• Journal of the Korean School Mathematics Society
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• v.12 no.3
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• pp.307-317
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• 2009

Formally p$\rightarrow$q means that affirming p one implicitly affirms q and that denying q one implicitly denies p. Denying p or affirming q do not lead to certain conclusions. Middle school students can recognize practical implication p$\rightarrow$q is true whenever p is false, but they don't recognize theoretical implication p$\rightarrow$q is true whenever p is false. They have not assimilated intuitively the complete structure of implication. Thus they do not distinguish naturally between the uncertain conclusion which can be drawn by affirming p and the certain rejection of p which follows from the negation of q. Also they can not recognize the uncertain conclusion which can be drawn by negation of p. There is no significant difference between practical conditional statements, formal conditional statements and conditional Inferences in advanced mathematics students. But there is a significant difference between formal conditional inferences and specific conditional inferences with statement p$\rightarrow$q is true when p is false.

• Korean Journal of Logic
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• v.15 no.3
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• pp.347-375
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• 2012

In my article published in 2008, I offered an inferentialist account of indicative conditionals. In her recent paper, Professor Seawha Kim raises three objections. First, I misunderstand Sellars-Brandom in that I take only concept-constitutive inferences as materially valid inferences. Second, Sellars and Brandom talk about the common features of all kinds of conditionals including counterfactual conditionals, but I construe their view as the analysis of the indicative conditionals only. Third, either my analysis is incompatible with Sellars-Brandom inferentialism or my analysis is too general. In this paper I argue that Seawha Kim's objections are all based on insufficient understandings of Sellars's and Brandom's views. First, it is Sellars's view that materially valid inferences are restricted within concept-constitutive inferences. Second, neither Sellars nor Brandom proposes a specific theory about the indicative conditional. Instead, they argue for the expressive role of the conditional. What I accept from their views is this expressive role of the conditional. The detailed proposals about the indicative conditional in my aforementioned article are my own. Third, the differences among conditionals have no direct bearing on Sellars-Brandom inferentialism. In addition, the meaning and role of the conditional expression 'if-then' do not require more than what I have argued for it.

• Journal of Families and Better Life
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• v.26 no.5
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• pp.333-354
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• 2008

This study aimed to investigate the role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on psychology and biology. Participants were 121 3-year-olds and 121 4-year-olds recruited from seven childcare centers in Seoul, Kyonggi Province, and Busan. After participants watched moving pictures on psychological and biological phenomena, they were asked to choose appropriate cause and justify their choices. Results of this study were as follows: First, young children made different inferences according to domain-specific causal mechanisms. Second, the developmental level of causal mechanisms has a gap between psychology and biology, and biological knowledge was proved to be separate from psychological knowledge during the preschool period. Third, young children's causal reasoning was different depending on the interaction effect of domain-specific mechanisms and domain-general conditional probability: children could make more inferences based on domain-specific causal mechanisms if conditional probability between domain-appropriate cause and effect was evident. To conclude, it can be inferred that the role of domain-specific causal mechanisms and domain-general conditional probability is not competitive but complementary in young children's causal reasoning.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.405-411
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• 2004

In the analysis of cancer data, it is important to make inferences of survival function and to assess the effects of covariates. Cox's proportional hazard model(PHM) and Beran's nonparametric method are generally used to estimate the survival function with covariates. We adjusted the incomplete survival time using the Buckley and James's(1979) pseudo random variables, and then proposed the estimator for the conditional survival function. Also, we carried out the simulation studies to compare the performances of the proposed method.

• Korean Journal of Child Studies
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• v.29 no.5
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• pp.243-269
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• 2008

The role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on physics and psychology was investigated with the participation of 121 3-year-olds and 121 4-year-olds recruited from seven child care centers in Seoul, Kyonggi Province, and Busan. Children watched moving pictures on physical and psychological phenomena, and were asked to choose an appropriate cause and justify their choice. Results showed that young children's causal reasoning differed depending on domain-specific mechanism. In addition, their causal reasoning on physics and psychology differed by the developmental level of causal mechanism. The interaction of domain-specific mechanism and domain-general conditional probability influenced children's causal reasoning : evident conditional probability between domain-appropriate cause and effect helped children make more inferences based on domain-specific causal mechanism.

• Korean Journal of Logic
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• v.15 no.2
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• pp.251-272
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• 2012

Recently Professor Lee has suggested the analysis of the indicative conditional based on Sellars-Brandom's inferentialism. In this paper, I raise three questions. First, Professor Lee seems to misunderstand Sellars-Brandom in that he considers only the analytically valid arguments as materially valid inferences. Second, Professor Lee seems to misunderstand Sellars-Brandom in that whereas Sellars-Brandom talks about the common features of all kinds of conditionals including counterfactual conditionals, Professor Lee takes it as the analysis of the indicative conditional only. Third, either Professor Lee's analysis is incompatible with Sellars-Brandom inferentialism or his analysis is too general.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

• Korean Journal of Logic
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• v.16 no.1
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• pp.87-115
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• 2013

In my previous article "The Uncontested Principle and Wonbae Choi's Objections", I argued that the validity of modus ponens (as a deductive inference) is compatible with the claim that the Uncontested Principle is controversial. In his recent paper "The Uncontested Principle and Modus Ponens", Wonbae Choi criticizes my view again by making the following three claims: First, even though I do not take an inference of the form 'If A then (probably) C. A. $\therefore$ C' as an instance of modus ponens, this form of inference can be taken to be such an instance. Second, there is no grammatical indicator which allows us to distinguish between an indicative conditional based on a deductive inference and an indicative conditional based on an inductive inference, so that inferences based on these conditionals should not be treated as different types of inferences. Third, if we allow an indicative conditional based on an inductive inference, we thereby violate the so-called 'principle of harmony', which any logical concept should preserve. In this paper, I reply that his criticisms are all implausible.

• Language and Information
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• v.4 no.2
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• pp.69-92
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• 2000

It is well-known that default rules require a nonmonotonic logic. Veltman proposed one dynamic theory which interprets default rules in such a way that correct inferences can be made at each information state. But his theory has some problems. First, this theory excludes the possibility that a default rule can be true of false. Second, his representation of an information state makes it difficult to interpret a default rule embedded in another sentence. Third, the notion of a frame which is introduced in the interpretation of a default rule and the adjustment of inferential expectation has a more complex structure than is necessary, In this paper, I propose a truth-conditional theory of default rules in which the meaning of a default rule is defined as a truth-condition in a possible world and which assumes a simpler structure of a frame. This makes it possible to interpret a default rule embedded in a sentence. A dynamic theory for default rules is also proposed for correct inferences based on default rules.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.193-207
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• 2001

When designing a test set, we need to consider constraints on items that are deemed important by item developers or test specialists. The constraints are essentially on the components of the test domain or abilities relevant to a given test set. And so if the test domain could be represented in a more refined form, test construction would be made in a more efficient way. We assume that relationships among task abilities are representable by a causal model and that the item response theory (IRT) is not fully available for them. In such a case we can not apply traditional item selection methods that are based on the IRT. In this paper, we use entropy as an uncertainty measure for making inferences on task abilities and developed an optimal item selection algorithm which reduces most the entropy of task abilities when items are selected from an item pool.