An Argumentation Model of Deliberative Decision-Making

2011 ◽  
pp. 1-17 ◽  
Author(s):  
Douglas Walton
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Previous Literature ◽  
Collaborative Decision Making ◽  
Worked Example ◽  
Collaborative Decision

This chapter presents deliberation dialogue as a framework for argumentation used in group decision-making. Drawing on and summarizing the previous literature in argumentation and artificial intelligence (AI), the chapter: (1) outlines the characteristics of deliberation as a type of dialogue; (2) distinguishes between deliberation dialogue and other types of dialogue it is closely related to and often confused with; (3) refines the existing models of deliberation to make them more useful for supporting reasoning communities engaged in collaborative decision making; (4) provides a worked example to show what the stages and characteristics of a deliberation dialogue are, and show how methods from AI and argumentation can be applied to analyzing it; and (5) outlines some further areas for research on deliberation that are currently being studied.

Decision rules, escalation of commitment and sensitivity to framing in group decision-making

2016 ◽  
Vol 54 (7) ◽  
pp. 1649-1668 ◽  
Author(s):  
Petru Lucian Curseu ◽  
Sandra G. L. Schruijer ◽  
Oana Catalina Fodor
Keyword(s):  
Decision Making ◽  
Decision Rule ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Rules ◽  
Escalation Of Commitment ◽  
Group Level ◽  
Content Type ◽  
The Impact ◽  
Collaborative Decision

Purpose – The purpose of this paper is to test the influence of collaborative and consultative decision rules on groups’ sensitivity to framing effect (FE) and escalation of commitment (EOC). Design/methodology/approach – In an experimental study (using a sample of 233 professionals with project management experience), the authors test the effects of collaborative and consultative decision rules on groups’ sensitivity to EOC and FE. The authors use four group decision-making tasks to evaluate decision consistency across gain/loss framed decision situations and six decision tasks to evaluate EOC for money as well as time as resources previously invested in the initial decisions. Findings – The results show that the collaborative decision rule increases sensitivity to EOC when financial resources are involved and decreases sensitivity to EOC when time is of essence. Moreover, the authors show that the collaborative decision rule decreases sensitivity to FE in group decision making. Research limitations/implications – The results have important implications for group rationality as an emergent group level competence by extending the insights concerning the impact of decision rules on emergent group level cognitive competencies. Due to the experimental nature of the design, the authors can probe the causal relations between the investigated variables, yet the authors cannot generalize the results to other settings. Practical implications – Managers can use the insights of this study in order to optimize the functioning of decision-making groups and to reduce their sensitivity to FEs and EOC. Originality/value – The study extends the research on group rationality and it is one of the few experimental attempts used to understand the role of decision rules on emergent group level rationality.

scholarly journals Literature review and discussion on collaborative decision making approaches in Industry 4.0

2021 ◽  
Vol 49 (4) ◽  
pp. 817-826
Author(s):  
A.C. Sousa ◽  
A.F. Bertachini ◽  
C. Cunha ◽  
R. Chaves ◽  
M.L.R. Varela
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Literature Review ◽  
Mathematical Models ◽  
Concurrent Engineering ◽  
Industry 4.0 ◽  
Collaborative Decision Making ◽  
Collaborative Decision

Nowadays, companies are faced with an increasingly higher level of competition while trying to adapt to the exigencies imposed by the Industry 4.0, regarding its usually referred dimensions and pillars, among which one that although is not so often referred is also expressing an increasing visibility and importance, related to collaboration, and more specifically to collaborative decision making and co-working. Thus, in this paper an analysis is carried out regarding the evolution of publications that have been put available over the last decade about collaborative decision making approaches, varying from approaches based on mathematical models up to the application of artificial intelligence and other kind of approaches. Moreover, a discussion about the relation between collaborative decision making, concurrent engineering and Industry 4.0 dimensions is also done.

Cloud-Based Collaborative Decision Making

2012 ◽  
Vol 4 (4) ◽  
pp. 39-59 ◽  
Author(s):  
Heiko Thimm
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Decision Support ◽  
System Architecture ◽  
Group Decision ◽  
Decision Processes ◽  
Integrated System ◽  
Background Information ◽  
Collaborative Decision Making ◽  
Collaborative Decision

The complexity of many decision problems of today’s globalized world requires new innovative solutions that are built upon proven decision support technology and also recent advancements in the area of information and communication technology (ICT) such as Cloud Computing and Mobile Communication. A combination of the cost-effective Cloud Computing approach with extended group decision support system technology bears several interesting unprecedented opportunities for the development of such solutions. These opportunities include ubiquitous accessibility to decision support software and, thus, the possibility to flexibly involve remote experts in group decision processes, guided access to background information, and facilitation support to direct group decision processes. The architects of such future solutions are challenged by numerous requirements that need to be considered and reflected in an integrated architectural approach. This article presents a thorough analysis of major design considerations for software solutions for collaborative decision making from a broad range of perspectives especially including the business process management perspective and the Cloud Computing perspective. The proposed architectural approach of the GRUPO-MOD system demonstrates how one can address the requirements in one integrated system architecture that supports different deployment options of Cloud Computing. A refinement of the high-level system architecture into a corresponding implementation architecture that builds on widely adopted standards such as OSGi and industry proven technology such as the Eclipse platform is also given in the article.

