Mohammad Mehdi Korjani

Charles Ragin's Fuzzy Set Qualitative Comparative Analysis (fsQCA) used for linguistic summarizations

Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It was developed by the eminent social scientist Prof. Charles C. Ragin, but has, as of this date, not been applied by engineers or computer scientists. Unlike more quantitative methods that are based on correlation, fsQCA seeks to establish logical connections between combinations of causal conditions and an outcome, the result being rules that summarize the sufficiency between subsets of all of the possible combinations of the causal conditions (or their complements) and the outcome. The rules are connected by the word OR to the output. Each rule is a possible path from the causal conditions to the outcome. This paper, for the first time, explains fsQCA in a very quantitative way, something that is needed if engineers and computer scientists are to use fsQCA.

Theoretical aspects of Fuzzy Set Qualitative Comparative Analysis (fsQCA)

Fuzzy set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It has recently been described as a collection of 13 steps [7]. In this paper we focus on how to greatly speed up some of the computationally intensive steps of fsQCA and show how to use the speed-up equations to obtain some interesting and important properties of fsQCA. These properties not only provide additional understanding about fsQCA, but also lead to different ways to implement fsQCA. One of the properties is so important (Section 8) that unless its results are adopted, when a variable is described by more than one term (e.g., Low and High), fsQCA will provide incorrect results.

Fuzzy set Qualitative Comparative Analysis (fsQCA): Challenges and applications

Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It was developed by the social scientist Prof. Charles C. Ragin. fsQCA seeks to establish logical connections between combinations of causal conditions and an outcome, the result being rules that describe how combinations of causal conditions would cause the desired outcome. So, each rule is a possible path from the causal conditions to the outcome. The rules are connected by the word OR to the output. To actually apply fsQCA to some engineering data problems, there are some challenges that had to be overcome. We explain the challenges and how they have been overcome. We also illustrate the application of fsQCA to the well-known Auto MPG dataset to obtain causal combinations that explain Low MPG 4-cylinder cars.

On establishing nonlinear combinations of variables from small to big data for use in later processing

Abstract This paper presents a very efficient method for establishing nonlinear combinations of variables from small to big data for use in later processing (e.g., regression, classification, etc.). Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition ) associated with it. Our Causal Combination Method uses fuzzy sets to model the terms and focuses on interconnections ( causal combinations ) of either a causal condition or its complement, where the connecting word is AND which is modeled using the minimum operation. Our Fast Causal Combination Method is based on a novel theoretical result, leads to an exponential speedup in computation and lends itself to parallel and distributed processing; hence, it may be used on data from small to big.

Fuzzy Love Selection by means of Perceptual Computing

The main contribution of this paper is to develop a Perceptual Computer for Fuzzy Love Selection problem. This is a problem of ranking all members (alternatives) in an individual list in order of preference. Uncertainty of the individual about criteria scores and weights assigned to each alternative is handled by means of Perceptual Computer. This paper also presents a comparison of two Perceptual Computer engines: linguistic weighted average and linguistic weighted power means. Results show the flexibility and range of logical inference provided by aggregation operators.

Fast Fuzzy set Qualitative Comparative Analysis (Fast fsQCA)

Fuzzy set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It has recently been described as a collection of 13 steps [3]. In this paper we focus on how to speed up some of the computationally intensive steps of fsQCA and how to use the speed-up equations to obtain some interesting properties of fsQCA.

Non-linear Variable Structure Regression (VSR) and its application in time-series forecasting

Variable Structure Regression (VSR) is a new kind of non-linear regression model, which simultaneously determines the exact mathematical structure of non-linear regressors and how many regressors there are, thereby freeing the end user from trial and error time-consuming studies to determine these. The results are based on an iterative procedure for optimizing parameters and automatically identifying the structure of the VSR model. A novel feature of this new model is it not only uses a linguistic term for a variable but it also uses the complement of that term. It also provides the end user with a physical understanding of the regressors. A Monte Carlo study shows the practical accuracy of VSR model on the classical Gas Furnace time-series prediction problem. VSR ranked #1 compared to five other methods.

Interval type-2 fuzzy set qualitative comparative analysis (IT2-fsQCA)

Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It seeks to establish logical connections between combinations of causal conditions and an outcome, the result being rules that summarize the sufficiency between subsets of all of the possible combinations of the causal conditions (or their complements) and the outcome. The rules are connected by the word OR to the output. Each rule is a possible path from the causal conditions to the outcome. FsQCA rules involve words that are modeled using type-1 fuzzy sets (T1 FSs). Unfortunately, once the T1 FS membership functions (MFs) have been chosen, all uncertainty about the words that are used in fsQCA disappears, because T1 MFs are totally precise. Interval type-2 FSs (IT2 FSs), on the other hand, are first-order uncertainty models for words. In this paper, we extend fsQCA to IT2 FSs. More specifically, we develop IT2-fsQCA by extending the steps of fsQCA from T1 FSs to IT2 FSs. In order to illustrate the application of IT2-f5QCA we consider Ragins Breakdown of Democracy example, which studies the effects of five causal conditions on the Breakdown of Democracy of 18 European countries between World Wars 1 and 2. We show results obtained for IT2-fsQCA and compare them with those obtained from fsQCA.

Validation of Fuzzy set Qualitative Comparative Analysis (fsQCA): Granular description of a function

Fuzzy set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It contains 13 steps which are mathematically described in [2]. In this paper we focus on the validation of fsQCA by using it to obtain a granular (linguistic) description of a function as a collection of fuzzy IF-THEN rules, where the rules may be viewed as a summary of the function.

A New Method for Calibrating the Fuzzy Sets Used in fsQCA

ABSTRACT This paper provides a new methodology for calibrating the fuzzy sets that are used in fsQCA, one that is based on clearly distinguishing between a linguistic variable and the linguistic terms for that variable, and that allows for uncertainties about those terms to be included in the calibration method. Each resulting fuzzy set, called an approximated reduced-information level 2 fuzzy set (RI L2 fuzzy set), is equivalent to a standard type-1 fuzzy set, but is for the linguistic variable, and, it has an S-shape, the kind of shape that is so widely used by fsQCA scholars, and is so important to fsQCA. This new calibration methodology is applied to Ragins Breakdown of Democracy example, using new data provided by him, and demonstrates that his earlier solutions are also obtained using our approximated RI L2 fuzzy sets, something that should be reassuring to fsQCA scholars. Additionally, because the S-shaped membership functions are derived from footprints of uncertainty for all of the linguistic variables terms, this paper shows how to obtain more precise statements of fsQCA causal combinations for their best instances, something that may be of added value to practitioners of fsQCA. Finally, we explain how different data-driven calibration robustness studies can be performed, something that may also be of great value to fsQCA practitioners.