Dissertations / Theses on the topic 'Game theory mechanism'

This thesis applies game theory to study optimal toehold bidding strategies during takeover competition, the problem of optimal design of voting rules and the design of package bidding mechanism to implement the core allocations. It documents three different research questions that are all related to auction theory. Chapter 2 develops a two-stage takeover game to explain toehold puzzle in the context of takeover. Potential bidders are allowed to acquire target shares in the open market, subject to some limitations. This pre-bid ownership is known as a toehold. Purchasing a toehold prior to making any takeover offer looks like a profitable strategy given substantial takeover premiums. However actual toehold bidding has decreased since 1980s and now is not common. Its time-series patter is centred on either zero or a large value. Chapter 2 develops a two-stage takeover game. In the first stage of this two-stage game, each bidder simultaneously acquires a toehold. In the second stage, bidders observe acquired toehold sizes, and process this information to update their beliefs about rival's private valuation. Then each bidder competes to win the target under a sealed-bid second-price auction. Different from previous toehold puzzle literature focusing on toehold bidding costs in the form of target managerial entrenchment, this chapter develops a two-stage takeover game and points another possible toehold bidding cost - the opportunity loss of a profitable resale. Chapter 2 finds that, under some conditions, there exists a partial pooling Bayesian equilibrium, in which low-value bidders optimally avoid any toehold, while high-value bidders pool their decisions at one size. The equilibrium toehold acquisition strategies coincide with the bimodal distribution of the actual toehold purchasing behaviour. Chapter 3 studies the problem of optimal design of voting rules when each agent faces binary choice. The designer is allowed to use any type of non-transferable penalty on individuals in order to elicit agents' private valuations. And each agent's private valuation is assumed to be independently distributed. Early work showed that the simple majority rule has good normative properties in the situation of binary choice. However, their results relay on the assumption that agents' preferences have equal intensities. Chapter 3 shows that, under reasonable assumptions, the simple majority is the best voting mechanism in terms of utilitarian efficiency, even if voters' preferences are comparable and may have varying intensities. At equilibrium, the mechanism optimally assigns zero penalty to every voter. In other words, the designer does not extract private information from any agent in the society, because the expected penalty cost of eliciting private information to select the better alternative is too high. Chapter 4 presents a package bidding mechanism whose subgame perfect equilibrium outcomes coincide with the core of an underlying strictly convex transferable utility game. It adopts the concept of core as a competitive standard, which enables the mechanism to avoid the well-known weaknesses of VCG mechanism. In this mechanism, only core allocations generate subgame perfect equilibrium payoffs, because non-core allocations provide arbitrage opportunities for some players. By the strict convexity assumption, the implementation of the core is achieved in terms of expectation.

Cette thèse développe de nouveaux outils de théorie des jeux et mechanism design pour de multiples application en économie/finance. Le premier chapitre étudie la possibilité d’implémentation d’équilibres de communication dans le cadre de jeux stratégiques lorsque tous les joueurs de réseau peuvent communiquer par l’intermédiaire d’un médiateur impartial. Je dérive les conditions nécessaires et suffisantes sur la structure du réseau de joueurs telles que, pour tout jeu, tout équilibre de communication puisse être implémenté. Le deuxième chapitre propose un modèle d’encombrement de la chaine de production dans lequel les contraintes de capacité produisent de multiples équilibres de Nash Pareto-inefficients. Ce chapitre montre comment l’utilisation d’équilibres corrélés peut résoudre de manière substantielle ces inefficiences. Les deux dernier chapitres traitent de questions liées à la conception des exigences de fonds propres de banques. Dans le chapitre 3, on caractérise les exigences optimales de fonds propres des banques lorsque celles-ci disposent d’informations privées sur la valeur de leurs actifs existants. On montre comment l’implémentation des exigences de fonds propres peut éliminer le coût de l’augmentation de capital pour la banque en révélant ses informations au marché, et les conditions dans lesquelles ce transfert d’informations est optimal. Dans le chapitre 4, on fait l’hypothèse que les banques possèdent de l’information privée sur le risque de leurs actifs plutôt que sur leur valeur. Dans c ecas, si les investisseurs sont suffisamment averses au risque, on montre que n’importe quelle exigence de fonds subordonée au risque des banques incitent ces derniéres à mentir sur leur niveau de risque effectif. Ce résultat met em lumière d’importants problèmes de robustesse
This thesis develops and utilizes tools in game theory and mechanism design to study multiple applications in economics and finance. The first chapter studies the problem of implementing communication equilibria of strategic games when players communicate with an impartial mediator through a network. I characterize necessary and sufficient conditions on the network structure such that any communication equilibrium of any game can be implemented on that network. The next chapter studies a model of supply chain congestion whereby capacity constraints lead to very inefficient Nash equilibria and I show how the use of correlsted equilibria can substantially resolve those inefficiencies. The final two chapters study related issues in the design of bank capital requirements. In Chapter 3, I characterize optimal bank capital requirements when banks have private information about the value of their existing assets. I show how the implementation of capital requirements can eliminate the bank’s cost of raising capital by revealing their information to the market and conditions under which doing soi s optimal. In Chapter 4, I show how when the bank’s private information is about the riskiness of its assets instead, then any risk sensitive capital requirement will lead banks to optimally misreport their risk whenever investors are sufficiently risk averse, highlighting important robustness concerns

In this work we present a general duality-theory framework for revenue maximization in additive Bayesian auctions involving multiple items and many bidders whose values for the goods follow arbitrary continuous joint distributions over some multi-dimensional real interval. Although the single-item case has been resolved in a very elegant way by the seminal work of Myerson [1981], optimal solutions involving more items still remain elusive. The framework extends linear programming duality and complementarity to constraints with partial derivatives. The dual system reveals the natural geometric nature of the problem and highlights its connection with the theory of bipartite graph matchings. We demonstrate the power of the framework by applying it to various special monopoly settings where a seller of multiple heterogeneous goods faces a buyer with independent item values drawn from various distributions of interest, to design both exact and approximately optimal selling mechanisms. Previous optimal solutions were only known for up to two and three goods, and a very limited range of distributional priors. The duality framework is used not only for proving optimality, but perhaps more importantly, for deriving the optimal mechanisms themselves. Some of our main results include: the proposal of a simple deterministic mechanism, which we call Straight-Jacket Auction (SJA) and is defined in a greedy, recursive way through natural geometric constraints, for many uniformly distributed goods, where exact optimality is proven for up to six items and general optimality is conjectured; a scheme of sufficient conditions for exact optimality for two-good settings and general independent distributions; a technique for upper-bounding the optimal revenue for arbitrarily many goods, with an application to uniform and exponential priors; and the proof that offering deterministically all items in a single full bundle is the optimal way of selling multiple exponentially i.i.d. items.

This thesis looks into the economics of crowdsourcing using game theoretic modeling. The art of aggregating information and expertise from a diverse population has been in practice since a long time. The Internet and the revolution in communication and computational technologies have made this task easier and given birth to a new era of online resource aggregation, which is now popularly referred to as crowdsourcing. Two important features of this aggregation technique are: (a) crowdsourcing is always human driven, hence the participants are rational and intelligent, and they have a payoff function that they aim to maximize, and (b) the participants are connected over a social network which helps to reach out to a large set of individuals. To understand the behavior and the outcome of such a strategic crowd, we need to understand the economics of a crowdsourcing network. In this thesis, we have considered the following three major facets of the strategic crowdsourcing problem. (i) Elicitation of the true qualities of the crowd workers: As the crowd is often unstructured and unknown to the designer, it is important to ensure if the crowdsourced job is indeed performed at the highest quality, and this requires elicitation of the true qualities which are typically the participants' private information. (ii) Resource critical task execution ensuring the authenticity of both the information and the identity of the participants: Due to the diverse geographical, cultural, socio-economic reasons, crowdsourcing entails certain manipulations that are unusual in the classical theory. The design has to be robust enough to handle fake identities or incorrect information provided by the crowd while performing crowdsourcing contests. (iii) Improving the productive output of the crowdsourcing network: As the designer's goal is to maximize a certain measurable output of the crowdsourcing system, an interesting question is how one can design the incentive scheme and/or the network so that the system performs at an optimal level taking into account the strategic nature of the individuals. In the thesis, we design novel mechanisms to solve the problems above using game theoretic modeling. Our investigation helps in understanding certain limits of achievability, and provides design protocols in order to make crowdsourcing more reliable, effective, and productive.

Thesis (Ph. D.)--University of Oregon, 2006.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 105-107). Also available for download via the World Wide Web; free to University of Oregon users.

In recent years, Software Defined and Cognitive Radios (SDRs and CRs) have become popular topics of research. Game theory has proven to be a useful set of tools for analyzing wireless networks, including Cognitive Networks (CNs). This thesis provides a game theoretic cross-layer mechanism that can be used to control SDRs and CRs. We have constructed an upper-layer Topology Control (TC) game, which decides which links each node uses. A TDMA algorithm which we have adapted is then run on these links. The links and the TDMA schedule are then passed to a lower-layer game, the Link Adaptation Game (LAG), where nodes adjust their transmit power and their link parameters, which in this case are modulation scheme and channel coding rate. It is shown that both the TC game and the LAG converge to a Nash Equilibrium (NE). It is also shown that the solution for the TC game approximates the topology that results from maximizing the utility function when appropriate link costs are used. Also seen is the increase in throughput provided by the LAG when compared to the results of Greedy Rate Packing (GRP).
Master of Science

The first chapter presents a theoretical model of electoral competition where two parties can increase campaign contributions by choosing policies benefiting a significant interest group. However, such decision will shrink their hardcore vote base where voters are well informed about the policy. The parties can then allocate the funds between campaigning and personal wealth. Different from the core voters, independent voters can be attracted by advertisements funded by campaign spending. Using a multi-stage extensive form game, I investigate how electoral competition interacts with diversions and policy distortions. My result shows that a higher level of electoral competition helps mitigate policy distortions but prompts the parties to divert more funds. Perfectly informed signal senders need to communicate their true type (productivity or ability) which is often private information to potential receivers. While tests are commonly used as measures of applicants' productivity, the accuracy of them has been questioned. Beginning with the framework of a two-type labor market signaling game, the second chapter investigates how tests of limited reliability affect the nature of equilibria in signaling games with asymmetric information. Our results show that, if a test is inaccurate and costly, only pooling PBE exists given certain conditions. Different forms of test inaccuracy may allow a separating PBE to exist. We also study the case of three types and find different PBEs. The central issue of siting noxious facilities is that the host community absorbs potential costs, while all others can share the benefits without paying as much. The third chapter presents a modified Clarke mechanism to facilitate the siting decision, taking into account all residents' strategies. Suppose that the social planner is able to reasonably estimate the possible costs, depending on the host location, to each resident created by the facility. Our proposed Clarke mechanism is characterized by strategy-proofness and yields an efficient siting outcome. The issue of budget imbalance is mitigated when the compensation scheme is fully funded with the tax revenue based on the benefits. We then use a simple example to show that a weighted version of the Clarke mechanism may yield a different outcome.
Ph. D.

The main aspiration behind the contributions of this research work is the achievement of simultaneuos delay-efficiency, autonomy, and security through innovative protocol design to address complex real-life problems. To achieve this, we take a holistic approach. We apply theoretical mathematical modeling implementing implications of social-economic behavioral characteristics to propose a cross-layer network security protocol. We further complement this approach by a layer-specific focus with implementations at two lower OSI layers. For the cross-layer design, we suggest the use of game and mechanism design theories. We design a network-wide consumer-centric and delay-efficient security protocol, DSIC-S. It induces a Dominant Strategy Incentive Compatible equilibrium among all rational and selfish nodes. We prove it is network-wide socially desirable and Pareto optimal. We address resource management and delay-efficiency through synergy of several design aspects. We propose a scenario-based security model with different levels. Furthermore, we design a valuation system to integrate the caused delay in selection of security algorithms at each node without consumer's knowledge of the actual delays. We achieve this by incorporating the consumer's valuation system, in the calculation of the credit transfers through the Vickrey-Clarke-Groves (VCG) payments with Clarke's pivotal rule. As the utmost significant contribution of this work, we solve the revelation theorem's problem of misrepresentation of agents' private information in mechanism design theory through the proposed design. We design an incentive model and incorporate the valuations in the incentives. The simulations validate the theoretical results. They prove the significance of this model and among others show the correlation of the credit transfers to actual delays and security valuations. In the layer-specific approach for the network-layer, we implement the DSIC-S protocol to extend current IPsec and IKEv2 protocols. IPsec-O and IKEv2-O inherit the strong properties of DSIC-S through the proposed extensions. Furthermore, we propose yet another layer-specific protocol, the SME_Q, for the datalink layer based on ATM. We develop an extensive simulation software, SMEQSIM, to simulate ATM security negotiations. We simulate the proposed protocol in a comprehensive real-life ATM network and prove the significance of this research work.
Ph. D.

