Avoidance of Partitions of a Three-element Set

Klazar defined and studied a notion of pattern avoidance for set partitions, which is an analogue of pattern avoidance for permutations. Sagan considered partitions which avoid a single partition of three elements. We enumerate partitions which avoid any family of partitions of a 3-element set as was done by Simion and Schmidt for permutations. We also consider even and odd set partitions. We provide enumerative results for set partitions restricted by generalized set partition patterns, which are an analogue of the generalized permutation patterns of Babson and Steingr{\'{\i}}msson. Finally, in the spirit of work done by Babson and Steingr{'{\i}}msson, we will show how these generalized partition patterns can be used to describe set partition statistics.Comment: 23 pages, 2 tables, 1 figure, to appear in Advances in Applied Mathematic

The Evolution Ontology

Existing ontologies model components of evolution, but none synthesize them or describe the framework of ideas used to conceptualize evolution. The Evolution Ontology (EO) aims to do just this. EO models processes (e.g. natural selection); contexts (e.g. habitats); the entities that undergo evolution; and the theories, methods, and disciplines of evolutionary science. Uses include data curation, data mining, and literature curation, EO’s developers working on the latter two for works of Darwin and the Biodiversity Heritage Library

Death and Paperwork Reduction

How does government value people\u27s time? Often the valuation is implicit, even mysterious. But in patches of the federal administrative state, paperwork burdens are quantified in hours and often monetized. When agencies do monetize, they look to how the labor market values the time of the people faced with paperwork. The result is that some people\u27s time is valued over ten times more than other people\u27s time. In contrast, when agencies monetize the value of statistical life for cost-benefit analysis, they look to how people faced with a risk of death subjectively value its reduction. In practice, agencies assign the same value to every statistical life saved by a given policy. This Article establishes these patterns of agency behavior and suggests that there is no satisfying justification for them. Welfarist and egalitarian principles, along with the logic of statistical life valuation, lean against the use of market wages to monetize a person\u27s time doing government paperwork. The impact of this practice might be limited, given the modest ambition of today\u27s paperwork reduction efforts. But time-related burdens—and benefits—are key consequences of government decisions in countless contexts. If we want to scale up a thoughtful process for valuing people\u27s time in the future, we will need new foundations

House price momentum and strategic complementarity

House prices exhibit substantially more momentum, positive autocorrelation in price changes, than existing theories can explain. I introduce an amplification mechanism to reconcile this discrepancy. Sellers do not set a unilaterally high or low list price because they face a concave demand curve: increasing the price of an above-average-priced house rapidly reduces its sale probability, but cutting the price of a below-average-priced house only slightly improves its sale probability. The resulting strategic complementarity amplifies frictions because sellers gradually adjust their price to stay near average. I provide empirical evidence for concave demand using a quantitative search model that amplifies momentum two- to threefold

Comparing Risks Thoughtfully

Dr. Finkel argues that comparing risks is neither impossible nor immoral - but is nonetheless very difficult. He then discusses two major pitfalls of making such comparisons, one commonly cited and one routinely ignored, before sketching a framework for improving them

SMCTC : sequential Monte Carlo in C++

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation