An Unbiased Pareto Improvement strategy for poverty alleviation

There are historical and institutional reasons behind our economic problems like poverty and environmental damage but it is not acceptable if they persist in years to come. The strategic use of information by individual economic agents establishes a biasing effect on our economy. In case of poverty, economic efforts of poor people are continuously undervalued and therefore, true welfare across an economy cannot be achieved without protecting the poor people for their immediate economic needs and simultaneously counteracting the biasing forces. This article describes an Unbiased Pareto Improvement (UPI) strategy to be implemented across an economy and eventually across the world to solve poverty problems. This strategy describes making business opportunities involving poor people as well as helping government to set up pro-poor economic policies and infrastructures. A new indicator RICR (Real Income to Contribution Ratio) is introduced to measure its performance.Bangladesh, Economics of information, micro credit, micro finance, Pareto improvement, poverty, social business, real income, Yunus

Fee-Setting Mechanisms: On Optimal Pricing by Intermediaries and Indirect Taxation

Mechanisms according to which private intermediaries or governments charge transaction fees or indirect taxes are prevalent in practice. We consider a setup with multiple buyers and sellers and two-sided independent private information about valuations. We show that any weighted average of revenue and social welfare can be maximized through appropriately chosen transaction fees and that in increasingly thin markets such optimal fees converge to linear fees. Moreover, fees decrease with competition (or the weight on welfare) and the elasticity of supply but decrease with the elasticity of demand. Our theoretical predictions fit empirical observations in several industries with intermediaries

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Smallholder Participation in Agricultural Value Chains: Comparative Evidence from Three Continents

Supermarkets, specialized wholesalers, and processors and agro-exporters’ agricultural value chains have begun to transform the marketing channels into which smallholder farmers sell produce in low-income economies. We develop a conceptual framework through which to study contracting between smallholders and a commodity-processing firm. We then conduct an empirical meta-analysis of agricultural value chains in five countries across three continents (Ghana, India, Madagascar, Mozambique, and Nicaragua). We document patterns of participation, the welfare gains associated with participation, reasons for non-participation, the significant extent of contract non-compliance, and the considerable dynamism of these value chains, as farmers and firms enter and exit frequently.

Shapley value: its algorithms and application to supply chains

Introduction− Coalitional game theorists have studied the coalition struc-ture and the payoff schemes attributed to such coalition. With respect to the payoff value, there are number ways of obtaining to “best” distribution of the value of the game. The solution concept or payoff value distribution that is canonically held to fairly dividing a coalition’s value is called the Shapley Value. It is probably the most important regulatory payoff scheme in coali-tion games. The reason the Shapley value has been the focus of so much interest is that it represents a distinct approach to the problems of complex strategic interaction that game theory tries to solve. Objective−This study aims to do a brief literature review of the application of Shapley Value for solving problems in different cooperation fields and the importance of studying existing methods to facilitate their calculation. This review is focused on the algorithmic view of cooperative game theory with a special emphasis on supply chains. Additionally, an algorithm for the calcu-lation of the Shapley Value is proposed and numerical examples are used in order to validate the proposed algorithm. Methodology−First of all, the algorithms used to calculate Shapley value were identified. The element forming a supply chain were also identified. The cooperation between the members of the supply chain ways is simulated and the Shapley Value is calculated using the proposed algorithm in order to check its applicability. Results and Conclusions− The algorithmic approach introduced in this paper does not wish to belittle the contributions made so far but intends to provide a straightforward solution for decision problems that involve supply chains. An efficient and feasible way of calculating the Shapley Value when player structures are known beforehand provides the advantage of reducing the amount of effort in calculating all possible coalition structures prior to the Shapley.Introducción: Los teóricos del juego cooperativos han estudiado la estructura de coalición y los esquemas de pago atribuidos a esas coaliciones. En relación al valor del pago, hay varias maneras de obtener la “mejor” distribución del valor del juego. El concepto de solución o la distribución del valor de recompensa que se mantiene canónicamente para dividir justamente el valor de una coalición se llama Valor de Shapley. Es probablemente el esquema de pago más importante en los juegos cooperativos. La razón por la cual el valor de Shapley ha sido el foco de tanto interés es que representa un acercamiento distinto a los problemas de la interacción estratégica compleja que la teoría del juego intenta resolver.Objetivo: Este estudio tiene como objetivo hacer una breve revisión bibliográfica de la aplicación del Valor de Shapley para resolver problemas en diferentes campos de cooperación y la importancia de estudiar los métodos existentes para facilitar su cálculo. Esta revisión se centra en la visión algorítmica de la teoría cooperativa de juegos con un énfasis especial en las cadenas de suministro. Adicionalmente se propone un algoritmo para el cálculo del Valor de Shapley y se utilizan ejemplos numéricos para validar el algoritmo propuesto.Metodología: En primer lugar, se identificaron los algoritmos utilizados para calcular el valor de Shapley. También se identificó los elementos que forman una cadena de suministro. Luego se simula la cooperación entre los miembros de las vías de la cadena de suministro y se calcula el valor de Shapley utilizando el algoritmo propuesto para comprobar su aplicabilidad.Resultados y Conclusiones: El enfoque algorítmico introducido en este documento no pretende menospreciar las contribuciones hechas hasta ahora, pero tiene la intención de proporcionar una solución directa para problemas de decisión que involucran cadenas de suministro. Una manera eficiente y factible de calcular el valor de Shapley cuando las estructuras de jugador se conocen de antemano proporciona la ventaja de reducir la cantidad de esfuerzo en el cálculo de todas las estructuras de coalición posibles antes del Shapley

