Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

HOW PERFECT ARE MARKETS FOR SOFTWARE SERVICES? AN ECONOMIC PERSPECTIVE ON MARKET DEFICIENCIES AND DESIRABLE MARKET FEATURES

Global service markets, which efficiently coordinate the supply of services with the demand, are a cornerstone for the breakthrough of service-oriented computing (SOC). With the increasing popularity of SOC, forecasts hence predicted that service marketplaces would rapidly evolve and work profitable. Despite such promising prospects, only a few marketplaces were able to establish themselves until now, however. Trying to explain this situation, we analyzed leading service marketplaces like Salesforce’s AppExchange or Google’s Apps Marketplace from an economic perspective. Based on the theory of perfect markets with perfect competition, we describe several characteristics of service markets that cause market deficiencies. To adapt to the special characteristics of service markets, agents have to adjust their business strategies accordingly. While current literature primarily focuses on providing strategies for providers and consumers, marketplace operators as essential intermedi-aries are barely considered. We therefore derive desirable market features that can be integrated into the business strategies of marketplace operators and summarize them in a conceptual architecture of a model service marketplace. As a validation, we conducted a series of semi-structured interviews with SOC experts, who corroborated most of our findings and attested their practical relevance

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

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

Models for Bundle Trading in Financial Markets

Bundle trading is a new trend in financial markets that allows traders to submit consolidated orders to sell and buy packages of assets. We propose a new formulation for portfolio bundle trading that extends the previous models of the literature through a more detailed representation of portfolios and the formulation of new bidding requirements. We also present post-optimality tie-breaking procedures intended to discriminate equivalent orders on the basis of their submission times. Numerical results evaluate the "bundle" effect as well as the bidding flexibility and the computational complexity of our formulation. Une nouvelle tendance dans les marchés financiers consiste à transiger des valeurs financières sous forme d'ordres composites d'achat et de vente. Nous proposons une nouvelle formulation basée sur les ordres composites du problème d'allocation de valeurs financières. Notre modèle, comparativement à ceux de la littérature, permet une représentation plus détaillée des portefeuilles financiers et la formulation de nouvelles contraintes transactionnelles. Nous présentons en outre une procédure de discrimination d'ordres équivalents sur la base de leur temps de soumission. Les résultats numériques de notre étude permettent d'évaluer empiriquement l'effet « ordres composites », ainsi que la flexibilité et la complexité numérique de notre formulation.Auction Design, Financial Markets, Bundle Trading, Discrimination Procedures, Mécanisme d'enchères, marchés financiers, ordres composites, procédures de discrimination

Lemon Jelly : spreading lemon through the internet

Lemon Jelly is a fashion brand that produces and sells plastic footwear. The brand belongs to the company Procalçado, a family-owned Portuguese company that already had an experience of 40 years as a soles and footwear components producer before creating Lemon Jelly. The company is characterized by innovative production processes that allow them to produce plastic injected shoes. Lemon Jelly is mostly being sold through distributors, however it also has an online presence. The brand’s official website represents from 5 to 10% of the brand’s sales, which were up to 3 million euros in 2015. The motivation for this dissertation was to write a case study about the e-commerce strategy of Lemon Jelly. The main challenge of the brand is to understand how it can increase its online sales, being this analyzed in the Teaching Note chapter that explores some issues relevant to the main problem of the case and proposes recommendations for the future. The previous chapter, Literature Review, was designed to support the Teaching Note through theoretical concepts. It is concluded that there are several factors that can influence a brand’s online sales, therefore Lemon Jelly should select some of them according to its needs, while monitoring the results at the same time. It is also important to benchmark competitors and other players in the industry in order to keep updated through the digital evolution.A Lemon Jelly é uma marca de moda que produz e vende sapatos de plástico. A marca pertence à Procalçado, uma empresa familiar Portuguesa que já contava com 40 anos de experiência enquanto produtora de solas e componentes de sapatos antes de criar a Lemon Jelly. A empresa é caraterizada por produzir através de processos inovadores, o que lhe permite criar calçado de plástico injetado. A Lemon Jelly é vendida principalmente através de distribuidores, no entanto também está presente online. O website oficial da marca representa entre 5 a 10% das suas vendas totais, que atingiram 3 milhões de euros em 2015. A motivação para esta dissertação passou por escrever um caso de estudo sobre a estratégia de comércio eletrónico da Lemon Jelly. O principal desafio da marca é perceber como pode aumentar as suas vendas online, e para isto é feita uma análise no capítulo da nota de ensino, que explora algumas questões relevantes para o principal problema do caso e onde são propostas recomendações para o futuro. O capítulo anterior, de revisão de literatura, destina-se a apoiar a nota de ensino através de conceitos teóricos. Conclui-se que há vários fatores que podem influenciar as vendas online de uma marca, pelo que a Lemon Jelly deve selecioná-los consoante as suas necessidades, controlando ao mesmo tempo os resultados obtidos. É também importante efetuar comparações com concorrentes e outras empresas da indústria para a marca se manter atualizada perante a evolução digital

Scalability and robustness of a market-based network resource allocation system

In this paper, we consider issues related to scalability and robustness in designing a market-based multi-agent system that allocates bandwidth in a communications network. Specifically, an empirical evaluation is carried out to assess the system performance under a variety of design configurations in order to provide an insight into network deployment issues. This extends our previous work in which we developed an application that makes use of market-based software agents that compete in decentralised marketplaces to buy and sell bandwidth resources. Our new results show that given a light to moderate network traffic load, partitioning the network into a few regions, each with a separate market server, gives a higher call success rate than by using a single market. Moreover, a trade-off in the number of regions was also noted between the average call success rate and the number of messages received per market server. Finally, given the possibility of market failures, we observe that the average call success rates increase with an increasing number of markets until a maximum is reached

Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data

Widespread e-commerce activity on the Internet has led to new opportunities to collect vast amounts of micro-level market and nonmarket data. In this paper we share our experiences in collecting, validating, storing and analyzing large Internet-based data sets in the area of online auctions, music file sharing and online retailer pricing. We demonstrate how such data can advance knowledge by facilitating sharper and more extensive tests of existing theories and by offering observational underpinnings for the development of new theories. Just as experimental economics pushed the frontiers of economic thought by enabling the testing of numerous theories of economic behavior in the environment of a controlled laboratory, we believe that observing, often over extended periods of time, real-world agents participating in market and nonmarket activity on the Internet can lead us to develop and test a variety of new theories. Internet data gathering is not controlled experimentation. We cannot randomly assign participants to treatments or determine event orderings. Internet data gathering does offer potentially large data sets with repeated observation of individual choices and action. In addition, the automated data collection holds promise for greatly reduced cost per observation. Our methods rely on technological advances in automated data collection agents. Significant challenges remain in developing appropriate sampling techniques integrating data from heterogeneous sources in a variety of formats, constructing generalizable processes and understanding legal constraints. Despite these challenges, the early evidence from those who have harvested and analyzed large amounts of e-commerce data points toward a significant leap in our ability to understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org