Agricultural Water Allocation Under Cyclical Scarcity: The Role of Priority Water Rights

Water is becoming an increasingly scarce resource worldwide, suggesting that water rationing methods should be revised to improve water allocation efficiency, especially during cyclical scarcity events (droughts). The proportional rule is the most widely used rationing method to allocate water in cases of water scarcity. However, this method fails to achieve Pareto-efficient allocation arrangements. Economic theory and international experience demonstrate that implementing security-differentiated water rights could improve allocative efficiency during cyclical scarcity periods. Moreover, it has been proven that this kind of priority rights regime is an efficient instrument to share risks related to water supply reliability, and can thus be considered as an adaptation measure to climate change. This evidence has enabled the development of an operational proposal for the implementation of security-differentiated water rights in the irrigation sector in Spain, as an alternative to the current rights based on the proportional rule. This proposal draws on the Australian case study, which is the most successful experience worldwide. Nevertheless, the insights obtained from the analysis performed and the proposal for reforming the water rights regime are applicable to any country with a mature water economy

SMART: Coordinated Double-Sided Seal Bid Multiunit First Price Auction Mechanism for Cloud-Based TVWS Secondary Spectrum Market

Spectrum trading is an important aspect of television white space (TVWS) and it is driven by the failure of spectrum sensing techniques. In spectrum trading, the primary users lease their unoccupied spectrum to the secondary users for a market fee. Although spectrum trading is considered as a reliable approach, it is confronted with a spectrum transaction completion time problem, which negatively impacts on end-users Quality of Service and Quality of Experience metrics. Spectrum transaction completion time is the duration to successfully conduct TVWS spectrum trading. To address this issue, this paper proposes simple mechanism auction reward truthful (SMART), a fast and iterative machine learning-assisted spectrum trading model to address this issue. Simulated results indicate thatSMART out-performs referenced VERUM algorithm in three key performance indicators: bit-error rate, instantaneous throughput, and probability of dropped packets by 10%, 5%, and 15%, respectively

Auctions, Bidding and Exchange Design

The different auction types are outlined using a classification framework along six dimensions. The economic properties that are desired in the design of auction mechanisms and the complexities that arise in their implementation are discussed. Some of the most interesting designs from the literature are analyzed in detail to establish known results and to identify the emerging research directions.Engineering and Applied Science

Incentives in One-Sided Matching Problems With Ordinal Preferences

One of the core problems in multiagent systems is how to efficiently allocate a set of indivisible resources to a group of self-interested agents that compete over scarce and limited alternatives. In these settings, mechanism design approaches such as matching mechanisms and auctions are often applied to guarantee fairness and efficiency while preventing agents from manipulating the outcomes. In many multiagent resource allocation problems, the use of monetary transfers or explicit markets are forbidden because of ethical or legal issues. One-sided matching mechanisms exploit various randomization and algorithmic techniques to satisfy certain desirable properties, while incentivizing self-interested agents to report their private preferences truthfully. In the first part of this thesis, we focus on deterministic and randomized matching mechanisms in one-shot settings. We investigate the class of deterministic matching mechanisms when there is a quota to be fulfilled. Building on past results in artificial intelligence and economics, we show that when preferences are lexicographic, serial dictatorship mechanisms (and their sequential dictatorship counterparts) characterize the set of all possible matching mechanisms with desirable economic properties, enabling social planners to remedy the inherent unfairness in deterministic allocation mechanisms by assigning quotas according to some fairness criteria (such as seniority or priority). Extending the quota mechanisms to randomized settings, we show that this class of mechanisms are envyfree, strategyproof, and ex post efficient for any number of agents and objects and any quota system, proving that the well-studied Random Serial Dictatorship (RSD) is also envyfree in this domain. The next contribution of this thesis is providing a systemic empirical study of the two widely adopted randomized mechanisms, namely Random Serial Dictatorship (RSD) and the Probabilistic Serial Rule (PS). We investigate various properties of these two mechanisms such as efficiency, strategyproofness, and envyfreeness under various preference assumptions (e.g. general ordinal preferences, lexicographic preferences, and risk attitudes). The empirical findings in this thesis complement the theoretical guarantees of matching mechanisms, shedding light on practical implications of deploying each of the given mechanisms. In the second part of this thesis, we address the issues of designing truthful matching mechanisms in dynamic settings. Many multiagent domains require reasoning over time and are inherently dynamic rather than static. We initiate the study of matching problems where agents' private preferences evolve stochastically over time, and decisions have to be made in each period. To adequately evaluate the quality of outcomes in dynamic settings, we propose a generic stochastic decision process and show that, in contrast to static settings, traditional mechanisms are easily manipulable. We introduce a number of properties that we argue are important for matching mechanisms in dynamic settings and propose a new mechanism that maintains a history of pairwise interactions between agents, and adapts the priority orderings of agents in each period based on this history. We show that our mechanism is globally strategyproof in certain settings (e.g. when there are 2 agents or when the planning horizon is bounded), and even when the mechanism is manipulable, the manipulative actions taken by an agent will often result in a Pareto improvement in general. Thus, we make the argument that while manipulative behavior may still be unavoidable, it is not necessarily at the cost to other agents. To circumvent the issues of incentive design in dynamic settings, we formulate the dynamic matching problem as a Multiagent MDP where agents have particular underlying utility functions (e.g. linear positional utility functions), and show that the impossibility results still exist in this restricted setting. Nevertheless, we introduce a few classes of problems with restricted preference dynamics for which positive results exist. Finally, we propose an algorithmic solution for agents with single-minded preferences that satisfies strategyproofness, Pareto efficiency, and weak non-bossiness in one-shot settings, and show that even though this mechanism is manipulable in dynamic settings, any unilateral deviation would benefit all participating agents

