An investigation of the trading agent competition : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand

The Internet has swept over the whole world. It is influencing almost every aspect of society. The blooming of electronic commerce on the back of the Internet further increases globalisation and free trade. However, the Internet will never reach its full potential as a new electronic media or marketplace unless agents are developed. The trading Agent Competition (TAC), which simulates online auctions, was designed to create a standard problem in the complex domain of electronic marketplaces and to inspire researchers from all over the world to develop distinctive software agents to a common exercise. In this thesis, a detailed study of intelligent software agents and a comprehensive investigation of the Trading Agent Competition will be presented. The design of the Risker Wise agent and a fuzzy logic system predicting the bid increase of the hotel auction in the TAC game will be discussed in detail

Integrating Energy Markets: Does Sequencing Matter?

This paper addresses three questions that are relevant to integrating different regional transmission areas. Market integrating normally increases the number of competitors and should therefore reduce prices but the first section shows that prices could rise when the number of generators initially increases. Regulatory effort will also be affected by market integration. If the number of generators in either market is low, then our analysis suggests that the outcome depends on whether the regulators act independently or co-ordinate. Finally, if markets are gradually combined into larger units, the choice of transmission allocation (auctions or market coupling) will affect the prospects of making further gains and hence could lead to incomplete reform.Cambridge-MIT Institut

Critical review of the e-loyalty literature: a purchase-centred framework

Over the last few years, the concept of online loyalty has been examined extensively in the literature, and it remains a topic of constant inquiry for both academics and marketing managers. The tremendous development of the Internet for both marketing and e-commerce settings, in conjunction with the growing desire of consumers to purchase online, has promoted two main outcomes: (a) increasing numbers of Business-to-Customer companies running businesses online and (b) the development of a variety of different e-loyalty research models. However, current research lacks a systematic review of the literature that provides a general conceptual framework on e-loyalty, which would help managers to understand their customers better, to take advantage of industry-related factors, and to improve their service quality. The present study is an attempt to critically synthesize results from multiple empirical studies on e-loyalty. Our findings illustrate that 62 instruments for measuring e-loyalty are currently in use, influenced predominantly by Zeithaml et al. (J Marketing. 1996;60(2):31-46) and Oliver (1997; Satisfaction: a behavioral perspective on the consumer. New York: McGraw Hill). Additionally, we propose a new general conceptual framework, which leads to antecedents dividing e-loyalty on the basis of the action of purchase into pre-purchase, during-purchase and after-purchase factors. To conclude, a number of managerial implementations are suggested in order to help marketing managers increase their customers’ e-loyalty by making crucial changes in each purchase stage

Sealed-Bid Auctions: Case Study

Auctions are an important link in supply chains.This paper presents an empirical investigation of a single-shot simultaneous or sealed-bid auction.This case study concerns the mussel trade in Yerseke, the Netherlands.It surprisingly demonstrates that companies buying large quantities of mussels pay higher unit prices.It also reveals that auction prices react sharply to changes in annual supply and that seasonality causes a bullwhip effect.Finally, purchase managers perform significantly differently from each other, when accounting for the above price factors and "hedonic price" factors, which represent objectively measured product characteristics.To derive these conclusions, this paper uses a simple linear regression model that: (i) extracts information from a database of 28,017 mussel lots enabling the rejection of four intuitive null-hypotheses; (ii) has signs for all explanatory variables that are correct from the viewpoint of economics or marine biology; and (iii) provides a novel tool for objective performance evaluation of purchase managers.auctions;performance measurement;purchasing;supply chain

Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL

NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

Application of Market Models to Network Equilibrium Problems

We present a general two-side market model with divisible commodities and price functions of participants. A general existence result on unbounded sets is obtained from its variational inequality re-formulation. We describe an extension of the network flow equilibrium problem with elastic demands and a new equilibrium type model for resource allocation problems in wireless communication networks, which appear to be particular cases of the general market model. This enables us to obtain new existence results for these models as some adjustments of that for the market model. Under certain additional conditions the general market model can be reduced to a decomposable optimization problem where the goal function is the sum of two functions and one of them is convex separable, whereas the feasible set is the corresponding Cartesian product. We discuss some versions of the partial linearization method, which can be applied to these network equilibrium problems.Comment: 18 pages, 3 table

Demand functions in Polish Treasury auctions

I introduce a new approach to modeling aggregate bidding functions (demand functions) submitted by participants of share auctions, the one based on (scaled) normal cumulative distribution functions. I provide a simple model illustrating how normal cdf-shaped demand might arise. Then, using new data from the Polish Treasury securities auctions, I show first, that assumptions of the model underlying the normal cdf specification fit the stylized characteristics of the data set and, second, that this approach actually generates a slightly better fit than the traditional approximation by logistic function. I also relate the parameters of the fitted function to economic variables known prior to the auction. This method appears to be a useful tool for early detection of slumps in the performance of a particular auction design.Treasury auctions, normal cumulative distribution function, underpricing

What Can Laboratory Experiments Teach Us About Emissions Permit Market Design?

The laboratory provides a test bed to inform many design choices for emissions permit markets. Experiments are sometimes strongly motivated and structured by specific theoretical models and predictions, but in other cases the experiment itself can be the model of the market and regulatory environment. We review specific experimental applications that address design issues for permit auction rules, permit expiration dates and banking, liability rules, and regulatory enforcement.cap-and-trade, auctions, liability, regulation, compliance, banking, Environmental Economics and Policy, Institutional and Behavioral Economics,