• Korean Journal of Logic
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• v.20 no.1
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• pp.21-67
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• 2017

In this paper, I critically examine Sartorio's (2006) argument for disjunctive cause, and put forth disjunctive causal structure in a different way. I show that the disjunctive causal structure meets not just what Sartorio means to claim but also our understanding of causal responsibility. First, I introduce Sartorio's argument for disjunctive cause. Second, I critically discuss Sartorio's responses to the criticisms of her arguments for disjunctive cause, and propose another problem with her arguments. Finally, I explicate in a different way Sartorio's disjunctive cause in terms of disjunctive causal structure founded on moderate variables, causal interaction, and probability trajectory. I notice, regarding the disjunctive causal structure, the role of causal interaction of cause events with moderate variables. I reveal, regarding the disjunctive causal structure, the significance of indetermination of cause events and effect events for our understanding of causal responsibility. I show that the disjunctive causal structure guides us more convincingly to assign causal responsibility to an agent. I come to three conclusions. First, there is no disjunctive cause event Sartorio argues for. Second, propensities of events to be causally connected to an effect event constitute disjunctive relation. Third, we should notice indetermination of cause events and effect events while assigning causal responsibility to an agent.

• 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 Journal of Cognitive Science
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• v.21 no.1
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• pp.47-75
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• 2010

Two experiments were conducted to explore whether the Or structure works as a default causal model in inferring the causal structure from the contingency data. The contingencies of three unfamiliar variables were used in Experiment 1. Participants inferred the Or structure quite well from the OR data, but incorrectly inferred the Or structure from the And data for about a little less than half of the time, and almost always inferred the Or structure from the chain data. The results suggested that the Or interpretation can be the default causal model. The prevalence of the Or interpretation from the contingency data was reported even when the three variables were familiar ones in Experiment 2. Multinomial modeling performed on the results of the two experiments strongly suggested that the Or interpretation work as a default causal model.

• International Journal of Contents
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• v.12 no.4
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• pp.69-75
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• 2016

Adolescent media addiction has emerged as an important social agenda in Korea. However, there has not been enough discussion on the causal structure of media addiction and policy interventions. The objective of this study is to identify and assess the mechanism of the existing and the revised Shutdown policy based on the systems thinking approach. To achieve this purpose, we establish the relationship between media usage, flow, and addiction, and develop a causal loop diagram. Based on the causal loop diagram, we explore the causal structure of two policy scenarios: shutdown policy and deregulation. Our study suggests that policy interventions inducing direct parental control on children's media usage time are ineffective since the time control reduces children's autonomy, which helps alleviate media addiction. Therefore, this study suggests that policy intervention should focus on alleviating addiction itself rather than on controlling media usage time.

• Journal of the Korean Mathematical Society
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• v.34 no.2
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• pp.257-264
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• 1997

We shall introduce the concept of almost causality condition. By defining the almost causality condition we would like to examine the relationship between Woodhouse's causality principle and other known causality conditions. We show that a series of causality conditions can be characterized by using the almost causality condition.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.120-121
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• 2018

The construction industry is a typical labor-intensive industry, and is heavily influenced by the employment structure. However, research on how the employment structure of construction workers affects the construction industry has not been sufficiently studied. In this study, the major factors influenced by the employment structure in the construction industry are derived and the causal relationship between the factors is illustrated. The results of this study are expected to be used as data for setting direction of policies related to the employment structure of construction workers.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.257-260
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• 2002

The impressions for odors are subjective and have individual differences. In this study, the Impressions of odors were investigated by covariance structure analysis. 46 subjects (men in their twenty) recorded their reactions to ten odorants by grading them on a seven-point scale in terms of twelve adjective pairs. Their reactions were quantified by using factor analysis and covariance structure analysis. The factors were extracted as "preference", "arousal" and "persistency". The subjects were classified into three groups according to the most suitable causal models (structural equation models). Each group had different causal relationship and different impression structure for odors. It was suggested that there is a possibility to evaluate the subjective impression of odor using covariance structure analysis.

• Korean System Dynamics Review
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• v.1 no.2
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• pp.91-111
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• 2000

Causal maps or cognitive maps have been widely used to get insights for complex systems or decision makers. When insights come from the system behavior rather than its structure, we need simulation of causal maps and cognitive maps. In this paper, a method for directly converting causal maps and cognitive maps into stock-flow diagrams that can be simulated in computers in proposed. This method is called as NUMBER. NUMBER is an abbreviation for 'Normal Unit Modeling By Elementary Relationship'. In this paper, NUMBER is applied to a cognitive map of policy maker to show its usefulness.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.365-376
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• 2022

This study aims to look into the causal relationships between corporate social responsibility and firm value, corporate social responsibility and institutional ownership, and firm value and institutional ownership. This study develops a triangle model of causal relationships among the three endogenous variables. Samples for this study are manufacturing companies listed on the Indonesia Stock Exchange for the period 2014-2018. The model is operated in the system of simultaneous equation models using the generalized method of moments technique to estimate parameter coefficients. After controlling the effects of trade-off/balancing capital structure and managerial ownership, the research findings show a positive causal relationship between CSR and firm value and firm value and institutional ownership. Institutional ownership has a positive effect on CSR, while the effect of CSR on institutional ownership is negative in the firms without managerial ownership and positive in the firms with managerial ownership. This study finds that the causal relationship between CSR and firm value is stronger after the trade-off/balancing of capital structure is included in the model. Capital structure has a convex effect on firm value and positively impacts institutional ownership. In addition, an independent commissioner has a negative impact on CSR but has no direct impact on firm value.