The author is glad to present to the readers, the book titled “Fuzzy Decision Making Tools to Sieve out the Poor in Nalanda District, Bihar”. The book is all about ‘how to identify and how to aggregate the poor’ in any given socio-economic context or condition, using the concept of Fuzzy Logic and Fuzzy Sets. This book will serve research students in applied mathematics and it will also be helpful to those who are involved in making socio-economic decision to distribute the resources available at their disposal. Why and How this book came into Being Decision making over the issue of ‘how to measure poverty’ has always been the subject of contentious debate for an economist, for a government, for a statistician, for a mathematician, or for any socio-economic plan. Several Decision Methods have been adopted to decide who would be considered poor or who would not. For examples: (i) Absolute and Relative Poverty Line Method, (ii) Uni- dimension poverty Method (iii) Income-Expenditure Method (iv) Head Count Ratio (HCR), (v) Income Gap Ratio (IGR) (vi) Poverty Gap Ratio (PGR), (vii) Advance Measure Method: – Foster-Greer-Thorbecke Measure, Sen – Shorrocks – Thon measure (SST), and Sen Index (viii) Multi-dimensional Poverty Approach :- (i) Counting Multi-dimensional Poverty (ii) Multi-dimensional Poverty Index (MPI) , and (iii) Capability Approach. These above mentioned methods were used at the global level. Identification of poor and non-poor in India is done based on Uni-dimensional model that is to say Income-consumption and expenditure model using following experts committee reports: (i) Dandekar and Rath (ii) Y.K. Alagh (iii) Lakdawala (iii) Suresh Tendulkar Committee (iv) C. Rangarajan Committee:- Modified Mixed Reference Period (MMRP), Poverty Line Basket (PLB), and Socio-Economic and Caste Census (SECC) 2011: BPL Identification in 2015. Nevertheless, every state in India is free to set its own standard of method to scale out the poor. The state of Bihar adopted the method of A Score Based Ranking Methodology to identify the poor. In Mathematical Modelling context all the above mentioned methods of making decisions fall under Crisp Decision Approach based on Aristotelian logic and Crisp Sets. In response to this method of decision making approach, fuzzy decision making approach was suggested as a better alternative to the process of poverty measurement method. Andrea Cerioli and Sergio Zani were the first one to apply fuzzy logic to poverty assessment in the year 1990. Later, Chiappero Martinetti and Qizilbash added the intrinsic vagueness of being poor by using so – called membership function for the identification of the poor. As the research continued further some more methods were addressed such as Totally Fuzzy (TF), Totally Fuzzy and Relative (TFR), Integrated Fuzzy Approach (Multidimensional and Longitudinal) to apply to identify and aggregate the poor. Key Concepts and Techniques This book further develops and introduces a new approach suggesting Multi-Criteria Fuzzy Decision-making Tools and Fuzzy Set Theory to capture the extent of poverty of households accommodating both the quantitative and qualitative factors such as Roti (Food), Kapda (Clothing), Makaan (Housing), Kaam (Job), and Samman (Social Status) and their fourteen respective sub-criteria. The fuzzification process is carried out by using Pentagonal Fuzzy Numbers (PFNs) and by introducing Stratified Fuzzy Analytical Hierarchy Process (SFAHP). Fuzzy poverty categorization is carried out by introducing Fuzzy Sieve Technique (FST). The judgment and scaling of the criteria and sub-criteria are done by adopting participative decision making method (interview method based on questionnaires). Stratified Fuzzy Analytical Hierarchy Process (SFAHP) categorizes the group of the poor into five subgroups such as (i) very poor, (ii) almost very poor (iii) poor, (iv) rather poor and (v) non-poor. Our fuzzy tools and methods are applied to the case study in Nalanda District, Bihar, India. The book also highlights the comparative studies between three models such as Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy Process (FAHP) and Stratified Fuzzy Analytical Hierarchy Process (SFAHP). The final results justify that Stratified Fuzzy Analytical Hierarchy Process (SFAHP) gives better results in identifying the Poverty Status. Special Features Computer Algorithmic approach via MATLAB: (Programme for 5 X 5 Matrix) is given to calculate the fuzzy centre value by using Matlab m-file which will minimize the time in carrying out the fuzzification and normalization process to measure poverty status. At the end the author shall ever be grateful to the inquisitive researchers and socio-economic planners for their valuable suggestions for further improvement of this book. DR. RAJ KUMAR St. Xavier’s College of Management and Technology, Patna Digha Ashiyan Road -11. Affliated to AKU, Patna, Bihar, India