Using fuzzy set approach in multi-attribute automated auctions

This paper designs a novel fuzzy attributes and competition based bidding strategy (FAC-Bid), in which the final best bid is calculated on the basis of the assessment of multiple attributes of the goods and the competition for the goods in the market. The assessment of attributes adapts the fuzzy sets technique to handle uncertainty of the bidding process. The bidding strategy also uses and determines competition in the market (based on the two factors i.e. no. of the bidders participating and the total time elapsed for an auction) using Mamdani's Direct Method. Then the final price of the best bid will be determined based on the assessed attributes and the competition in the market using fuzzy reasoning technique

Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system

Design and Effects of Information Feedback in Continuous Combinatorial Auctions

Advancements in information technologies offer opportunities for designing and deploying innovative market mechanisms. For example, combinatorial auctions, in which bidders can bid on combinations of goods, can increase the economic efficiency of a trade when goods have complementarities. However, lack of real-time bidder support tools has been a major obstacle preventing this mechanism from reaching its full potential. This study uses novel feedback mechanisms to aid bidders in formulating bids in real-time to facilitate participation in continuous combinatorial auctions. Laboratory experiments examine the effectiveness of our feedback mechanisms; the study is the first to examine how bidders behave in such information-rich environments. Our results indicate that feedback results in higher efficiency and higher seller’s revenue compared to the baseline case where bidders are not provided feedback. Furthermore, contrary to conventional wisdom, even in complex economic environments, individuals effectively integrate rich information in their decision making

Agent-based simulation of electricity markets: a literature review

Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets. --

An agent for online auctions: bidding and bundling goods for multiple clients.

by Chi-Lun Chau.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 91-93).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivTable of Contents --- p.vList of Figures --- p.viiiDefinitions --- p.1Chapter Chapter 1 - --- Introduction --- p.2Chapter 1.1 --- Background --- p.2Chapter 1.2 --- Testing Environment --- p.4Chapter 1.2.1 --- Game Overview --- p.4Chapter 1.2.2 --- Auctions --- p.5Chapter 1.2.3 --- Utility and Scores --- p.8Chapter 1.3 --- Thesis Contribution and Organization --- p.10Chapter Chapter 2 - --- Relatfd Work --- p.12Chapter 2.1 --- Traditional auction theory --- p.12Chapter 2.2 --- Technologies related to online auctions --- p.13Chapter 2.3 --- Recent researches on online auctions --- p.14Chapter 2.3.1 --- Priceline (proposed by Amy Greenwald) --- p.16Chapter 2.3.2 --- ATTac: Integer Linear Programming (ILP) --- p.17Chapter 2.3.3 --- RoxyBot: Beam search --- p.19Chapter Chapter 3 - --- Theoretical model for agents in online auctions --- p.20Chapter 3.1 --- High-level planning --- p.20Chapter 3.2 --- Mathematical model --- p.21Chapter Chapter 4 - --- Agent Architecture and Mechanisms --- p.26Chapter 4.1 --- Architecture --- p.26Chapter 4.2 --- Cost Estimator (CE) --- p.29Chapter 4.2.1 --- Closed auction --- p.29Chapter 4.2.2 --- "Open ""take-it or leave-it"" market" --- p.30Chapter 4.2.3 --- Open continuous double auction (CDA) --- p.31Chapter 4.2.4 --- Open multi-unit ascending auction --- p.33Chapter 4.4.2.1 --- Historical clearing prices --- p.33Chapter 4.4.2.2 --- Increasing marginal costs --- p.35Chapter 4.4.2.3 --- Bid winning probability --- p.37Chapter 4.3 --- Allocation and Acquisition Solver (AAS) --- p.39Chapter 4.3.1 --- Un-coordinated VS coordinated aspiration --- p.39Chapter 4.3.2 --- Optimal VS heuristic approach --- p.40Chapter 4.3.3 --- An greedy approach with coordinated aspiration --- p.41Chapter 4.4 --- The Bidders --- p.44Chapter 4.4.1 --- """Take-it or leave-it"" market" --- p.44Chapter 4.4.2 --- Multi-unit ascending auction --- p.46Chapter 4.4.2.1 --- Budget bidding --- p.47Chapter 4.4.2.2 --- Low price bidding --- p.49Chapter 4.4.3 --- Continuous double auction (CDA) --- p.51Chapter 4.4.3.1 --- Review of fuzzy reasoning mechanism --- p.52Chapter 4.4.3.2 --- Fuzzy Reasoning in FL-strategy --- p.54Chapter 4.4.3.3 --- Adaptive Risk Attitude --- p.59Chapter Chapter 5 - --- Results --- p.61Chapter 5.1 --- TAC '02 Competition --- p.62Chapter 5.1.1 --- Tournament result of our working agent --- p.63Chapter 5.1.2 --- "Comparisons between CUHK, ATTac and Roxybot" --- p.65Chapter 5.1.3 --- Low-price Bidding --- p.66Chapter 5.2 --- Controlled Environment --- p.67Chapter 5.2.1 --- Software platform --- p.67Chapter 5.2.2 --- Aggressive agent vs. Adaptive agent --- p.68L-agent (aggressive agent) --- p.68S-agent (adaptive agent) --- p.69Experimental Setting --- p.70Experimental Results --- p.71The Hawk-Dove Game --- p.72Chapter 5.2.3 --- Our agent model --- p.73Experimental Setting --- p.73Experimental Results --- p.74Chapter 5.2.4 --- Historical clearing price --- p.75Experimental Setting --- p.76Experimental Result --- p.76Comparisons among different approaches --- p.77Chapter 5.2.5 --- Increasing marginal cost --- p.79Experimental Setting --- p.79Experimental Result --- p.79Chapter 5.2.6 --- Bid winning probability --- p.81Experimental Setting --- p.81Experimental Result --- p.81Chapter 5.2.7 --- FL-strategy --- p.82A-strategy --- p.83Experimental Setting --- p.84Experimental Result --- p.85Chapter Chapter 6 - --- Conclusion and Future work --- p.87Reference --- p.9

