The goal of the first part of this thesis is to obtain a high-level theoretical understanding of how an alliance can be managed such that its resources are used in an optimal manner. We propose a pricing mechanism to manage the interactions of carriers, through the allocation of alliance resources and profits, in a manner that encourages individual carriers to make decisions that are optimal for the alliance. Our methodology is based on modeling carrier behavior as linear programs, which are incorporated into a mechanism that manages carrier interactions by appropriately setting resource prices. After introducing two distinct behavioral models, the performance of the mechanism using each model is analyzed for its ability to ensure alliance optimal behavior is attained. We find that the behavioral model selected can significantly impact the characteristics of allocations obtained using the mechanism. In the second part of the thesis, we seek to establish practical insights regarding how the characteristics of potential partners impact the benefit that can be gained by collaborating with these partners. Computational experiments are conducted to evaluate the impact of network size, fleet capacity, demand distribution, and network compatibility on the benefit associated with collaborating. A comprehensive study for simulated two and three-carrier alliances establishes general insights regarding the compatibility of carriers with varying network sizes and fleet capacities. The impact of increasing hub-to-hub connectivity between partnering carriers is then investigated, followed by a study of the effect of market overlap on alliance success. Finally, a real-world cargo alliance is analyzed. In the third and final part of this thesis, we develop new approaches for determining and inducing fair profit allocations in alliances, providing alternatives to traditional approaches which equate minimum acceptance requirements and satisfaction. The mechanism established in the first part of the thesis is adapted to more precisely control the profit allocations obtained, in particular so that an allocation as close to some predetermined fair" allocation is obtained. Several measures of fairness are proposed and implemented, and their performance analyzed for each of the behavioral models discussed in the first part of the thesis.

<div>In recent years, the amount of data in the information processing systems has significantly increased, which is also referred to as big-data. The design of systems handling big-data calls for a scalable approach, which brings distributed systems into the picture. In contrast to centralized systems, data are spread across the network of agents in the distributed system, and agents cooperatively complete tasks through local communications and local computations. However, the design and analysis of distributed systems, in which no central coordinators with complete information are present, are challenging tasks. In order to support communication among agents to enable multi-agent coordination among others, practical communication constraints should be taken into consideration in the design and analysis of such systems. The focus of this dissertation is to provide design and analysis of distributed network processing using finite-rate communications among agents. In particular, we address the following open questions: 1) can one design algorithms balancing a graph weight matrix using finite-rate and simplex communications among agents? 2) can one design algorithms computing the average of agents’ states using finite-rate and simplex communications? and 3) going beyond of ad-hoc algorithmic designs, can one design a black-box mechanism transforming a general class of algorithms with unquantized communication to their finite-bit quantized counterparts?</div><div><br></div><div>This dissertation addresses the above questions. First, we propose novel distributed algorithms solving the weight-balancing and average consensus problems using only finite-rate simplex communications among agents, compliant to the directed nature of the network topology. A novel convergence analysis is put forth, based on a new metric inspired by the</div><div>positional system representations. In the second half of this dissertation, distributed optimization subject to quantized communications is studied. Specifically, we consider a general class of linearly convergent distributed algorithms cast as fixed-point iterate, and propose a novel black-box quantization mechanism. In the proposed mechanism, a novel quantizer preserving linear convergence is proposed, which is proved to be more communication efficient than state-of-the-art quantization mechanisms. Extensive numerical results validate our theoretical findings.</div>