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

We present a computational approach that autonomous software agents can adopt to make tactical decisions, such as product pricing, and strategic decisions, such as product mix and production planning, to maximize profit in markets with supply and demand uncertainties. Using a combination of machine learning and optimization techniques, the agent is able to characterize economic regimes, which are historical microeconomic conditions reflecting situations such as over-supply and scarcity. We assume an agent is capable of using real-time observable information to identify the current dominant market condition and we show how it can forecast regime changes over a planning horizon. We demonstrate how the agent can then use regime characterization to predict prices, price trends, and the probability of receiving a customer order in a dynamic supply chain environment. We validate our methods by presenting experimental results from a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM). The results show that our agent outperforms traditional short- and long-term predictive methodologies (such as exponential smoothing) significantly, resulting in accurate prediction of customer order probabilities, and competitive market prices. This, in turn, has the potential to produce higher profits. We also demonstrate the versatility of our computational approach by applying the methodology to prediction of stock price trends

Time Changes Everything: An Examination and Application of Time-Varying Coefficients in Information Systems Research

The Information Systems research field is inherently dynamic. New technologies, new standards, new legislation, and changing user expectations are some of the reasons why topics of interest to the IS field remain in flux. As researchers, we seek to uncover and explain relationships among variables, but due to the dynamism of IS phenomena, these relationships are apt to change over time. For example, the effect of informational features such as product diagnosticity or seller reputation on the price of an electronic commerce transaction is likely to change over time as users become more comfortable with online trading. This paper describes several statistical methods to model these changes in relationships. Specifically, we discuss methods to investigate time-varying coefficients in regression models, including rolling regression, “parameterizing” the coefficients using process functions, and testing for structural change. Importantly, we describe how the structure of many of the data sets used in IS research differs from that of data sets often used in other fields such as finance, economics, or marketing. This has implications for the investigation of time-based effects. We illustrate each method using a data set gathered from the wholesale automotive market, which not only helps us explain the methods, but also allows us to investigate the evolution of market practice in one empirical context. Thus, we address both methodological and substantive issues. Given that our field is inherently dynamic, an understanding of how effects change over time should be central to the overall IS research agenda. This paper is designed to familiarize IS researchers with methods available for this purpose

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

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

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management

Cryptocurrency and the Foreign Account Tax Compliance Act An empirical study on cryptocurrency as a method for tax evasion

The Foreign Account Compliance Act (FATCA) was passed into law in 2010, the objective was to reduce offshore tax evasion. We examine one avenue left open for U.S. based taxpayers, namely cryptocurrencies. We study the short-term effect of FATCA on bitcoin trading volume, making use of available trading data between different fiat currencies to bitcoin. We document a statistically significant increase in bitcoin trading volume with British pound after the endorsement of FATCA. We argue that this is indirect evidence showing U.S. based taxpayers avoiding the information exchange under FATCA.nhhma

Work stream on data:Final report

Online platforms are intermediaries in the digital economy that enable the exchange of goods, services or information between two or more parties. They facilitate matching and make trade more efficient. The mechanisms and strategies by which these digital intermediaries provide these efficiencies universally revolve around the use of technology that intensively and extensively builds on data. The way data is generated and shared becomes a critical issue in a context where online services are increasingly diversified. Such data is the subject of this report. Data generated through or in relation to online platforms fosters innovation. Data plays an increasingly important role in business intelligence, product development, and process optimization. Data has become a new currency at times where many online services are provided for “free”, fuelled by the data provided by their users. Data is also the basis for competition and further innovation. While a number of national, EU and international reports clearly recognise the importance of data for the online platform economy, they rarely highlight the complexity and heterogeneity of data in the platform environment. This report provides a structured overview of how data is generated, collected and used in the online platform economy. It maps out the diversity and heterogeneity of data-related practices and expands on what different types of data require a careful examination in order to better understand their importance for both the platforms and their users as well as the issues and challenges arising in their interactions