scholarly journals Social Setting, Intuition, and Experience in Lab Experiments Interact to Shape Cooperative Decision-Making

2015 ◽  
Author(s):  
Valerio Capraro ◽  
Giorgia Cococcioni
Keyword(s):  
Decision Making ◽  
Social Setting ◽  
Lab Experiments ◽  
Cooperative Decision Making

scholarly journals Social setting, intuition and experience in laboratory experiments interact to shape cooperative decision-making

2015 ◽  
Vol 282 (1811) ◽  
pp. 20150237 ◽  
Author(s):  
Valerio Capraro ◽  
Giorgia Cococcioni
Keyword(s):  
Decision Making ◽  
Dynamic Process ◽  
Laboratory Experiments ◽  
Time Pressure ◽  
Social Setting ◽  
Cooperative Behaviour ◽  
Economic Games ◽  
Controlled Choice ◽  
Cooperative Decision Making ◽  
Positive Effect

Recent studies suggest that cooperative decision-making in one-shot interactions is a history-dependent dynamic process: promoting intuition versus deliberation typically has a positive effect on cooperation (dynamism) among people living in a cooperative setting and with no previous experience in economic games on cooperation (history dependence). Here, we report on a laboratory experiment exploring how these findings transfer to a non-cooperative setting. We find two major results: (i) promoting intuition versus deliberation has no effect on cooperative behaviour among inexperienced subjects living in a non-cooperative setting; (ii) experienced subjects cooperate more than inexperienced subjects, but only under time pressure. These results suggest that cooperation is a learning process, rather than an instinctive impulse or a self-controlled choice, and that experience operates primarily via the channel of intuition. Our findings shed further light on the cognitive basis of human cooperative decision-making and provide further support for the recently proposed social heuristics hypothesis.

scholarly journals Cooperative Decision Support Systems

2011 ◽  
pp. 109-115 ◽  
Author(s):  
Pascale Zaraté
Keyword(s):  
Decision Making ◽  
Search Space ◽  
Optimal Decision ◽  
Decision Making Process ◽  
Collaborative Decision Making ◽  
Distributed Reasoning ◽  
Cooperative Decision Making ◽  
Optimal Decision Making ◽  
Analytical Tools ◽  
Collaborative Decision

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.

Competitive and Cooperative Decision-Making Groups

1980 ◽  
Vol 7 (4) ◽  
pp. 457-478 ◽  
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.

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

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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