Deux situations de comportement égoïste des agents dans les réseaux de communication sont considérées dans le cadre de la théorie des jeux.La première situation concerne les réseaux de communication utilisant un routage décentralisé basé sur des agents autonomes. Nous étudions les propriétés de convergence des dynamiques de meilleures réponses dans un jeu de routage sur des liens parallèles. Le jeu implique un nombre fini d'agents, chacun décidant comment son trafic est routé sur les liens de manière à minimiser son propre coût. Nous proposons l'utilisation du rayon spectral généralisé des matrices Jacobiennes de l'opérateur de meilleure réponse pour démontrer la convergence.La seconde situation apparaît dans les réseaux tolérants aux délais dont l'objectif est de permettre la communication dans des environnements où la connectivité n'est qu'intermittente et où les délais de communication peuvent être très longs. Nous proposons tout d'abord un mécanisme d'incitation basé sur une récompense pour convaincre les noeuds mobiles de relayer les messages, et analysons l'influence de l'information donnée par la source (nombre de copies du message, âge de ces copies) aux relais sur le prix à payer pour transmettre le message. Nous considérons ensuite un modèle dans lequel la source propose une récompense fixe. Les noeuds mobiles peuvent alors décider d'accepter ou non le message, et s'ils l'acceptent, peuvent ensuite à tout moment décider de l'abandonner. Nous modélisons l'interaction entre les noeuds mobiles sous la forme d'un jeu stochastique partiellement observable et analysons les politiques optimales pour les relais
This thesis focuses on the issues related to the selfish behavior of the agents in the communication networks. We are particularly interested in two situations in which these issues arise and we address game-theoretical framework to study them.The first situation relates to communication networks using a distributed routing based on autonomous agents. Compared to a centralized routing, this type of routing offers significant advantages in terms of scalability, ease of deployment or robustness to failures and environmental disturbances. We investigate the convergence properties of the sequential best-response dynamics in a routing game over parallel links. The game involves a finite number of routing agents each of which decides how much flow to route on each of the links with the objective of minimizing its own costs. For some particular cases (e.g., two players), the convergence of the best-response dynamics can be proved by showing that this game has a potential function. For other cases, a potential function has remained elusive. We propose the use of non-linear spectral radius of the Jacobian of the best-response dynamics as an alternative approach to proving its convergence.The second situation occurs in Delay Tolerant Networks (DTNs) that have been the subject of intensive research over the past decade. DTN has an idea to support communication in environments where connectivity is intermittent and where communication delays can be very long. We focus on game-theoretic models for DTNs. First, we propose an incentive mechanism to persuade selfish mobile nodes to participate in relaying messages, and investigate the influence of the information given by the source (number of existing copies of the message, age of these copies) to the relays on the rewards proposed. For static information polices, that is the same type of information given to all the relays, it is shown that the expected reward paid by the source is independent of the policy. However, the source can reduce the reward by dynamically adapting the type of information based on the meeting times with the relays. For the particular cases, we give some structural results of the optimal adaptive policy. Next, we consider the model where the source proposes a fixed reward. The mobile relays can decide to accept or not the packet and then to drop the packet in the future. This game can be modelled as a partially-observable stochastic game. For two relays, we have shown that the optimal policies for the relays relates to the threshold type

The emergence of blockchain technology and increasing penetration of distributed energy resources (DERs) have created a new opportunity for peer-to-peer (P2P) energy trading. However, challenges arise in such transactive markets to ensure individual rationality, incentive compatibility, budget balance, and economic efficiency during the trading process. This thesis creates an hour-ahead P2P energy trading network based on the Hyperledger Fabric blockchain and explores a comparative analysis of different auction mechanisms that form the basis of smart contracts. Considered auction mechanisms are discriminatory and uniform k-Double Auction with different k values. This thesis also investigates effects of four consumer and prosumer bidding strategies: random, preference factor, price-only game-theoretic approach, and supply-demand game-theoretic approach. A custom simulation framework that models the behavior of the transactive market is developed. Case studies of a 100-home microgrid at various photovoltaic (PV) penetration levels are presented using typical residential load and PV generation profiles in the metropolitan Washington, D.C. area. Results indicate that regardless of PV penetration levels and employed bidding strategies, discriminatory k-DA can outperform uniform k-DA. Despite so, discriminatory k-DA is more sensitive to market conditions than uniform k-DA. Additionally, results show that the price-only game-theoretic bidding strategy leads to near-ideal economic efficiencies regardless of auction mechanisms and PV penetration levels.
MS

En el marco de los Objetivos del Milenio, la falta de provisión de servicios públicos esenciales constituye en el Perú una carga histórica que debe ser superada con el objetivo de implementar de manera eficiente políticas de desarrollo social, establecer índices sostenibles de crecimiento micro y macroeconómico y aportar a la reducción de las tasas de pobreza en el país. Sin embargo, es indiscutible que la prestación de tales servicios esenciales o de interés público o general (servicios eléctricos, de transporte, ferroviarios, aeroportuarios, portuarios, etc.) no es posible sin contar con el soporte o sostén de una infraestructura que permita su viabilidad. De esta manera, en los países latinoamericanos, la falta de provisión de servicios públicos se ha intentado explicar en la denominada “Brecha de Infraestructura en América del Sur”, término que la Comisión Económica para América Latina y el Caribe (“CEPAL”) y la Secretaría General de la Unión de Naciones Suramericanas (“UNASUR”) han utilizado en diversas investigaciones Ejemplo claro de esta brecha es que, en los últimos 20 años, los países latinoamericanos (con distintos modelos económicos y políticos) han invertido aproximadamente entre el 2% y el 3% de su Producto Bruto Interno (“PBI”) en infraestructura, salvo en los casos de Chile con 6% y Colombia con 4%
Tesis

The efficient allocation of power is a major concern in today’s wireless communications systems. Due to the high demand in data rate and the scarcity of wireless resources such as power, the multi-user communication systems like the multiple access channel (MAC) and broadcast channel (BC) have become highly competitive environments for the users as well as the system itself. Theory of microeconomics and game theory provide the good analytical manner for the selfish and social welfare conflict problems. Instead of maximizing the system sum rate, our proposed system deals with fulfilling the utility (rate) requirement of all the users with efficient power allocation. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements. We propose the framework to allocate the power to each user with universal pricing mechanisms. The prices act as the control signal and are assumed to be some virtual currency in the wireless system. They can influence the physical layer operating points to meet the desired utility requirements. Centralized and distributed power allocation frameworks are discussed separately in the thesis with different pricing schemes. In wireless systems we have users that are rational in the game theoretic sense of making decisions consistently in pursuit of their own individual objectives. Each user’s objective is to maximize the expected value of its own payoff measured on a certain utility scale. Selfishness or self-interest is an important implication of rationality. Therefore, the mobiles which share the same spectrum have incentives to misinterpret their private information in order to obtain more utility. They might behave selfishly and show also malicious behavior by creating increased interference for other mobiles. Therefore, it is important to supervise and influence the operation of the system by pricing and priority (weights) optimization. In the centralized resource allocation, we study the general MAC and BC (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The regulator ensures the QoS requirements of all users by clever pricing and prevents cheating. The simple system optimizer solves a certain system utility maximization problem to allocate the power with the given prices and weights (priorities). The linear and nonlinear pricing mechanisms are analyzed, respectively. It is shown that linear pricing is a universal pricing only if successive interference cancellation (SIC) for uplink transmission or dirty paper coding (DPC) for downlink transmission is applied at the base station (BS). For MAC without SIC, nonlinear pricing which is logarithmic in power and linear in prices is a universal pricing scheme. The prices, the resulting cost terms, the optimal power allocation to achieve the QoS requirement of each user in the feasible rate region are derived in closed form solutions for MAC with and without SIC using linear and nonlinear pricing frameworks, respectively. The users are willing to maximize their achievable rate and minimize their cost on power by falsely reporting their channel state information (CSI). By predicting the best cheating strategy of the malicious users, the regulator is able to detect the misbehavior and punish the cheaters. The infinite repeated game (RG) is proposed as a counter mechanism with the trigger strategy using the trigger price. We show that by anticipating the total payoff of the proposed RG, the users have no incentive to cheat and therefore our framework is strategy-proof. In the distributed resource allocation, each user allocates its own power by optimizing the individual utility function. The noncooperative game among the users is formulated. The individual prices are introduced to the utility function of each user to shift the Nash equilibrium (NE) power allocation to the desired point. We show that by implicit control of the proposed prices, the best response (BR) power allocation of each user converges rapidly. The Shannon rate-based QoS requirement of each user is achieved with minimum power at the unique NE point. We analyse different behavior types of the users, especially the malicious behavior of misrepresenting the user utility function. The resulting NE power allocation and achievable rates of all users are derived when malicious behavior exists. The strategy-proof mechanism is designed using the punishment prices when the types of the malicious users are detected. The algorithm of the strategy-proof noncooperative game is proposed. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations. The uplink transmission within the single cell of heterogeneous networks is exactly the same model as MAC. Therefore, the results of the pricing-based power allocation for MAC can be implemented into heterogeneous networks. Femtocells deployed in the Macrocell network provide better indoor coverage to the user equipments (UEs) with low power consumption and maintenance cost. The industrial vendors show great interest in the access mode, called the hybrid access, in which the macrocell UEs (MUEs) can be served by the nearby Femtocell Access Point (FAP). By adopting hybrid access in the femtocell, the system energy efficiency is improved due to the short distance between the FAP and MUEs while at the same time, the QoS requirements are better guaranteed. However, both the Macrocell base station (MBS) and the FAP are rational and selfish, who maximize their own utilities. The framework to successively apply the hybrid access in femtocell and fulfill the QoS requirement of each UE is important. We propose two novel compensation frameworks to motivate the hybrid access of femtocells. To save the energy consumption, the MBS is willing to motivate the FAP for hybrid access with compensation. The Stackelberg game is formulated where the MBS serves as the leader and the FAP serves as the follower. The MBS maximizes its utility by choosing the compensation prices. The FAP optimizes its utility by selecting the number of MUEs in hybrid access. By choosing the proper compensation price, the optimal number of MUEs served by the FAP to maximize the utility of the MBS coincides with that to maximize the utility of the FAP. Numerous simulation results are conducted, showing that the proposed compensation frameworks result in a win-win solution. In this thesis, based on game theory, mechanism design and pricing framework, efficient power allocation are proposed to guarantee the QoS requirements of all users in the wireless networks. The results are applicable in the multi-user systems such as heterogeneous networks. Both centralized and distributed allocation schemes are analyzed which are suitable for different communication scenarios
Aufgrund der hohen Nachfrage nach Datenrate und wegen der Knappheit an Ressourcen in Funknetzen ist die effiziente Allokation von Leistung ein wichtiges Thema in den heutigen Mehrnutzer-Kommunikationssystemen. Die Spieltheorie bietet Methoden, um egoistische und soziale Konfliktsituationen zu analysieren. Das vorgeschlagene System befasst sich mit der Erfüllung der auf Signal-zu-Rausch-und-Interferenz-Verhältnis (SINR) basierenden Quality-of-Service (QoS)-Anforderungen aller Nutzer mittels effizienter Leistungsallokation, anstatt die Übertragungsrate zu maximieren. Es wird ein Framework entworfen, um die Leistungsallokation mittels universellen Pricing-Mechanismen umzusetzen. In der Dissertation werden zentralisierte und verteilte Leistungsallokationsalgorithmen unter Verwendung verschiedener Pricing-Ansätze diskutiert. Die Nutzer in Funksystemen handeln rational im spieltheoretischen Sinne, indem sie ihre eigenen Nutzenfunktionen maximieren. Die mobilen Endgeräte, die dasselbe Spektrum nutzen, haben den Anreiz durch bewusste Fehlinterpretation ihrer privaten Informationen das eigene Ergebnis zu verbessern. Daher ist es wichtig, die Funktionalität des Systems zu überwachen und durch Optimierung des Pricings und Priorisierungsgewichte zu beeinflussen. Für den zentralisierten Ressourcenallokationsansatz werden der allgemeine Mehrfachzugriffskanal (Multiple Access Channel, MAC) und der Broadcastkanal (BC) mit linearen bzw. nichtlinearen Empfängern untersucht. Die Preise, die resultierenden Kostenterme und die optimale Leistungsallokation, mit der die QoS-Anforderungen in der zulässigen Ratenregion erfüllt werden, werden in geschlossener Form hergeleitet. Lineare und nichtlineare Pricing-Ansätze werden separat diskutiert. Das unendlich oft wiederholte Spiel wird vorgeschlagen, um Spieler vom Betrügen durch Übermittlung falscher Kanalinformationen abzuhalten. Für die verteilten Ressourcenvergabe wird das nichtkooperative Spiel in Normalform verwendet und formuliert. Die Nutzer wählen ihre Sendeleistung zur Maximierung ihrer eigenen Nutzenfunktion. Individuelle Preise werden eingeführt und so angepasst, dass die QoS-Anforderungen mit der Leistungsallokation im eindeutigen Nash-Gleichgewicht erfüllt werden. Verschiedene Arten des Nutzerverhaltens werden bezüglich der Täuschung ihrer Nutzenfunktion analysiert, und ein Strategy-Proof-Mechanismus mit Strafen wird entwickelt. Die Ergebnisse für den MAC sind anwendbar auf heterogene Netzwerke, wobei zwei neuartige Ansätze zur Kompensation bereitgestellt werden, die den hybriden Zugang zu Femtozell-Netzwerken motivieren. Mithilfe des Stackelberg-Spiels wird gezeigt, dass die vorgeschlagenen Ansätze in einer Win-Win-Situation resultieren