Two Auction-Based Resource Allocation Environments: Design and Experience

Many computer systems have reached the point where the goal of resource allocation is no longer to maximize utilization; instead, when demand exceeds supply and not all needs can be met, one needs a policy to guide resource allocation decisions. One natural policy is to seek efficient usage, which allocates resources to the set of users who have the highest utility for the use of the resources. Researchers have frequently proposed market-based mechanisms to provide such a goal-oriented way to allocate resources among competing interests while maximizing overall utility of the users.Engineering and Applied Science

Supercomputing futures : the next sharing paradigm for HPC resources : economic model, market analysis and consequences for the Grid

À la croisée des chemins du génie informatique, de la finance et de l'économétrie, cette thèse se veut fondamentalement un exercice en ingénierie économique dont l' objectif est de contribuer un système novateur, durable et adaptatif pour le partage de resources de calcul haute-performance. Empruntant à la finance fondamentale et à l'analyse technique, le modèle proposé construit des ratios et des indices de marché à partir de statistiques transactionnelles. Cette approche, encourageant les comportements stratégiques, pave la voie à une métaphore de partage plus efficace pour la Grid, où l'échange de ressources se voit maintenant pondéré. Le concept de monnaie de Grid, un instrument beaucoup plus liquide et utilisable que le troc de resources comme telles est proposé: les Grid Credits. Bien que les indices proposés ne doivent pas être considérés comme des indicateurs absolus et contraignants, ils permettent néanmoins aux négociants de se faire une idée de la valeur au marché des différentes resources avant de se positionner. Semblable sur de multiples facettes aux bourses de commodités, le Grid Exchange, tel que présenté, permet l'échange de resources via un mécanisme de double-encan. Néanmoins, comme les resources de super-calculateurs n'ont rien de standardisé, la plate-forme permet l'échange d'ensemble de commodités, appelés requirement sets, pour les clients, et component sets, pour les fournisseurs. Formellement, ce modèle économique n'est qu'une autre instance de la théorie des jeux non-coopératifs, qui atteint éventuellement ses points d'équilibre. Suivant les règles du "libre-marché", les utilisateurs sont encouragés à spéculer, achetant, ou vendant, à leur bon vouloir, l'utilisation des différentes composantes de superordinateurs. En fin de compte, ce nouveau paradigme de partage de resources pour la Grid dresse la table à une nouvelle économie et une foule de possibilités. Investissement et positionnement stratégique, courtiers, spéculateurs et même la couverture de risque technologique sont autant d'avenues qui s'ouvrent à l'horizon de la recherche dans le domaine

Systems-compatible Incentives

Originally, the Internet was a technological playground, a collaborative endeavor among researchers who shared the common goal of achieving communication. Self-interest used not to be a concern, but the motivations of the Internet's participants have broadened. Today, the Internet consists of millions of commercial entities and nearly 2 billion users, who often have conflicting goals. For example, while Facebook gives users the illusion of access control, users do not have the ability to control how the personal data they upload is shared or sold by Facebook. Even in BitTorrent, where all users seemingly have the same motivation of downloading a file as quickly as possible, users can subvert the protocol to download more quickly without giving their fair share. These examples demonstrate that protocols that are merely technologically proficient are not enough. Successful networked systems must account for potentially competing interests. In this dissertation, I demonstrate how to build systems that give users incentives to follow the systems' protocols. To achieve incentive-compatible systems, I apply mechanisms from game theory and auction theory to protocol design. This approach has been considered in prior literature, but unfortunately has resulted in few real, deployed systems with incentives to cooperate. I identify the primary challenge in applying mechanism design and game theory to large-scale systems: the goals and assumptions of economic mechanisms often do not match those of networked systems. For example, while auction theory may assume a centralized clearing house, there is no analog in a decentralized system seeking to avoid single points of failure or centralized policies. Similarly, game theory often assumes that each player is able to observe everyone else's actions, or at the very least know how many other players there are, but maintaining perfect system-wide information is impossible in most systems. In other words, not all incentive mechanisms are systems-compatible. The main contribution of this dissertation is the design, implementation, and evaluation of various systems-compatible incentive mechanisms and their application to a wide range of deployable systems. These systems include BitTorrent, which is used to distribute a large file to a large number of downloaders, PeerWise, which leverages user cooperation to achieve lower latencies in Internet routing, and Hoodnets, a new system I present that allows users to share their cellular data access to obtain greater bandwidth on their mobile devices. Each of these systems represents a different point in the design space of systems-compatible incentives. Taken together, along with their implementations and evaluations, these systems demonstrate that systems-compatibility is crucial in achieving practical incentives in real systems. I present design principles outlining how to achieve systems-compatible incentives, which may serve an even broader range of systems than considered herein. I conclude this dissertation with what I consider to be the most important open problems in aligning the competing interests of the Internet's participants