Pricing the Cloud: An Auction Approach

Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research. One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints. Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice

Two Auction-Based Resource Allocation Environments: Design and Experience

Many computer systems have reached the point where the goal of resource allocation is no longer to maximize utilization; instead, when demand exceeds supply and not all needs can be met, one needs a policy to guide resource allocation decisions. One natural policy is to seek efficient usage, which allocates resources to the set of users who have the highest utility for the use of the resources. Researchers have frequently proposed market-based mechanisms to provide such a goal-oriented way to allocate resources among competing interests while maximizing overall utility of the users.Engineering and Applied Science

e-Reverse logistics for remanufacture-to-order : an online auction-based and multi- agent system supported solution

Due to the rapid obsolescent nature of consumer products, the remanufacture-to-stock strategy, in which remanufacturers tend to collect certain amount of end-of-life products, remanufacturing them as many as they can and keep these remanufactured products in stock waiting for customers come to buy, is not always an optimal solution. Under this circumstance, remanufacture-to-order policy, as an effective complement, provides a good trade-off for remanufacturers between meeting consumers’ demand and, in the meantime, keeping the inventory cost at a lower level. To remanufacture the used items, the manufacturer must retrieve them from the market where they are dispersed among consumers. This is accomplished by means of a reverse logistics chain that is comparable to the new product distribution system in reverse. However, the current reverse logistics do not respond to remanufacture-to-order at an efficient level. Therefore it is a necessity to develop a novel infrastructure, which can deal with these issues. This paper presents a framework called e-reverse logistics that aims at filling this gap. The major features and architecture of the proposed e-reverse logistics are detailed in this work

Adaptive bidding strategies in agent-based combinatorial auctions.

Sui, Xin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 91-97).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- MAS-Based Resource Allocation Problems --- p.1Chapter 1.2 --- Combinatorial Auction As a Solution --- p.4Chapter 1.3 --- Strategy Issues --- p.5Chapter 1.4 --- Structure of This Work --- p.7Chapter 2 --- Combinatorial Auctions and Bidding Strategies --- p.9Chapter 2.1 --- Combinatorial Auctions for Resource Allocation --- p.9Chapter 2.2 --- Bidding Strategies in Combinatorial Auctions --- p.11Chapter 2.2.1 --- Berhault's Strategies --- p.11Chapter 2.2.2 --- An's Strategies --- p.13Chapter 2.2.3 --- Schwind´ةs Strategies --- p.15Chapter 2.2.4 --- Wilenius´ةs Strategy --- p.17Chapter 2.2.5 --- Overview of Previous Strategies --- p.18Chapter 3 --- An Adaptive Bidding Strategy in Static Markets --- p.21Chapter 3.1 --- Basic Concepts --- p.22Chapter 3.2 --- The Core Algorithm --- p.24Chapter 3.3 --- Experimental Evaluation --- p.31Chapter 3.3.1 --- Experiment Setup --- p.32Chapter 3.3.2 --- Experiment Results and Analysis --- p.33Chapter 4 --- An Adaptive Bidding Strategy in Dynamic Markets --- p.38Chapter 4.1 --- Basic Concepts --- p.39Chapter 4.2 --- The Core Algorithm --- p.42Chapter 4.3 --- Experimental Evaluation --- p.48Chapter 4.3.1 --- Experiment Setup --- p.49Chapter 4.3.2 --- Experiment Results and Analysis --- p.51Chapter 5 --- A Q-Learning Based Adaptive Bidding Strategy in Static Mar-kets --- p.59Chapter 5.1 --- An Overview of Q-Learning --- p.60Chapter 5.2 --- Basic Concepts --- p.63Chapter 5.3 --- The Core Algorithm --- p.65Chapter 5.4 --- Experimental Evaluation --- p.70Chapter 5.4.1 --- Experiment Setup --- p.70Chapter 5.4.2 --- Experiment Results and Analysis --- p.72Chapter 6 --- Discussion --- p.82Chapter 6.1 --- Applicability of the Adaptive Strategies --- p.82Chapter 6.2 --- Generalization of the Adaptive Strategies --- p.83Chapter 7 --- Conclusion and Future Work --- p.86Chapter 7.1 --- Conclusion --- p.86Chapter 7.2 --- Future Work --- p.88Bibliography --- p.9