-------- Forwarded Message --------
*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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