Anti-Trust and Economic Theory: Some Observations from the US Experience

Recent developments in US anti-trust can be characterised as reflecting the uneasy interaction of two quite separate phenomena: first, the increased emphasis on economic analysis as the overriding organising principle of anti-trust policy and on economic efficiency as the primary (perhaps only) relevant goal for anti-trust; second, the long-standing reluctance of the federal judiciary to involve itself in any substantive economic analysis, and the preference, instead, for simple rules of thumb or ‘pigeon holes’ to sort out lawful from unlawful conduct. The result has been that while economics has played a major role, it has not influenced American anti-trust as thoroughly or as uniformly as might have been imagined; rather the extent and the nature of its influence have depended on the degree to which the relevant economics could be reduced to the kind of simple rules or pigeon holes that the judiciary favours. The present paper will illustrate that theme, first by reporting on the two developments separately and then by illustrating their joint influence with reference to two important areas of American anti-trust: predatory conduct and so-called vertical restraints. Finally, a contrast will be made between judicial development in those two areas and recent American merger policy which, it is argued, is carried out largely independently of the judiciary, and hence the opportunities for economics to influence the process are less inhibited by the judicial reluctance to undertake extensive economic analysis

Computing: report leaps geographical barriers but stumbles over gender

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62917/1/441025a.pd

Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk

BACKGROUND: In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. METHODS: ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in R(N), classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R(2), based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan™. RESULTS: The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22–67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51–60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. CONCLUSION: ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters