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

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.

Calculating the interaction index of a fuzzy measure: a polynomial approach based on sampling

In this paper we address the problem of fuzzy measures index calculation. On the basis of fuzzy sets, Murofushi and Soneda proposed an interaction index to deal with the relations between two individuals. This index was later extended in a common frame-work by Grabisch. Both indices are fundamental in the literature of fuzzy measures. Nevertheless, the corresponding calculation still presents a highly complex problem for which no approximation solution has been proposed yet. Then, using a representation of the Shapley based on orders, here we suggest an alternative calculation of the interaction index, both for the simple case of pairs of individuals, and for the more complex situation in which any set could be considered. This alternative representation facilitates the handling of these indices. Moreover, we draw on this representation to define two polynomial methods based on sampling to estimate the interaction index, as well as a method to approximate the generalized version of it. We provide some computational results to test the goodness of the proposed algorithms.Comment: 17 page

Prediction-led prescription: Optimal decision-making in times of turbulence and business performance improvement

Can you have prescription without prediction? Most scholars and practitioners would argue that a good forecast drives an optimal decision, thus promoting the concept of prediction-led prescription. In times of turbulence, Special events like promotions and supply chain disruptions are impacting businesses severely. Nevertheless, limited research has been carried out to date to accurately forecast the impact of, and consequentially prescribe in the presence of special events. Nowadays Artificial Intelligence (AI) predictive analytics methods and heuristics imitate and even improve human intelligence, progressively leading towards innovative cognitive analytics solutions. This research aims to contribute to applying advancements in AI-based predictive analytics to improve business performance. We provide empirical evidence that these AI solutions outperform the popular (especially among practitioners) linear regression models. We corroborate the stream of literature arguing that AI predictive analytics could − via a natural path-dependent process − enhance prescriptive analytics solutions, and thus improve business performance

Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces

The ever-increasing take-up of machine learning techniques requires ever-more application-specific training data. Manually collecting such training data is a tedious and time-consuming process. Data marketplaces represent a compelling alternative, providing an easy way for acquiring data from potential data providers. A key component of such marketplaces is the compensation mechanism for data providers. Classic payoff-allocation methods such as the Shapley value can be vulnerable to data-replication attacks, and are infeasible to compute in the absence of efficient approximation algorithms. To address these challenges, we present an extensive theoretical study on the vulnerabilities of game theoretic payoff-allocation schemes to replication attacks. Our insights apply to a wide range of payoff-allocation schemes, and enable the design of customised replication-robust payoff-allocations. Furthermore, we present a novel efficient sampling algorithm for approximating payoff-allocation schemes based on marginal contributions. In our experiments, we validate the replication-robustness of classic payoff-allocation schemes and new payoff-allocation schemes derived from our theoretical insights. We also demonstrate the efficiency of our proposed sampling algorithm on a wide range of machine learning tasks

Improving polynomial estimation of the Shapley value by stratified random sampling with optimum allocation

In this paper, we propose a refinement of the polynomial method based on sampling theory proposed by Castro et al. (2009) to estimate the Shapley value for cooperative games. In addition to analyzing the variance of the previously proposed estimation method, we employ stratified random sampling with optimum allocation in order to reduce the variance. We examine some desirable statistical features of the stratified approach and provide some computational results by analyzing the gains due to stratification, which are around 30% on average and more than 80% in the best case