Impossibilities with Kemeny updating

Impossibility theorems for preference correspondences based on a new monotonicity concept arediscussed. Here monotonicity means that if preferences update in such a way that they get closerto an outcome then at the new situation this outcome remains chosen. Strong monotonicity requiresfurther that in those cases the outcome at the new profile is a subset of the outcome at the oldprofile. It is shown that only dictatorial preference correspondences are unanimous and stronglymonotone.microeconomics ;

Update Monotone Preference Rules

Collective decisions are modeled by preference correspondences (rules). In particular, we focus ona new condition: "update monotonicity" for preference rules. Although many so-called impossibilitytheorems for the choice rules are based on -or related to- monotonicity conditions, this appealingcondition is satisfied by several non-trivial preference rules. In fact, in case of pairwise,Pareto optimal, neutral, and consistent rules; the Kemeny-Young rule is singled out by thiscondition. In case of convex valued, Pareto optimal, neutral and replication invariant rules;strong update monotonicity implies that the rule equals the union of preferences which extend allpreference pairs unanimously agreed upon by k agents, where k is related to the number ofalternatives and agents. In both cases, it therewith provides a charaterization of these rules.microeconomics ;

QUALITY LOSSES IN TEMPORARY SUNFLOWER SEED STORES AND INFLUANCES OF STORAGE CONDITIONS ON QUALITY LOSSES DURING STORAGE

The aim of storage is to preserve properties of products and their freshness. If suitable storage conditions are not supplied according to product variety, quality and quantity losses increase. Decreasing these losses is possible with providing suitable storage conditions and storage management. In this study were aimed at determining of storage losses in the temporary sunfl ower seed stores and investigating the influences of storage condition on quality losses of sunfl ower during storage

Turkish lexicon expansion by using finite state automata

© 2019 The Authors. Published by The Scientific and Technological Research Council of Turkey. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://journals.tubitak.gov.tr/elektrik/issues/elk-19-27-2/elk-27-2-25-1804-10.pdfTurkish is an agglutinative language with rich morphology. A Turkish verb can have thousands of different word forms. Therefore, sparsity becomes an issue in many Turkish natural language processing (NLP) applications. This article presents a model for Turkish lexicon expansion. We aimed to expand the lexicon by using a morphological segmentation system by reversing the segmentation task into a generation task. Our model uses finite-state automata (FSA) to incorporate orthographic features and morphotactic rules. We extracted orthographic features by capturing phonological operations that are applied to words whenever a suffix is added. Each FSA state corresponds to either a stem or a suffix category. Stems are clustered based on their parts-of-speech (i.e. noun, verb, or adjective) and suffixes are clustered based on their allomorphic features. We generated approximately 1 million word forms by using only a few thousand Turkish stems with an accuracy of 82.36%, which will help to reduce the out-of-vocabulary size in other NLP applications. Although our experiments are performed on Turkish language, the same model is also applicable to other agglutinative languages such as Hungarian and Finnish.Published versio

STORING SUNFLOWER SEEDS AND QUALITY LOSSES DURING STORAGE

In this research, sunfl ower storage buildings in the Thrace region were examined. Infl uences of storage condition on product losses were investigated. According to the results of experiments in selected stores, the worst storage conditions and the most quality losses were determined in the concreate store, on the other hand the most suitable conditions and the least losses were determined in model store

Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations (AQMS). In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS (A-D) located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH (group method of data handling)-type neural network method (namely the self-organizing deep learning approach), a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training (%80) and a testing set (%20). Based on R2, RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS (reaching 0.94, 10.95, and 6.65, respectively for station A) and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables (without using NOx data as model input) with R2, RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS

An axiomatic re-characterization of the Kemeny rule

The Kemeny rule is one of the well studied decision rules. In this paper we show that the Kemeny rule is the only rule which is unbiased, monotone, strongly tie-breaking, strongly gradual, and weighed tournamental. We show that these conditions are logically independent

Economic Design of Things

Economics is a social science, so is economic design as a field. This short article discusses, in particular, the future of economic design, and of economic theory in general. By suggesting some examples, I hope to convince the readers that the recent technological advances in science and technology will not only be disruptive to the social machinery that surrounds us but also to the future of economic design as a field. However, economic design, as an established field, has the potential to add value to the society by offering an axiomatic framework to the design of the future with a social sciences perspective