A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

Decentralized Supply Chain Formation: A Market Protocol and Competitive Equilibrium Analysis

Supply chain formation is the process of determining the structure and terms of exchange relationships to enable a multilevel, multiagent production activity. We present a simple model of supply chains, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. To decentralize the formation process, we introduce a market price system over the resources produced along the chain. In a competitive equilibrium for this system, agents choose locally optimal allocations with respect to prices, and outcomes are optimal overall. To determine prices, we define a market protocol based on distributed, progressive auctions, and myopic, non-strategic agent bidding policies. In the presence of resource contention, this protocol produces better solutions than the greedy protocols common in the artificial intelligence and multiagent systems literature. The protocol often converges to high-value supply chains, and when competitive equilibria exist, typically to approximate competitive equilibria. However, complementarities in agent production technologies can cause the protocol to wastefully allocate inputs to agents that do not produce their outputs. A subsequent decommitment phase recovers a significant fraction of the lost surplus

Resource Allocation through Auction-based Incentive Scheme for Federated Learning in Mobile Edge Computing

openMobile Edge Computing (MEC) combinedly with Federated Learning is con- sidered as most capable solutions to AI-driven services. Most of the studies focus on Federated Learning on security aspects and performance, but the re- search is lacking to establish an incentive mechanism for the devices that are connected with a server to perform different task. In MEC, edge nodes would not participate voluntarily in learning process, nodes differ in the accusation of multi-dimensional resources, which also affects the performance of federated learning. In a competitive market scenario, the auction game theory has been widely popular for designing efficient resource allocation mechanisms, as it particularly focuses on regulating the strategic interactions among the self-interested play- ers.In this thesis, I investigate auction-based approach that based on incentive mechanism and encourage nodes to share their resources and take part in train- ing process as well as to maximize the auction revenue. To achieve this research goal, I developed auction mechanism considering the network dynamics and neglecting the devices computation and design a novel generalized first price auction mechanism to encourage participation of connected devices. Furthermore, I studied the K top best-response bidding strategies that maximize the profits of the resource sellers and guarantee the stability and effectiveness of the auction by satisfying desired economic properties. To this end, I validate the performance of the proposed auction mechanisms and bidding strategies through numerical result analysis.Mobile Edge Computing (MEC) combinedly with Federated Learning is con- sidered as most capable solutions to AI-driven services. Most of the studies focus on Federated Learning on security aspects and performance, but the re- search is lacking to establish an incentive mechanism for the devices that are connected with a server to perform different task. In MEC, edge nodes would not participate voluntarily in learning process, nodes differ in the accusation of multi-dimensional resources, which also affects the performance of federated learning. In a competitive market scenario, the auction game theory has been widely popular for designing efficient resource allocation mechanisms, as it particularly focuses on regulating the strategic interactions among the self-interested play- ers.In this thesis, I investigate auction-based approach that based on incentive mechanism and encourage nodes to share their resources and take part in train- ing process as well as to maximize the auction revenue. To achieve this research goal, I developed auction mechanism considering the network dynamics and neglecting the devices computation and design a novel generalized first price auction mechanism to encourage participation of connected devices. Furthermore, I studied the K top best-response bidding strategies that maximize the profits of the resource sellers and guarantee the stability and effectiveness of the auction by satisfying desired economic properties. To this end, I validate the performance of the proposed auction mechanisms and bidding strategies through numerical result analysis

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

Green Power for Africa: Overcoming the Main Constraints

Inadequate power supply in sub-Saharan Africa (SSA) means that only 37 per cent of sub-Saharan Africans have access to electricity. Those with access are prone to experience problems with regular power outages. In many sub- SSA countries, electricity access rates are decreasing because electrification efforts are slower than population growth. In recent years, however, some SSA countries have demonstrated that with political will and opportunities for appropriate finance, access to electricity can be accelerated. Alongside increased awareness in the international development community of the importance of energy for human development, the requirement for energy to be ‘green’ means that calls for the provision of clean, renewable energy sources cannot be ignored. The authors of this IDS Bulletin provide insights from power systems engineering, macroeconomics, microeconomics, and political economy on how to overcome constraints to green electricity in Africa. One of the biggest contributions of this issue is that is allows a dialogue between academics and practitioners that would not normally be published in the same journal. What also emerges as an underlying thread is the essential role of donors to achieve sustainable energy for all in Africa. The contributions to the IDS Bulletin underline the enormity of the clean electrification challenge in Africa, and demonstrate the benefits of a multidisciplinary approach where technical, economic, and political perspectives are involved in the design of interventions

Dagstuhl News January - December 2002

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Issues in evaluating tax and payment arrangements for publicly owned minerals

Many developing countries are still heavily dependent on mineral extraction to generate fiscal revenue and to earn foreign exchange. When minerals form a significant proportion of the country's asset base it is particularly important to have a framework to evaluate the adequacy of compensation schemes. Are these countries collecting enough in return for depleting their reserves? Are these countries carrying too much of the risk? This paper describes work in progress in developing such a framework. In many mineral dependent countries, the government holds the mineral rights and enters into compensation agreements with public or private firms that will extract the resources. Given the high degree of risk and uncertainty associated with mineral development, determining tax/payment arrangements is further complicated by the need to develop risk-sharing schemes between government and the resource extractors. This paper reviews these issues briefly and concludes that when objectives are not perfectly correlated it is preferable to use multiple instruments and to match each instrument to an objectives.Economic Theory&Research,Environmental Economics&Policies,Health Economics&Finance,Banks&Banking Reform,Insurance&Risk Mitigation