Thesis (Ph.D.)--University of Western Sydney, 2009.
A thesis presented to the University of Western Sydney, College of Health and Science, School of Computing and Mathematics for the degree of Doctor of Philosophy and Docteur de L'Université de Toulouse, Faculté d'Informatique, under a cotutelle agreement. Includes bibliographies.

Resource sharing is a common collaborative strategy used in practice. It has the potential to create synergistic value and leads to higher system efficiency. However, realizing this synergistic value can be challenging given the prevalence of decentralization in practice, where individual operators manage resources based on their own benefits. Hence, optimizing a decentralized system requires understanding not only the optimal operational strategy in terms of the overall system efficiency, but also the implementation of the strategy through proper management of individual incentives. However, traditional network optimization approaches typically assume a centralized perspective. The classic game theory framework, on the other hand, addresses incentive issues of decentralized decision makers, but mainly takes a high-level, economic perspective that does not fully capture the operational complexity involved in optimizing systems with resource sharing. The purpose of this thesis is to bridge this gap between practice and theory by studying the design of tools to manage and optimize the operations in decentralized systems with resource sharing using approaches that combine optimization and game theory. In particular, we focus on decentralized network systems and analyze two research streams in two application domains: (i) implementation of environmental legislation, and (ii) managing collaborative transportation systems. These applications are characterized by their decentralized multi-stakeholder nature where the conflicts and tension between the heterogeneous individual perspectives make system management very challenging. The main methodology used in this thesis is to adopt game theory models where individual decisions are endogenized as the solutions to network optimization problems that reflect their incentives. Such an approach allows us to capture the connection between the operational features of the system (e.g., capacity configuration, network structure, synergy level from resource sharing) and the individual incentives thus the effectiveness of the management tools, which is a main research contribution of this thesis. In the first research stream, we consider designing effective, efficient and practical implementation of electronic waste take-back legislation based on the widely-adopted Extended Producer Responsibility (EPR) concept that mandates the financial responsibility of post-use treatment of their products. Typical implementations of EPR are collective, and allocate the resulting operating cost to involved producers. In this thesis, we demonstrate the complexity of collective EPR implementation due to the tension among different stakeholder perspectives, based on a case analysis of the Washington implementation. We then perform analytical studies of the two prominent challenges identified in current implementations: (i) developing cost allocation mechanisms that induce the voluntary participation of all producers in a collective system, thus promoting implementation efficiency; and (ii) designing collective EPR so as to encourage environmentally-friendly product design, thus promoting implementation effectiveness. Specifically, we prescribe new cost allocation methods to address the first challenge, and demonstrate the practicality and economic impact of the results using implementation data from the state of Washington. We then analyze the tensions between design incentives, efficiency and the effectiveness of the cost allocation to induce voluntary participation under collective EPR implementation. We show there exists a tradeoff among the three dimensions, driven by the network effects inherent in a collective system. The main contribution of this research stream is to demonstrate how the implementation outcomes of an environmental policy is influenced by the way that the policy ``filters' through operational-level factors, and to propose novel and implementation mechanisms to achieve efficient and effective EPR implementation. Hence, our study has the potential to provide guidance for practice and influence policy-making. In the second research stream, motivated by the practice of transportation alliances, we focus on a decentralized network setting where the individual entities make independent decisions regarding the routing of their own demand and the management of their own capacity, driven by their own benefits. We study the use of market-based exchange mechanisms to motivate and regulate capacity sharing so as to achieve the optimal overall routing efficiency in a general multicommodity network. We focus on the design of capacity pricing strategies in the presence of several practical operational complexities, including multiple ownership of the same capacity, uncertainty in network specifications, and information asymmetry between the central coordinator and individual operators. Our study in this research stream produces two sets of results. First, we demonstrate the impact of the underlying network structure on the effectiveness of using market-based exchange mechanisms to coordinate resource sharing and to allocate the resulting synergistic benefit, and characterize the network properties that matter. Second, we propose efficient and effective pricing policies and other mechanism design strategies to address different operational complexities. Specifically, we develop duality-based pricing algorithms, and evaluate different pricing strategies such as commodity-based price discrimination, which is shown to have an advantage in coordinating networks under uncertainty.

Doctor of Philosophy (Economics)
Auctions are an ancient economic institution. Since Vickrey (1961), the development of auction theory has lead to an extremely detailed description of the often desirable characteristics of these simple selling procedures, in the process explaining their enduring popularity. Given the pervasiveness of auctions, the question of how a seller should engineer the rules of these mechanisms to maximize her own profits is a central issue in the organization of markets. The seminal paper of Myerson (1981) shows that when facing buyers with Independent Private Values (IPVs) a standard auction with a specifically selected reserve price (or prices) is optimal, that is, maximizes a seller's expected profits among all conceivable selling mechanisms. In this model, it is assumed that the buyers have perfect information as to the existence of gains from trade. We shall argue that the consequences of this assumption for the design of the optimal auction are not well understood, which motivates our analysis. The three essays of this thesis relax the `known seller valuation' assumption by examining the optimal auction program when the seller (and principal) holds private information representing her reservation value for the good. In the first essay we provide an original technique for comparing ex ante expected profits across mechanisms for a seller facing N>1 potential buyers when all traders hold private information. Our technique addresses mechanisms that cannot be ranked point-by-point through their allocation rules using the Revenue Equivalence Theorem. We find conditions such that the seller's expected profits increase in the slope of each buyer's allocation probability function. This provides new intuition for the fact that a principal does not benefit from holding private information under risk neutrality. Monopoly pricing induces steep probability functions so the seller/principal benefits from announcing a fixed price, and implicitly her private information. An application is presented for the well known k double auction of the bilateral trade literature. In the second and third essays of this thesis, we extend the above framework to allow for informational externalities. Specifically, we allow for the situation in which the seller's private information represents a common value component in buyers' valuations. Thus the seller's private information (say regarding the quality of the good) is of interest to bidders independently of any strategic effects. In recent work Cai, Riley and Ye (2007) have demonstrated that a seller who holds private information about the quality of a good faces an extra consideration in designing an auction; the reserve price signals information to bidders. In a separating equilibrium signalling is costly in the sense that reserves are higher than would be optimal under complete information. We examine the returns to the seller in an English auction from using different types of secret reserve regimes. We find that immediate disclosure of a reserve is preferable to announcement after the auction in the form of a take-it-or-leave-it offer to the winning bidder. Sale occurs less often during the auction for a given reserve price strategy under secret reserve regimes, which increases the incentive for the seller to report more favourable information though the reserve price offer. Separating equilibria involving later announcement therefore generate even lower expected profits to the seller (signalling is more costly) than under immediate disclosure. In the third essay we compare the benchmark signalling equilibrium of immediate disclosure to a screening regime which we call the Right of Refusal. In this extreme form of a secret reserve the seller never announces the reserve price, she simply accepts or rejects the auction price. We find that the Right of Refusal dominates immediate disclosure if the seller's valuation is a sufficient statistic for the private information of interest. Thus a seller with market-relevant private preference information can benefit from not exercising monopoly price setting power. The result also provides conditions under which a competitive screening equilibrium is more efficient than a signalling mechanism. Broadly speaking, screening is better when the common value aspect in the preferences of the informed and uninformed parties are `aligned', and potential gains from trade to the uninformed party are significant. We believe this conclusion to be of particular interest to the design of privatization schemes.

Nous présentons notre contribution sur la régulation et l’optimisation de la production d’électricité.La première partie concerne l’optimisation de la gestion d’un micro réseau. Nous formulons le programme de gestion comme un problème de commande optimal en temps continu, puis nous résolvons ce problème par programmation dynamique à l’aide d’un solveur développé dans ce but : BocopHJB. Nous montrons que ce type de formulation peut s’étendre à une modélisation stochastique. Nous terminons cette partie par l’algorithme de poids adaptatifs, qui permet une gestion de la batterie du micro réseau intégrant le vieillissement de celle-ci. L’algorithme exploite la structure à deux échelles de temps du problème de commande.La seconde partie concerne des modèles de marchés en réseaux, et en particulier ceux de l’électricité. Nous introduisons un mécanisme d’incitation permettant de diminuer le pouvoir de marché des producteurs d’énergie, au profit du consommateur. Nous étudions quelques propriétés mathématiques des problèmes d’optimisation rencontrés par les agents du marché (producteurs et régulateur). Le dernier chapitre étudie l’existence et l’unicité des équilibres de Nash en stratégies pures d’une classe de jeux Bayésiens à laquelle certains modèles de marchés en réseaux se rattachent. Pour certains cas simples, un algorithme de calcul d’équilibre est proposé.Une annexe rassemble une documentation sur le solveur numérique BocopHJB
We present our contribution on the optimization and regulation of electricity produc- tion.The first part deals with a microgrid Energy Management System (EMS). We formulate the EMS program as a continuous time optimal control problem and then solve this problem by dynamic programming using BocopHJB, a solver developed for this application. We show that an extension of this formulation to a stochastic setting is possible. The last section of this part introduces the adaptative weights dynamic programming algorithm, an algorithm for optimization problems with different time scales. We use the algorithm to integrate the battery aging in the EMS.The second part is dedicated to network markets, and in particular wholesale electricity markets. We introduce a mechanism to deal with the market power exercised by electricity producers, and thus increase the consumer welfare. Then we study some mathematical properties of the agents’ optimization problems (producers and system operator). In the last chapter, we present some pure Nash equilibrium existence and uniqueness results for a class of Bayesian games to which some networks markets belong. In addition we introduce an algorithm to compute the equilibrium for some specific cases.We provide some additional information on BocopHJB (the numerical solver developed and used in the first part of the thesis) in the appendix

Wireless sensor networks are emerging as effective tools in the gathering and dissemination of data. They can be applied in many fields including health, environmental monitoring, home automation and the military. Like all other computing systems it is necessary to include security features, so that security sensitive data traversing the network is protected. However, traditional security techniques cannot be applied to wireless sensor networks. This is due to the constraints of battery power, memory, and the computational capacities of the miniature wireless sensor nodes. Therefore, to address this need, it becomes necessary to develop new lightweight security protocols. This dissertation focuses on designing a suite of lightweight trust-based security mechanisms and a cooperation enforcement protocol for wireless sensor networks. This dissertation presents a trust-based cluster head election mechanism used to elect new cluster heads. This solution prevents a major security breach against the routing protocol, namely, the election of malicious or compromised cluster heads. This dissertation also describes a location-aware, trust-based, compromise node detection, and isolation mechanism. Both of these mechanisms rely on the ability of a node to monitor its neighbors. Using neighbor monitoring techniques, the nodes are able to determine their neighbors’ reputation and trust level through probabilistic modeling. The mechanisms were designed to mitigate internal attacks within wireless sensor networks. The feasibility of the approach is demonstrated through extensive simulations. The dissertation also addresses non-cooperation problems in multi-user wireless sensor networks. A scalable lightweight enforcement algorithm using evolutionary game theory is also designed. The effectiveness of this cooperation enforcement algorithm is validated through mathematical analysis and simulation. This research has advanced the knowledge of wireless sensor network security and cooperation by developing new techniques based on mathematical models. By doing this, we have enabled others to build on our work towards the creation of highly trusted wireless sensor networks. This would facilitate its full utilization in many fields ranging from civilian to military applications.

