A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing

Agent-based Modeling And Market Microstructure

In most modern financial markets, traders express their preferences for assets by making orders. These orders are either executed if a counterparty is willing to match them or collected in a priority queue, called a limit order book. Such markets are said to adopt an order-driven trading mechanism. A key question in this domain is to model and analyze the strategic behavior of market participants, in response to different definitions of the trading mechanism (e.g., the priority queue changed from the continuous double auctions to the frequent call market). The objective is to design financial markets where pernicious behavior is minimized.The complex dynamics of market activities are typically studied via agent-based modeling (ABM) methods, enriched by Empirical Game-Theoretic Analysis (EGTA) to compute equilibria amongst market players and highlight the market behavior (also known as market microstructure) at equilibrium. This thesis contributes to this research area by evaluating the robustness of this approach and providing results to compare existing trading mechanisms and propose more advanced designs.In Chapter 4, we investigate the equilibrium strategy profiles, including their induced market performance, and their robustness to different simulation parameters. For two mainstream trading mechanisms, continuous double auctions (CDAs) and frequent call markets (FCMs), we find that EGTA is needed for solving the game as pure strategies are not a good approximation of the equilibrium. Moreover, EGTA gives generally sound and robust solutions regarding different market and model setups, with the notable exception of agents’ risk attitudes. We also consider heterogeneous EGTA, a more realistic generalization of EGTA whereby traders can modify their strategies during the simulation, and show that fixed strategies lead to sufficiently good analyses, especially taking the computation cost into consideration.After verifying the reliability of the ABM and EGTA methods, we follow this research methodology to study the impact of two widely adopted and potentially malicious trading strategies: spoofing and submission of iceberg orders. In Chapter 5, we study the effects of spoofing attacks on CDA and FCM markets. We let one spoofer (agent playing the spoofing strategy) play with other strategic agents and demonstrate that while spoofing may be profitable in both market models, it has less impact on FCMs than on CDAs. We also explore several FCM mechanism designs to help curb this type of market manipulation even further. In Chapter 6, we study the impact of iceberg orders on the price and order flow dynamics in financial markets. We find that the volume of submitted orders significantly affects the strategy choice of the other agents and the market performance. In general, when agents observe a large volume order, they tend to speculate instead of providing liquidity. In terms of market performance, both efficiency and liquidity will be harmed. We show that while playing the iceberg-order strategy can alleviate the problem caused by the high-volume orders, submitting a large enough order and attracting speculators is better than taking the risk of having fewer trades executed with iceberg orders.We conclude from Chapters 5 and 6 that FCMs have some exciting features when compared with CDAs and focus on the design of trading mechanisms in Chapter 7. We verify that CDAs constitute fertile soil for predatory behavior and toxic order flows and that FCMs address the latency arbitrage opportunities built in those markets. This chapter studies the extent to which adaptive rules to define the length of the clearing intervals — that might move in sync with the market fundamentals — affect the performance of frequent call markets. We show that matching orders in accordance with these rules can increase efficiency and selfish traders’ surplus in a variety of market conditions. In so doing, our work paves the way for a deeper understanding of the flexibility granted by adaptive call markets

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

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

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 analysis in online auctions using clustering and regression tree approach

Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a price forecasting agent (PFA) is proposed using data mining techniques to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by k-means clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Based on the transformed data after clustering, bid selector nominates the cluster for the current auction whose price is to be forecasted. Regression trees are employed to predict the end-price and designing the optimal bidding strategies for the current auction. Our results show the improvements in the end price prediction using clustering and regression tree approach

