Poverty in Tunisia: A Fuzzy measurement approach

Belhadj, Besma; Matoussi, Mohamed Salah

doi:10.1007/bf03399322

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Fuzzy logic is first proposed by Lotfi Aliasker Zadeh in 1965 [88], and since then it has been used in fields as diverse as Economics, Statistics, Finance, Management, Engineering, etc. [89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104] Fuzzy logic uses membership functions to determine the percent of win for each party. To simplify the illustration of the proposed model, we consider two parties of the negotiation.…”

Section: The Proposed Fuzzy Win-win Modelmentioning

confidence: 99%

Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic

Hassanat

Altarawneh

Tarawneh

2021

Preprint

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The classic win-win has a key flaw in that it cannot offer the parties with right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs the Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiation scenarios such as the Iranian uranium enrichment negotiations, the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.

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Section: The Proposed Fuzzy Win-win Modelmentioning

confidence: 99%

Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic

Hassanat

Altarawneh

Tarawneh

2021

Preprint

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“…Therefore, the accuracy, the precision, minimizing error and time effectiveness could be achieving and increases the performance knowledge of the students. The function of membership is a curve showing the mapping of data input points into their membership value (also often called as membership degree) which has intervals between 0 and 1 [25,26].…”

Section: Dss Modulementioning

confidence: 99%

Fuzzy logic applications for data acquisition systems of practical measurement

Haddin¹,

Marwanto²,

Suprajitno³

et al. 2020

IJECE

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In laboratory works, the error in measurement, reading the measurring devices, similarity of experimental data and lack of understanding of practicum materials are often found. These will lead to the inacurracy and invalid in data obtanined. As an alternative solution, application of fuzzy logic to the data acquisition system using a web server can used. This research focuses on the design of data acquisition systems with the target of reducing the error rate in measuring experimental data on the laboratory. Data measurement on laboratory practice module is done by taking the analog data resulted from the measurement. Furthermore, the data are converted into digital data via arduino and stored on the server. To get valid data, the server will process the data by using fuzzy logic method. The valid data are integrated into a web server so that it can be accessed as needed. The results showed that the data acquisition system based on fuzzy logic is able to provide recommendation of measurement result on the lab works based on the degree value of membership and truth value. Fuzzy logic will select the measured data with a maximum error percentage of 5% and select the measurement result which has minimum error rate.

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“…Moreover, application of fuzzy approaches to unidimensional poverty measurement is still grounded on parametric and axiomatic modeling (see, for instance, Belhadj and Limam, ). Belhadj and Matoussi () have proposed a parametric approach based on the configuration of three fuzzy privation states for the measurement of unidimensional poverty.…”

Section: Fuzzy Non‐parametric Privation Statesmentioning

confidence: 99%

“…Indeed, the choice of the form of the membership function should convey the nature and the real features of the phenomenon. Here, we make the choice for a membership function form more adapted to the real features of poverty phenomenon (used in Belhadj and Matoussi, ) and which is defined as follows:

F N (x, a, b, c) = (\begin{array}{l} left & 0 & x < a, \\ left & 1 & a \leq x < b, \\ left & x \cdot \frac{- 1}{(c - b)} + \frac{(c)}{(c - b)} & b \leq x < c, \\ left & 0 & x \geq c . \end{array}

The parameters a , b , and c are set among estimators of the percentiles of the poverty predicate variable X , which are estimated via the bootstrap technique (Efron, ). Then, the membership functions corresponding to the 100 fuzzy sets are defined as follows:…”

Section: Fuzzy Non‐parametric Privation Statesmentioning

confidence: 99%

A New Approach to Unidimensional Poverty Analysis: Application to the Tunisian Case

Zedini¹,

Belhadj²

2014

Review of Income and Wealth

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Fuzzy conceptualization of privation has been a step closer to more realistic handling of poverty. However, fuzzy approaches to poverty are still grounded on parametric axioms. Moreover, construction of poverty lines within these approaches still relies on ad-hoc methods. In this paper, we advance instead a fuzzy procedure based on the non-parametric bootstrap method, allowing us to depict fuzzy unidimensional privation states with boundaries drawn spontaneously from data. Fuzzy nonparametric measures of privation within each state as well as a collective fuzzy non-parametric index of poverty are derived, along with their corresponding confidence intervals. The new approach is applied to the analysis of poverty in Tunisia in 2005. JEL Code: I32

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Poverty in Tunisia: A Fuzzy measurement approach

Abstract: a Higher Institute of Management, University of Tunisia. The researchers wish to thank the anonymous reviewers for their comments and reviews including all the important points raised.

Cited by 8 publications

References 12 publications

Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic

Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic

Fuzzy logic applications for data acquisition systems of practical measurement

A New Approach to Unidimensional Poverty Analysis: Application to the Tunisian Case

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