Market Engineering

This open access book provides a broad range of insights on market engineering and information management. It covers topics like auctions, stock markets, electricity markets, the sharing economy, information and emotions in markets, smart decision-making in cities and other systems, and methodological approaches to conceptual modeling and taxonomy development. Overall, this book is a source of inspiration for everybody working on the vision of advancing the science of engineering markets and managing information for contributing to a bright, sustainable, digital world. Markets are powerful and extremely efficient mechanisms for coordinating individuals’ and organizations’ behavior in a complex, networked economy. Thus, designing, monitoring, and regulating markets is an essential task of today’s society. This task does not only derive from a purely economic point of view. Leveraging market forces can also help to tackle pressing social and environmental challenges. Moreover, markets process, generate, and reveal information. This information is a production factor and a valuable economic asset. In an increasingly digital world, it is more essential than ever to understand the life cycle of information from its creation and distribution to its use. Both markets and the flow of information should not arbitrarily emerge and develop based on individual, profit-driven actors. Instead, they should be engineered to serve best the whole society’s goals. This motivation drives the research fields of market engineering and information management. With this book, the editors and authors honor Professor Dr. Christof Weinhardt for his enormous and ongoing contribution to market engineering and information management research and practice. It was presented to him on the occasion of his sixtieth birthday in April 2021. Thank you very much, Christof, for so many years of cooperation, support, inspiration, and friendship

Reputation-based Strategies for the Evolution of Cooperative Behaviour

Cooperation between strangers can be difficult to explain. Several mechanisms have been shown to sustain cooperation among which one of the most general is Indirect Reciprocity. This describes how reputation-based social norms can distinguish between appropriate and inappropriate behaviours and sustain cooperation through the promise of future reciprocity from other members of a population. We present three experiments that investigate how a social norm’s ability to sustain cooperation is affected when: information flow is restricted to between neighbours, anyone can punish and anyone can be punished, and when people are capable of fine tuning their behaviour in response to their environment. Using simulations and a series of agent-based models, we find that - in the two-person prisoner's dilemma - restricting the flow of information and ensuring people learn from their neighbours, benefits the maintenance of good behaviour. In such scenarios, the best chances for cooperation occur when actions are judged harshly, ensuring that a good reputation once lost, is difficult to regain. For social norms to sustain cooperation in collective action problems, similar harshness is required through the ongoing threat of punishment. These situations can be highly cooperative if withdrawal from the social dilemma is possible and such behaviour is not judged to be morally worse than defection. However, if people are not able to punish badly behaving peers, then free-riding runs rampant unless the population considers defection to be worse than withdrawing from the social dilemma. We show that an improvement on this state of affairs, can be obtained when agents are able to fine-tune their behaviour when confronted with various reputational environments. Regardless of how actions are morally viewed, cooperation has a good chance if people can be sufficiently deliberate

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A study on the potential of reward-based crowdfunding in supporting sustainable entrepreneurship

The dissertation sets out to explore the often ignored role of the consumer (end-user) within sustainable innovation by examining the potential of reward-based crowdfunding in enabling sustainable entrepreneurship. It explores under which conditions and to what extent rewardbased crowdfunding could benefit entrepreneurs with social and/or environmentally-oriented products. The dissertation employs four articles in order to explore this. The first sets the stage by systematically reviewing the various roles that end-users can adopt within sustainable innovation process. The second serves to present a conceptual understanding of how the process of crowdfunding is organized. Finally papers three and four respectively present the dissertation’s empirical evidence. Paper three focuses on uncovering the distributive qualities of reward-based crowdfunding in terms of its ability to increase innovation finance access, while paper four introduces the experimental evidence on the role of individual and product details in shaping pledging behavior as it relates to a diversity of (un)sustainable campaigns

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

The drivers of Corporate Social Responsibility in the supply chain. A case study.

Purpose: The paper studies the way in which a SME integrates CSR into its corporate strategy, the practices it puts in place and how its CSR strategies reflect on its suppliers and customers relations. Methodology/Research limitations: A qualitative case study methodology is used. The use of a single case study limits the generalizing capacity of these findings. Findings: The entrepreneur’s ethical beliefs and value system play a fundamental role in shaping sustainable corporate strategy. Furthermore, the type of competitive strategy selected based on innovation, quality and responsibility clearly emerges both in terms of well defined management procedures and supply chain relations as a whole aimed at involving partners in the process of sustainable innovation. Originality/value: The paper presents a SME that has devised an original innovative business model. The study pivots on the issues of innovation and eco-sustainability in a context of drivers for CRS and business ethics. These values are considered fundamental at International level; the United Nations has declared 2011 the “International Year of Forestry”