Authors:
Diego de Siqueira Braga
1
;
Marco Niemann
1
;
Bernd Hellingrath
1
and
Fernando Buarque de L. Neto
2
Affiliations:
1
Westfälische Wilhelms-Universität Münster, Germany
;
2
University of Pernambuco, Brazil
Keyword(s):
Social Simulation, Trust, Bullwhip Effect, ABSS, Behavioral Experiment, Behavior Modeling.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Operational Research
;
Simulation
;
Software Engineering
;
Symbolic Systems
Abstract:
Trust is seen as one of the most important dimensions in developing and maintaining fruitful business relationships and has deep impact on the decision-making process in the supply chain planning. Despite its importance, very limited research has been done in the trust-aware decision-making field. This paper aims to experimentally examine how trust can be assessed over different dimensions and then be used to support decision-making in order to reduce the Bullwhip Effect, which is one of the biggest efficiency problems shown by supply chains of highly interconnected organizations. As industry is generally reluctant to provide data due to privacy concerns and trade secret protection, the authors of this paper, designed and conducted a web-based trust behavioral experiment. The data collected was used to evaluate the proposed trust mechanism through an Agent-Based Social Simulation. The results revealed that it is possible to infer trust relationships from behavioral experiments and hi
storical based data, and use these relationships to influence the procurement, ordering and information sharing process. Although additional research is still necessary, the preliminary results revealed that the use of computational trust mechanisms can be helpful to lower the Bullwhip Effect.
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