scholarly journals Decision making with support of artificial intelligence

2012 ◽  
Vol 51 (No. 9) ◽  
pp. 385-388
Author(s):  
I. Rábová ◽  
V. Konečný ◽  
A. Matiášová
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Process ◽  
Process Model ◽  
Decision Processes ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Software Modules ◽  
Competition Ability

  Development of software modules for decision support is currently a basic trend in the creation of enterprise Information Systems (IS). The IS is basically a support system of the enterprise Decision System, therefore we can regard it as a very important factor of the competition ability and enterprise prosperity. Conventional IS modules provide the enterprise managers a lot of useful information. Nevertheless, own decision process in view of difficulty, complexity or creation disability of decision process model is very often problematic. This contribution is oriented by its content to appropriate choice realization of modules for support decision processes by using of artificial intelligence methods.      

Advancing Cybersecurity for Business Transformation and Enterprise Architecture Projects

2021 ◽  
pp. 288-331
Author(s):  
Antoine Trad
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Model ◽  
Decision Making ◽  
Enterprise Architecture ◽  
Research Approach ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Business Transformation ◽  
Action Research Approach ◽  
Enterprise Transformation

In this chapter, the author bases his research project on his authentic mixed multidisciplinary applied mathematical model for transformation projects. His mathematical model, named the applied holistic mathematical model for projects (AHMM4P), is supported by a tree-based heuristics structure. The AHMM4P is similar to the human empirical decision-making process and is applicable to any type of project; it is aimed to support the evolution of organisational, national, or enterprise transformation initiatives. The AHMM4P can be used for the development of the cybersecurity subsystems, enterprise information systems, and their decision-making systems, based on artificial intelligence, data sciences, enterprise architecture, big data, deep learning, and machine learning. The author attempts to prove that an AHMM4P-based action research approach can unify the currently frequently-used siloed MLI4P and DLI4P trends.

Conversation-Oriented Decision Support Systems for Organizations

Data Mining ◽  
2013 ◽  
pp. 1376-1389
Author(s):  
Paulo Garrido
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Decision Process ◽  
Support Systems ◽  
Decision Processes ◽  
Group Decision Support Systems ◽  
Compound Decision

This chapter proposes concepts for designing and developing decision support systems that acknowledge, explore and exploit the fact that conversations among people are the top-level “supporting device” for decision-making. The goal is to design systems that support, configure and induce increasingly effective and efficient decision-making conversations. This includes allowing and motivating participation in decision-making conversations of any people who may contribute positively to decision-making and to the quality of its outcomes. The proposal sees the sum total of decisions being taken in an organization as the global decision process of the organization. The global decision process of the organization is structured in decision processes corresponding to organizational domains. Each organizational domain has associated a unit decision process. If the organizational domain contains organizational sub-domains, then its compound decision process is the union and composition of its unit decision process and the unit decision processes of its sub-domains. The proposal can be seen as extending, enlarging and integrating group decision support systems into an organization-wide system. The resulting organizational decision support system, by its conversational nature, may become the kernel decision support system of an organization or enterprise. In this way, the global decision process of the organization may be made explicit and monitored. It is believed that this proposal is original.

scholarly journals Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2360
Author(s):  
Mostefa Mohamed-Seghir ◽  
Krzysztof Kula ◽  
Abdellah Kouzou
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Energy Consumption ◽  
Collision Avoidance ◽  
Trajectory Optimization ◽  
Process Model ◽  
Human Error ◽  
Subjective Evaluation ◽  
Ship Control

Ship collisions cause major losses in terms of property, equipment, and human lives. Therefore, more investigations should be focused on this problem, which mainly results from human error during ship control. Indeed, to reduce human error and considerably improve the safe traffic of ships, an intelligent tool based on fuzzy set theory is proposed in this paper that helps navigators make fast and competent decisions in eventual collision situations. Moreover, as a result of selecting the shortest collision avoidance trajectory, our tool minimizes energy consumption. The main aim of this paper was the development of a decision-support system based on an artificial intelligence technique for safe ship trajectory determination in collision situations. The ship’s trajectory optimization is ensured by multistage decision making in collision situations in a fuzzy environment. Furthermore, the navigator’s subjective evaluation in decision making is taken into account in the process model and is included in the modified membership function of constraints. A comparative analysis of two methods, i.e., a method based on neural networks and a method based on the evolutionary algorithm, is presented. The proposed technique is a promising solution for use in real time in onboard decision-support systems. It demonstrated a high accuracy in finding the optimal collision avoidance trajectory, thus ensuring the safety of the crew, property, and equipment, while minimizing energy consumption.