В условиях тендерных закупок значимым является учет влияния многих факторов при выборе стратегии ценового предложения участника, которые выходят за пределы классической экономики. Целью магистерской диссертации является разработка модели прогнозирования ценового предложения участников тендерных закупок. В работе рассматривается понятие прогнозирования ценового предложения, влияние факторов на участника тендерных закупок и принципы поведенческой экономики. В качестве источников использовалась научно-исследовательская и методическая литература, нормативно-правовые акты и статистические данные различных электронно-торговых площадок в открытом доступе. В магистерской диссертации была разработана модель прогнозирования ценового предложения участника тендерных закупок, базирующаяся на функции полезности Неймана-Моргенштерна, отличающаяся учетом влияния релевантных факторов, позволяющая корректировать тактику поведения участника для каждого шага торгов и максимизировать полезность предложения с точки зрения принципов поведенческой экономики.
In terms of tender purchases, it is important to take into account the influence of many factors when choosing a bidder's price proposal strategy, which go beyond the classical economy. The aim of the master's thesis is to develop a model for forecasting the price offer of bidders. The paper discusses the concept of forecasting the price offer, the influence of factors on the participant in tender purchases and the principles of behavioral economics. The sources used were scientific research and methodological literature, regulatory legal acts and statistical data of various electronic trading platforms in the public domain. In the master's thesis, a model for predicting the price offer of a bidder was developed based on the Neumann-Morgenstern utility function, which takes into account the influence of relevant factors, which allows you to adjust the bidder's behavior tactics for each bidding step and maximize the utility of the offer in terms of the principles of behavioral economics.

Abstract The aim of this thesis is to propose an array of novel cooperative content delivery (CCD) methods and related incentive mechanisms for future fifth-generation (5G) and beyond networks. CCD using multiple air interfaces is a powerful solution to mitigate the problem of congestion in wireless networks, in which the available multiple air interfaces on smart devices are utilized intelligently to distribute data content among a group of users that are in the vicinity of one another. The requirements for higher capacity, reliability, and energy efficiency in the 5G networks have warranted the development of methods focusing on CCD. Moreover, critical to the efficiency of a CCD process are incentive mechanisms to induce cooperation among the mobile users engaged in CCD. The first part of the thesis studies an ideal condition of reliable and error-free distribution of content using cellular and short-range links. The main contribution is to introduce different device selection CCD methods that take into account only the link quality of the devices’ primary (cellular) interfaces. The proposed methods provide frequency carrier savings for the operator while allowing users to enjoy higher downlink rates. The second part of the thesis studies a more realistic CCD situation where users with low data rate wireless links can be a bottleneck in terms of CCD performance. The main contribution is to propose a novel device selection CCD method that considers the link quality of both primary (cellular) and secondary (short-range) interfaces of the devices. Additionally, a carrier aggregation-based incentive mechanism for the proposed method is introduced to address the challenge of selfish deviating users. The proposed mechanism maximizes individual and network payoffs, and is an equilibrium against unilateral selfish deviations. The third part of the thesis addresses the adverse selection problem in CCD scenarios. The operator is assumed to have incomplete information about the willingness of the users to participate in CCD. The main contribution is to introduce contract-based methods through which the operator could motivate users to reveal their true willingness towards participation. The proposed methods incentivize users according to their willingness and improve system performance in terms of the utility of the operator and the users
Tiivistelmä Tämän väitöskirjan tavoitteena on kehittää menetelmiä yhteistyössä tapahtuvaan sisällön jakamiseen (cooperative content delivery, CCD) sekä siihen liittyviä kannustinmekanismeja viidennen sukupolven (5G) ja sen jälkeisille matkaviestinverkoille. CCD:n käyttö hyödyntämällä älylaitteessa olevia useita ilmarajapintoja on tehokas ratkaisu välttää langattomien verkkojen ruuhkautumista. CCD-menetelmissä laiteen ilmarajapintoja käytetään älykkäästi datan jakamiseen käyttäjäryhmälle, kun käyttäjät ovat lähellä toisiaan. 5G-verkkojen vaatimukset korkeammalle kapasiteetille, luotettavuudelle ja energiatehokkuudelle ovat motivoineet CCD-menetelmien kehitystyötä. Erityisen tärkeää CCD-menetelmien tehokkuudelle on kannustinmekanismien kehittäminen mahdollistamaan yhteistyö mobiilikäyttäjien välillä. Väitöskirjatyön ensimmäinen osuus käsittelee ideaalista tilannetta luotettavalle ja virheettömälle sisällön jakamiselle hyödyntämällä solukkoverkkoa ja lyhyen kantaman linkkejä. Tässä osuudessa päätuloksena on kehitetty käyttäjien valinnalle menetelmiä, jotka huomioivat linkin laadun solukkoverkon ilmarajapinnassa. Ehdotetut menetelmät tuovat operaattorille säästöjä taajuusresurssien käytön osalta ja käyttäjät saavuttavat korkeampia laskevan siirtotien datanopeuksia. Työn toinen osuus tutkii todenmukaisempaa CCD-tilannetta, jossa alhaisen datanopeuden linkkien käyttäjät voivat olla pullonkaula CCD:n suorituskyvylle. Päätulos tässä on uusi käyttäjien valintamenetelmä, joka ottaa huomioon linkkien laadun sekä solukkoverkossa että lyhyen kantaman linkeissä. Lisäksi esitellään eri taajuuksien yhdistämistä hyödyntävä kannustinmenetelmä, joka ottaa huomioon itsekkäiden käyttäjien aiheuttamat ongelmat. Ehdotettu mekanismi maksimoi yksittäisen käyttäjän ja verkon hyödyt ja saavuttaa tasapainotilan käyttäjien yksipuolista itsekkyyttä vastaan. Väitöskirjan kolmannessa osuudessa tutkitaan haitallisen valikoitumisen mahdollisuutta CCD:ssä. Operaattorilla oletetaan olevan epätäydellistä tietoa käyttäjien halukkuudesta osallistua yhteistyöhön CCD:ssä. Tämän osuuden päätulos on esitellä sopimuksiin perustuvia kannustinmenetelmiä, joiden avulla operaattori voi motivoida käyttäjiä paljastamaan heidän todellinen tahtotilansa osallistua yhteistyöhön. Ehdotetut menetelmä kannustavat käyttäjiä heidän todellisen tahtotilan perusteella ja parantavat järjestelmän suorituskykyä operaattorin ja käyttäjien saavuttamien hyötyjen osalta

Computer agents are required to make appropriate decisions quickly and efficiently. As the environments in which they act become increasingly complex, efficient decision-making becomes significantly more challenging. This thesis examines the positive ways in which human emotions influence people’s ability to make good decisions in complex, uncertain contexts, and develops computational analogues of these beneficial functions, demonstrating their usefulness in agent decision-making and communication. For decision-making by a single agent in large-scale environments with stochasticity and high uncertainty, the thesis presents GRUE (Goal Re-prioritization Using Emotion), a decision-making technique that deploys emotion-inspired computational operators to dynamically re-prioritize the agent’s goals. In two complex domains, GRUE is shown to result in improved agent performance over many existing techniques. Agents working in groups benefit from communicating and sharing information that would otherwise be unobservable. The thesis defines an affective signaling mechanism, inspired by the beneficial communicative functions of human emotion, that increases coordination. In two studies, agents using the mechanism are shown to make faster and more accurate inferences than agents that do not signal, resulting in improved performance. Moreover, affective signals confer performance increases equivalent to those achieved by broadcasting agents’ entire private state information. Emotions are also useful signals in agents’ interactions with people, influencing people’s perceptions of them. A computer-human negotiation study is presented, in which virtual agents expressed emotion. Agents whose emotion expressions matched their negotiation strategy were perceived as more trustworthy, and they were more likely to be selected for future interactions. In addition, to address similar limitations in strategic environments, this thesis uses the theory of reasoning patters in complex game-theoretic settings. An algorithm is presented that speeds up equilibrium computation in certain classes of games. For Bayesian games, with and without a common prior, the thesis also discusses a novel graphical formalism that allows agents’ possibly inconsistent beliefs to be succinctly represented, and for reasoning patterns to be defined in such games. Finally, the thesis presents a technique for generating advice from a game’s reasoning patterns for human decision-makers, and demonstrates empirically that such advice helps people make better decisions in a complex game.
Engineering and Applied Sciences

Neste projeto apresentamos três modelos para publicidade na Internet, e mecanismos associados a cada modelo, analisando suas propriedades como estabilidade, otimalidade, prova de estratégia, e consumo de tempo. Primeiramente apresentamos um dos primeiros artigos publicados sobre o problema de leilões para publicidade na Internet, que descreve essencialmente o funcionamento dos atuais leilões para publidade na Internet. Em seguida, apresentamos outros dois modelos. O primeiro deles permite publicidade com exclusividade. O segundo modelo permite mais opções ao leiloeiro e aos anunciantes, ao permitir preços mínimos de venda e preços máximos de compra.
In this project we present three Internet sponsored search advertising models, and mechanisms related to each model, analyzing properties such as stability, optimality, strategy-proof, and time consumption. We start presenting one of the first articles on the problem of sponsored search advertising. Next, we present other two models. The first one allows exclusivity advertising. The second model allows a wider variety of options for both the auctioneer and the bidders, including minimum selling prices and maximum buying prices.

La douleur chronique représente un problème médical majeur et les thérapies actuelles sont souvent inefficaces et présentent des effets secondaires indésirables. Récemment, notre équipe a identifié une nouvelle molécule pour le traitement de la douleur chronique : TAFA4. Ce neuropeptide d’une centaine d’acide-aminé est sécrété par une sous-population de neurones des ganglions de la racine dorsale appelés mécanorécepteurs à bas seuil de type C (C-LTMR). Nous avons montré que TAFA4 réverse l'hypersensibilité mécanique induite par une lésion de type inflammatoire, neuropathique ou post-opératoire. Cet effet anti-nociceptif de TAFA4 passe par l’intermédiaire du récepteur LRP1 (Low density lipoprotein receptor-related protein). Nous avons validé biochimiquement et fonctionnellement le rôle de LRP1 dans l’action de TAFA4. Par ailleurs, nous avons identifié une sous-population d'interneurones inhibiteurs dans la couche laminaire II interne de la corne dorsale de la moelle épinière qui présentent une augmentation du courant potassique transitoire de type A et une diminution du courant h suite à un modèle de douleur neuropathique. De façon intéressante, nous montrons que TAFA4 permet de reverser cette modulation des courants et que cette réversion est bloquée par un antagoniste de LRP1. Mes travaux montrent que TAFA4 est un nouveau et puissant antalgique et que les C-LTMR sont des acteurs cruciaux dans la modulation des mécanismes douloureux. Ces résultats mettent en évidence, pour la première fois, des mécanismes moléculaires et cellulaires qui étayent une des théories les plus débattues dans le domaine de la douleur, « The Gate Control Theory of Pain»
Chronic pain denotes a major worldwide medical problem with current therapies being ineffective and often displaying adverse side effects. Recently, our group identified the secreted neuropeptide TAFA4 as a novel candidate for treating chronic pain. Specifically, we showed that TAFA4 is highly expressed in a subpopulation of dorsal root ganglion neurons called C-low threshold mechanoreceptors, that project into the inner laminaII (LIIi) of the dorsal horn of the spinal cord. TAFA4 reverses injury-induced mechanical hypersensitivity after inflammation, and nerve injury, and increases the frequency of spontaneous inhibitory postsynaptic currents in LIIi. Here, we show that TAFA4 reverses inflammatory, neuropathic and postoperative-induced mechanical hypersensitivity, and that the analgesic effect of TAFA4 is mediated via the low density lipoprotein receptor-related protein (LRP1), which, when removed from the cell surface using its antagonist receptor associated protein, blocks the effect of TAFA4. Also, we identified a subpopulation of inhibitory interneurons in LIIi (LIIi-IhIN) that display an increase of the A-type transient potassium current and decrease of outward h-current after spared nerve injury. Bath application of TAFA4 modulates and reverses these currents, and this effect can be negated by blocking LRP1. Our findings highlight TAFA4 as a new powerful painkiller, in addition to contributing to the understanding of the Gate Control Theory of Pain, through the identification of a novel spinal cord circuit mediating a cross talk between C-LTMRs, LRP1 and a subset of LIIi-IhIN that modulate injury-induced loss of inhibition and the establishment of pain

