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Most financial markets operate as double auction markets in which buyers and sellers submit limit and market orders. In this case the traders have to decide firstly whether they want to submit a buy or sell order and then secondly what the limit price of this order is. In this thesis I develop further a theoretical model based on Chatterjee and Samuelson (1983) in which two traders trade with each other in a double auction market. Assuming that both traders assign a private value to the asset they are trading, which is known only to them but not their trading partner, I determine whether the traders should submit a buy or sell order and what the optimal limit price should be. I develop a single-period model in which traders only trade once and thus cannot learn each other’s private values from trading as well as a multi-period model that allows to infer to some degree the other trader’s private value from their order submission behavior. Using this theoretical model as a benchmark, I then conducted experiments with students to evaluate whether the actual behavior of students fits the theory developed. Although we find that in general the behavior of traders is consistent with the proposed theory, there are some significant differences. Most notably traders seem to underreact to differences in their own private value, i.e. do not adjust their limit price to the extend suggested by theory. I evaluate these outcomes in light of results established results in behavioral finance.

In this thesis, we explore the use of double auction markets as a general approach to tackle resource allocation problems in large-scale distributed systems, which are traditionally solved using optimization techniques. Prevalently adopted in real-world markets, double auctions have the power of arbitrating mappings between participating players and trading commodities in a decentralized fashion, with every player trying to maximize her own utility selfishly. Through the design of prefetching strategies in peer-assisted video-on-demand systems, we show how the problem of minimizing server bandwidth costs by reallocating media contents can be solved by double auction markets gracefully. However, not every resource allocation problem satisfies requirements of double auctions. We illustrate the limitation of double auctions with an example of virtual machine migration in container-based datacenters, which is then modeled into a Nash bargaining game and solved by a Nash bargaining solution.

In this thesis, we explore the use of double auction markets as a general approach to tackle resource allocation problems in large-scale distributed systems, which are traditionally solved using optimization techniques. Prevalently adopted in real-world markets, double auctions have the power of arbitrating mappings between participating players and trading commodities in a decentralized fashion, with every player trying to maximize her own utility selfishly. Through the design of prefetching strategies in peer-assisted video-on-demand systems, we show how the problem of minimizing server bandwidth costs by reallocating media contents can be solved by double auction markets gracefully. However, not every resource allocation problem satisfies requirements of double auctions. We illustrate the limitation of double auctions with an example of virtual machine migration in container-based datacenters, which is then modeled into a Nash bargaining game and solved by a Nash bargaining solution.

If the market of military procurement conforms to the double auction (Chatterjee and Samuelson, 1983), the government and the manufacturer can try to reach a binding agreement and maximize the monopoly profit. When they both comply with the agreement, the game constitutes a cooperative game. The government and the firm can extend the periods of the game from one to two through signing the research and development contract. When they carry out the research and development contract in the second phase, the trading probability in the first phase that is double auction model will rise. However the R&D contract causes an increase of transaction probability, the market offers an opportunity let the ineligible manufacturer participate in the defense procurement. We can find the trade-off relationship between the advancement of trading probability and the appearances of unqualified firm. The result is consistent with Inefficiency theorem that an incentive-compatible mechanism which is ex post efficient will not be individual theorem.

The present work discuss the continuous double auction mechanisms and the order book models. After a brief introduction to selected models, a general model of the the continuous double auction from the thesis title is described. Further, a structure of british market data is given as well as an approach to them. Based on these data the validity of Smith Farmer's model and Cont Stoikov's model is tested in the context of general model by linear regression. Finally, based on the previous results, the own order book model is suggested and its validity tested.

The thesis deals with two kinds of double auction - with the continuous auction and a sequence of call auctions. We explain their rules and we define their models. We present results of simulations of the both kinds of double auction - the aim is to look for the call auction with such parameters that the prices and the traded volume of the continuous auction are approximated best. Finally, in a theoretical part, we characterize the dis- tribution of the order book in the continuous auction and then we specify the joint distribution of the price and the traded volume in the call auction (the distribution of bid, ask and the traded volume given by the continuous auction may be immediately devised from the distribution of the order book).

The main purpose is to develop the auction system that enables enterprise employees to create and update information in website fast and easily. Such e–shops are one of the main components of auctions. System of auctions suggests great opportunity to sell goods or services on Internet of all over the world. There is an opportunity to obtain goods that we gained in a traditional market, staying at home or at your job place. This web system is suitable for sale companies. Either goods or facilities (services) can be sold in web market. Such type of web market is available for everyone. Both, enterprise companies or private persons can use it. There is an opportunity not only to buy, but to sell by you too. The increase of Internet users in Lithuania causes a development of such kind of systems and becomes more available. The system has user – friendly interface, good performance and ensures secure access. It can work on any operating system, which supports Apache Web server with PHP and MySQL database.

Financial markets use complex computer trading systems whose failures can cause serious economic damage, making reliability a major concern. Automated random testing has been shown to be useful in finding defects in these systems, but its inherent test oracle problem (automatic generation of the expected system output) is a drawback that has typically prevented its application on a larger scale. Two main tasks have been carried out in this thesis as a solution to the test oracle problem. First, an independent model of a real trading system based on constraint programming, a method for solving combinatorial problems, has been created. Then, the model has been integrated as a true test oracle in automated random tests. The test oracle maintains the expected state of an order book throughout a sequence of random trade order actions, and provides the expected output of every auction triggered in the order book by generating a corresponding constraint program that is solved with the aid of a constraint programming system. Constraint programming has allowed the development of an inexpensive, yet reliable test oracle. In 500 random test cases, the test oracle has detected two system failures. These failures correspond to defects that had been present for several years without being discovered neither by less complete oracles nor by the application of more systematic testing approaches. The main contributions of this thesis are: (1) empirical evidence of both the suitability of applying constraint programming to solve the test oracle problem and the effectiveness of true test oracles in random testing, and (2) a first attempt, as far as the author is aware, to model a non-theoretical continuous double auction using constraint programming. / Winner of the Swedish AI Society's prize for the best AI Master's Thesis 2010.

Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.