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.

Evaluating two-stage holistic production systems using novel CSDEA approach

2018 ◽  
Vol 13 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Dujun Zhai ◽  
Minyue Jin ◽  
Jennifer Shang ◽  
Chenfeng Ji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Production Process ◽  
Group Decision Making ◽  
Group Decision ◽  
Collective Decision ◽  
Data Envelopment ◽  
Two Stage ◽  
Decision Strategies ◽  
Content Type

Purpose The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences. Design/methodology/approach The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences. Findings The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set. Originality/value The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

scholarly journals Towards Trust, Transparency and Liability in AI / AS systems

2017 ◽  
Author(s):  
Eva Thelisson
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Autonomous Systems ◽  
Research Problem ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Global Level ◽  
Governance Mechanisms ◽  
Decision Making Processes ◽  
Artificial Intelligence Systems

The research problem being investigated in this article is how to develop governance mechanisms and collective decision-making processes at a global level for Artificial Intelligence systems (AI) and Autonomous systems (AS), which would enhance confidence in AI and AS.

scholarly journals Borda Count in Collective Decision Making: A Summary of Recent Results

2019 ◽  
Vol 33 ◽  
pp. 9830-9836
Author(s):  
Jörg Rothe
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Strategic Behavior ◽  
Collective Decision ◽  
Borda Count ◽  
Collective Decision Making ◽  
Computational Social Choice ◽  
Indivisible Goods ◽  
Voting Rules ◽  
Ordinal Preferences

Borda Count is one of the earliest and most important voting rules. Going far beyond voting, we summarize recent advances related to Borda in computational social choice and, more generally, in collective decision making. We first present a variety of well known attacks modeling strategic behavior in voting—including manipulation, control, and bribery—and discuss how resistant Borda is to them in terms of computational complexity. We then describe how Borda can be used to maximize social welfare when indivisible goods are to be allocated to agents with ordinal preferences. Finally, we illustrate the use of Borda in forming coalitions of players in a certain type of hedonic game. All these approaches are central to applications in 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 Decision-Making in Forestry: A Review of the Hybridisation of Multiple Criteria and Group Decision-Making Methods

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 375 ◽  
Author(s):  
Ortiz-Urbina ◽  
González-Pachón ◽  
Diaz-Balteiro
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Forest Management ◽  
Case Studies ◽  
Group Decision Making ◽  
Social Protection ◽  
Group Decision ◽  
Multiple Criteria ◽  
Collective Decision Making ◽  
Critical Overview

The degree of complexity in forest management has increased in the last few decades, not only due to the inclusion of specific new issues (e.g., climate change, social protection, etc.), but also because these new, as well as classic, issues have to be dealt with in a context characterised by multiple conflicting criteria that are evaluated by different stakeholders. Nowadays, the multicriteria issue enjoys a relatively sound tradition in forest management. However, the consideration of several stakeholders, which requires the formulation of management models within a collective decision-making setting, is not that advanced. This paper aims to provide a critical overview of forestry case studies that have been published in primary journals and that deal with multiple criteria and several stakeholders. Based on this overview, some highlights of the most promising methods were obtained, and recommendations for the fruitful use of these combined methodologies for dealing with numerous types of forest management problems are provided.

Reengineering the Group Decision-Making Process

2000 ◽  
Vol 4 (2) ◽  
pp. 29-35
Author(s):  
Subash Wadhwa ◽  
Souresh Bhattacharya
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Work Group ◽  
Collaborative Work ◽  
Group Decision Support System ◽  
Competitive Edge ◽  
Area Of Interest ◽  
Decision Making Processes ◽  
Wide Scale

Organisations, the world over are increasingly focussing their efforts on radical measures to enhance their competitive edge. One such area of interest focuses on reduction in the time taken in classical process of managerial group decision-making. The rapid strides made in the field of Information Technology have opened new opportunities to qualitatively enhance productivity of collaborative work. Group Decision Support System (GDSS) focuses on the aspect of introducing technology, in a graded manner, into the process of group decisionmaking. It seeks to fundamentally alter the angularities of such interaction and present a technology driven platform for the purpose. However, the basic process of group interaction needs to be more clearly understood before any wide-scale implementation of GDSS can prove beneficial. A model of the evolving GDSS process is presented which affords a framework for further research into the contemporary technology-driven collaborative process. It is proposed that the advent of IT offers a unique opportunity to re-engineer the group decision making processes so as to significantly benefit from the GDSS paradigm.

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.

Nonmanipulable Collective Decision-Making for Games

2012 ◽  
pp. 67-81
Author(s):  
Rob LeGrand ◽  
Timothy Roden ◽  
Ron K. Cytron
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Collective Decision ◽  
The Other ◽  
Collective Decision Making ◽  
Game Development ◽  
New Approach ◽  
Collective Decisions ◽  
Higher Dimensional ◽  
Game Players

This chapter explores a new approach that may be used in game development to help human players and/or non-player characters make collective decisions. The chapter describes how previous work can be applied to allow game players to form a consensus from a simple range of possible outcomes in such a way that no player can manipulate it at the expense of the other players. Then, the text extends that result and shows how nonmanipulable consensus can be found in higher-dimensional outcome spaces. The results may be useful when developing artificial intelligence for non-player characters or constructing frameworks to aid cooperation among human players.

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.

scholarly journals Interval-Valued Intuitionistic Fuzzy Multiple Attribute Group Decision Making with Uncertain Weights

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Zhuosheng Jia ◽  
Yingjun Zhang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Collective Decision ◽  
Decision Makers ◽  
Fuzzy Decision ◽  
Decision Matrix ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Interval Valued

The theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been an impactful and convenient tool in the construction of advanced multiple attribute group decision making (MAGDM) models to counter the uncertainty in the developing complex decision support system. To satisfy much more demands from fuzzy decision making problems, we propose a method to solve the MAGDM problem in which all the information supplied by the decision makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by an interval-valued intuitionistic fuzzy number, and the information about the weights of both decision makers and attributes may be completely unknown or partially known. Firstly, we introduce a consensus-based method to quantify the weights of all decision makers based on all interval-valued intuitionistic fuzzy decision matrices. Secondly, we utilize the interval-valued intuitionistic fuzzy weighted arithmetic (IVIFWA) operator to aggregate all interval-valued intuitionistic fuzzy decision matrices into the collective one. Thirdly, we establish an optimization model to determine the weights of attributes depending on the collective decision matrix and the given attribute weight information. Fourthly, we adopt the weighted correlation coefficient of IVIFSs to rank all the alternatives from the perspective of TOPSIS via the collective decision matrix and the obtained weights of attributes. Finally, some examples are used to illustrate the validity and feasibility of our proposed approach by comparison with some existing models.

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