Pricing and Equilibrium Analysis of Network Market Systems

Markets have been the most successful method of identifying value of goods and services. Both large and small scale markets have gradually been moving into the Internet domain, with increasingly large numbers of diverse participants. In this dissertation, we consider several problems pertaining to equilibria in networked marketplaces under different application scenarios and market sizes. We approach the question of pricing and market design from two perspectives. On the one hand, we desire to understand how self-interested market participants would set prices and respond to prices resulting in certain allocations. On the other hand, we wish to evaluate how best to allocate resources so as to attain efficient equilibria. There might be a gap between these viewpoints, and characterizing this gap is desirable. Our technical approaches follow the number of market participants, and the nature of trades happening in the market. In our first problem, we consider a market of providing communication services at the level of providing Internet transit. Here, the transit Internet Service Provider (ISP) must determine billing volumes and set prices for its customers who are _rms that are content providers, sinks, or subsidiary ISPs. Demand from these customers is variable, and they have different impacts on the resources that the transit ISP needs to provision. Using measured data from several networks, we design a fair and flexible billing scheme that correctly identifies the impact of each customer on the amount of provisioning needed. While the customer set in the first problem is finite, many marketplaces deal with a very large number of agents that each have ephemeral lifetimes. Here, agents arrive, participate in the market for some time, and then vanish. We consider two such markets in such a regime. The first is one of apps on mobile devices that compete against each other for cellular data service, while the second is on service marketplaces wherein many providers compete with each other for jobs that consider both prices and provider reputations while making choices between them. Our goal is to show that a Mean Field Game can be used to accurately approximate these systems, determine how prices are set, and characterize the nature of equilibria in such markets. Finally, we consider efficiency metrics in large scale resource sharing networks in which bilateral exchange of resources is the norm. In particular, we consider peer-to-peer (P2P) file sharing under which peers obtain chunks of a file from each other. Here, contrary to the intuition that chunks must be shared whenever one peer has one of value to another, we show that a measure of suppression is needed to utilize resources efficiently. In particular, we propose a simple and stable algorithm entitled Mode suppression that attains near optimal file sharing times by disallowing the sharing of the most frequent chunks in the system

Influence diagrams : a new approach to modelling games

Game theory seeks to describe the interaction of two or more actors with distinct objectives. This is achieved using a mathematical model known as a game. Virtually all game theory relies on either the extensive form or the normal form to represent the games being studied. By drawing on the previously unrelated fields of game theory and graphical modelling, and by taking a new approach to the way in which a game is modelled, an alternative to the extensive and normal forms is developed: the belief influence diagram (BID). Starting from the basic definition of a game and using a new form of conditional belief called a prospective function, it is shown how the decision influence diagram can be adapted to model games. The advantages of the BID over the extensive and normal forms are explored, particularly its ability to model some of the qualitative aspects of games and to model games of greater complexity. By using BIDs in the modelling of games, fresh insight can be gained into certain features of the game, such as what sources of information an actor in the game should take account of. New concepts of sufficiency and parsimony are defined which relate to the BID. It is shown how these concepts, when combined with different forms of rationality, can lead to a variety of methods for simplifying a BID, and hence simplifying the game which it represents. It is shown that such simplifications arc invariant with respect to the order in which the simplifying steps are carried out. A schematic version of the BID is used to model finite repeated games and to develop concepts of learning and local sufficiency. It is shown how BIDs can be used to facilitate an induction proof in a finite repeated game and to model a highly complex competitive market. This last example is used to illustrate how BIDs can be helpful in evaluating some qualitative aspects of a model

Pricing and Equilibrium Analysis of Network Market Systems

Markets have been the most successful method of identifying value of goods and services. Both large and small scale markets have gradually been moving into the Internet domain, with increasingly large numbers of diverse participants. In this dissertation, we consider several problems pertaining to equilibria in networked marketplaces under different application scenarios and market sizes. We approach the question of pricing and market design from two perspectives. On the one hand, we desire to understand how self-interested market participants would set prices and respond to prices resulting in certain allocations. On the other hand, we wish to evaluate how best to allocate resources so as to attain efficient equilibria. There might be a gap between these viewpoints, and characterizing this gap is desirable. Our technical approaches follow the number of market participants, and the nature of trades happening in the market. In our first problem, we consider a market of providing communication services at the level of providing Internet transit. Here, the transit Internet Service Provider (ISP) must determine billing volumes and set prices for its customers who are _rms that are content providers, sinks, or subsidiary ISPs. Demand from these customers is variable, and they have different impacts on the resources that the transit ISP needs to provision. Using measured data from several networks, we design a fair and flexible billing scheme that correctly identifies the impact of each customer on the amount of provisioning needed. While the customer set in the first problem is finite, many marketplaces deal with a very large number of agents that each have ephemeral lifetimes. Here, agents arrive, participate in the market for some time, and then vanish. We consider two such markets in such a regime. The first is one of apps on mobile devices that compete against each other for cellular data service, while the second is on service marketplaces wherein many providers compete with each other for jobs that consider both prices and provider reputations while making choices between them. Our goal is to show that a Mean Field Game can be used to accurately approximate these systems, determine how prices are set, and characterize the nature of equilibria in such markets. Finally, we consider efficiency metrics in large scale resource sharing networks in which bilateral exchange of resources is the norm. In particular, we consider peer-to-peer (P2P) file sharing under which peers obtain chunks of a file from each other. Here, contrary to the intuition that chunks must be shared whenever one peer has one of value to another, we show that a measure of suppression is needed to utilize resources efficiently. In particular, we propose a simple and stable algorithm entitled Mode suppression that attains near optimal file sharing times by disallowing the sharing of the most frequent chunks in the system

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Proceedings of the 11th Workshop on Nonmonotonic Reasoning

These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods