Improving estimates accuracy of voter transitions. Two new algorithms for ecological inference based on linear programming

10.31124/advance.14716638.v1 ◽

2021 ◽

Author(s):

JoseM Pavia ◽

Rafael Romero

Keyword(s):

Linear Programming ◽

Mathematical Programming ◽

Contingency Tables ◽

R Package ◽

Programming Approach ◽

Ecological Inference ◽

Probability Estimates ◽

Inference Algorithms ◽

Baseline Algorithm ◽

New Algorithms

The estimation of RxC ecological inference contingency tables from aggregate data defines one of the most salient and challenging problems in the field of quantitative social sciences. From the mathematical programming framework, this paper suggests a new direction for tackling this problem. For the first time in the literature, a procedure based on linear programming is proposed to attain estimates of local contingency tables. Based on this and the homogeneity hypothesis, we suggest two new ecological inference algorithms. These two new algorithms represent an important step forward in the ecological inference mathematical programming literature. In addition to generating estimates for local ecological inference contingency tables and amending the tendency to produce extreme transfer probability estimates previously observed in other mathematical programming procedures, they prove to be quite competitive and more accurate than the current linear programming baseline algorithm. The new algorithms place the linear programming approach once again in a prominent position in the ecological inference toolkit. We use a unique dataset with almost 500 elections, where the real transfer matrices are known, to assess their accuracy. Interested readers can easily use these new algorithms with the aid of the R package lphom.

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Optimization of production planning in Czech agricultural co-operative via linear programming

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun200957060099 ◽

2009 ◽

Vol 57 (6) ◽

pp. 99-104 ◽

Cited By ~ 2

Author(s):

Jitka Janová ◽

Pavla Ambrožová

Keyword(s):

Linear Programming ◽

Czech Republic ◽

Mathematical Programming ◽

Production Planning ◽

Crop Rotation ◽

Programming Model ◽

Resource Scarcity ◽

Linear Programming Model ◽

Programming Approach ◽

Ongoing Research

The production planning is one of the key managerial decisions in agricultural business, which must be done periodically every year. Correct decision must cover the agriculture demands of planting the crops such as crop rotation restrictions or water resource scarcity, while the decision maker aims to plan the crop design in most profitable way in sense of maximizing the total profit from the crop yield. This decision problem represents the optimization of crop design and can be treated by the methods of linear programming which begun to be extensively used in agriculture production planning in USA during 50’s. There is ongoing research of mathematical programming applications in agriculture worldwide, but the results are not easily transferable to other localities due to the specific local restrictions in each country. In Czech Republic the farmers use for production planning mainly their expert knowledge and past experience. However, the mathematical programming approach enables find the true optimal solution of the problem, which especially in the problems with a great number of constraints is not easy to find intuitively. One of the possible barriers for using the general decision support systems (which are based on mathematical programming methods) for agriculture production planning in Czech Republic is its expensiveness. The small farmer can not afford to buy the expensive software or to employ a mathematical programming specialist. The aim of this paper is to present a user friendly linear programming model of the typical agricultural production planning problem in Czech Republic which can be solved via software tools commonly available in any farm (e.g. EXCEL). The linear programming model covering the restrictions on total costs, crop rotation, thresholds for the total area sowed by particular crops, total amount of manure and the need of feed crops is developed. The model is applied in real-world problem of Czech agriculture cooperative and the results of its solution are compared to the real decision made. The applicability of the model in every day agriculture managerial practice in Czech Republic is discussed and its possible enlargement is mentioned.

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ei.Datasets: Real Data Sets for Assessing Ecological Inference Algorithms

Social Science Computer Review ◽

10.1177/08944393211040808 ◽

2021 ◽

pp. 089443932110408

Author(s):

Jose M. Pavía

Keyword(s):

Simulated Data ◽

Ground Truth ◽

Real Data ◽

R Package ◽

Data Sets ◽

Ecological Inference ◽

Inference Models ◽

Individual Level ◽

Inference Algorithms ◽

Cross Classification

Ecological inference models aim to infer individual-level relationships using aggregate data. They are routinely used to estimate voter transitions between elections, disclose split-ticket voting behaviors, or infer racial voting patterns in U.S. elections. A large number of procedures have been proposed in the literature to solve these problems; therefore, an assessment and comparison of them are overdue. The secret ballot however makes this a difficult endeavor since real individual data are usually not accessible. The most recent work on ecological inference has assessed methods using a very small number of data sets with ground truth, combined with artificial, simulated data. This article dramatically increases the number of real instances by presenting a unique database (available in the R package ei.Datasets) composed of data from more than 550 elections where the true inner-cell values of the global cross-classification tables are known. The article describes how the data sets are organized, details the data curation and data wrangling processes performed, and analyses the main features characterizing the different data sets.

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