Income Distribution and Demand-Induced Innovations

We introduce non-homothetic preferences into an innovation-based growth model and study how income and wealth inequality affect economic growth. We identify a (positive) price effect—where increasing inequality allows innovators to charge higher prices and (negative) market-size effects—with higher inequality implying smaller markets for new goods and/or a slower transition of new goods into mass markets. It turns out that price effects dominate market-size effects. We also show that a redistribution from the poor to the rich may be Pareto improving for low levels of inequalit

El valor de Shapley: sus algoritmos y aplicación en cadenas de suministro

Introduction: Coalitional game theorists have studied the coalition structure and the payoff schemes attributed to such coalition. With respect to the payoff value, there are number ways of obtaining to “best” distribution of the value of the game. The solution concept or payoff value distribution that is canonically held to fairly dividing a coalition’s value is called the Shapley Value. It is probably the most important regulatory payoff scheme in coalition games. The reason the Shapley value has been the focus of so much interest is that it represents a distinct approach to the problems of complex strategic interaction that game theory tries to solve.Objective: This study aims to do a brief literature review of the application of Shapley Value for solving problems in different cooperation fields and the importance of studying existing methods to facilitate their calculation. This review is focused on the algorithmic view of cooperative game theory with a special emphasis on supply chains. Additionally, an algorithm for the calculation of the Shapley Value is proposed and numerical examples are used in order to validate the proposed algorithm.Methodology: First of all, the algorithms used to calculate Shapley value were identified. The element forming a supply chain were also identified. The cooperation between the members of the supply chain ways is simulated and the Shapley Value is calculated using the proposed algorithm in order to check its applicability.Results and Conclusions: The algorithmic approach introduced in this paper does not wish to belittle the contributions made so far but intends to provide a straightforward solution for decision problems that involve supply chains. An efficient and feasible way of calculating the Shapley Value when player structures are known beforehand provides the advantage of reducing the amount of effort in calculating all possible coalition structures prior to the Shapley.Introducción: Los teóricos del juego cooperativos han estudiado la estructura de coalición y los esquemas de pago atribuidos a esas coaliciones. En relación al valor del pago, hay varias maneras de obtener la “mejor” distribución del valor del juego. El concepto de solución o la distribución del valor de recompensa que se mantiene canónicamente para dividir justamente el valor de una coalición se llama Valor de Shapley. Es probablemente el esquema de pago más importante en los juegos cooperativos. La razón por la cual el valor de Shapley ha sido el foco de tanto interés es que representa un acercamiento distinto a los problemas de la interacción estratégica compleja que la teoría del juego intenta resolver.Objetivo: Este estudio tiene como objetivo hacer una breve revisión bibliográfica de la aplicación del Valor de Shapley para resolver problemas en diferentes campos de cooperación y la importancia de estudiar los métodos existentes para facilitar su cálculo. Esta revisión se centra en la visión algorítmica de la teoría cooperativa de juegos con un énfasis especial en las cadenas de suministro. Adicionalmente se propone un algoritmo para el cálculo del Valor de Shapley y se utilizan ejemplos numéricos para validar el algoritmo propuesto.Metodología: En primer lugar, se identificaron los algoritmos utilizados para calcular el valor de Shapley. También se identificó los elementos que forman una cadena de suministro. Luego se simula la cooperación entre los miembros de las vías de la cadena de suministro y se calcula el valor de Shapley utilizando el algoritmo propuesto para comprobar su aplicabilidad.Resultados y Conclusiones: El enfoque algorítmico introducido en este documento no pretende menospreciar las contribuciones hechas hasta ahora, pero tiene la intención de proporcionar una solución directa para problemas de decisión que involucran cadenas de suministro. Una manera eficiente y factible de calcular el valor de Shapley cuando las estructuras de jugador se conocen de antemano proporciona la ventaja de reducir la cantidad de esfuerzo en el cálculo de todas las estructuras de coalición posibles antes del Shapley.

