[EGOV LIST] Call For Papers: DGO 2024 "Computational Methods for Data-driven Governance" Track

[Charalampos Alexopoulos]

Dear colleagues,

We created a new track for the DGO 2024 <https://dgsociety.org/dgo-2024/>.
If you are working on data and algorithms using AI, ML, big data, data
science, and etc, please consider sending your paper to this track. If you
have any questions about this track, please feel free to contact any of the
track chairs listed below. Hope to see many of you in Taiwan next year!

*Title of the track:* Computational Methods for Data-driven Governance
*Track chairs: *Loni Hagen, Charalampos Alexopoulos, David Valle-Cruz,
Shefali Virkar, and Kellyton Brito
*Submission Deadline: *January 26, 2024

The exponential growth in the amount of data generated, collected, stored
and processed in a digitalized world compels us to take a fresh look at how
information is accessed and used by governments, and at the computer
systems that facilitate these processes. Data-driven Governance corresponds
to a new way of accessing, retrieving and analysing appropriate and/or
relevant information towards more effective and efficient policy and
decision making. This involves the application of a wide range of
computational methods in order to provide meaningful insights, assess the
relative significance of alternative solutions against a set of values or
preferences and finally support decision and policy making. Due to the
complexity of the process, papers adopting computational methods require
computational expertise that is often not taught to behavioral scholars.

This track is for scholars whose interests are in developing or adopting
computational methods. Having this track can benefit both computational
scholars and behavioral scholars. On the one hand, computational scholars
can receive meaningful feedback from scholars with computational expertise.
On the other hand, behavioral scholars can provide feedback to enhance
theoretical contribution of method-oriented papers and get familiarized in
computational methods.

Papers selected for this track will advance knowledge, research, and
practice in government service provision, developing prototypes of
technical platforms, government interaction with the public, and innovative
decision-making from methods development. Topics and methodologies covered
by this track may include:

   - Use of design science research to develop and evaluate artifacts
   including new metrics and novel methods for the measurement and evaluation
   or the examination of user aspects such as interaction, contextualization,
   personalization and behavior.
   - Development of technologies for advanced Decision Support enhancing
   government decision-making through data science approaches and big data
   analytics
   - Artificial Intelligence and machine learning approaches for innovative
   government service development or administrative function.
   - Computational methods (Information retrieval and text mining) for
   better information quality
   - Computational methods for better decision making under conditions of
   uncertainty.
   - Technical, political, social, economic aspects of Decision Support
   Systems development
   - Applications development and evaluation such as chatbots,
   conversational agents, and digital virtual assistants, recommender systems,
   web and social media apps, professional and domain-specific search etc.
   - Open government data infrastructures for decision making.
   - Linked Data Analytics and Management including semantic data
   interoperability and ontological approaches.


Best regards,
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*Charalampos (Harris) Alexopoulos*
Assistant Professor of Open Data
Department of Information and Communication Systems Engineering
<http://www.icsd.aegean.gr/>
University of the Aegean <https://www1.aegean.gr/aegean2/index.html> | 2
Palama St., Karlovassi, GR-83200, Samos, Greece
_______________________________________________________________________
(mob): +30 697 2425 051 | (e-mail): alexop at aegean.gr
<xalexopoulos at gmail.com> | (skype/twitter): xalexopoulos
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