À cause des stratégies de production et de marketing de plus en plus ambitieuses tellesque le Juste-À-Temps et la production adaptée au client, les approches de collaboration logistique verticale qui sont courantes atteignent une limite d'efficacité notamment en transport. La collaboration logistique horizontale (CLH) et plus particulièrement la mutualisation, dont l'efficacité a été prouvée dans la littérature et dans les cas réels, a attiré l'attention des chercheurs ainsi que des praticiens. Cependatn, un des obstacles principaux à la mise en œuvre des CLHs est l'absence d'un mécanisme de collaboration raisonné, en particulier un mécanisme de partage des gains. Nous identifions deux formes d'organisation des CLHs : centralisée limité en pratique à de petites coalitions et décentralisée pouvant comprendre de nombreux participants. Pour des CLHs centralisées, nous proposons un modèle de collaboration qui est un processus de conduite qui intègre les outils d'aide à la décision. Nous développons également un mécanisme de partage par la théorie des jeux. Ce mécanisme est applicable aux différentes catégories des CLHs centralisées, qui peuvent être modélisées par des jeux coopératifs super-additif et non-super-additifs. Afin de proposer un plan de partage crédible aux collaborateurs, ce mécanisme de partage prend en compte la contribution de chacun des collaborateurs, la stabilité de la coalition et leur pouvoir de négociation. Ce cadre est illustré par des exemples numériques issus de cas logistiques. Pour la mise en œuvre des CLHs décentralisées, nous proposons un cadre de travail de logistique collaborative qui est ouvert aux participants potentiels, et avons conçu des protocoles fondés sur le mécanisme d'enchère combinatoire, qui spécifient l'allocation de demande de livraison et la détermination de paiement pour faciliter les collaborations. Cette dernière partie s'appuie sur la théorie dite de Mechanism design.

This thesis employs perspectives inspired by General Systems Theory to address issues in philosophy, including moral philosophy and philosophy of mind. I present an overview of a range of ideas from the study of physical systems that may be used to provide a firm physicalist foundation to explorations of some common questions in philosophy. I divide these topics into three categories: the Physical Category, the Relevance Category and the Signal Elements Category. I interpret concepts from General Systems Theory, including information and entropy, in a way that I believe facilitates their incorporation into philosophical discussion. I also explain various points arising from General Systems Theory, such as order and disorder, stability, complexity, and self-organisation, and show how ideas from these areas can be applied to certain philosophical problems. I explain relevance in terms of stability, in order to link these scientific perspectives to questions in moral philosophy. I suggest a possible physical foundation for a theory of morality, which takes the form of a variety of Utilitarianism, intended to balance the competing needs of open systems to manage entropy. Such a theory of morality must be capable of dealing with limitations arising from the physicality of information; I propose game theory as a solution to this problem. This thesis also covers issues connected to the above points regarding the nature of consciousness and communication. In particular, I examine the role of linguistic associations in consciousness; and some related features of language and other non-linear representational schemes.

O modelo tradicional de ensino está pautado em uma educação bancária, prioritariamente informativa, desestimulante e descontextualizada de seu momento histórico, que é caracterizado pela informação de fácil acesso e comunicação em esfera global. Neste panorama, pesquisas apontam que os estudantes vêm apresentando baixo desempenho em ciências. Há, portanto, a necessidade de estudos que levem em consideração esta realidade educacional no intuito de propor estratégias inovadoras de ensino. Neste cenário, games digitais apresentam grande potencialidade, pois são atrativos, contribuem para o desenvolvimento cognitivo e podem propiciar o desenvolvimento de preceitos científicos. O grande problema é que, apesar de atraírem legiões de jogadores, games digitais comerciais geralmente não apresentam grandes preocupações pedagógicas, uma vez que não são desenvolvidos para esta finalidade. Ainda que se pense nos games digitais educativos, estes apresentam características antagônicas aos comerciais, pois são normalmente constituídos de um arcabouço excessivo de informações, sem muita preocupação com aspectos imersivos. Assim, na tentativa de correlacionar ensino de ciências com os aspectos epistemológicos presentes nos games digitais educativos e com os aspectos imersivos presentes nos games digitais comerciais, o objetivo deste trabalho consiste no desenvolvimento de uma tipologia e na criação de uma matriz de análise para jogos com potencial para uso educacional. Na etapa teórica do projeto, desenvolvemos a fundamentação da matriz, baseada em três elementos constituintes. São eles: Referencial educativo, baseado na Teoria Antropológica do Didático; Imersão, baseada na Teoria do Flow; e Mecânica Lúdica. Posteriormente, verificamos a aplicabilidade da tipologia e da matriz na análise de jogos e das interações entre jogadores e situações de jogo. É importante destacar que a pesquisa foi desenvolvida com alunos do ensino médio de uma escola pública da rede estadual de ensino de São Paulo. Dentre os resultados obtidos, destaca-se correlação entre aspectos imersivos e praxeológicos das plataformas de jogos.
The traditional Teaching model is based on a banking education, primarily informative, discouraging and decontextualized of its historical moment, which is characterized by information of easy access and communication in global sphere. In this panorama, research shows that students have been performing poorly in science. Therefore, there is a need for studies that take into account this educational reality in order to propose innovative teaching strategies. In this scenario, digital games present great potentiality, as they are attractive, contribute to cognitive development and can foster the development of scientific precepts. The big problem is that, despite attracting legions of players, commercial digital games generally do not present major pedagogical concerns as they are not developed for this purpose. Although digital educational games are thought of, they present characteristics antagonistic to the commercial ones, since they are usually constituted of an excessive information framework, without much concern with immersive aspects. Thus, in an attempt to correlate science with the epistemological aspects present in digital educational games and with the immersive aspects present in commercial digital games, The objective of this work is the development of a typology and the creation of an analysis matrix for games with potential for educational use. In the theoretical stage of the project, we developed the foundation of the matrix, based on three constituent elements: Educational Reference, based on the Didactic Anthropological Theory (TAD); Immersion, based on Flow Theory; And playful Mechanics. Subsequently, we verified the applicability of typology and matrix in game analysis and interactions between players and game situations. It is important to highlight that the research was developed with high school students of a Public School of the State of São Paulo. Among the results obtained, there is a correlation between immersive and praxeological aspects of gaming platforms.

Gamification är appliceringen av spelmekanismer i icke-spelkontextuella miljöer för att skapa motivation som kan användas för att utföra uppgifter. Denna uppsats avsåg utforska hur det appliceras och utvecklas på djupet, med fokusering på hur spelmekanismer påverkar inre och yttre motivation. I denna studie intervjuades gamification-konsulter för sin erfarenhet inom området och Self-Determination Theory (SDT) användes som analysmodell. Resultaten visar att utvecklandet av ett gamification-system har två oberoende variabler från vilka flera beroende variabler stammar. För att bestämma de mekanismer som bör användas behöver dels tidshorisont bestämmas, dels spelartyp. Således är processen för att bestämma hur eller om gamification ska appliceras lång och komplex, men att bestämma vilken typ av motivation som ska skapas beroende på tidshorisont är ett första steg för att konkretisera gamification-processen.
Gamification is the application of game mechanics in non-game contexts to create motivation to accomplish tasks. This thesis sought to delve deeper into how it is applied and developed, with a focus on how game mechanics affect intrinsic and extrinsic motivation. In the study gamification consultants were interviewed for their experience in the field and a Self-Determination Theory (SDT) view was applied in the analysis of their answers. The results show that the development of a gamification system has two independent variables from which several dependable variables spring. To determine the mechanics that should be used the time horizon has to be determined as well as player type. Thus, the decision of how or if to gamify a situation is long and complex in its application, but the determination of which type of motivation to induce is a first step to concretize how to gamify a system.

Le déploiement massif de ressources d'énergie renouvelable distribuée (RED) représente une opportunité majeur pour atteindre les objectifs de réduction des émissions de carbone en Europe, mais aussi dans le monde entier. Visant à mobiliser des capitaux publics et privés, plusieurs plans ont été développés pour placer les clients finaux au cœur de la transition énergétique, dans l'espoir d'accélérer l'adoption de l'énergie verte en augmentant son attractivité et sa rentabilité. Certains systèmes proposés incluent la création de marchés locaux d'énergie, où les résidences peuvent vendre leur énergie dans leur quartier à un prix plus élevé que celui que les fournisseurs classiques seraient prêts à payer (mais inférieur à ce que les autres clients paieraient à ces fournisseurs); des investissement partagés, les consommateurs possédant dans ce cadre un générateur décarboné et/ou du stockage pour lesquels ils obtiennent des dividendes dans un contexte d’autoconsommation collective où plusieurs familles sont `` cachées '' derrière le même compteur intelligent, leur permettant d'optimiser leur profil de consommation agrégé et donc maximiser la valeur de leur investissement. L'un des principaux objectifs de la thèse est de fournir des méthodes pour augmenter les gains potentiels et des modèles pour évaluer l’impact que l’on peut attendre de ces différentes solutions, afin qu’elles deviennent une incitation plus forte à la génération et usage d’énergies renouvelables, car celles-ci joueront un rôle crucial dans la lutte contre le changement climatique seulement si elles sont correctement mises en œuvre. Pour ce faire, nous concevons un cadre permettant de concevoir et de comparer divers paradigmes «d’investissements partagés et d’échanges monétisés locaux de l’énergie», dont le potentiel de «gains» se traduit par une incitation forte à leur mise en œuvre. Dans le cadre d’échanges monétisés locaux d'énergie, nous étudions les interactions entre prosommateurs (consommateurs avec capacité de production et éventuellement de stockage) situés dans le même réseau Basse Tension, éventuellement derrière le même départ. Dans nos systèmes, ces prosommateurs seront toujours connectés au réseau électrique principal et ils auront la possibilité, comme ils le font aujourd'hui, d'acheter et de vendre à un opérateur de services de distribution d’électricité, suivant une politique tarifaire connue à l’avance (un taux forfaitaire ou un temps d'utilisation, pour exemple). Pour que ces agents bénéficient pleinement des avantages des échanges locaux d'énergie, nous supposons qu'ils possèdent des appareils (tels que des batteries) qui, sans modifier leur demande énergétique interne (sans changer leur comportement d’usage), peuvent leur permettre de modifier leur demande énergétique nette vue de l'extérieur de leur domicile. En modélisant les prosommateurs comme des maximisateurs rationnels de l'utilité (que nous définissons), ils planifieront les flux entrant et sortant de leur batterie pour diminuer le coût associé à leur demande nette d'énergie (avec comme signalé, une demande perçue qui reste inchangée). Dans la première partie de la thèse, nous étudions des modèles concurrentiels dans lesquels les prosommateurs vendent leur surplus à leurs voisins via un marché local d'énergie. Nous analysons différents types de marchés et donc différentes stratégies que les acteurs pourraient utiliser pour participer à ces marchés, ainsi que leur impact sur le réseau électrique et sur le gestionnaire du réseau de distribution. Dans la deuxième partie de la thèse, nous explorons les incitations qui peuvent être mises en œuvre par la coopération. À cet égard, nous utilisons la théorie des jeux coopératifs pour modéliser l'investissement partagé dans l’acquisition de dispositifs de stockage énergie et de panneaux photovoltaïques (PV) par un groupe de prosommateurs
To meet carbon reduction goals in Europe but worldwide too, a large number of renewable distributed energy resources (DER) still need to be deployed.Aiming at mobilizing private capitals, several plans have been developed to put end-customers at the heart of the energy transition, hoping to accelerate the adoption of green energy by increasing its attractiveness and profitability.Some of the proposed models include the creation of local energy markets where households can sell their energy to their neighbors at a higher price than what the government would be willing to pay (but lower than what other customers normally pay), shared investment models in which consumers own a carbon-free power plant such as a wind turbine or a solar farm and they obtain dividends from its production to collective auto-consumption models in which several families are ‘hidden’ behind the same smart meter, allowing them to optimize their aggregated consumption profile and therefore maximizing the value of their DER.One of the main objectives of the thesis is to understand these different incentives as they will play a crucial role in tackling climate change if correctly implemented. To do so, we design a framework ‘local energy trading’ that encompasses a large number of incentives.In the context of local energy trading, we study the interactions of prosumers (consumers with generation capabilities) located in the same Low Voltage network, possibly behind the same feeder. These prosumers will still be connected to the main power grid and they will have the option, as they do today, to buy and sell to/from their utility company at a fixed price (a flat rate or a Time-of-Use, for example). For these agents to fully benefit from the advantages of local energy trading, we shall assume that they own appliances (such as batteries) that, without changing their perceived energy demand, can enable them to change their net energy demand as seen from outside their homes. Modeling prosumers as rational utility maximizers, they will schedule their battery to decrease the cost associated with their net energy demand (as their perceived demand remains unchanged).In the first part of the thesis, we investigate competitive models in which prosumers sell their surplus to their neighbors via a local energy market. We analyze different strategies that players could use to participate in these markets and their impact on the normal operation of the power grid and the Distribution System Operator. In this regard, it is shown that sequential markets can pose a problem to the system and a new market mechanism that exploits domain knowledge is proposed to increase the efficiency of the local trades.In the second part of the thesis, we delve into incentives that can be implemented through cooperation. In this regard, we use cooperative game theory to model the shared investment into energy storage and photovoltaic panels (PV) by a group of prosumers. For the studied model we show that a stable solution (in the core of the game) exists in which all participants cooperate and we provide an efficient algorithm to find it. Furthermore, we also show that cooperation is stable for participants that already own batteries and PVs but prefer to operate them in coordination to increase their value, effectively implementing collective auto-consumption.Finally, we demonstrate how to integrate both models: the shared investment and the cooperative control of existing resources into a single cooperative framework which also enjoys the existence of stable outcomes. For this later model, we propose to decouple the return over investments (ROI) obtained between the ROI produced by the investment in hardware and the ROI obtained by cooperation itself. By doing so, we can offer the former profit to external investors to raise the required capital (although nothing forbids the member of the coalition to contribute) and the latter to the actual consumers

