• Korean Journal of Child Studies
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• v.34 no.6
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• pp.1-11
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• 2013

The purpose of this study was to investigate the process of counterfactual reasoning which children undergo, based on mental model theory and dual process theory. The subjects were 120 four-year-olds and 120 five-year-olds from Ulsan. Counterfactual reasoning task conditions were created, including task type and content, which were type 1-specific, type 1-general, type 2-specific, type 2-general. There were two stories used for each task condition. Children's counterfactual reasoning score range was 0 to 8. Data were analyzed using SPSS by mean, standard deviation, one sample t-test, repeated measures of Anova. The results of this study were as follows. First, children's counterfactual reasoning was above chance level regardless of the task condition. Second, children's counterfactual reasoning was lowest when type 1-specific or type 2-specific tasks were given, slightly higher when type1-general tasks were given, and the highest when type 2-general tasks were given. There was no significant difference between 4-year-old and 5-year-old children's counterfactual reasoning.

• Analyses & Alternatives
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• v.8 no.3
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• pp.125-150
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• 2024

This paper explores the integration of artificial intelligence and causal inference in social science research, focusing on causal deep learning. We examine key theories including Pearl's Structural Causal Model, Rubin's Potential Outcomes Framework, and Schölkopf's Causal Representation Learning. Methodologies such as structural causal models with deep learning, counterfactual reasoning, and causal discovery algorithms are discussed. The paper presents applications in social media analysis, economic policy, public health, and education, demonstrating how causal deep learning enables nuanced understanding of complex social phenomena. Key challenges addressed include model complexity, causal identification, interpretability, and ethical considerations like fairness and privacy. Future research directions include developing new AI architectures, real-time causal inference, and multi-domain generalization. While limitations exist, causal deep learning shows significant potential for enhancing social science research and informing evidence-based policy-making, contributing to addressing complex social challenges globally.

• 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 Korean Philosophical Society
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• v.146
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• pp.307-328
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• 2018

The use of thought experiments has a long history in many disciplines including science. In the field of philosophy, thought experiments have frequently appeared in the pre-existing literature on the contemporary Analytic Philosophy. A thought experiment refers to a synthetic environment where the designer of the experiment-with his or her intuition and imagination-tests common-sense knowledge. It can be understood as a conceptual tool for testing the validity of the common understanding of an issue or a phenomenon. However, we are not certain about the usefulness or efficacy of a thought experiment in knowledge production. The design of a thought experiment is meant to lure readers into believing as intended by the experiment itself. Thus, regardless of the purpose of a thought experiment, many readers who encounter the experiment could feel deceived. In this paper, to analyze the logic of thought experiments and to seek the source of uneasiness the readers and critics may feel about thought experiments, I draw lessons from three renowned thought-experiments: Thomson's 'ailing violinist', Putnam's 'brain in a vat', and Searle's 'Chinese room'. Imaginative thought experiments are usually constructed around a gap between the reality and the knowledge/information at hand. From the three experiments, several lessons can be learned. First, the evidence of the existence of a gap provided via thought experiments can serve as arguments for counterfactual situations. At the same time, the credibility and efficacy of the thought experiments can be damaged as soon as the thought-experiments are carried out with inappropriate and/or murky directions regarding the procedures of the experiment or the background of the study. According to D. R. Hofstadter and D. C. Dennett(1981), the 'knob setting' in a thought experiment can be altered in the middle of a simulation of the experimental condition, and then the implications of the thought experiment change altogether, indicating that an entirely different conclusion can be deduced from thought experiment. Lastly, some pre-suppositions and bias of the experiment designers play a considerable role in the validity and the chances of success of a thought experiment; thus, it is recommended that the experiment-designers refrain from exercising too much of their imagination in order to avoid contaminating the design of the experiment and/or wrongly accepting preconceived/misguided conclusions.