Market Engineering: An Interdisciplinary Research Challenge

Market engineering is making markets work. Markets are information processing and information producing information systems which mediate allocation of resources within or between organizations. Setting up and operating a market in a way that it works effectively and efficiently is an art and a science. This paper outlines challenges in this interdisciplinary field of research and presents frameworks for assessing markets

The blockchain, plums, and lemons: Information asymmetries & transparency in decentralized markets

Despite a growing interest, researchers and practitioners still struggle to transfer the blockchain concept introduced by Bitcoin to market-oriented application scenarios. To shed light on the technology\u27s usage in markets with asymmetric information, this study analyzes the eect of the blockchain\u27s public transparency paradigm on behavioral patterns and market outcomes. In line with prior research, our ndings indicate that the blockchain\u27s shared record mitigates adverse selection eects and reduces moral hazard of good market participants (plums). In addition, we identify an incentive for bad market participants (lemons) to behave opportunistically in the presence of perfect quality information. More specically, the disclosed information allows them to learn about quality dierences between plums and lemons, deceive their counterparties, and move to a new equilibrium with increased utility. As a result, the market collapses despite a welfare gain and future generations are denied market access. In addition, plums and lemons are committed to inecient equilibria following irrational behavior. In total, this study aims to provide initial guidance for blockchain adoption in the context of markets with information asymmetries and highlights risks that arise from competition, the exposure to irrational behavior, and the implementation of services on the infrastructure level

Identifying Experts in Virtual Forecasting Communities

Macroeconomic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest in the scientific world and in industry. An arising question is how to detect valuable user input and identify experts in such online communities. Detecting such input would possibly enable us to improve the information aggregation mechanism and the forecast performance of such systems. We design a prediction market for economic derivatives that aggregates macro-economic information. Using market-based measures we find that user input can be evaluated ad-hoc. Further analysis shows that aggregated measures outperform established methods -such as reputation- in identifying forecasting experts. Moreover, using data from a two year field-experiment we find that expertise is stable for longer time horizons

TOWARDS AN EFFICIENT DECISION POLICY FOR CLOUD SERVICE PROVIDERS

Cloud service providers may face the problem of how to price infrastructure services and how this pricing may impact the resource utilization. One aspect of this problem is how Cloud service providers would decide to accept or reject requests for services when the resources for offering these services become scarce. A decision support policy called Customized Bid-Price Policy (CBPP) is proposed in this paper to decide efficiently, when a large number of services or complex services can be offered over a finite time horizon. This heuristic outperforms well-known policies, if bid prices cannot be updated frequently during incoming requests and an automated update of bid prices is required to achieve more accurate decisions. Since CBPP approximates the revenue offline before the requests occur, it has a low runtime compared to other approaches during the online phase. The performance is examined via simulation and the pre-eminence of CBPP is statistically proven

Towards an Economic Analysis of Routing in Payment Channel Networks

Payment channel networks are supposed to overcome technical scalability limitations of blockchain infrastructure by employing a special overlay network with fast payment confirmation and only sporadic settlement of netted transactions on the blockchain. However, they introduce economic routing constraints that limit decentralized scalability and are currently not well understood. In this paper, we model the economic incentives for participants in payment channel networks. We provide the first formal model of payment channel economics and analyze how the cheapest path can be found. Additionally, our simulation assesses the long-term evolution of a payment channel network. We find that even for small routing fees, sometimes it is cheaper to settle the transaction directly on the blockchain.Comment: 6 pages, 3 figures, SERIAL '17 Worksho

Requirements and Design Principles for Blockchain-enabled Matchmaking-Marketplaces in Additive Manufacturing

Blockchain-enabled marketplaces offer considerable potential for cross-company networks. The area of additive manufacturing appears particularly promising. However, the practical impact of business-to-business marketplaces in today’s organizations are still scarce, and academic literature contains limited design guidelines. Synthesizing knowledge from literature, practice, and qualitative expert interviews, our study explores 27 mandatory requirements, six optional requirements, and 12 design principles

Decision Support for Electric Vehicle Charging

Many hopes lie on the successful introduction of electric vehicles (EV): reduction of transportation-related emissions, reduced dependence on oil imports, electricity storage provision for the grid, or improved integration of renewable energy sources. Meeting these goals will not only require a significant number of EVs on the streets, but it will also require intelligent decision making with respect to their charging schedules. Through dynamic rates or local energy trading smart grids incentivize load flexibility required for taking advantage of renewable generation availability. For EVs to respond to these incentives intelligent charging protocols are required. These protocols should aim to minimize electricity costs and/or emissions while at the same time securing the customers’ driving requirements. We describe and characterize the relevant problems and solution concepts on how to achieve smart charging behavior. Currently discussed smart charging concepts are not directly applicable for practical decision support system. To address this shortcoming we develop relaxed and heuristic optimization approaches. We evaluate these solutions approaches using simulations based on empirical mobility and electricity price data

Ensuring Energy Affordability through Digital Technology: A Research Model and Intervention Design

In order to ensure energy affordability, we propose a design-oriented behavioral research study with the aim of helping low-income tenants to develop an efficient energy behavior by increasing their energy self-efficacy. We propose to compare different digital interventions in field tests to understand, in an unfiltered way, what helps low-income tenants to be able to reduce their energy costs. We thereby contribute towards understanding how the vulnerable group of low-income tenants with their limitations and needs regarding their energy consumption behavior can be effectively supported digitally. In addition, we contribute initial measurement instruments for energy worries, energy literacy and energy self-efficacy to evaluate the effects of digital interventions