Game Theoretic Approach to the Stabilization of Heterogeneous Multiagent Systems Using Subsidy

We consider a multiagent system consisting of selfish and heterogeneous agents. Its behavior is modeled by multipopulation replicator dynamics, where payoff functions of populations are different from each other. In general, there exist several equilibrium points in the replicator dynamics. In order to stabilize a desirable equilibrium point, we introduce a controller called a government which controls the behaviors of agents by offering them subsidies. In previous work, it is assumed that the government determines the subsidies based on the populations the agents belong to. In general, however, the government cannot identify the members of each population. In this paper, we assume that the government observes the action of each agent and determines the subsidies based on the observed action profile. Then, we model the controlled behaviors of the agents using replicator dynamics with feedback. We derive a stabilization condition of the target equilibrium point in the replicator dynamics.Comment: 6 pages, IEEE Conference on Decision and Control, 201

Big Data in MultiAgent Systems: Market Design Solutions

El objetivo principal de esta Tesis es presentar un conjunto de novedosos y diferentes métodos en los que los sistemas multiagente pueden jugar un papel clave en predicciones y modelos económicos en un amplio conjunto de contextos. La hipótesis principal es que los sistemas multiagente permiten la creación de modelos macroeconómicos con microfundamentos reales que son capaces de representar la economía en los diferentes niveles de acuerdo con diferentes propósitos y necesidades. La investigación se estructura en seis capítulos. El Capítulo 1 es una introducción teórica al resto de los capítulos que presentan aplicaciones empíricas. En él se compara los sistemas multiagente con dos alternativas: los modelos de equilibrio general computable y la econometría espacial. El resto de los capítulos son intencionadamente diferentes en sus objetivos y sus contenidos. Estas cinco aplicaciones incorporan diferentes tipos de agentes: incluyen individuos (2, 5, 6), familias (2, 5), empresas (3, 5, 6), establecimientos (5), instituciones financieras (6) y usuarios (4). En el ámbito espacial, la desagregación espacial es deliberadamente diferente en cada aplicación: El capítulo 4 no incluye el espacio, El capítulo 6 es una aplicación para la zona euro en su conjunto y en el capítulo 3 se toma España en su conjunto. Los capítulos 2 y 5 exploran las dos de las principales posibilidades para la incorporación del espacio en los sistemas multiagente: el capítulo 2 incluye las regiones NUTS 3 de la Unión Europea y en el capítulo 5 se geolocalizan los agentes. En el capítulo 2 se desarrolla un sistema multiagente que incluye a todos los individuos de la Unión Europea. Con este sistema podemos predecir la población a escala regional para toda la Unión Europea y cómo distintos niveles de crecimiento económico repercuten asimismo sobre el empleo. En el capítulo 3 se presenta un modelo de simulación con los principales puntos de vista de la teoría de negocios para estudiar el crecimiento empresarial y la demografía empresarial en un modelo evolutivo estocástico. El modelo que se presenta también muestra cómo las empresas se adaptan a los cambios en las características deseadas del producto y el efecto de la crisis sobre estas dinámicas. El capítulo 4 discute el papel clave de los incentivos en la seguridad de los sistemas de información. Trabajos anteriores realizan este estudio utilizando un enfoque de teoría de juegos, pero el capítulo muestra que un modelo basado en agentes es capaz de incluir la heterogeneidad y las interrelaciones entre los individuos, y no se centra en el equilibrio alcanzado sino en la dinámica antes de su aparición. El objetivo del capítulo 5 es el estudio de los efectos de la Ley para la Revitalización Comercial (Ley de Dinamización Comercial) que fue aprobada en la Comunidad de Madrid durante el año 2012. Por último, el objetivo del capítulo 6 es explicar los determinantes de la inflación y pronosticar la tasa de inflación en la zona euro en los próximos cinco años. Se predice una inflación para la zona euro creciente hasta 2018 con un límite cercano al 2,5% en tasa interanual siempre que no se produzcan perturbaciones externas relevantes

A survey on the analysis and control of evolutionary matrix games

In support of the growing interest in how to efficiently influence complex systems of interacting self interested agents, we present this review of fundamental concepts, emerging research, and open problems related to the analysis and control of evolutionary matrix games, with particular emphasis on applications in social, economic, and biological networks. (C) 2018 Elsevier Ltd. All rights reserved

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

Automated Markets and Trading Agents

Computer automation has the potential, just starting to be realized, of transforming the design and operation of markets, and the behaviors of agents trading in them. We discuss the possibilities for automating markets, presenting a broad conceptual framework covering resource allocation as well as enabling marketplace services such as search and transaction execution. One of the most intriguing opportunities is provided by markets implementing computationally sophisticated negotiation mechanisms, for example combinatorial auctions. An important theme that emerges from the literature is the centrality of design decisions about matching the domain of goods over which a mechanism operates to the domain over which agents have preferences. When the match is imperfect (as is almost inevitable), the market game induced by the mechanism is analytically intractable, and the literature provides an incomplete characterization of rational bidding policies. A review of the literature suggests that much of our existing knowledge comes from computational simulations, including controlled studies of abstract market designs (e.g., simultaneous ascending auctions), and research tournaments comparing agent strategies in a variety of market scenarios. An empirical game-theoretic methodology combines the advantages of simulation, agent-based modeling, and statistical and game-theoretic analysis.http://deepblue.lib.umich.edu/bitstream/2027.42/49510/1/ace_galleys.pd