Les futurs réseaux de distribution d’électricité devront héberger une part importante et croissante de sources d’énergies renouvelables intermittentes. De plus, ils devront faire face à une part croissante de véhicules électriques. Ces tendances induisent le besoin de nouveaux paradigmes et architectures d’exploitation du réseau de distribution, afin de fiabiliser les réseaux et d'assurer la qualité de fourniture d’électricité. Dans cette thèse nous proposons une nouvelle architecture capable de favoriser la collaboration entre les acteurs du marché de gros, les gestionnaires de réseau de distribution et les clients finaux, afin de tirer parti des ressources énergétiques distribuées tout en prenant en compte les contraintes des réseaux de distribution. L’architecture est conçue pour fournir des services innovants de gestion de la demande résidentielle, dans le cadre de l'autoconsommation individuelle et collective (à l'échelle d'un quartier). La thèse apporte trois contributions principales. D'abord, sur la base de l'internet des objets et de la technologie blockchain, la thèse fournit les éléments de base pour les futures architectures de gestion de l'énergie au niveau du réseau de distribution. Ensuite, en focalisant sur les services rendus par de telles architectures, nous proposons un marché intra-journalier au pas horaire pour l'échange local de l'énergie renouvelable entre maisons, associé à un mécanisme d'allocation dynamique des phases afin d'améliorer la qualité de fourniture. Finalement, nous proposons un mécanisme de contrôle en temps réel pour l'ajustement des transactions du marché vers des échanges finaux d'électricité qui respectent les restrictions posées par le gestionnaire du réseau électrique
Future electricity distribution grids will host an important and growing share of variable renewable energy sources and local storage resources. Moreover, they will face new load structures due for example to the growth of the electric vehicle market. These trends raise the need for new distribution grid architecture and operation paradigms to keep the grid stable and to ensure quality of supply. In addition, these new paradigms will enable the provision of advanced new services. In this thesis we propose a novel architecture capable of fostering collaboration among wholesale market actors, distribution system operators and end customers, to leverage flexible distributed energy resources while respecting distribution system constrains. The architecture is designed for providing innovative residential demand side management services, with a special focus on services enabled by self-consumption at the household and neighborhood level. Following these general objectives, the thesis provides three main contributions. First, based on internet of things and blockchain technology, we propose the building blocks for future distribution grid energy management architectures. Then, focusing on the services enabled by such architectures, we propose hour-ahead markets for the local exchange of renewable energy among households together with dynamic phase allocation mechanism to improve the quality of electricity supply. Finally, we propose a real time control mechanism for the adjustment of market decisions to satisfy distribution system operator constraints

L'objectif principal de la présente thèse est d'examiner l'impact des mécanismes internes de gouvernance sur le risque fiscal dans le contexte tunisien. Les notions de risque fiscal et de gestion du risque fiscal ont été définies dans un premier chapitre. Un deuxième chapitre présente le fondement théorique de notre étude. La théorie des jeux est présentée en premier lieu. Elle constitue un cadre propice pour analyser les comportements des deux acteurs principaux dans le cadre de cette étude, à savoir l'entreprise et l'administration fiscale, vis-à-vis du risque fiscal. La théorie partenariale de la gouvernance a été mobilisée, en deuxième lieu, afin d'examiner le rôle des mécanismes de gouvernance dans la réduction du risque fiscal. Les hypothèses issues de cette théorie supposent l'existence d'un effet négatif de l'indépendance du conseil, de la séparation des postes de directeur général et de président du conseil d'administration, de l'expertise comptable ou fiscale du comité d'audit et de l'expertise comptable ou fiscale de la fonction d'audit interne sur le risque fiscal. La méthodologie de notre recherche comporte trois étapes. D'abord, une analyse de contenu des arrêts fiscaux rendus en cassation entre les entreprises et l'administration fiscale a permis de dégager les divers types de risques fiscaux des entreprises détectés lors du contrôle et confirmés par la procédure juridictionnelle. Ensuite, l'analyse de contenu des états financiers et des rapports généraux des commissaires aux comptes des sociétés tunisiennes cotées sur une période de cinq ans a mis en évidence la divulgation d'informations sur le risque fiscal et ce conformément aux exigences réglementaires. Enfin, l'analyse de régressions logistiques a permis de mettre en évidence le rôle joué par le conseil d'administration et le conseil externe dans la gestion comptable du risque fiscal. Cette analyse a montré aussi que les acteurs qui exercent un impact sur la gestion comptable du risque fiscal différent entre les entreprises non financières et celles appartenant au secteur financier. Globalement, l'analyse de l'interaction entre le système de gouvernance et le risque fiscal a permis d'identifier les acteurs qui participent activement dans la gestion comptable du risque fiscal Ces acteurs semblent en faveur de la transparence fiscale de l'entreprise à l'égard des actionnaires et de l'administration fiscale. Cette thèse s'insère ainsi pleinement dans la problématique plus générale de la responsabilité sociale de l'entreprise.

碩士
國立成功大學
資訊工程學系碩博士班
101
The taxicab becomes one of the most important public transportations in many big cities. Customers always suffer from waiting a long time for taxis. Similarly, the taxi drivers spend much time on cruising on the road for finding passengers. Therefore, we present a recommendation mechanism for both taxis and passengers. When taxis and passengers have requests for recommendation, the server provides them with paths and locations. The first aim of our model is to respectively recommend taxis and passengers for picking up passengers quickly and finding taxis easily. The second purpose is providing taxi-sharing service for passengers who want to save the payment. In our method, we analyze the historical Global Positioning System (GPS) trajectories generated by 10,357 taxis during 110 days and present the service region with time-dependent R-Tree. We formulate the problem of choosing the paths among the taxis in the same region by using non-cooperative game theory, and find out the solution of this game which is known as Nash equilibrium. When a taxi is occupied and the on-board passengers who want taxi-sharing service, the taxi checks the proper passengers for sharing periodically. In order to verify the proposed recommendation mechanism, the simulation of SUMO, MOVE, and TraCI are adopted to fit our model. The results show that our method can find taxis and passengers efficiently. In addition, applying our method can reduce the payment of passengers and increase the taxi revenue by taxi-sharing.

Game balance has been a challenge for game developers since the time games have become more complex. There have been a handful of proposals for game balancing processes outside the manual labor-intensive play testing methods, which most game developers often are forced to use simply due to the lack of better methods. Simple solutions, like restrictive game play, are limited because of their inability to provide insight on interdependencies among the mechanisms in the game. Complex techniques framed around the potential of AI algorithms are limited by computational budgets or cognition inability to assess human actions. In order to find a middle ground we investigate Game Theory and Mechanism Design concepts. Both have proven to be effective tools to analyse strategic situations among interacting participants, or in this case `players'. We test the feasibility of using these techniques in an Real Time Strategy (RTS) game domain to understand game balance. MicroRTS, a small and simple execution of an RTS game is employed as our model. The results provide promising insight on the effectiveness of the method in detecting imbalances and further inspection to find the cause. An additional benefit out of this technique, besides detecting for game imbalances, the approach can be leveraged to create imbalances. This is useful when the designer or player desires to do so.

博士
國立中山大學
資訊工程學系研究所
102
Due to the low cost, easy deployment, wireless multi-hop networks have attracted many researchers’ attention in recent years. In wireless multi-hop network, the nodes are willing to forward packets for other nodes, so as to packets can successfully reach the destination. Therefore, cooperation between nodes is a critical factor in multi-hop networks. In order to encourage nodes to cooperate in networks, two approaches that refer to game theory are proposed. The first approach is incentive mechanism for packet forwarding. We design a Stag Hunt payoff matrix that is different to traditional Prisoner’s Dilemma model. Our model stimulates nodes to forward more packets to get more profits. It also compensates idle time to increase boarder nodes’ gains to guarantees the existence of boarder nodes. The second approach is an interactive strategy, which is called as Adaptive-Cooperator to refine the two equilibrium points of the Stag Hunt game. This strategy is an active cooperator; however, it may as well punish uncooperative nodes, making them impossible to obtain gains by free-rider. Then, we make some analyses by game and evolutionary game theory to deduct the correctness of our approach. According to the simulation result, the approaches we proposed are proved effective. To apply our model to wireless multi-hop network, all nodes can tend to cooperate and get fair treatment.

Multicast is a bandwidth efficient mechanism to provide wireless services for a group of nodes. Providing reliable wireless multicast is challenging due to channel fading. This thesis investigates cooperation among receiving nodes to enhance the reliability of wireless multicast. A time division based cooperative multicast strategy is proposed, and the optimal scheduling scheme is found to maximize the system throughput. It is shown that the optimal relay number is bounded by a threshold, and the optimal time allocation can be found using an efficient algorithm. Numerical results show that the proposed strategy can enhance network performance when the average channel condition between receiving nodes is better than that of the direct link. To provide incentive for cooperation, this thesis further studies the interactions among selfish nodes using game theoretic approaches. The cooperative multicast process is modeled as a repeated game and the desired cooperation state which satisfies the absolute fairness and the Pareto optimality criteria is found. A Worst Behavior Tit-for-Tat incentive strategy is designed to enforce cooperation and its effectiveness is studied under both the perfect and the imperfect monitoring scenarios. To address the issue of imperfect monitoring, an interval based estimation method is proposed. Simulation results show that the proposed strategy can enforce cooperation efficiently even the monitoring is imperfect.
Signal and Image Processing

碩士
淡江大學
電機工程學系碩士班
100
In recent years, with the emergence of P2P technology, there are many of the applications for P2P technology has emerged and how to effectively inhibit the free-riders is a hot issue. According to statistics, most of the P2P applications used Gnutella protocol and BitTorrent protocol. BitTorrent uses the Choking algorithm to isolate free-riders, but Choking algorithm is simply giving free-riders choke. Gnutella uses the EigenTrust algorithm to combat free-riders. However according to the actuality, EigenTrust algorithm is not a consummated algorithm. Therefore, both of Choking algorithm and EigenTrust algorithm have a bed effect. A research was conducted in 2005, there are nearly 85 percent of Gnutella network users are free-riders from all users. Furthermore, there is only 1 percent of users voluntary to share new files. Due to the resource in the P2P applications are like public goods that everyone can use it for free. It makes most of the users were not sharing their own resource, to cause tragedy of the commons and inefficiency. Therefore, in this paper, we propose an incentive mechanism on the basis of game theory; it promotes the peers to spontaneous sharing their own resource. We also use social network to promote users to share their resource, to maintain a long-term cooperation between users, and also use user''s relationship of social network to exclude free-riders. To deploy the mechanism to the architecture, the system will give users counters by considering bandwidth, computing power and electricity of each user. These counters will be stored in server and managed by server. In addition, the sharing model of the system can be divided into live media streaming sharing and file sharing. In live media streaming sharing, users can participate in the auction and using their counters to bid the tickets of high-quality live media streaming. In file sharing, users have to pay counters for every unit download bandwidth. In other words, users need to use the counters to bid or buy service from server. Therefore, the mechanism can promotes the peers to spontaneous sharing their own resource if they need some resources from server.

