presented by Herrera and Martínez (“A model based on linguistic 2‐tuples for dealing with multigranularity hierarchical linguistic contexts in multi‐expert decision‐making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.     

Inter‐functional Integration and Industrial New Product Portfolio Decision Making: Exploring and Articulating the Linkages

This study investigates the relationship between the nature of inter‐functional integration and industrial new product/service portfolio decision making. While inter‐functional integration has been widely researched, there is little understanding of its influence on resource allocation decisions within the context of a broad portfolio of development projects. The detailed activities and decisions underpinning the inter‐functional management of the new product portfolio, as well as three specific new product/service projects, at a large European industrial product manufacturer are analysed. Detailed findings are provided. Two critical inter‐functional dimensions – functional domination and nature of evaluation criteria – are derived and discussed. Implications for managerial action are given.     

Optimal repair decisions for integrated circuits manufacturing

The sequential manufacturing process has been widely applied to the making of complicated integrated circuits (IC). The basic idea is to break an IC product line into a linear sequence of simple operations, and then, a final product with desired high complexity can be obtained by performing the individual operations step by step. To assure the quality of the in-process products, we may conduct a test after each operation. When an in-process product is fault-free, it will pass the test and be moved to the next operation. On the other hand, for a defective in-process product which fails the test, we have to make a decision to either scrap or repair it so that no fault propagates to later operations. The repair/scrapping decisions certainly affect the manufacturing cost. Our goal is to find a set of optimal repair decisions so that the total manufacturing cost per fault-free IC product is minimized. For an IC product line with n sequential operations, there are 2ⁿpossible decision combinations in total. In this paper, we introduce a dynamic programming approach with an algorithm of 0(n₂) complexity to solve the problem.     

The Role of Intuition and Deliberation for Exploration and Exploitation Success

Advances in social and cognitive psychology and related fields have drawn attention to the role of intuition in organizational decision making. In this study we link intuitive and deliberate decision‐making styles to the success of exploration and exploitation activities, which are understood as two qualitatively opposing strategies that organizations can adopt. We provide empirical evidence that the two opposing strategies are linked to two opposing styles of decision making – intuitive and deliberate. In doing so, we draw on data which we received from 140 entrepreneurs and managing partners of Austrian companies and show that exploration is strongly related to intuitive decision making whereas exploitation draws on both intuitive and deliberate decision making. Based on our findings, we stress the complementarity of the two decision‐making styles, and point out that particularly in the light of the fast‐changing premises in which organizations have to manoeuvre today, decision makers are well advised to use both decision‐making styles to their best benefit.     

Induced intuitionistic fuzzy Choquet integral operator for multicriteria decision making

Yager (Fuzzy Sets, Syst 2003;137:59–69) extended the idea of order‐induced aggregation to the Choquet aggregation and defined induced Choquet ordered averaging operator. In this paper, an induced intuitionistic fuzzy Choquet (IFC) integral operator is proposed for the multiple criteria decision making. Some of its properties are investigated. Furthermore, an induced generalized IFC integral operator is introduced. It is worth mentioning that most of the existing intuitionistic fuzzy aggregation operators are special cases of this induced aggregation operator. A decision procedure based on the proposed induced aggregation operator is developed for solving the multicriteria decision‐making problem in which all the decision information is represented by intuitionistic fuzzy values. An illustrative example is given for demonstrating the applicability of the proposed decision procedure. © 2011 Wiley Periodicals, Inc.     

Strategies to improve selection of creative ideas: An experimental test of epistemic and social motivation in groups

Pre‐implementation activities like idea selection play a crucial role in the innovation process. However, groups of people seem to perform rather poorly when it comes to selecting creative ideas for implementation. The Motivated Information Processing in Groups model (MIP‐G) provides an explanation as to why some groups outperform others when it comes to making group decisions. On the basis of the MIP‐G framework, we hypothesized that groups that are both epistemically and prosocially motivated would outperform other groups in selecting creative ideas. Contrary to our hypothesis, a 2 × 2 experiment in a field sample (N = 240 or 80 three‐person groups) showed that under conditions of high epistemic motivation, proself motivated groups selected significantly more creative and original ideas than prosocial groups. Proself motivated groups did not differ significantly from the prosocial motivated groups in selecting feasible ideas under conditions of high epistemic motivation. Our results suggest that the MIP‐G framework may need refinement to increase our future understanding of group idea selection. To this end, we propose three specific avenues for future research.     

It's the Thought that Counts: The Mediating Effects of Information Processing in Virtual Team Decision Making

Abstract

Previous research shows that although collaboration technology can improve the exchange of information during team decision making, one of the challenges is that this better discussion often does not lead to better decisions. In this paper, we investigate the impact of individual information processing on team decision making. We investigated the effects of two types of collaboration technology when participants were either the member of a team majority or a team minority. There was little impact due to the majority/minority, but the use of collaboration technology that required users to categorize the comments they received from others resulted in increased information processing, which in turn led to better decisions and more satisfied participants.     

Trust,Performance, and the Communication Process in Ad Hoc Decision‐Making Virtual Teams

This research explores the communication process variables that potentially determine trust and performance quality in ad hoc virtual teams to better inform the choice of communication media for virtual groups engaged in decision‐making tasks. Results of a survey indicate that virtual copresence is significantly correlated with intrateam trust and the performance quality that teams achieve. Results also accentuate the prominent role of trust in the virtual collaborative decision‐making process; they indicate that trust mediates the relationship between virtual copresence and performance. It is therefore recommended that the efforts related to the design and adoption of communication systems for decision making teams strongly consider that virtual copresence potentially promotes both high levels of trust and high quality performance in virtual teams.