Integrating Economic Knowledge in Data Mining Algorithms

The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.knowledge;neural network;data mining;decision trees

Derivation of Monotone Decision Models from Non-Monotone Data

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.decision models;knowledge;decision theory;operational research;data mining

Application of Neural Networks to House Pricing and Bond Rating

Feed forward neural networks receive a growing attention as a data modelling tool in economic classification problems. It is well-known that controlling the design of a neural network can be cumbersome. Inaccuracies may lead to a manifold of problems in the application such as higher errors due to local optima, overfitting and ill-conditioning of the network, especially when the number of observations is small. In this paper we provide a method to overcome these difficulties by regulating the flexibility of the network and by rendering measures for validating the final network. In particular a method is proposed to equilibrate the number of hidden neurons and the value of the weight decay parameter based on 5 and 10-fold cross-validation. In the validation process the performance of the neural network is compared with a linear model with the same input variables. The degree of monotonicity with respect to each explanatory variable is calculated by numerical differentiation. The outcomes of this analysis is compared to what is expected from economic theory. Furthermore we propose a scheme for the application of monotonic neural networks to problems where monotonicity with respect to the explanatory variables is known a priori. The methods are illustrated in two case studies: predicting the price of housing in Boston metropolitan area and the classification of bond ratings.Classification;error estimation;monotonicity;finance;neural-network models

Defending Against Speculative Attacks

While virtually all currency crisismodels recognise that the fate of a currency peg depends on how tenaciously policy makers defend it, they seldom model how this is done. We incorporate themechanics of speculation and the interest rate defence against it in the model ofMorris and Shin (American Economic Review 88, 1998). Our model captures that the interest rate defence reduces speculators’ profits and thus postpones the crisis. It predicts that well before the fall of a currency interest rates are increased to offset the buildup of exchange market pressure, and this then unravels in a sharp depreciation. This pattern is at odds with predictions of standard models, but we show that it fits well with reality.Exchange Market Pressure, Currency Crisis, Interest Rate Defence, Global Game

News broadcasts between fuṣḥā and Lebanese: Language choice as an implicit comment on national identity in Lebanon

This article presents an analysis of the news bulletins broadcasted by South Lebanese radio station Ṣawt al-Ğanūb (SaJ, Voice of the South). SaJ broadcasted its news bulletins in fuṣḥā (Standard Arabic), as well as in Lebanese. This is interesting because in most news bulletins tend to be broadcasted in the standard language, rather than in spoken varieties. This is definitely the case for so-called diglossic societies, such as Arabic-speaking societies, in which the linguistic metanorm for ‘serious programs’ is fuṣḥā. After presenting a brief linguistic description of a small corpus of news bulletins that were broadcasted in January 1998, this article focuses on how language (choice) functions symbolically in the extra-linguistic world. It argues that the choice to breach the metapragmatic norms, while framing the language use in the news bulletins explicitly as ‘the Lebanese language’ (al-luġa al-lubnānīye) can be interpreted as an implicit comment on Lebanese national identity

Integrating Economic Knowledge in Data Mining Algorithms

The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.

Derivation of Monotone Decision Models from Non-Monotone Data

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.