• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.435-440
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• 2004

Most of our GIS implementation activities have been focussed on the management of urban information in local municipalities. Management of urban facilities has been the major issue of concern and has little role in providing the decision maker with alternatives from which one can analyse and choose the optimum solution. For this reason, the spatial decision support system is in need. Business analysis software is effectively used for site analysis of new stores, customer prospecting and other issues of decision making for business purposes, The same geoprocessing module of business analysis software would be useful if put to use for the management of disaster management especially for storm surge management. Application of the business analysis model for disaster management has been reviewed. Specially in case of storm surge, where quick response is crucial, the spatial decision support system will be most effective.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.4
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• pp.111-126
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• 2002

This paper presents an interactive decision procedure to aggregate each group member's preferences when each group member articulates his or her preference information incompletely. An index, an indicative for the preference strength between alternatives, is derived to aid each decision maker to articulate preference information about alternatives. We develop a mathematical programming model that can establish dominance relations when the preference information about values of alternatives, attribute weights, and group member's importance weights are provided incompletely. Also, the preference relation between alternatives is to be considered in the model. Based on the preference strength measure and mathematical model, we develop an interactive group decision support procedure.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.57-67
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• 2012

This paper presents the Multi Criteria Decision Making (MCDM) to evaluate water resources plan for agricultural reservoir. Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) and Analytic Hierarchy Process (AHP) were used to estimate weight and priority of alternatives to find out the most reasonable and efficient way of water resources assessment. The 6 criteria that both decision maker and beneficiary are satisfied have been identified to secure agricultural water resources and then the priority of 10 subcriteria was set. An enhanced PROMETHEE-AHP model was used to perform pairwise comparison and find out the priority of each alternative because the existing decision making model have uncertainty and ambiguity. Comparison analysis of decision making models was carried out to find a way of suitable decision making and validity of PROMETHEE-AHP model was suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.1
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• pp.131-140
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• 1992

For a real-time flood forecasting and reservoir control problem of a multipurpose dam, the online acquisition of hydro-meteorological data and computerized analysis of the acquired data are absolutely necessary for the prompt decision of reservoir discharges which can minimize the possible damages and simultaneously maximize the utilization of the runoff. By introducing a man-machine interface such as condensed color graphics of the analyzed results, it is much easier and faster to transform the information to the decision maker who can decide the reservoir discharge. The newly developed PC-REFCON, which represents the PC based real-time flood forecasting and reservoir control, can easily handle the above problems by adopting a innovative decision support system. The system has three principal components of, a data base subsystem which acquires and manages real-time data, a model subsystem which forecasts the flood runoff and simulates the reservoir operation, and a dialogue subsystem which helps decision maker and system engineers using various graphics and tables with renovative methodologies. The developed PC-REFCON will be utilized from the coming Summer of 1992 for the flood control of all the nine multipurpose reservoirs in Korea.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.2
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• pp.15-20
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• 1979

Information on the future availability of invested funds provides a decision maker with additional insight into the characteristics of alternatives in sequential investment analysis. The flexibilty to pursue newly emergent investment opportunities is an important property of decision alternatives that describes the future mobility of invested funds. The primary purpose of this paper is to propose a method for measuring flexibility using the concept of negative project balance with project abandonment option.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.41-50
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• 1997

In this paper, we presents the method for finding the compensatory solution for fuzzy multiobjective nonlinear programming problem with fuzzy parameters involved in the problem-formulation process and fuzzy equal goals of the decision maker for each of the objective functions. The fuzzy parameters in the objective functions and the constraints characterized by fuzzy numbers. The proposed method can be applied to case with multiobjective problems and guarantee an efficient solution. An illustrative numerical example is presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.1-8
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• 1988

This study suggests on optimal process when decision maker is confronted with expert's biased information under the situation that the bias is caused mainly by the difference of their interest. In order to make honest transmission of expert's probabilistic information, the concept of expert use and scoring rule to provide expert with an incentive is used in this paper. And expected regret concept is introduced to evaluate the value of expert's information. A simple example is also shown.