Conversation-Oriented Decision Support Systems for Organizations

2010 ◽  
pp. 118-131 ◽  
Author(s):  
Paulo Garrido
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Decision Process ◽  
Support Systems ◽  
Decision Processes ◽  
Group Decision Support Systems ◽  
Compound Decision

This chapter proposes concepts for designing and developing decision support systems that acknowledge, explore and exploit the fact that conversations among people are the top-level “supporting device” for decision-making. The goal is to design systems that support, configure and induce increasingly effective and efficient decision-making conversations. This includes allowing and motivating participation in decision-making conversations of any people who may contribute positively to decision-making and to the quality of its outcomes. The proposal sees the sum total of decisions being taken in an organization as the global decision process of the organization. The global decision process of the organization is structured in decision processes corresponding to organizational domains. Each organizational domain has associated a unit decision process. If the organizational domain contains organizational sub-domains, then its compound decision process is the union and composition of its unit decision process and the unit decision processes of its sub-domains. The proposal can be seen as extending, enlarging and integrating group decision support systems into an organization-wide system. The resulting organizational decision support system, by its conversational nature, may become the kernel decision support system of an organization or enterprise. In this way, the global decision process of the organization may be made explicit and monitored. It is believed that this proposal is original.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

An Exploration of Five Stages in Faculty Retirement Decision Making

2020 ◽  
pp. 193672442098298
Author(s):  
Beverlee B. Anderson ◽  
Jennifer Jeffries ◽  
Janet McDaniel
Keyword(s):  
Decision Making ◽  
Qualitative Research ◽  
Decision Process ◽  
Process Model ◽  
Working Life ◽  
Substantial Portion ◽  
Retirement Decision ◽  
Faculty Retirement ◽  
Cognitive Decision

Humans make thousands of decisions each day. Most of the decisions we make are trivial or relatively unimportant in possible consequences. However, there are a few decisions we make in life that are lifechanging; one of those is the decision to retire from the professoriate. Voluntarily deciding to leave a profession where one has spent a substantial portion of one’s working life is one of life’s major decisions. This qualitative research looks at the various influences, actions, and feelings through the process of deciding to retire. Using a five-stage cognitive decision-process model as a framework, this paper reports on the reflections of 20 recent retirees over the five stages of the decision process from when first seriously considering the decision to postretirement activities and feelings. The results show that while all faculty progressed through the five stages, the timeframe, influences, feelings, and actions were unique to each individual.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

The Role of Business Case Development in the Diffusion of Innovations Theory for Enterprise Information Systems

2011 ◽  
pp. 3322-3331 ◽  
Author(s):  
Francisco Chia Cua ◽  
Tony C. Garrett
Keyword(s):  
Information Systems ◽  
Decision Process ◽  
Radical Innovation ◽  
Business Case ◽  
Enterprise Information ◽  
Implementation Phase ◽  
Enterprise Information Systems ◽  
Initiation Phase ◽  
Course Of Action ◽  
Case Development

A successful organisation continually initiates and implements radical innovations. The innovation must not only be new. A radical innovation has a significant impact on how the organisation undertakes its business process. Impacting is different from affecting. The former has a more substantial effect on the organisation. This is precisely why new enterprise information systems represent a radical innovation. To be successful, the organisation undertakes an innovation-decision process to align itself, as much as possible, with the ever-changing external realities. The innovation-decision process dictates selling an idea (the business case) that the new enterprise information systems possess economic value to upper management. This paper depicts a bird’s-eye view of how innovation, in this case, the new enterprise information systems, diffuses (episteme) via business case development (techne) in the innovation-decision process. As shown in Figure 1, the adoption and implementation of new enterprise information systems constitute a radical change (prerequisite F). New enterprise information systems represent radical innovation. An innovation-decision process starts with an initiation phase through which the individuals or decision-making units move from identifying and knowing the new enterprise information systems, to the forming of an attitude toward the different competing software packages, and subsequently to deciding whether to adopt or reject the implementation and use of the new idea. A business case is a formally written document that argues about the adoption to a certain course of action. It contains a point-by-point analysis to making a decision for a set of alternative courses of action to accomplish a specific goal. A business case process walks through the initiation phase of the innovation-decision process and talks about the project plans that concern the implementation phase, which follows the initiation phase. The business case document justifies, in detail, the innovation-decision process: what has transpired in the initiation phase and what will transpire in the implementation phase. It takes into account the innovation-decision process. In short, a business case process develops a detailed business case document of the innovation-decision process. Thus, a business case is both a means and an end.

Decision Support Software

2009 ◽  
pp. 440-447
Author(s):  
John Wang ◽  
Huanyu Ouyang ◽  
Chandana Chakraborty
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Mathematical Logic ◽  
Decision Support ◽  
Support Systems ◽  
Scientific Methods ◽  
Simulation And Optimization ◽  
Business Software ◽  
The Right ◽  
Support Software

Throughout the years many have argued about different definitions for DSS; however they have all agreed that in order to succeed in the decision-making process, companies or individuals need to choose the right software that best fits their requirements and demands. The beginning of business software extends back to the early 1950s. Since the early 1970s, the decision support technologies became the most popular and they evolved most rapidly (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002). With the existence of decision support systems came the creation of decision support software (DSS). Scientists and computer programmers applied analytical and scientific methods for the development of more sophisticated DSS. They used mathematical models and algorithms from such fields of study as artificial intelligence, mathematical simulation and optimization, and concepts of mathematical logic, and so forth.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

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