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.

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.

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.

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.

scholarly journals Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making

2019 ◽  
Vol 61 (4) ◽  
pp. 84-109 ◽  
Author(s):  
Lynn Metcalf ◽  
David A. Askay ◽  
Louis B. Rosenberg
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Swarm Intelligence ◽  
Group Decision Making ◽  
Group Decision ◽  
Business Decisions ◽  
Collaboration Technology ◽  
Business Decision

This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.

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