碩士
國立臺灣大學
商學研究所
99
Small and Medium Enterprises (SME) have been a major force in Taiwan.They not only play the part as a bridge between larger enterprises and the suppliers but also refrain the industrial supply chain from controlled by single monopolists. Employment opportunities are also created by SME for the society. Moreover, most of Taiwanese large enterprises were evolved from SME which demonstrated the importance of existence of SMEs in the local business environment. In short, SMEs are essential foundations for the economic development in Taiwan. However, due to low transparency of financial information and unhealthy financial/accounting system, for reduce the risk of loaning, SMEs are required to provide collateralization as grantee when applying for financial support. While few supports from the governmental side, this requirements turn out to be a barrier for small scale SMEs who are unable to provide such proof. As a result, this study is aimed to discuss the Credit Guarantee when the violation of contract between large enterprises and bank occurs, providing collateralization of upstream SMEs as well as the expected return after the loan of bank, in order to reduce the risk of loaning and increase the wiliness of loaning, in hope to achieve the win-win situation for suppliers and bank. A leader-follower game is introduced in this paper, with the upstream suppliers as a follower, downstream suppliers as a leader. Both sides of the suppliers are seeking for profit maximization and build up supply chain purchase model. Backward calculation starts from obtaining the optimal decision and profit maximization result which lead to the sensitivity analysis on important factors as well as the conclusion. The result is projected to provide reference and solutions to the loaning decision marking process for all parties in the financial supply chain.

In the first part of this dissertation we study the problem of designing desirable mechanisms for economic environments with different types of informational and consumption externalities. We first study the mechanism design problem for the class of Bayesian environments where preferences of individuals depend not only on their allocations but also on the welfare of other individuals. For these environments, we fully characterize interim efficient mechanisms and examine their properties. This set of mechanisms is compelling since interim efficient mechanisms are the best in the sense that there is no other mechanism which generates unanimous improvement. For public good environments, we show that these mechanisms produce the public good closer to the efficient level of production as the degree of altruism in the preferences increases. For private good environments, we show that altruistic agents trade more often than selfish agents.

We next consider mechanism design problem for matching markets where externalities are present. We present mechanisms that implement the core correspondence of many-to-one matching markets, such as college admissions problems, where the students have preferences over the other students who would attend the same college. With an unrestricted domain of preferences the non-emptiness of the core is not guaranteed. We present a sequential mechanism implementing the core without any restrictions on the preferences. We also show that simple two-stage mechanisms cannot be used to implement the core correspondence in subgame perfect Nash equilibrium without strong assumptions on agents' preferences.

In the final part of the dissertation we focus on another matching market, one-to-one assignment games with money. We present an alternative way to characterize the core as the fixed points of a certain mapping. We also introduce the first algorithm that finds all core outcomes in assignment games. The lattice property of the stable payoffs, as well as its non-emptiness, are proved using Tarski's fixed point theorem. We show that there is a polarization of interests in the core by using our formulation.

碩士
國立成功大學
電腦與通信工程研究所
98
An Intrusion detection system (IDS) is a network security tool that can check packets passing through it. However, with the rapid development of Internet, network bandwidth has steadily increased. So a major issue with IDS is an overly high volume of traffic where the NIDS is unable to process all data resulting in “dropped” traffic. Scaling NIDS to high speed networks can be achieved by using multiple NIDS operating in parallel. We propose a Dynamic Parallel Intrusion Detection System (DPIDS) with dynamic allocation and a load balancing mechanism to handle the increased load. For improving performance of using multiple IDS, we introduce a taskmaster, which is the core of the DPIDS. The taskmaster oversees division and allocation of responsibility and performs packet control, pre-filtering, and state management. This taskmaster uses active analysis to achieve intelligent assignment of work distribution using game theory contrasting against the passive distribution methods proposed by previous works. The active mechanism improves division of labor by dynamically loading the slave IDSs and can account for sudden increases in traffic or slave IDS crashes. In addition, this mechanism also allows for different grades of IDSs to work in tandem within the DPIDS architecture. The overall system is designed as session-oriented signature-based IDS, which provides stateful analysis to aggregate related events for detection by a single Slave IDS. Our experimental results show that DPIDS maintains stable loading as a function of the taskmaster. This feature allows better performance of the overall system as a result of more efficient use of IDS capacity.

碩士
淡江大學
電機工程學系碩士班
104
P2P file-sharing system has been developed rapidly over the past years. P2P file-sharing mechanisms such as Gnutella, BitTorrent and Private Tracker have been used popularly. However the free-rider and malicious node problem hinders the efficient utilization of P2P networks. Hence this paper makes improvements on the original BitTorrent by proposing a novel bilateral incentive mechanism (NBIM) to restrain free riders and malicious nodes simultaneously. The impact of proposed mechanism on free-riders and malicious nodes is analyzed using Peersim. The simulated results prove that the proposed mechanism can restrain average 31 percent behavioral capability of free riders and average 41 percent behavioral capability of malicious nodes to improve the performance of the P2P network.

The field of algorithmic mechanism design is concerned with the design of computationally efficient algorithms for use when inputs are provided by rational agents, who may misreport their private values in order to strategically manipulate the algorithm for their own benefit. We revisit classic problems in this field by considering settings of incomplete information, where the players' private values are drawn from publicly-known distributions. Such Bayesian models of partial information are common in economics, but have been largely unexplored by the computer science community. In the first part of this thesis we show that, for a very broad class of single-parameter problems, any computationally efficient algorithm can be converted without loss into a mechanism that is truthful in the Bayesian sense of partial information. That is, we exhibit a transformation that generates mechanisms for which it is in each agent's best (expected) interest to refrain from strategic manipulation. The problem of constructing mechanisms for use by rational agents therefore reduces to the design of approximation algorithms without consideration of game-theoretic issues. We furthermore prove that no such general transformation is possible if we require mechanisms that are truthful in the stronger non-Bayesian sense of dominant strategies. In the second part of the thesis we study simple greedy methods for resolving complex auctions. We show that while such greedy algorithms are not truthful, they suffer very little loss in worst-case performance bounds when agents apply strategies at equilibrium, even in settings of partial information. Our analysis applies to various different equilibrium concepts, including Bayes-Nash equilibrium, regret-minimizing strategies, and asynchronous best-response dynamics. Thus, even though greedy auctions are not truthful, they may be appropriate for use as mechanisms under the goal of achieving high social efficiency at equilibrium. Moreover, we prove that no algorithm in a broad class of greedy-like methods can be used to create a deterministic truthful mechanism while retaining a non-trivial approximation to the optimal social welfare. Our overall conclusion is that while full-information models of agent rationality currently dominate the algorithmic mechanism design literature, a relaxation to settings of partial information is well-motivated and provides additional power in solving central problems in the field.

Unit-demand auctions have been well studied with applications in several areas. In this dissertation, we discuss new variants of the unit-demand auction that are motivated by practical applications. We design mechanisms for these variants that have strong properties related to truthfulness, efficiency, scalability, and privacy. The main contributions of this dissertation can be divided into two parts. In the first part, we introduce a new variant of the classic sealed-bid unit-demand auction in which each item is associated with a put option; the put option of an item gives the seller the right to sell the item at a specified strike price to a specified bidder, regardless of market conditions. We motivate our unit-demand auction setting by discussing applications to the reassignment of leases, and to the design of multi-round auctions. For the classic sealed-bid unit-demand framework, the VCG mechanism provides a truthful auction with strong associated guarantees, including efficiency and envy-freedom. For an item in our auction, the strike price of the associated put imposes a lower bound on the auction price. Due to these lower bound constraints on auction prices, we find that the VCG mechanism is not suitable for our setting. Instead, our work draws on two fundamental techniques, one from the realm of mechanism design for numerical preferences -- the dynamic unit-demand approximate auction of Demange, Gale, and Sotomayor -- and one from the realm of mechanism design for ordinal preferences -- the Top Trading Cycles algorithm -- to obtain a natural auction that satisfies the lower bound constraints on auction prices. While we cannot, in general, achieve either efficiency or envy-freedom in our setting, our auction achieves suitably relaxed versions of these properties. For example, this auction is envy-free for all bidders who do not acquire an item via the exercise of a put. We provide a polynomial time implementation of this auction. By breaking ties in an appropriate manner, we are able to prove that this auction is truthful. In the second part, we specify rules for a dynamic unit-demand auction that supports arbitrary bid revision. In each round, the dynamic auction takes a tentative allocation and pricing as part of the input, and allows each bidder -- including a tentatively allocated bidder -- to submit an arbitrary unit-demand bid. Each round of our dynamic auction is implemented via a single application of the sealed-bid unit-demand auction proposed in the first part. We show that our dynamic auction satisfies strong properties related to truthfulness and efficiency. Using a certain privacy preservation property of each round of the auction, we show that the overall dynamic auction is highly resistant to shilling. We present a fast algorithm for implementing the proposed auction. Using this algorithm, the amortized cost of processing each bidding operation is upper bounded by the complexity of solving a single-source shortest paths problem on a graph with nonnegative edge weights and a node for each item in the auction. We also propose a dynamic price adjustment scheme that discourages sniping by providing bidders with incentives to bid early in the auction.
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Global warming is currently a major challenge facing the world. There are widespread ongoing efforts in the form of summits, conferences, etc., to find satisfactory ways of surmounting this challenge. The basic objective of all such efforts can be summarized as conception and formation of protocols to reduce the pace of global carbon levels. Game theory and mechanism design provide a natural modeling tool for capturing the strategic dynamics involved in global warming related problems. This dissertation explores for the first time the use of voting mechanisms in the context of solving the central problems, namely, allocation of emission caps and reduction quotas to strategic emitting agents (countries). The contribution of this dissertation is two-fold. The first contribution is to develop an elegant game theoretic model that accurately captures the strategic interactions among different emitting agents in a global warming setting. This model facilitates a convenient way of exploring a mechanism design approach for solving important allocation problems in the global warming context. The second contribution is to propose and explore a novel approach, based on voting mechanisms, to solve two problems: (1) allocating emission caps and (2) allocating reduction quotas to strategic agents. Our work investigates the use of voting mechanisms that satisfy four desirable properties: (1) non-dictatorship, (2) strategy-proofness, (3) efficiency, and (4) anonymity. In particular, we explore the median selection, maximum order statistic selection, and general Kth order statistic selection voting mechanisms. Our results clearly show that only trivial allocations satisfy all the above properties simultaneously. We next investigate the use of voting mechanisms for the dual problem, namely, allocation of emission reductions to emitting agents. Here, we show that non-trivial allocations are possible, however an important property, individual rationality, might be compromised. The investigations in the thesis bring out certain limitations in applying voting mechanisms that satisfy all the four properties above. Nevertheless, the insights obtained provide valuable guidelines for solving emission allocation related problems in a principled and informed way.

博士
國立交通大學
資訊科學與工程研究所
106
Contemporary trends in education and research have emphasized active learning. However, how to allow learners to maintain learners’ autonomy and continue problem-solving are the key factors has not been meticulously explored. Using digital games as a learning guidance tool can support and reinforce problem-solving ability, attention and concentration, learning effectiveness and learning motivation. In recent years, digital game-based learning (DGBL) has gradually become an important role in various work and learning fields. Gaming concepts and mechanisms stimulate learners’ interest, maintain motivation and produce creativity and then learners willingly overcome unnecessary obstacles. This helps learners to develop cognitive skills and acquire new knowledge. However, the problem that learners often “stuck at the level” caused by the imbalance between challenges and skills is the educators and researchers are eager to improve. DGBL environments have a variety of digital assistance tools that can guide learners and let them pursuit active failure. In the past, the scaffolding tools were mostly designed from the instructors' perspective. In order to design scaffolding tool for learners' needs, this study used eye movement data to analyze how learners observed the DGBL environment. In addition, the digital games’ rule systems generate immediate feedback after learners act. How to make good use of the feedback and reward mechanisms in the games is even more important. Therefore, our goal is to maintain the learners’ continuous active learning by combining the appropriate game learning designs and analyze learners’ cognitive and problem-solving process. This study used three quasi-experimental designs based on self-regulation theory, scaffold theory, the eye movement measurement, rhythm game and Sudoku game to explore how learners self-regulate their problem-solving process. In the first study, we utilized eye tracking movement to analyze how learners perceived a new Sudoku game learning environment and how the attention was diverted between various information. The second study used a rhythm game with difficulty level design to explore how learners can achieve continuous learning through the process of self-regulation, complete stage goals and maintain the flow experience when facing the obstacles. Based on the results of two studies, the third study was designed the scaffold with auxiliary functions to analyze how learners use the restricted resources (different scaffold presentation and reward mechanisms) to solve problems and create their own problem strategies. Results from experiments involving elementary, senior high and junior school students suggest that (a) the learners will solve the problem faster, if they understand the rules clearly at the beginning; (b) flow state was continuously influenced by self-regulation over time; (c) active failure enables learners to continue advanced learning in game-based digital learning; (d) experiences toward the end of gaming sessions had an influence on learners' continuous learning willingness; (e) providing a hidden scaffolds in rule-based environment allows learners to create new problem-solving strategies. We applied two scaffolding presented principles to the teaching scene and received positive feedback from the students.