Integrated management of chemical processes in a competitive environment

El objetivo general de esta Tesis es mejorar el proceso de la toma de decisiones en la gestión de cadenas de suministro, tomando en cuenta principalmente dos diferencias: ser competitivo considerando las decisiones propias de la cadena de suministro, y ser competitivo dentro de un entorno global. La estructura de ésta tesis se divide en 4 partes principales: La Parte I consiste en una introducción general de los temas cubiertos en esta Tesis (Capítulo 1). Una revisión de la literatura, que nos permite identificar las problemáticas asociadas al proceso de toma de decisiones (Capítulo 2). El Capítulo 3 presenta una introducción de las técnicas y métodos de optimización utilizados para resolver los problemas propuestos en esta Tesis. La Parte II se enfoca en la integración de los niveles de decisión, buscando mejorar la toma de decisiones de la propia cadena de suministro. El Capítulo 4 presenta una formulación matemática que integra las decisiones de síntesis de procesos y las decisiones operacionales. Además, este capítulo presenta un modelo integrado para la toma de decisiones operacionales incluyendo las características del control de procesos. El Capítulo 5 muestra la integración de las decisiones del nivel táctico y el operacional, dicha propuesta está basada en el conocimiento adquirido capturando la información relacionada al nivel operacional. Una vez obtenida esta información se incluye en la toma de decisiones a nivel táctico. Finalmente en el capítulo 6 se desarrolla un modelo simplificado para integrar múltiples cadenas de suministro. El modelo propuesto incluye la información detallada de las entidades presentes en una cadena de suministro (suministradores, plantas de producción, distribuidores y mercados) introduciéndola en un modelo matemático para su coordinación. La Parte III propone la integración explicita de múltiples cadenas de suministro que tienen que enfrentar numerosas situaciones propias de un mercado global. Asimismo, esta parte presenta una nueva herramienta de optimización basada en el uso integrado de métodos de programación matemática y conceptos relacionados a la Teoría de Juegos. En el Capítulo 7 analiza múltiples cadenas de suministro que cooperan o compiten por la demanda global del mercado. El Capítulo 8 incluye una comparación entre el problema resuelto en el Capítulo anterior y un modelo estocástico, los resultados obtenidos nos permiten situar el comportamiento de los competidores como fuente exógena de la incertidumbre típicamente asociada la demanda del mercado. Además, los resultados de ambos Capítulos muestran una mejora sustancial en el coste total de las cadenas de suministro asociada al hecho de cooperar para atender de forma conjunta la demanda disponible. Es por esto, que el Capítulo 9 presenta una nueva herramienta de negociación, basada en la resolución del mismo problema (Capítulo 7) bajo un análisis multiobjetivo. Finalmente, la parte IV presenta las conclusiones finales y una descripción general del trabajo futuro.This Thesis aims to enhance the decision making process in the SCM, remarking the difference between optimizing the SC to be competitive by its own, and to be competitive in a global market in cooperative and competitive environments. The structure of this work has been divided in four main parts: Part I: consists in a general introduction of the main topics covered in this manuscript (Chapter I); a review of the State of the Art that allows us to identify new open issues in the PSE (Chapter 2). Finally, Chapter 3 introduces the main optimization techniques and methods used in this contribution. Part II focuses on the integration of decision making levels in order to improve the decision making of a single SC: Chapter 4 presents a novel formulation to integrate synthesis and scheduling decision making models, additionally, this chapter also shows an integrated operational and control decision making model for distributed generations systems (EGS). Chapter 5 shows the integration of tactical and operational decision making levels. In this chapter a knowledge based approach has been developed capturing the information related to the operational decision making level. Then, this information has been included in the tactical decision making model. In Chapter 6 a simplified approach for integrated SCs is developed, the detailed information of the typical production‐distribution SC echelons has been introduced in a coordinated SC model. Part III proposes the explicit integration of several SC’s decision making in order to face several real market situations. As well, a novel formulation is developed using an MILP model and Game Theory (GT) as a decision making tool. Chapter 7 includes the tactical and operational analysis of several SC’s cooperating or competing for the global market demand. Moreover, Chapter 8 includes a comparison, based on the previous results (MILP‐GT optimization tool) and a two stage stochastic optimization model. Results from both Chapters show how cooperating for the global demand represent an improvement of the overall total cost. Consequently, Chapter 9 presents a bargaining tool obtained by the Multiobjective (MO) resolution of the model presented in Chapter 7. Finally, final conclusions and further work have been provided in Part IV.Postprint (published version