Agent-orientated auction mechanism and strategy design

Agent-based technology is playing an increasingly important role in today’s economy. Usually a multi-agent system is needed to model an economic system such as a market system, in which heterogeneous trading agents interact with each other autonomously. Two questions often need to be answered regarding such systems: 1) How to design an interacting mechanism that facilitates efficient resource allocation among usually self-interested trading agents? 2) How to design an effective strategy in some specific market mechanisms for an agent to maximise its economic returns? For automated market systems, auction is the most popular mechanism to solve resource allocation problems among their participants. However, auction comes in hundreds of different formats, in which some are better than others in terms of not only the allocative efficiency but also other properties e.g., whether it generates high revenue for the auctioneer, whether it induces stable behaviour of the bidders. In addition, different strategies result in very different performance under the same auction rules. With this background, we are inevitably intrigued to investigate auction mechanism and strategy designs for agent-based economics. The international Trading Agent Competition (TAC) Ad Auction (AA) competition provides a very useful platform to develop and test agent strategies in Generalised Second Price auction (GSP). AstonTAC, the runner-up of TAC AA 2009, is a successful advertiser agent designed for GSP-based keyword auction. In particular, AstonTAC generates adaptive bid prices according to the Market-based Value Per Click and selects a set of keyword queries with highest expected profit to bid on to maximise its expected profit under the limit of conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments. The TAC CAT tournament provides an environment for investigating the optimal design of mechanisms for double auction markets. AstonCAT-Plus is the post-tournament version of the specialist developed for CAT 2010. In our experiments, AstonCAT-Plus not only outperforms most specialist agents designed by other institutions but also achieves high allocative efficiencies, transaction success rates and average trader profits. Moreover, we reveal some insights of the CAT: 1) successful markets should maintain a stable and high market share of intra-marginal traders; 2) a specialist’s performance is dependent on the distribution of trading strategies. However, typical double auction models assume trading agents have a fixed trading direction of either buy or sell. With this limitation they cannot directly reflect the fact that traders in financial markets (the most popular application of double auction) decide their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Experiments are conducted under both dynamic and static settings of the continuous BDA market. We find that the allocative efficiency of a continuous BDA market mainly comes from rational selection of trading directions. Furthermore, we introduce a high-performance Kernel trading strategy in the BDA market which uses kernel probability density estimator built on historical transaction data to decide optimal order prices. Kernel trading strategy outperforms some popular intelligent double auction trading strategies including ZIP, GD and RE in the continuous BDA market by making the highest profit in static games and obtaining the best wealth in dynamic games

Optimizing energy market participation with batteries

Due to the fact that the energy sector is in transition, there are goals for lowering the energy cost with the use of renewables and batteries. This presents challenges to the system and the solution is the issuing of energy communities that can be used to make electricity provision more clean and secure. It is also to see how energy flexibility elements or elements on the consumption side can make the system more efficient and cheaper, which is being done in this paper concerning the day-ahead bid and batteries. Traditional day-ahead bidding methods have become costly, mainly when the forecasted energy consumption differs from the actual consumption, which has to be resolved by penalizing with an imbalance cost. This thesis is part of a more significant project (Layered Energy System) that is to be deployed in Spain. Applying such changes to the electricity system first requires becoming familiar with and understanding Spain's context. The first part of this thesis provides research to understand the Spanish regulatory framework, how the market works, and the status of these technologies in Spain. Following that, this thesis's primary work is to explore how day-ahead market bid could be improved through the use of batteries for better planning and error assumptions. It mentions several day-ahead bidding strategies in the context of energy and batteries. And then selects a subset (three) of the studied strategies and implements them, comparing their performance on actual electricity data. Finally, selects the one that best fits various scenarios and requirements. A particular objective function is opted to be minimized with respect to the battery constraints that involve the variables. A linear program will find the values that best fits those variables at every time step $t$ of a single day. The methodology is an improvement over traditional predictive models. After comparing different strategies, Results show that strategy one, namely "Stochastic Chance-constraint optimization", yields the best results. In this strategy, the battery would have the freedom to maximize profit even if it sometimes increases imbalance. The preferred error distribution for this strategy is the Gamma distribution. Using a battery to offset imbalances can help to minimize total energy cost for a whole day (up to 26%). The last part of the thesis is ongoing research about capacity traders and market performance. It surveys the literature on trading strategies in various contexts and markets relevant to capacity traders. The market performance in capacity trading needs to consider how well the buildings can reach their desired capacity through bidding and selling. Performance metrics that are typically used to evaluate those trading strategies were documented. This feature is being worked on with python, but it will not be able to be shown