scholarly journals Reflections on Concepts of Employment for Modern Information Fusion and Artificial Intelligence Technologies: Situation Management, Decision Making under Varying Uncertainty and Ambiguity, Sequential Decision-Making, Learning, Prediction, and Trust

2011 ◽  
pp. 31-31
Author(s):  
James Llinas
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information Fusion ◽  
Sequential Decision Making ◽  
Management Decision ◽  
Sequential Decision ◽  
Management Decision Making ◽  
Modern Information

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

scholarly journals Research of Modern Information Technology on Tree Growth Management Decision Making Model

2013 ◽  
Vol 347-350 ◽  
pp. 2418-2421
Author(s):  
Yuan Li ◽  
Wen Qing Zhang ◽  
Hua Liu ◽  
Hui Qin Yang ◽  
Xu Ning Liu
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Tree Growth ◽  
Growth Management ◽  
Growth Conditions ◽  
Management Decision ◽  
Modern Information Technology ◽  
Management Decision Making ◽  
Modern Information ◽  
Decision Making Model

Tree growth management decision-making model can simulate growth management of tree and perform quantitative analysis of tree growth conditions. This paper explores the feasibility of modern information technology in management assessment of tree growth, information technology include neural network, ontology and expert system technology, then ontology technology is used to establish ontology database and knowledge base of tree growth management resource, the growth simulation and tree growth management ontology technology are used to build simulation models of tree growth, then expert systems and neural network technology are combined to simulate tree growth development process of decision-making model. The practice has proved that the research can not only predict the growth conditions of tree and dynamic grasp the growth process of the tree, but also can provide theoretical basis for the analysis and evaluation of tree growth management, greatly improving the level of tree growth management.

Organizational Decision-Making Structures in the Age of Artificial Intelligence

2019 ◽  
Vol 61 (4) ◽  
pp. 66-83 ◽  
Author(s):  
Yash Raj Shrestha ◽  
Shiko M. Ben-Menahem ◽  
Georg von Krogh
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Search Space ◽  
Sequential Decision Making ◽  
Decision Making Process ◽  
Sequential Decision ◽  
Organizational Decision Making ◽  
Process And Outcome ◽  
Alternative Set

How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid—human-to-AI and AI-to-human—sequential decision making; and aggregated human–AI decision making.

Centralization and Effectiveness of Reward Management in Multinational Enterprises

2018 ◽  
Vol 17 (2) ◽  
pp. 55-65 ◽  
Author(s):  
Michael Tekieli ◽  
Marion Festing ◽  
Xavier Baeten
Keyword(s):  
Decision Making ◽  
Social Identity Theory ◽  
Multinational Enterprises ◽  
Role Identity ◽  
Management Decision ◽  
Role Identities ◽  
Management Decision Making ◽  
New Perspective ◽  
Subsidiary Managers ◽  
The Relationship

Abstract. Based on responses from 158 reward managers located at the headquarters or subsidiaries of multinational enterprises, the present study examines the relationship between the centralization of reward management decision making and its perceived effectiveness in multinational enterprises. Our results show that headquarters managers perceive a centralized approach as being more effective, while for subsidiary managers this relationship is moderated by the manager’s role identity. Referring to social identity theory, the present study enriches the standardization versus localization debate through a new perspective focusing on psychological processes, thereby indicating the importance of in-group favoritism in headquarters and the influence of subsidiary managers’ role identities on reward management decision making.

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