On Negligence Rules and Self-Selection

Economic models of negligence ordinarily involve a single standard of care that all injurers must meet to avoid liability. When injurers differ in their costs of care, however, this leads to distortions in their care choices. This paper derives the characteristics of a generalized negligence rule that induces injurers to self-select their optimal care levels. The principal features of the rule are (1) the due standard of care is maximal, and (2) liability increases gradually as injurers depart further from this standard. The results are broadly consistent with the gradation in liability under certain causation rules and under comparative negligence.

Varying Injurer Costs of Care, Negligence, and Self-Selection

Standard economic models of negligence set a single standard of care to which all injurers must conform. When injurers differ in their costs of care, this leads to distortions in individual care choices. This paper derives the characteristics of a negligence rule that induces optimal care by all injurers by means of self-selection. The principal features of the rule are (1) the due standard is set at the optimal care of the lowest cost injurer, and (2) liability increases gradually rather than abruptly as care falls below this standard. The results are consistent with the gradation in liability under certain causation rules and under comparative negligence.

Current Legal Rules Benefit Spray Applicators When It Comes to Pesticide Drift

Homeowners, landowners, pesticide applicators, and farmers are concerned about pesticide drift. It may injure a homeowner’s garden or flowers or ruin a neighboring farmer’s crop. While no Maryland court has considered the issue of liability from pesticide drift, courts in other states have. These decisions provide some guidance on how a Maryland court might handle the issue. Depending on the facts of the drift case, pesticide applicators and farmers could owe damages for nuisance or trespass case, or for uses inconsistent with the pesticide label.

Behaviour disorders and teacher negligence

The purpose of this paper is to examine the legal implications of the continuing rise in the number of school children diagnosed with behaviour disorders. Not only are teachers now subject to a dense grid of legal regulation, they are also increasingly vulnerable to actions in tort. It will be argued here that as more and more children are labelled ‘disordered’, the duty of care become more onerous, and hence harder for teachers to meet. As a consequence, teachers are more likely to face claims of negligence. It is concluded that while the schooling system needs to retain a healthy scepticism about each new pathologising disorder that seeks special status for its sufferers, it also needs to provide greater training and resources for teachers regarding disorder management. It is also concluded that recent changes to negligence law regarding the issue of ‘reasonable foreseeability’ within breach of duty of care, may not be as significant as might have been hoped by the teaching community. Indeed, the elevated standard of care required by the increasing numbers of disordered pupils, places teachers in an ever more difficult legal position.

A Reliable Basis for Approximate Association Rules

For most of the work done in developing association rule mining, the primary focus has been on the efficiency of the approach and to a lesser extent the quality of the derived rules has been emphasized. Often for a dataset, a huge number of rules can be derived, but many of them can be redundant to other rules and thus are useless in practice. The extremely large number of rules makes it difficult for the end users to comprehend and therefore effectively use the discovered rules and thus significantly reduces the effectiveness of rule mining algorithms. If the extracted knowledge can’t be effectively used in solving real world problems, the effort of extracting the knowledge is worth little. This is a serious problem but not yet solved satisfactorily. In this paper, we propose a concise representation called Reliable Approximate basis for representing non-redundant approximate association rules. We prove that the redundancy elimination based on the proposed basis does not reduce the belief to the extracted rules. We also prove that all approximate association rules can be deduced from the Reliable Approximate basis. Therefore the basis is a lossless representation of approximate association rules.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.