Fairness takes time: Development of cooperative decision making in fairness context

The subject of our research aims to support in the most suitable way the collaborative decision-making process. Several scientific approaches deal with collaborative decision-making: decision analysis (Carlsson & Turban, 2002; Doyle & Thomason, 1999; Keeney & Raiffa, 1976) developing different analytical tools for optimal decision-making; in management sciences the observation of decision-making styles activity (Nuut, 2005; Fong, Wyer, & Robert 2003); decision-making as a group work (Esser, 1998; Matta & Corby, 1997); studies concerning different types of decisions focalised on number of actors: individual (Keeney & Raiffa, 1976), group (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002), cooperative (Zaraté, 2005), and collaborative (Karacapilidis & Papadias, 2001). For the collaborative decision-making field, the situation is clear. In most of research studies, the concept of collaborative decision-making is used as a synonym for cooperative decision-making. Hence, the collaborative decision-making process is considered to be distributed and asynchronous (Chim, Anumba, & Carillo, 2004; Cil, Alpturk, & Yazgan, 2005). However, we can stand out several works, having different research approaches, considering collaborative decision-making process as multi-actor decision-making process, where actors have different goals. Considering (Panzarasa, Jennings, & Norman, 2002) the collaborative decision-making process is seen as “a group of logically decentralised agents that cooperate to achieve objectives that are typically beyond the capacities of an individual agent. In short, the collaborative decision-making has generally been viewed and modelled as a kind of distributed reasoning and search, whereby a collection of agents collaboratively go throughout the search space of the problem in order to find a solution.” The main interrogation of this article is to study the best way to support collaborative decision-making process.

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Community Involvement in Cooperative Decision Making: Some Lessons Learned

Educational Evaluation and Policy Analysis ◽

10.3102/01623737001006037 ◽

1979 ◽

Vol 1 (6) ◽

pp. 37-46 ◽

Cited By ~ 3

Author(s):

Joseph A. Mercurio

Keyword(s):

Decision Making ◽

Community Involvement ◽

Lessons Learned ◽

Cooperative Decision Making

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Competitive and Cooperative Decision-Making Groups

Communication Research ◽

10.1177/009365028000700402 ◽

1980 ◽

Vol 7 (4) ◽

pp. 457-478 ◽

Cited By ~ 6

Author(s):

Dorothy Lenk Krueger

Keyword(s):

Decision Making ◽

Sequential Analysis ◽

Group Interaction ◽

Communication Patterns ◽

Sample Group ◽

Lag Sequential Analysis ◽

Patterns Of Communication ◽

Two Measures ◽

Cooperative Decision Making

This study investigates differences among four decision-making groups and describes the patterns of communication unique to two groups. In the first part of the investigation, four decision-making groups are given either competitive or cooperative inducements and are compared on two measures: competition and satisfaction. The two groups given the competitive inducement (Groups I and III) were found to have significantly higher competition and lower satisfaction than the groups given cooperative inducements (Groups II and IV). In the second part of the study a lag sequential analysis is conducted on the coded communicative sequences in the highest and lowest competition groups (I and II, respectively). This analysis yields patterns to decision-making unique to each sample group. Group I's communication is characterized by highly probable (above-chance) sequences of disagreement messages and few probable agreement messages. Group II's communication patterns consist of highly probable sequences of decision development and probable agreement/support messages throughout the group interaction.

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Axioms of Cooperative Decision Making

10.1017/ccol0521360552 ◽

1988 ◽

Cited By ~ 446

Author(s):

Hervi Moulin

Keyword(s):

Decision Making ◽

Cooperative Decision Making

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Cooperative Decision Making for Evaluating Ports’ Reception Facilities

Lecture Notes in Computer Science - Cooperative Design, Visualization, and Engineering ◽

10.1007/978-3-642-16066-0_16 ◽

2010 ◽

pp. 101-104

Author(s):

Sylvia Encheva ◽

Sharil Tumin

Keyword(s):

Decision Making ◽

Cooperative Decision Making

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Handling Different Format Initial Data in a Cooperative Decision Making Process

Lecture Notes in Computer Science - Cooperative Design, Visualization, and Engineering ◽

10.1007/978-3-642-23734-8_11 ◽

2011 ◽

pp. 74-77

Author(s):

Sylvia Encheva

Keyword(s):

Decision Making ◽

Initial Data ◽

Decision Making Process ◽

Cooperative Decision Making

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A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations

E-Collaboration ◽

10.4018/978-1-60566-652-5.ch073 ◽

2009 ◽

pp. 941-963

Author(s):

Faezeh Afshar ◽

John Yearwood ◽

Andrew Stranieri

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Computational Intelligence ◽

Group Decision ◽

Shared Understanding ◽

Deliberative Process ◽

Computer Mediated ◽

Decision Making Processes ◽

Cooperative Decision Making

This chapter introduces an approach, ConSULT (Consensus based on a Shared Understanding of a Leading Topic), to enhance group decision-making processes within organizations. ConSULT provides a computer-mediated framework to allow argumentation, collection and evaluation of discussion and group decision-making. This approach allows for the articulation of all reasoning for and against propositions in a deliberative process that leads to cooperative decision-making. The chapter argues that this approach can enhance group decision-making and can be used in conjunction with any computational intelligence assistance to further enhance its outcome. The approach is particularly applicable in an asynchronous and anonymous environment.

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