Does Political Affirmative Action Work, and for Whom? Theory and Evidence on India’s Scheduled Areas

Does political affirmative action undermine or promote development, and for whom? We examine Scheduled Areas in India, which reserve political office for the historically disadvantaged Scheduled Tribes. We apply a new theoretical framework and dataset of 217,000 villages to evaluate the overall impact of affirmative action on development, as well as its distributional consequences for minorities and non-minorities. Examining effects on the world’s largest employment program, the National Rural Employment Guarantee Scheme, we find that reservations deliver no worse overall outcomes, that there are large gains for targeted minorities, and that these gains come at the cost of the relatively privileged, not other minorities. We also find broader improvements in other pro-poor policies, including a rural roads program and general public goods. Contrary to the expectations of affirmative action skeptics, our results indicate that affirmative action can redistribute both political and economic power without hindering overall development

Agent-based Simulation of District-based Elections

In district-based elections, electors cast votes in their respective districts. In each district, the party with maximum votes wins the corresponding seat in the governing body. The election result is based on the number of seats won by different parties. In this system, locations of electors across the districts may severely affect the election result even if the total number of votes obtained by different parties remains unchanged. A less popular party may end up winning more seats if their supporters are suitably distributed spatially. This happens due to various regional and social influences on individual voters which modulate their voting choice. In this paper, we explore agent-based models for district-based elections, where we consider each elector as an agent, and try to represent their social and geographical attributes and political inclinations using probability distributions. This model can be used to simulate election results by Monte Carlo sampling. The models allow us to explore the full space of possible outcomes of an electoral setting, though they can also be calibrated to actual election results for suitable values of parameters. We use Approximate Bayesian Computation (ABC) framework to estimate model parameters. We show that our model can reproduce the results of elections held in India and USA, and can also produce counterfactual scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1186

The machine learning in the prediction of elections

Resúmen: Este artículo de investigación presenta el análisis y comparación de tres algoritmos diferentes: A.- método de agrupamiento K-media, B.- expectativa de criterios de convergencia y C.- metodología de clasificación LAMDA usando dos softwares de clasificación, Weka y SALSA, como auxiliares para la predicción de las futuras elecciones en el estado de Quintana Roo. Cuando se trabaja con datos electorales, éstos son clasificados en forma cualitativa y cuantitativa, de tal virtud que al final de este artículo tendrá los elementos necesarios para decidir que software tiene un mejor desempeño para el aprendizaje de dicha clasificación. La principal razón para hacer este trabajo es demostrar la eficiencia de los algoritmos con diferentes tipos de datos. Al final se podrá decidir sobre el algoritmo con mejor desempeño para el manejo de información. Palabras clave: aprendizaje automático, lógica fuzzy, agrupamiento, Weka, SALSA, LAMDA, elecciones estatales, predicción

Spatial Patterns and Irregularities of the electoral data: general elections in Canada

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Democratic elections are one of the most important social phenomena of the last centuries. Countries which publish elections results on the polling station level provide a valuable source of data for different groups of scientists like geographers and statisticians. In this work, we combined geographical and statistical analysis, pursuing a goal of defining the spatial patterns and irregularities of the electoral data. From theoretical point of view, it will help to find out if the electoral behavior has any spatial dependency. From practical perspective, it can give a new insight about the electoral fraud detection. We have applied a set of statistical methods to estimate the distribution and variability of the electoral behavior in space and time for different geographic units. Canada was selected as a study area because it is an old democracy where the elections are considered being fair, and all the necessary data are available

Votemandering: Strategies and Fairness in Political Redistricting

Gerrymandering, the deliberate manipulation of electoral district boundaries for political advantage, is a persistent issue in U.S. redistricting cycles. This paper introduces and analyzes a new phenomenon, 'votemandering'- a strategic blend of gerrymandering and targeted political campaigning, devised to gain more seats by circumventing fairness measures. It leverages accurate demographic and socio-political data to influence voter decisions, bolstered by advancements in technology and data analytics, and executes better-informed redistricting. Votemandering is established as a Mixed Integer Program (MIP) that performs fairness-constrained gerrymandering over multiple election rounds, via unit-specific variables for campaigns. To combat votemandering, we present a computationally efficient heuristic for creating and testing district maps that more robustly preserve voter preferences. We analyze the influence of various redistricting constraints and parameters on votemandering efficacy. We explore the interconnectedness of gerrymandering, substantial campaign budgets, and strategic campaigning, illustrating their collective potential to generate biased electoral maps. A Wisconsin State Senate redistricting case study substantiates our findings on real data, demonstrating how major parties can secure additional seats through votemandering. Our findings underscore the practical implications of these manipulations, stressing the need for informed policy and regulation to safeguard democratic processes

BARD: Better Automated Redistricting

BARD is the first (and at time of writing, only) open source software package for general redistricting and redistricting analysis. BARD provides methods to create, display, compare, edit, automatically refine, evaluate, and profile political districting plans. BARD aims to provide a framework for scientific analysis of redistricting plans and to facilitate wider public participation in the creation of new plans. BARD facilitates map creation and refinement through command-line, graphical user interface, and automatic methods. Since redistricting is a computationally complex partitioning problem not amenable to an exact optimization solution, BARD implements a variety of selectable metaheuristics that can be used to refine existing or randomly-generated redistricting plans based on user-determined criteria. Furthermore, BARD supports automated generation of redistricting plans and profiling of plans by assigning different weights to various criteria, such as district compactness or equality of population. This functionality permits exploration of trade-offs among criteria. The intent of a redistricting authority may be explored by examining these trade-offs and inferring which reasonably observable plans were not adopted. Redistricting is a computationally-intensive problem for even modest-sized states. Performance is thus an important consideration in BARD's design and implementation. The program implements performance enhancements such as evaluation caching, explicit memory management, and distributed computing across snow clusters.