Multcriteria Decision Analysis for Collaborative GIS

2011 ◽  
pp. 167-185 ◽  
Author(s):  
Jacek Malczewski
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Analysis ◽  
Group Decision Making ◽  
Critical Review ◽  
Group Decision ◽  
Multicriteria Decision ◽  
Collaborative Gis ◽  
Collaborative Decision

This chapter provides a critical review of GIS-based multicriteria decision analysis (GIS-MCDA) for supporting group (collaborative and participatory) decision making. The review is based on a survey of referred papers that have been published over the last 15 years or so. The chapter offers a classification of the GIS-MCDA approaches for group decision making. First, the articles are classified according to the generic elements of the MCDA methods. Second, the GIS-MCDA methods are classified according to the various perspectives on collaborative decision support. These taxonomies of the GIS-MCDA approaches provide a background for an evaluation of the contribution of MCDA to GIS-based collaborative decision making.

The Elements of Collective Decision Making

2012 ◽  
pp. 1-31
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Group Decision Making ◽  
Real World ◽  
Group Decision ◽  
Collaborative Work ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Decision Science ◽  
Computer Supported Collaborative Work

In this chapter, the concept of a reasoning community is introduced. The overarching motivation is to understand reasoning within groups in real world settings so that technologies can be designed to better support the process. Four phases of the process of reasoning by a community are discerned: engagement of participants, individual reasoning, group coalescing, and, ultimately, group decision making. A reasoning community is contrasted with communities of practice and juxtaposed against concepts in related endeavours including computer supported collaborative work, decision science, and artificial intelligence.

scholarly journals Group Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1566
Author(s):  
Ruben Heradio ◽  
David Fernandez-Amoros ◽  
Cristina Cerrada ◽  
Manuel J. Cobo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Bibliometric Analysis ◽  
Group Decision Making ◽  
Group Decision ◽  
Single Person ◽  
Group Members ◽  
Linguistic Approaches ◽  
Fuzzy Linguistic ◽  
The Impact

Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.

scholarly journals Improving Group Decision-Making by Artificial Intelligence

2017 ◽  
Author(s):  
Lirong Xia
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Recent Work ◽  
Group Decision Making ◽  
Group Decision ◽  
Unified Approach ◽  
Future Directions

We summarize some of our recent work on using AI to improve group decision-making by taking a unified approach from statistics, economics, and computation. We then discuss a few ongoing and future directions.

scholarly journals Discrepancies and Analogies in Artificial Intelligence and Engineering Design Approaches in Addressing Collaborative Decision-Making

2013 ◽  
pp. 135-148
Author(s):  
Marija Jankovic ◽  
Pascale Zaraté
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Engineering Design ◽  
Local Area ◽  
Collaborative Decision Making ◽  
Geographical Dispersion ◽  
Early Design Stages ◽  
Collaborative Decision ◽  
Scientific Fields

One of the trends in the decision-making field in the past 20 years has been the migration from individual decision-making to collective one. Several changes of working conditions influenced this trend: geographical dispersion due to the business internationalisation, concurrent work in order to satisfy time delays, facilitation of the information sharing induced by the development of local area networks (LAN), and internet. This study examines the discrepancies and analogies in addressing the collaborative decision making in two scientific fields: artificial intelligence and engineering design. These two fields have different considerations and approaches in view to the decision-making support. This paper exposes a comparative study concerning two research studies, both decision support oriented: the first one concerns the collaborative decision-making in early design stages in vehicle development projects (Jankovic, Bocquet, Stal Le Cardinal, & Bavoux, 2006) and the second one concerns the development of an architecture of a Cooperative decision Support Systems (CDSS) (Zaraté, 2005).

Research on group decision making with interval numbers based on plant growth simulation algorithm

Kybernetes ◽  
2014 ◽  
Vol 43 (2) ◽  
pp. 250-264 ◽  
Author(s):  
Lei Li ◽  
Xiaolu Xie ◽  
Rui Guo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Interval Number ◽  
Simulation Algorithm ◽  
Content Type ◽  
Growth Simulation ◽  
Plant Growth Simulation Algorithm ◽  
Preference Matrix

Purpose – This paper aims at multi-attribute and multi-program group decision making when the attribute weights are completely unknown and the attribute value information is in the form of the interval number. Design/methodology/approach – This is an artificial intelligence algorithm for designing information gathering in group decision making. The authors propose the nonlinear programming model to gather information based on plant growth simulation algorithm (PGSA). The authors collect each program on each attribute group decision preference ordering interval and then use them to find the preference vector and the preference matrix. The entropy method is used to determine the weight of each attribute by the constructed preference matrix. According to the possibility degree matrix of each attribute, the combined effect vector is established by the priority weight vector method, which sorts and selects the best decision making program. Findings – To the authors' knowledge, the application of PGSA in the field of management decisions to collect program on each attribute group decision making preference interval number is the first trial in literature. It has retained more valuable decision making information from all experts without distortion. Practical implications – In practice, a real number may not be an accurate representation, but only gives a range of values to describe the attributes. This study provides a useful measurement of interval number information for managers to evaluate military science, venture capital, and environmental assessment, etc. Originality/value – The methodology considers the complete information to ensure no information distortion even with large and complex systems. The authors adopt computer artificial intelligence algorithms to obtain the objective evaluation, which is meaningful for both research studies and practical use.

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