The electricity transmission and distribution (T&D) grid is undergoing a paradigm shift as renewable generation explodes while flexible, storage-like loads are being massively adopted. We address the intermittency and volatility issues of renewable resources in connection with spatiotemporal distribution location-specific marginal-cost-based prices (DLMPs) that guide flexible loads to utilize their significant degrees of freedom for the purpose of providing valuable storage-like services to the grid including demand response, energy charge/discharge arbitrage and regulation reserve services. Dynamic DLMPs can induce socially optimal energy and reserve schedules to be adopted by flexible load. To this end, existing transmission wholesale markets must be extended to include distribution network connected participants. Since the inclusion of the complex preferences of many flexible loads renders familiar centralized transmission market designs intractable, we propose and investigate tractable decentralized market designs with Electric Vehicle (EV) battery charging selected as the representative flexible load. We address the equilibrium existence, uniqueness, and efficiency issues that arise with decentralized market designs, using game theory techniques. We investigate various multi-hour and multi-commodity (energy and reserves) market designs including EV self-scheduling under distribution network information aware/unaware conditions, and single or multiple load aggregator(s) scheduling groups of EVs. We investigate the role of network related information in enabling partially price anticipating EVs to acquire market power and self-schedule to achieve individual benefits at the expense of social welfare. Our contribution is the proof of existence and uniqueness of decentralized market equilibria, as well as analytical and numerical comparative analysis. Secondly, we depart from the usual ideal battery assumption, employing instead a realistic two bucket model. We then develop a novel Markovian Decision Process (MDP) application to estimate the regulation tracking cost incurred over an hour by an EV charger employing an optimal controller to respond to the regulation signal which is reset every two seconds by the system operator. The hourly tracking error increases when the EV promises higher regulation reserves while at the same time demanding an achievable albeit high average charging rate. We solve the MDP repeatedly, in fact off line, to capture the impact of the average charging rate and the regulation reserves promised at the beginning of an hour to the resulting hourly regulation tracking error. We then estimate a convex closed form relationship mapping hourly charging rate and regulation reserve offerings to the expected hourly tracking error cost. These convex tracking cost functions provide crucial input to the day ahead hourly energy bids and regulation reserve offers made by individual EVs to the Day Ahead market in response to spatiotemporal DLMPs.

In this paper an extension of the well-known binomial approach to option pricing is presented. The classical question is: What is the price of an option on the risky asset? The traditional answer is obtained with the help of a replicating portfolio by ruling out arbitrage. Instead a two-person game from the Nash equilibrium of which the option price can be derived is formulated. Consequently both the underlying asset’s price at expiration and the price of the option on this asset are endogenously determined. The option price derived this way turns out, however, to be identical to the classical no-arbitrage option price of the binomial model if the expiration-date prices of the underlying asset and the corresponding risk-neutral probability are properly adjusted according to the Nash equilibrium data of the game.

The increasing popularity and widespread deployment of wireless data systems fuel the increasing demand for more spectrum. On the other hand, various studies measuring spectrum utilization show that there is a huge variation in spectrum utilization at different times and locations. In view of this, various dynamic spectrum access (DSA) methods have been proposed in order to achieve more efficient utilization of spectrum resources by virtue of exploiting the variations in spectrum demand over time and space. Implementing DSA systems in a centralized way can lead to complexity and scalability problems due to the extensive control signaling involved. Therefore distributed implementations of DSA systems in which the users can access the system resources at their own discretion have been proposed. These distributed mechanisms typically incorporate cognitive radio systems which act as agents on behalf of users to measure the radio environment and make decisions based on these measurements. On the other hand, the freedom of the users in distributed systems to form their actions can lead each user to try to maximize its benefit from the system without regard the overall performance of the DSA system. Therefore, selfish behavior can prevail in distributed systems, which is likely to degrade the system performance.In this thesis we investigate the implications of selfish decision making in dynamic spectrum access systems. To address this broad problem, we focus our analysis on a particular system which can represent the essential properties of DSA systems and thus can shed light on the performance of the broad class of DSA systems with selfish users. Specifically, we model a DSA system as a multichannel random access system which uses ALOHA for medium access, and we analyze the behavior of the selfish users by modeling the system as a non-cooperative game. In this analysis we incorporate the effect of channel state information on the decision making of the users; we consider both cases when the users act on global (complete) and on local (incomplete) information. We determine the behavior of the selfish users at the Nash equilibria of the non-cooperative game and measure the performance of the system in terms of sum and individual utilities for various user loads and amount of available resources. We try to identify how the performance of the DSA system with selfish users compares with its cooperative counterpart. By performing these analyses we provide insights into the broader question of whether selfish users can utilize spectrum resources in a DSA system as well as cooperative users. / <p>QC 20111208</p> / MultiOperator Dynamic Spectrum access (MODyS)

No description available.