Combinatorial exchange models for a user-driven air traffic flow management in Europe

2008/2009Air Traffic Flow Management (ATFM) is the service responsible to guarantee that the available capacity of the air transportation system is efficiently used and never exceeded. It guarantees safety of air transportation by adopting a series of measures which range from strategic long-term ones to the imposition of ground delays to flights at a tactical level. These ATFM delays are imposed to individual flights at the departure airport prior to their take-off, since it is safer and less costly to anticipate on the ground any delay predicted somewhere in the system. They are assigned by a central authority according to a First-Planned-First-Served principle, without taking into account individual Airlines' preferences. This criteria of assignment can cause an aggregated cost of delay experienced by users, higher than the minimal one, due to the fact that the cost of delay is a non-linear function of the duration and it depends on many variables such as the type of aircraft, the specific origin-destination pair, ecc. This thesis tackles the issue of formalizing and analyzing alternative models for the assignment of ATFM resources which take into account individual airlines preferences. In particular mathematical programming models are analyzed, that extend the concept of ATFM slot currently adopted to the one of Target Window, as proposed in the CATS European project. Such a concept is in line with the SESAR program, recently adopted in Europe to develop the new generation system of Air Traffic Management, which imposes a direct involvement of Airspace users whenever external constraints need to be enforced that modify their original requests. The first Chapter provides a general introduction to the context of Air Traffic Management and Air Traffic Control. In the second Chapter the principles, methods and performances of the ATFM system are described according to the current situation as well as to the SESAR target concept. The problem of optimally assign ATFM resources is then described mathematically and then analyzed to uncover two fundamental structures that determine its tractability: one corresponds to the case in which there is a unique capacity constrained resource while in the second there is an unrestricted number of constrained resources. In Chapter three a number of properties are proved that give insight into the applicability of different mechanisms for a central calculation of the optimal solution by the ATFM authority. Since such mechanisms involve cost minimization for several agents they are formulated as exchanges, i.e. particular types of auctions in which each participant may buy and/or sell several indivisible goods. The last part of the thesis included in Chapter four deals with the design of iterative exchange mechanisms, whose application in real world presents several advantages with respect to centralized models, from the distribution of computational complexity among participants to the preservation of disclosure of private information by Aircraft Operators. In this case an optimal model based on the Lagrangian relaxation of the separable central problem is first formulated and analyzed. To overcome practical issues possibly deriving from its application in real operations, an heuristic iterative Market-based mechanism is finally formalized. This algorithm exploits some of the underlying characteristics specific to the problem to derive near-optimal solutions in an acceptable time. Computational results are obtained by simulating its implementation on real traffic data and they show that considerable cost savings are possible with respect to a First-Planned-First-Served central allocation. The contribute of this thesis is twofold. The first is to provide a mathematical description, modeling and analysis of the ATFM resource exchange problem faced by Airspace users when network capacity needs to be rationed among them. The second consists in the methodological innovation represented by the formulation of the Market Mechanism which is compliant with several requirements represented by legislative and practical constraints and whose simulation provided encouraging results.XXII Cicl

Reasoning effectively under uncertainty for human-computer teamwork

As people are increasingly connected to other people and computer agents, forming mixed networks, collaborative teamwork offers great promise for transforming the way people perform their everyday activities and interact with computer agents. This thesis presents new representations and algorithms, developed to enable computer systems to function as effective team members in settings characterized by uncertainty and partial information. For a collaboration to succeed in such settings, participants need to reason about the possible plans of others, to be able to adapt their plans as needed for coordination, and to support each other's activities. Reasoning on general teamwork models accordingly requires compact representations and efficient decision-theoretic mechanisms. This thesis presents Probabilistic Recipe Trees, a probabilistic representation of agents' beliefs about the probable plans of others, and decision-theoretic mechanisms that use this representation to manage helpful behavior by considering the costs and utilities of computer agents and people participating in collaborative activities. These mechanisms are shown to outperform axiomatic approaches in empirical studies. The thesis also addresses the challenge that agents participating in a collaborative activity need efficient decision-making algorithms for evaluating the effects of their actions on the collaboration, and they need to reason about the way other participants perceive these actions. This thesis identifies structural characteristics of settings in which computer agents and people collaborate and presents decentralized decision-making algorithms that exploit this structure to achieve up to exponential savings in computation time. Empirical studies with human subjects establish that the utility values computed by this algorithm are a good indicator of human behavior, but learning can help to better understand the way these values are perceived by people. To demonstrate the usefulness of these teamwork capabilities, the thesis describes an application of collaborative teamwork ideas to a real-world setting of ridesharing. The computational model developed for forming collaborative rideshare plans addresses the challenge of guiding self-interested people to collaboration in a dynamic setting. The empirical evaluation of the application on data collected from the real-world demonstrates the value of collaboration for individual users and environment