• Proceedings of the Korean System Dynamics Society
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• 2004.08a
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• pp.109-128
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• 2004

The complexity of information and communication policy has been increasing due to its rapid changes and its expansions toward various fields. I used the2001, 2002 and 2003 White Papers on MIC(Ministry of Information and Communication Republic of Korea) as a reference and the Vensim PLE program to create a causal map. According to my analysis, no major feedback loop was found among the information and communication policies. Thus, it was impossible to conduct a causal map analysis on these policies. The causal map analysis is usually employed to understand a complex mechanism of entire policies by finding feedback loops among them. A lack of feedback loops makes it impossible to conduct the causal map analysis and means that the mechanism of such policies is even more complex to understand. The most important conclusion is that to consider feedback thought among the policies based on the systems thinking before making the policies.

• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• Korean System Dynamics Review
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• v.11 no.2
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• pp.77-102
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• 2010

This paper examines a mechanism of the Electronic Territory Expansion and the Information-oriented Society. Especially, a strategy for the territory development based on intelligence is suggested. The strategy is divided into a strategy for the domestic electronic territory and a plan for the global electronic territory. To examine the strategy and the plan, this paper is using the causal map analysis based on the System Thinking Approach. The causal map of the mechanism is characterized by a positive feedback loop. The paper has concluded that it is important to make the positive loops as a virtuous circle. It means that when a society dominates the advantageous position firstly in the field of intelligent and electronic territory, the competitiveness can grow in arithmetical progression.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• Korean System Dynamics Review
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• v.10 no.1
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• pp.33-60
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• 2009

This study explores the limitation in making a causal model through an existing case and proposes an alternative plan to improve a theoretical system of causation modeling. To make a dynamic and actual model, several principles are needed such as reality based analysis of system structures and dynamics, consistent expression of causations, conversion of numerical formulas to causal relations, classification and arrangement of variables by size of concept, etc. However, it is hard to find cases to apply these considerations from existing models in System Dynamics. Therefore, this study verifies errors of derived models from literatures and proposes principles and guides that should be considered to make a sound dynamic model on a causal map. It contributes to making an opportunity for exciting public opinion to improve theory about causal maps, yet it has limitation that the study does not advance forward to the experimental step. For future study, it plans to make up by classifying and leveling causal variables, developing a dynamic BSC model.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.210-218
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• 2007

Road freight transport industry in Korea contains many and complicate problems such as over supply of the vehicle caused by rapid policy changes, illegal multilevel transactions, poor truck drivers working environment, lack of road freight transport related statistics and so on. Korean government has developed various logistics industrial policy trying to solve these problems in various ways. However, the relationship among the problems and action plans has been more and more entangled since the part of suggested policies have made another perverted problems. These complex structure of the toad freight transport industry in Korea makes difficult to identify and to solve the problems. Causal map method helps to give a clean picture to understand the complex industry at a glance. This study contributes for visualization of the causal relationships among the existing problems and related policy issues in the road freight transport industry in Korea by causal map. This study could be helpful to develop the actual road freight transport industrial policies including the illogical multilevel and unfair transaction in Korea.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.342-348
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• 2004

As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. However, literature still shows that only structured conditions have been explicitly considered, despite the fact that unstructured conditions should be rendered as well. In this sense, this paper proposes a new negotiation support mechanism to incorporate causal relationships between structured and unstructured conditions in the process of B2B negotiation. Fuzzy cognitive map was used as a main source of causal knowledge as well causal inference engine. A prototype named CAKES-NEGO was developed to perform experiments with an illustrative example. Results revealed the robustness of our proposed negotiation support mechanism.

• Korean System Dynamics Review
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• v.14 no.3
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• pp.75-103
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• 2013

The purpose of this study is to use system dynamics to establish the relation among each variable through the construction process of Community Planning Model, and examine what changes policy scenarios per alternative cause in Community Planning through policy simulation of the constructed model. Therefore, this study extracted chief variables of Community Planning Projects through precedent researches related to Community Planning, and extracted variables were prepared as causal map to examine in what causal cycle feedback structure within Community Planning they can be explained. Next, Community Planning Model was constructed based on the prepared causal map. The model was verified by specialists' interviews and simulation of example areas. This study, which aimed to construct Community Planning Model using system dynamics, has a significance in that it prepared the foundation to provide useful methodology in monitoring the progress of project or establishing the plan of future Community Planning Projects.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.860-863
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• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme. However, the previously proposed scheme has the problem of lower diagnostic resolution. In order to improve the diagnostic resolution, a new diagnostic scheme based on extended FCM which incorporates the concept of fuzzy number into FCM is developed in this paper. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and pattern matching scheme are also proposed.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.