-------- Forwarded Message -------- Subject: [AISWorld] CFP -- HICSS 55 Minitrack: Data Models and Digital Twins for the Digital Economy: Real, Augmented, and Synthetic Data in Decision Support Systems Date: Fri, 21 May 2021 13:01:34 +0000 From: Roßmann, Alexander Alexander.Rossmann@Reutlingen-University.DE To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org

*Call for Papers*

*2022 Hawaii International Conference on System Sciences (HICSS-55)*

*Minitrack: Data Models and Digital Twins for the Digital Economy: Real, Augmented, and Synthetic Data in Decision Support Systems*

Digital transformation is an ongoing, long-term and disruptive change process based on the continuous progress of information technology. It is reasonable to assume that the advent of digital transformation is changing the way the economy works on a macro and micro level. Therefore, the theory and practice of economics needs to shift to digital economics with a strong emphasis on the impact of information technologies on eco-systems, enterprises, households and other relevant stakeholder groups. An important approach to research on digital economics is related to models and data. Relevant research problems in the real world encompass a large number of variables, dynamic interdependences, high complexity, ambiguity, and multiple options. Therefore, models are used to understand real-world processes and to explore the impact of decision options. Usually, such models are linked to or tested with empirical data. Approaches for data modeling cover probabilistic and deterministic approache s from different sub-fields, such as econometrics, machine learning, structural equation modeling and system dynamics. Such data models are often used as digital twins for real-life problems and deployed as a decision support system. An important limitation of the application of data models and digital twins is the absence, incompleteness or reliabilty of data. Therefore, real-world data must be captured and approved by multiple procedures (e.g. surveys, census, sensors). Real-world data may also be complemented by augmented data or substituted by synthetic data. This leads to the notion of augmented or synthetic data twins and the possibility of rapid prototyping, simulations and shorter time-to-market cycles for model-based decision support systems.

This minitrack aims to explore these issues, paying particular attention to the challenges of data model development, data model evaluation, augmented or synthetic data models and digital twins. It also focuses on the application of data models in different domains of a digital economy.

Topics of interest include, but are not limited to:

- Impact of information technology on economic theory and practice - Data models for decision support systems - Application of data-driven models for the digital economy in different domains and application areas (e.g. eco-systems, enterprises, households, comsumers) - Data twins as images of real-world decision problems - Procedures for the creation or implementation of augmented or synthetic data, implementation of data incubators - Simulation of real-world processes with (real, augmented, synthetic) data models - Application of data and models for rapid prototyping - Application of machine learning for the development of data models - Econometrics, system dynamics, structural equation modeling, machine learing and other relevant approachs to develop and evaluate data models

*Submission deadline:* June 15, 2021 (11:59 pm HST)

*Website:* https://bit.ly/3rka0FY

*Minitrack co-chairs:* Alexander Rossmann alexander.rossmann@reutlingen-university.de, Reutlingen University, Germany Dieter Hertweck dieter.hertweck@reutlingen-university.de, Reutlingen University, Germany

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