A proposal for media component brokerage

This paper describes how MPEG-4 object based video (obv) can be used to allow selected objects to be inserted into the play-out stream to a specific user based on a profile derived for that user. The application scenario described here is for personalized product placement, and considers the value of this application in the current and evolving commercial media distribution market given the huge emphasis media distributors are currently placing on targeted advertising. This level of application of video content requires a sophisticated content description and metadata system (e.g., MPEG-7). The scenario considers the requirement for global libraries to provide the objects to be inserted into the streams. The paper then considers the commercial trading of objects between the libraries, video service providers, advertising agencies and other parties involved in the service. Consequently a brokerage of video objects is proposed based on negotiation and trading using intelligent agents representing the various parties. The proposed Media Brokerage Platform is a multi-agent system structured in two layers. In the top layer, there is a collection of coarse grain agents representing the real world players – the providers and deliverers of media contents and the market regulator profiler – and, in the bottom layer, there is a set of finer grain agents constituting the marketplace – the delegate agents and the market agent. For knowledge representation (domain, strategic and negotiation protocols) we propose a Semantic Web approach based on ontologies. The media components contents should be represented in MPEG-7 and the metadata describing the objects to be traded should follow a specific ontology. The top layer content providers and deliverers are modelled by intelligent autonomous agents that express their will to transact – buy or sell – media components by registering at a service registry. The market regulator profiler creates, according to the selected profile, a market agent, which, in turn, checks the service registry for potential trading partners for a given component and invites them for the marketplace. The subsequent negotiation and actual transaction is performed by delegate agents in accordance with their profiles and the predefined rules of the market

Designing a regional e-logistics portal

A variety of optimization and negotiation technologies hold the promise of delivering value to the logistics processes of businesses both small and large, yet they tend to remain inaccessible to SMEs (largely due to price and complexity concerns). This paper describes the early-phase steps in a project to develop a regional e- logistics portal. The project seeks to make constraint-based optimization and automated negotiation technologies accessible to SMEs within a portal that also serves their information needs. The paper highlights several novel aspects of the design of the portal, as well as a novel requirements gathering process involving community consultation

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Operational project for the development of an agricultural product auction in Greece.

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.Σκοπός της εργασίας είναι να εξεταστούν τα δημοπρατήρια αγροτικών προϊόντων στην Ελλάδα τα οποία μετά το 2011 διαθέτουν νομικό πλαίσιο εφαρμογής και εξάπλωσης και να διερευνηθεί αν το εγχείρημα κρίνεται επιτυχημένο. Τα δημοπρατήρια αγροτικών προϊόντων αποτελούν μια εναλλακτική στρατηγική εμπορίας αγροτικών προϊόντων. Στην Ελλάδα αυτή η στρατηγική έχει αρχίσει να εφαρμόζεται πολύ πρόσφατα μόλις τα τελευταία οκτώ χρόνια. Στόχος είναι να καταγραφεί η σημερινή κατάσταση των δημοπρατηρίων αγροτικών προϊόντων στην Ελλάδα και η εφαρμογή τους από τους ελληνικούς αγροτικούς συνεταιρισμούς. Για την εκπόνηση της εργασίας χρησιμοποιήθηκαν τόσο δευτερογενείς όσο και πρωτογενείς πηγές πληροφόρησης. Αφού εξετάστηκε η ελληνική νομοθεσία και έρευνες που αφορούν τα δημοπρατήρια αγροτικών προϊόντων τόσο στην Ελλάδα όσο και στο εξωτερικό στη συνέχεια πραγματοποιήθηκε δειγματοληπτική έρευνα σε αγροτικούς συνεταιρισμούς της χώρας και συγκεκριμένα σε δύο κατηγορίες συμμετεχόντων, σε στελέχη της διοίκησης των συνεταιρισμών και σε μέλη αυτών. Τα δύο ερωτηματολόγια που διανεμήθηκαν είχαν κάποιες κοινές και κάποιες διαφορετικές ερωτήσεις. Η στατιστική ανάλυση που πραγματοποιήθηκε έγινε με τη χρήση του Microsoft Excel και τη χρήση ορισμένων βασικών στατιστικών μεγεθών όπως ο μέσος όρος και η τυπική απόκλιση. Σύμφωνα με τα αποτελέσματα της έρευνας η συχνότητα αγοράς των αγροτικών προϊόντων στους αγροτικούς συνεταιρισμούς είναι έως δύο φορές την εβδομάδα, η προμήθεια αγροτικών προϊόντων γίνεται από παραγωγούς των γύρω περιοχών και μάλιστα τα προϊόντα προέρχονται από την τοπική αγορά. Ακόμη, η πλειοψηφία των συμμετεχόντων δεν γνωρίζει σχετικά με τη λειτουργία των δημοπρατηρίων αγροτικών προϊόντων ενώ όσοι γνωρίζουν είναι εξοικειωμένοι με τη συμβατική τους μορφή. Ταυτόχρονα, παρατηρείται έλλειψη εκπαίδευσης και γνώσεων σχετικά με τη λειτουργία ηλεκτρονικών δημοπρατηρίων αγροτικών προϊόντων η οποία πρέπει να διορθωθεί άμεσα ώστε να καταστούν οι αγροτικοί συνεταιρισμοί ανταγωνιστικοί και πάλι στην παγκόσμια πια αγορά. Τέλος, απαιτείται μεγαλύτερη οργάνωση και συντονισμός ώστε οι ελληνικοί αγροτικοί συνεταιρισμοί να διαθέτουν καθ’ όλη τη διάρκεια του έτους τα αγροτικά τους προϊόντα

Automated Auction Mechanism Design with Competing Markets

Resource allocation is a major issue in multiple areas of computer science. Despite the wide range of resource types across these areas, for example real commodities in e-commerce and computing resources in distributed computing, auctions are commonly used in solving the optimization problems involved in these areas, since well designed auctions achieve desirable economic outcomes. Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. Following this line of work, we present what we call a grey-box approach to automated auction mechanism design using reinforcement learning and evolutionary computation methods. We first describe a new strategic game, called \cat, which were designed to run multiple markets that compete to attract traders and make profit. The CAT game enables us to address the imbalance between prior work in this field that studied auctions in an isolated environment and the actual competitive situation that markets face. We then define a novel, parameterized framework for auction mechanisms, and present a classification of auction rules with each as a building block fitting into the framework. Finally we evaluate the viability of building blocks, and acquire auction mechanisms by combining viable blocks through iterations of CAT games. We carried out experiments to examine the effectiveness of the grey-box approach. The best mechanisms we learnt were able to outperform the standard mechanisms against which learning took place and carefully hand-coded mechanisms which won tournaments based on the CAT game. These best mechanisms were also able to outperform mechanisms from the literature even when the evaluation did not take place in the context of CAT games. These results suggest that the grey-box approach can generate robust double auction mechanisms and, as a consequence, is an effective approach to automated mechanism design. The contributions of this work are two-fold. First, the grey-box approach helps to design better auction mechanisms which can play a central role in solutions to resource allocation problems in various application domains of computer science. Second, the parameterized view and the reinforcement learning-based search method can be used in other strategic, competitive situations where decision making processes are complex and difficult to design and evaluate manually

GAF: A General Auction Framework for Secure Combinatorial Auctions

Auctions are an economic mechanism for allocating goods to interested parties. There are many methods, each of which is an Auction Protocol. Some protocols are relatively simple such as English and Dutch auctions, but there are also more complicated auctions, for example combinatorial auctions which sell multiple goods at a time, and secure auctions which incorporate security solutions. Corresponding to the large number of protocols, there is a variety of purposes for which protocols are used. Each protocol has different properties and they differ between how applicable they are to a particular domain. In this thesis, the protocols explored are privacy preserving secure combinatorial auctions which are particularly well suited to our target domain of computational grid system resource allocation. In grid resource allocation systems, goods are best sold in sets as bidders value different sets of goods differently. For example, when purchasing CPU cycles, memory is also required but a bidder may additionally require network bandwidth. In untrusted distributed systems such as a publicly accessible grid, security properties are paramount. The type of secure combinatorial auction protocols explored in this thesis are privacy preserving protocols which hide the bid values of losing bidder’s bids. These protocols allow bidders to place bids without fear of private information being leaked. With the large number of permutations of different protocols and configurations, it is difficult to manage the idiosyncrasies of many different protocol implementations within an individual application. This thesis proposes a specification, design, and implementation for a General Auction Framework (GAF). GAF provides a consistent method of implementing different types of auction protocols from the standard English auction through to the more complicated combinatorial and secure auctions. The benefit of using GAF is the ability to easily leverage multiple protocols within a single application due to the consistent specification of protocol construction. The framework has be tested with three different protocols: the Secure Polynomial auction protocol, the Secure Homomorphic auction protocol and the Secure Garbled Circuits auction protocol. These three protocols and a statistics collecting application is a proof of concept for the framework and provides the beginning of an analysis designed at determining suitable protocol candidates for grid systems

Using Transfer Learning in Network Markets

Mechanism design is the sub-field of microeconomics and game theory, which considers agents have their own private information and are self-interested and tries to design systems that can produce desirable outcomes. In recent years, with the development of internet and electronic markets, mechanism design has become an important research field in computer science. This work has largely focused on single markets. In the real world, individual markets tend to connect to other markets and form a big “network market”, where each market occupies a node in the network and connections between markets reflect constraints on traders in the markets. So, it is interesting to find out how the structure of connected network markets impacts the performance of the resulting network markets and how we can optimize performance by varying the things that one could control in a network market. In this dissertation, I aim to find out whether we can apply transfer learning to other machine learning techniques like reinforcement learning in the design of network markets to help optimize the performance of the network markets. I applied transfer learning on both machine learning trading strategies and machine learning strategies for selecting which market to trade in. I found that, in most cases, by applying transfer learning to machine learning trading strategies or machine learning market selection strategies, we can improve the performance of the network market significantly