Finding Core Members of a Hedonic Game

Agent-based modeling (ABM) is a frequently used paradigm for social simulation; however, there is little evidence of its use in strategic coalition formations. There are few models that explore coalition formation and even fewer that validate their results against an expected outcome. Cooperative game theory is often used to study strategic coalition formation but solving games involving a significant number of agents is computationally intractable. However, there is a natural linkage between ABM and the study of strategic coalition formation. A foundational feature of ABM is the interaction of agents and their environment. Coalition formation is primarily the result of interactions between agents to form collective groups. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro level effect without large computational requirements. This research proposes a hybrid model combining Agent-based modeling and cooperative game theory to find members of a cooperative game’s solution. The algorithm will be applied to the core solution of hedonic games. The core solution is the most common solution set. Hedonic games are a subset of cooperative games whereby agents’ utilities are defined solely by a preference relation over the coalitions of which they are members. The utility of an agent is non-transferrable; there can be no transfer, wholly or in part, of the utility of one agent to another. Determining the core of a hedonic game is NP-complete. The heuristic algorithm utilizes the stochastic nature of ABM interactions to minimize computational complexity. The algorithm has seven coalition formation functions. Each function randomly selects agents to create new coalitions; if the new coalition improves the utility of the agents, it is incorporated into the coalition structure otherwise it is discarded. This approach reduces the computational requirements. This work contributes to the modeling and simulation body of knowledge by providing researchers with a generalized ABM algorithm for forming strategic coalition structures. It provides an empirically validated model based on existing theory that utilizes sound mathematics to reduce the computational complexity and demonstrates the advantages of combining strategic, analytical models with Agent-based models for the study of coalition formation

Coalition Formation For Distributed Constraint Optimization Problems

This dissertation presents our research on coalition formation for Distributed Constraint Optimization Problems (DCOP). In a DCOP, a problem is broken up into many disjoint sub-problems, each controlled by an autonomous agent and together the system of agents have a joint goal of maximizing a global utility function. In particular, we study the use of coalitions for solving distributed k-coloring problems using iterative approximate algorithms, which do not guarantee optimal results, but provide fast and economic solutions in resource constrained environments. The challenge in forming coalitions using iterative approximate algorithms is in identifying constraint dependencies between agents that allow for effective coalitions to form. We first present the Virtual Structure Reduction (VSR) Algorithm and its integration with a modified version of an iterative approximate solver. The VSR algorithm is the first distributed approach for finding structural relationships, called strict frozen pairs, between agents that allows for effective coalition formation. Using coalition structures allows for both more efficient search and higher overall utility in the solutions. Secondly, we relax the assumption of strict frozen pairs and allow coalitions to form under a probabilistic relationship. We identify probabilistic frozen pairs by calculating the propensity between two agents, or the joint probability of two agents in a k-coloring problem having the same value in all satisfiable instances. Using propensity, we form coalitions in sparse graphs where strict frozen pairs may not exist, but there is still benefit to forming coalitions. Lastly, we present a cooperative game theoretic approach where agents search for Nash stable coalitions under the conditions of additively separable and symmetric value functions

Are EU Environmental Policies Too Demanding for New Members States?

In 2004, ten new states entered the European Union. Relative to the pre-2004 member states, these accession states have lower environmental standards, and some worry that it will be too demanding for these new EU members to fully comply with European environmental provisions. In this paper, we assess one rationale for such harmonization. Specifically, we analyze the determinants of environmental policies’ stringency, and show that differences in corruption levels are more important as explanatory factor when compared to income differentials. Since high levels of corruption characterize some countries in the enlarged EU, we argue that this is a good reason for an upward harmonization of environmental policies at the EU level.Corruption, European union, Environmental policy

Water Consumption and Long-Run Urban Development: The Case of Milan

Analyses of long run consumption series are rare in literature. We study the evolution of water consumption in Milan in the twentieth century. The objective is twofold: on one side, the univariate analysis tries both to assess the impact of relevant socio-economic and environmental changes on water consumption in Milan and verify if consumers have deeply rooted consumption habits. On the other side, the multivariate analysis is used to identify the socio-economic factors that are relevant in explaining consumption evolution. Results indicate both that water users have well entrenched consumption habits and that population, climate and economic structure behave more similarly, in Euclidean terms, to water consumption than to other economic and social variables.Urban consumption, Long-run, Development, Environmental changes

The Determinants of Individuals’ Attitudes Towards Preventing Environmental Damage

This paper investigates empirically the determinants of individuals’ attitudes towards preventing environmental damage in Spain using data from the World Values Survey and European Values Survey for the periods 1990, 1995 and 1999/2000. Compared to many previous studies, we present a richer set of independent variables and found that strongly neglected variables such as political interest and social capital have a strong impact on individuals’ preferences to prevent environmental damage. An interesting aspect in our study is the ability to investigate environmental preferences over time. The results show strong differences over time. Finally, using disaggregated data for Spanish regions, we also find significant regional differences.Environment, Regional and time preferences, Political interest, Social capital

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Dynamic Formation and Strategic Management of Web Services Communities

In the last few years, communities of services have been studied in a certain numbers of proposals as virtual pockets of similar expertise. The motivation is to provide these services with high chance of discovery through better visibility, and to enhance their capabilities when it comes to provide requested functionalities. There are some proposed mechanisms and models on aggregating web services and making them cooperate within their communities. However, forming optimal and stable communities as coalitions to maximize individual and group efficiency and income for all the involved parties has not been addressed yet. Moreover, in the proposed frameworks of these communities, a common assumption is that residing services, which are supposed to be autonomous and intelligent, are competing over received requests. However, those services can also exhibit cooperative behaviors, for instance in terms of substituting each other. When competitive and cooperative behaviors and strategies are combined, autonomous services are said to be "coopetitive". Deciding to compete or cooperate inside communities is a problem yet to be investigated. In this thesis, we first identify the problem of defining efficient algorithms for coalition formation mechanisms. We study the community formation problem in two different settings: 1) communities with centralized manager having complete information using cooperative game-theoretic techniques; and 2) communities with distributed decision making mechanisms having incomplete information using training methods. We propose mechanisms for community membership requests and selections of web services in the scenarios where there is interaction between one community and many web services and scenarios where web services can join multiple established communities. Then in order to address the coopetitive relation within communities of web services, we propose a decision making mechanism for our web services to efficiently choose competition or cooperation strategies to maximize their payoffs. We prove that the proposed decision mechanism is efficient and can be implemented in time linear in the length of the time period considered for the analysis and the number of services in the community. Moreover, we conduct extensive simulations, analyze various scenarios, and confirm the obtained theoretical results using parameters from a real web services dataset

Bargaining Coalitions in the Agricultural Negotiations of the Doha Round: Similarity of Interests or Strategic Choices? An Empirical Assessment

The paper aims at understanding the structural features of the bargaining coalitions in the Doha Round of the WTO. We provide an empirical assessment of the preferences of each negotiating actor looking at general economics indicators, development levels, structure of the agricultural sectors, and trade policies for agricultural products. Bargaining coalitions are analyzed by grouping countries through a cluster analysis procedure. The clusters are compared with existing coalitions, in order to assess their degree of internal homogeneity as well as their common interests. Such a comparison allows the detection of possible “defectors”, i.e. countries that according to their economic conditions and policies seem to be relatively less committed to the positions of the coalition they join.Agricultural trade negotiations, Bargaining coalitions, WTO, Cluster analysis

Comparing Open and Sealed Bid Auctions: Theory and Evidence from Timber Auctions

We study entry and bidding patterns in sealed bid and open auctions with heterogeneous bidders. Using data from U.S. Forest Service timber auctions, we document a set of systematic effects of auction format: sealed bid auctions attract more small bidders, shift the allocation towards these bidders, and can also generate higher revenue. We propose a model, which extends the theory of private value auctions with heterogeneous bidders to capture participation decisions, that can account for these qualitative effects of auction format. We then calibrate the model using parameters estimated from the data and show that the model can explain the quantitative effects as well. Finally, we use the model to provide an assessment of bidder competitiveness, which has important consequences for auction choice.Auctions, Timber

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field