Game theory and mechanism design have emerged as an important tool to model, analyze,and solve decentralized design problems involving multiple agents that interact strategically in a rational and intelligent way. Some examples of these design problems include: auctions and markets in electronic commerce; network economics; dynamic pricing; routing protocols in wireless networks; resource allocation in computational grids; algorithms for selfish agents;etc. The motivation for this doctoral work springs from the high level of current interest indesigning innovative mechanisms for solving emerging game theoretic problems in the area of electronic commerce. In this thesis, we focus on three such problems and advance the current art in mechanism design while developing new, innovative mechanisms to solve the problems. The first problem we explore is the highly strategic problem of forming a high value E-business supply chain by choosing the best mix of supply chain partners. In our research, we formulate the supply chain formation problem as a mechanism design problem in a generic wayand show that the well known VCG (Vickrey-Clarke-Groves) mechanisms provide an apt frame-work for solving this problem. We provide a compelling example of a three stage automotive distribution network to illustrate the power and e±cacy of the proposed methodology. The second problem we model and solve is that of designing a revenue maximizing sponsored search auction. This is a problem that is faced by every Internet search engine, such as Google,MSN, and Yahoo!, whenever it receives a search query. In our research, we take a comprehensivelook at existing auction mechanisms for this problem. Our work leads to an innovative new auction mechanism, which we call OPT (optimal mechanism), that exhibits a superior level ofperformance. The proposed mechanism extends, in a non-trivial way, the well known Myersonoptimal auction to the specific setting of sponsored search auctions. The proposed mechanism maximizes the revenue to the search engine and also satisfies two crucial properties, Bayesian incentive compatibility and interim individual rationality. The third contribution is in respect of Stackelberg problems, which are game theoretic problems that involve hierarchical and sequential decision making. By focusing attention on an important subclass of these problems, namely the Single-Leader-Rest-Followers (SLRF) problems, we extend all relevant aspects of classical mechanism design theory to the case of SLRF problems. We derive many important results in respect of procurement auctions with reserve prices using the developed theory. The research carried out as part of this doctoral work, we believe, advances the current art in mechanism design while developing innovative mechanisms to solve those problems.

Bilateral incomplete-information negotiations of multiple issues present a difficult yet common negotiation problem that is complicated to solve from a mechanism design perspective. Unlike multilateral situations, where the individual aspirations of multiple agents can potentially be used against one another to achieve socially desirable outcomes, bilateral negotiations only involve two agents; this makes the negotiations appear to be a zero-sum game pitting agent against agent. While this is essentially true, the gain of one agent is the loss of the other, with multiple issues, it is not unusual that issues are valued asymmetrically such that agents can gain on issues important to them but suffer losses on issues of less importance. Being able to make trade-offs amongst the issues to take advantage of this asymmetry allows both agents to experience overall benefit. The major complication is negotiating under the uncertainty of incomplete information, where agents do not know each other's preferences and neither agent wants to be taken advantage of by revealing its private information to the other agent, or by being too generous in its negotiating. This leaves agents stumbling in the dark trying to find appropriate trade-offs amongst issues. In this work, we introduce the Bilateral Automated Mediation (BAM) framework. The BAM framework is aimed at helping agents alleviate the difficulties of negotiating under uncertainty by formulating a negotiation environment that is suitable for creating agreements that benefit both agents jointly. Our mediator is a composition of many different negotiation ideas and methods put together in a novel third-party framework that guides agents through the agreement space of the negotiation, but instead of arbitrating a final agreement, it allows the agents themselves to ratify the final agreement.

The efficient allocation of power is a major concern in today’s wireless communications systems. Due to the high demand in data rate and the scarcity of wireless resources such as power, the multi-user communication systems like the multiple access channel (MAC) and broadcast channel (BC) have become highly competitive environments for the users as well as the system itself. Theory of microeconomics and game theory provide the good analytical manner for the selfish and social welfare conflict problems. Instead of maximizing the system sum rate, our proposed system deals with fulfilling the utility (rate) requirement of all the users with efficient power allocation. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements. We propose the framework to allocate the power to each user with universal pricing mechanisms. The prices act as the control signal and are assumed to be some virtual currency in the wireless system. They can influence the physical layer operating points to meet the desired utility requirements. Centralized and distributed power allocation frameworks are discussed separately in the thesis with different pricing schemes. In wireless systems we have users that are rational in the game theoretic sense of making decisions consistently in pursuit of their own individual objectives. Each user’s objective is to maximize the expected value of its own payoff measured on a certain utility scale. Selfishness or self-interest is an important implication of rationality. Therefore, the mobiles which share the same spectrum have incentives to misinterpret their private information in order to obtain more utility. They might behave selfishly and show also malicious behavior by creating increased interference for other mobiles. Therefore, it is important to supervise and influence the operation of the system by pricing and priority (weights) optimization. In the centralized resource allocation, we study the general MAC and BC (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The regulator ensures the QoS requirements of all users by clever pricing and prevents cheating. The simple system optimizer solves a certain system utility maximization problem to allocate the power with the given prices and weights (priorities). The linear and nonlinear pricing mechanisms are analyzed, respectively. It is shown that linear pricing is a universal pricing only if successive interference cancellation (SIC) for uplink transmission or dirty paper coding (DPC) for downlink transmission is applied at the base station (BS). For MAC without SIC, nonlinear pricing which is logarithmic in power and linear in prices is a universal pricing scheme. The prices, the resulting cost terms, the optimal power allocation to achieve the QoS requirement of each user in the feasible rate region are derived in closed form solutions for MAC with and without SIC using linear and nonlinear pricing frameworks, respectively. The users are willing to maximize their achievable rate and minimize their cost on power by falsely reporting their channel state information (CSI). By predicting the best cheating strategy of the malicious users, the regulator is able to detect the misbehavior and punish the cheaters. The infinite repeated game (RG) is proposed as a counter mechanism with the trigger strategy using the trigger price. We show that by anticipating the total payoff of the proposed RG, the users have no incentive to cheat and therefore our framework is strategy-proof. In the distributed resource allocation, each user allocates its own power by optimizing the individual utility function. The noncooperative game among the users is formulated. The individual prices are introduced to the utility function of each user to shift the Nash equilibrium (NE) power allocation to the desired point. We show that by implicit control of the proposed prices, the best response (BR) power allocation of each user converges rapidly. The Shannon rate-based QoS requirement of each user is achieved with minimum power at the unique NE point. We analyse different behavior types of the users, especially the malicious behavior of misrepresenting the user utility function. The resulting NE power allocation and achievable rates of all users are derived when malicious behavior exists. The strategy-proof mechanism is designed using the punishment prices when the types of the malicious users are detected. The algorithm of the strategy-proof noncooperative game is proposed. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations. The uplink transmission within the single cell of heterogeneous networks is exactly the same model as MAC. Therefore, the results of the pricing-based power allocation for MAC can be implemented into heterogeneous networks. Femtocells deployed in the Macrocell network provide better indoor coverage to the user equipments (UEs) with low power consumption and maintenance cost. The industrial vendors show great interest in the access mode, called the hybrid access, in which the macrocell UEs (MUEs) can be served by the nearby Femtocell Access Point (FAP). By adopting hybrid access in the femtocell, the system energy efficiency is improved due to the short distance between the FAP and MUEs while at the same time, the QoS requirements are better guaranteed. However, both the Macrocell base station (MBS) and the FAP are rational and selfish, who maximize their own utilities. The framework to successively apply the hybrid access in femtocell and fulfill the QoS requirement of each UE is important. We propose two novel compensation frameworks to motivate the hybrid access of femtocells. To save the energy consumption, the MBS is willing to motivate the FAP for hybrid access with compensation. The Stackelberg game is formulated where the MBS serves as the leader and the FAP serves as the follower. The MBS maximizes its utility by choosing the compensation prices. The FAP optimizes its utility by selecting the number of MUEs in hybrid access. By choosing the proper compensation price, the optimal number of MUEs served by the FAP to maximize the utility of the MBS coincides with that to maximize the utility of the FAP. Numerous simulation results are conducted, showing that the proposed compensation frameworks result in a win-win solution. In this thesis, based on game theory, mechanism design and pricing framework, efficient power allocation are proposed to guarantee the QoS requirements of all users in the wireless networks. The results are applicable in the multi-user systems such as heterogeneous networks. Both centralized and distributed allocation schemes are analyzed which are suitable for different communication scenarios.
Aufgrund der hohen Nachfrage nach Datenrate und wegen der Knappheit an Ressourcen in Funknetzen ist die effiziente Allokation von Leistung ein wichtiges Thema in den heutigen Mehrnutzer-Kommunikationssystemen. Die Spieltheorie bietet Methoden, um egoistische und soziale Konfliktsituationen zu analysieren. Das vorgeschlagene System befasst sich mit der Erfüllung der auf Signal-zu-Rausch-und-Interferenz-Verhältnis (SINR) basierenden Quality-of-Service (QoS)-Anforderungen aller Nutzer mittels effizienter Leistungsallokation, anstatt die Übertragungsrate zu maximieren. Es wird ein Framework entworfen, um die Leistungsallokation mittels universellen Pricing-Mechanismen umzusetzen. In der Dissertation werden zentralisierte und verteilte Leistungsallokationsalgorithmen unter Verwendung verschiedener Pricing-Ansätze diskutiert. Die Nutzer in Funksystemen handeln rational im spieltheoretischen Sinne, indem sie ihre eigenen Nutzenfunktionen maximieren. Die mobilen Endgeräte, die dasselbe Spektrum nutzen, haben den Anreiz durch bewusste Fehlinterpretation ihrer privaten Informationen das eigene Ergebnis zu verbessern. Daher ist es wichtig, die Funktionalität des Systems zu überwachen und durch Optimierung des Pricings und Priorisierungsgewichte zu beeinflussen. Für den zentralisierten Ressourcenallokationsansatz werden der allgemeine Mehrfachzugriffskanal (Multiple Access Channel, MAC) und der Broadcastkanal (BC) mit linearen bzw. nichtlinearen Empfängern untersucht. Die Preise, die resultierenden Kostenterme und die optimale Leistungsallokation, mit der die QoS-Anforderungen in der zulässigen Ratenregion erfüllt werden, werden in geschlossener Form hergeleitet. Lineare und nichtlineare Pricing-Ansätze werden separat diskutiert. Das unendlich oft wiederholte Spiel wird vorgeschlagen, um Spieler vom Betrügen durch Übermittlung falscher Kanalinformationen abzuhalten. Für die verteilten Ressourcenvergabe wird das nichtkooperative Spiel in Normalform verwendet und formuliert. Die Nutzer wählen ihre Sendeleistung zur Maximierung ihrer eigenen Nutzenfunktion. Individuelle Preise werden eingeführt und so angepasst, dass die QoS-Anforderungen mit der Leistungsallokation im eindeutigen Nash-Gleichgewicht erfüllt werden. Verschiedene Arten des Nutzerverhaltens werden bezüglich der Täuschung ihrer Nutzenfunktion analysiert, und ein Strategy-Proof-Mechanismus mit Strafen wird entwickelt. Die Ergebnisse für den MAC sind anwendbar auf heterogene Netzwerke, wobei zwei neuartige Ansätze zur Kompensation bereitgestellt werden, die den hybriden Zugang zu Femtozell-Netzwerken motivieren. Mithilfe des Stackelberg-Spiels wird gezeigt, dass die vorgeschlagenen Ansätze in einer Win-Win-Situation resultieren.