• Korean Journal of Logic
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• v.18 no.1
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• pp.133-153
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• 2015

In his paper "Structural Equations Approach to Token Causation: The Active Route Account Revisited" Professor Sungsu Kim defends the active route account. The active route account is the theory of causation which overcomes counterexamples to the counterfactual theories of causation, while maintaining the counterfactual theorist's essential intuition that an effect depends counterfactually on a cause. Unfortunately, there are counterexamples to the active route account itself. Professor Sungsu Kim attempts to defend the active route account by rebutting those counterexamples. In this paper, I argue that his defense of the active route account is not successful.

• Korean Journal of Logic
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• v.18 no.2
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• pp.265-289
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• 2015

The idea that an effect counterfactually depends on its cause is simple and intuitive. However, this simple idea runs into various difficulties. The active route account, in order to avoid the difficulties, analyzes causation in terms of counterfactual dependence under certain control. In her recent article, Seahwa Kim criticizes Sungsu Kim's earlier attempt to defend the active route account from its counterexamples. Her criticisms are convincing, and defenders of the active route account or counterfactual analysis of causation in general need another defense. In response, a two-step defense is proposed. First, the scope of the active route account is restricted to 'proximate' causal relation. Second, a control over factors that are in proximate causal relation is offered to figure out 'distant' causal relation. The result is that with proper control, an effect indeed counterfactually depends on its cause.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.946-952
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• 2014

Mobility prediction is one of hot topics using location history information. It is useful for not only user-level applications such as people finder and recommendation sharing service but also for system-level applications such as hand-off management, resource allocation, and quality of service of wireless services. Most of current prediction techniques often use a set of significant locations without taking into account possible location information changes for prediction. Markov-based, LZ-based and Prediction by Pattern Matching techniques consider interesting locations to enhance the prediction accuracy, but they do not consider interesting location changes. In our paper, we propose an algorithm which integrates the changing or emerging new location information. This approach is based on Active LeZi algorithm, but both of new location and all possible location contexts will be updated in the tree with the fixed depth. Furthermore, the tree will also be updated even when there is no new location detected but the expected route is changed. We find that our algorithm is adaptive to predict next location. We evaluate our proposed system on a part of Dartmouth dataset consisting of 1026 users. An accuracy rate of more than 84% is achieved.