No description available.

Policy Hill Climbing (PHC) is a reinforcement learning algorithm that extends Q-learning to learn probabilistic policies for multi-agent games. WoLF-PHC extends PHC with the "win or learn fast" principle. A proof that PHC will diverge in self-play when playing Shapley's game is given, and WoLF-PHC is shown empirically to diverge as well. Various WoLF-PHC based modifications were created, evaluated, and compared in an attempt to obtain convergence to the single shot Nash equilibrium when playing Shapley's game in self-play without using more information than WoLF-PHC uses. Partial Commitment WoLF-PHC (PCWoLF-PHC), which performs best on Shapley's game, is tested on other matrix games and shown to produce satisfactory results.

<p> In this thesis, we focus on the rationing polices for the hotel room inventory control problems. Our study begins with a brief overview of revenue management in hotel industry, emphasizing the importance of room inventory control in revenue management problems. Mathematical models for controlling the room inventory in the literature are then reviewed along with recently developed game theoretic applications in revenue management. In game theoretic context, we establish three types of models to solve the hotel room inventory control problem in three different situations: 1) two-player two-fare-class static single-period game with complete information; 2) two-player two-fare-class dynamic multiple-period game with complete information; and 3) two-player two-fare-class single-period game with incomplete information.</p> <p> In the first situation, we find the existence of unique Nash equilibrium and Stackelberg equilibrium in the non-cooperative case. We provide the exact forms for these equilibria and corresponding conditions. Next, under the dynamic game settings, we provide the sufficient conditions for the unique Nash equilibrium. In the last situation, we consider the static single-period games with incomplete information and discuss the optimal strategies for the uninformed case, secret information case, private information case and public information case. The unique Bayesian Nash equilibrium in each case is found. We then analyze the values of different types of information and study their relations in different situations. Under each game theoretic setting, we present the managerial implications of our solutions along with the numerical examples. The thesis is concluded by a discussion of how game theory can is useful in hotel industry, and its relationship to other topics in revenue management.</p> / Thesis / Doctor of Philosophy (PhD)

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This dissertation builds a model of information exchange, where the information is skills. A two-sided matching market for skills is employed that includes two distinct sides, skilled and unskilled agents, and the matches that connect these agents. The unskilled agents wish to purchase skills from the skilled agents, who each possess one valuable and unique skill. Skilled agents may match with many unskilled agents, while each unskilled agent may match with only one skilled agent. Direct interaction is necessary between the agents to teach and learn the skill. Thus, there must be mutual consent for a match to occur and the skill to be exchanged. In this market for skills, a discrete, simultaneous move game is employed where all agents announce their strategies at once, every skilled agent announcing a price and every unskilled agent announcing the skill she wishes to purchase. First, both Nash equilibria and a correlated equilibrium are determined for an example of this skills market game. Next, comparative statics are employed on this discrete, simultaneous move game through computer simulations. Finally, a continuous, simultaneous move game is studied where all agents announce their strategies at once, every skilled agent announcing a price and every unskilled agent announcing a skill and price pair. For this game, an algorithm is developed that if used by all agents to determine their strategies leads to a strong Nash equilibrium for the game. / Economics

Networks, generically, refer to any application of graph theory in economics. Consider an undirected graph where nodes represent players and links represent relationships between them. Players can both form and delete links by which we mean that they can both form new relationships and terminate existing ones. A stable network is one in which no incentives exist to change the network structure. There can be various forms of stability depending on how many links players are allowed to form or delete at a time. Under strong pairwise stability, each player is allowed to delete any number of links at a time while any pair of players can form one link at a time. We introduce a network-value function, which assigns to each possible network a certain value. The value is allocated according to the component-wise egalitarian allocation rule, which divides the value generated by a component equally among members of the component (where a component refers to a maximally connected subgraph). An efficient network is one that maximizes the network value function. We show that there is an underlying conflict between strong pairwise stability and efficiency. Efficient networks are not necessarily strongly pairwise stable. This conflict can be resolved only if value functions satisfy a certain property called "middlemen-security". We further find that there is a broad class of networks called "middlemen-free networks" for which the above condition is automatically satisfied under all possible value functions. We also look at three network applications. A peering contract is an arrangement between Internet Service Providers under which they exchange traffic with one another free of cost. We analyze incentives for peering contracts among Internet service providers using the notion of pairwise stability. A hierarchy is a directed graph with an explicit top-down structure where each pair of linked agents have a superior-subordinate relationship with each other. We apply the notion of conjunctive permission value to demonstrate the formation of hierarchical firms in a competitive labor market. Comparative or targeted advertising is defined as any form of advertising where a firm directly or indirectly names a competitor. We also examine a model of targeted advertising between oligopolistic firms using non-cooperative game theoretic tools. / Ph. D.