Shared Mobility - Operations and Economics

In the last decade, ubiquity of the internet and proliferation of smart personal devices have given rise to businesses that are built on the foundation of the sharing economy. The mobility market has implemented the sharing economy model in many forms, including but not limited to, carsharing, ride-sourcing, carpooling, taxi-sharing, ridesharing, bikesharing, and scooter sharing. Among these shared-use mobility services, ridesharing services, such as peer-to-peer (P2P) ridesharing and ride-pooling systems, are based on sharing both the vehicle and the ride between users, offering several individual and societal benefits. Despite these benefits, there are a number of operational and economic challenges that hinder the adoption of various forms of ridesharing services in practice. This dissertation attempts to address these challenges by investigating these systems from two different, but related, perspectives. The successful operation of ridesharing services in practice requires solving large-scale ride-matching problems in short periods of time. However, the high computational complexity and inherent supply and demand uncertainty present in these problems immensely undermines their real-time application. In the first part of this dissertation, we develop techniques that provide high-quality, although not necessarily optimal, system-level solutions that can be applied in real time. More precisely, we propose a distributed optimization technique based on graph partitioning to facilitate the implementation of dynamic P2P ridesharing systems in densely populated metropolitan areas. Additionally, we combine the proposed partitioning algorithm with a new local search algorithm to design a proactive framework that exploits historical demand data to optimize dynamic dispatching of a fleet of vehicles that serve on-demand ride requests. The main purpose of these methods is to maximize the social welfare of the corresponding ridesharing services. Despite the necessity of developing real-time algorithmic tools for operation of ridesharing services, solely maximizing the system-level social welfare cannot result in increasing the penetration of shared mobility services. This fact motivated the second stream of research in this dissertation, which revolves around proposing models that take economic aspects of ridesharing systems into account. To this end, the second part of this dissertation studies the impact of subsidy allocation on achieving and maintaining a critical mass of users in P2P ridesharing systems under different assumptions. First, we consider a community-based ridesharing system with ride-back guarantee, and propose a traveler incentive program that allocates subsidies to a carefully selected set of commuters to change their travel behavior, and thereby, increase the likelihood of finding more compatible and profitable matches. We further introduce an approximate algorithm to solve large-scale instances of this problem efficiently. In a subsequent study for a cooperative ridesharing market with role flexibility, we show that there may be no stable outcome (a collusion-free pricing and allocation scheme). Hence, we introduced a mathematical formulation that yields a stable outcome by allocating the minimum amount of external subsidy. Finally, we propose a truthful subsidy scheme to determine matching, scheduling, and subsidy allocation in a P2P ridesharing market with incomplete information and a budget constraint on payment deficit. The proposed mechanism is shown to guarantee important economic properties such as dominant-strategy incentive compatibility, individual rationality, budget-balance, and computational efficiency. Although the majority of the work in this dissertation focuses on ridesharing services, the presented methodologies can be easily generalized to tackle related issues in other types of shared-use mobility services.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169843/1/atafresh_1.pd

Symbiotic strategies in enterprise ecology : modeling commercial aviation as an Enterprise of Enterprises

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology, Management, and Policy Program, 2007.Includes bibliographical references (p. 339-351).We investigate the effectiveness of strategic alternatives that are designed to dampen the cyclicality manifest in the commercial aviation (CA)-related industries. In this research we introduce the conceptual framework of Enterprise of Enterprises (EoE) as an extension and special case of a System of Systems, to facilitate the design of strategic alternatives in an enterprise ecosystem characterized by loosely coupled enterprises. The constituent enterprises in an EoE exhibit managerial and operational independence and have diverse value functions that are often viewed by the enterprises as zero-sum games. We argue that this may not always be the case; for example, in the CA EoE both airline and airframe manufacturers constituents would benefit from a steadier influx of aircraft that counters the current situation that is characterized by relatively stable demand growth rate for air travel while airline profitability and aircraft ordering fluctuate intensely. A strategic alternative geared towards this EoE-wide desired state is "symbiotic". In order to identify such strategies, we use the EoE framework to analyze the CA-related industries and to specify their local value functions and the salient interfaces among them based on an extensive review of the literature on commercial aviation. We develop working hypotheses about the driving mechanisms of the cycle in the CA EoE informed by the literature on economywide and supply chain cyclicality. To test these hypotheses, we extend a system dynamics model of commercial aviation. After testing several individual strategic alternatives, we find that capacity management is key to cycle moderation. We then compare two diverse, non-collusive ways for capacity management: faster aircraft deliveries and semi-fixed production schedules generated by long-term forecasts.(cont.) While both are promising, only the latter alternative is shown to be Pareto optimal. We also examine the potential synergistic effects from combining more than one strategic alternatives for which we also discuss implementation implications. The EoE framework and some of our findings can be applicable and generalizable to other industries facing intense cyclical behavior.by Sgouris P. Sgouridis.Ph.D

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc