Comment on Alternative Monies and the Demand for Media of Exchange
Professors Santomero and Seater have put considerable energy into building and analyzing a carefully crafted mathematical model with a narrow microbanking focus. They explore the representative consumer's willingness to substitute electronic money (which they portray as plastic-based "smart cards" with no nontransactional informational processing or communications usages) for paper-based "traditional money." Their model concerns itself only with what we could call pocket money and "pocket electronic platforms." The question I wish to explore in my remarks is where can we go from here: How can this first-cut model be made to cast more light on the problem of persuading households to dive deeply into electronic banking?
SOBRE LA DEMANDA DE EFECTIVO
Within by the electronic money definition , the plastics have a significant place , both the debit cards and the new option , even in the experimental stage known as the electronic purse or prepayment. The advanced technological changes , especially the incorporated microchips into plastics and the internet have facilitated the payment system. These circumstances ( the facilities with plastics) have generated the acceptation on the debit card and the electronic purse by the people , and it has altered and transformed several habits deeply rooted in our customs, in particular in Latin America. The goal of this work is to develop a demand model of bills and coins model , such as the Baumol-Tobin inventory model. It includes the option of the buyers to buy their items with cash or debit cards. The sellers cash demand is added to the model because it is a necessary condition in order to find the solution. The sellers could accept debit card only if they have a machine capable of making the necessary transactions between the bank account's client and bank account's seller. If they don't have
Mining Words in the Minds of Second Language Learners: Learner-Specific Word Difficulty
While there have been many studies on measuring the size of learners’ vocabulary or the vocabulary they should learn, there have been few studies on what kind of words learners actually know. Therefore, we investigated theoretically and practically important models for predicting second language learners’ vocabulary and propose another model for this vocabulary prediction task. With the current models, the same word difficulty measure is shared by all learners. This is unrealistic because some learners have special interests. A learner interested in music may know special music-related terms regardless of their difficulty. To solve this problem, our model can define a learner-specific word difficulty measure. Our model is also an extension of these current models in the sense that these models are special cases of our model. In a qualitative evaluation, we defined a measure for how learner-specific a word is. Interestingly, the word with the highest learner-specificity was “twitter”. Although “twitter” is a difficult English word, some low-ability learners presumably knew this word through the famous micro-blogging service. Our qualitative evaluation successfully extracted such interesting and suggestive examples. Our model achieved an accuracy competitive with the current models.