---
_id: '62885'
author:
- first_name: Malin
full_name: Osnabrügge, Malin
id: '79748'
last_name: Osnabrügge
- first_name: Claudia
full_name: Tenberge, Claudia
id: '67302'
last_name: Tenberge
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: Osnabrügge M, Tenberge C, Fechner S. Artificial Intelligence in primary science
and technology education with a focus on implementation of AI in learning context
– Results of a Scoping Review.
apa: Osnabrügge, M., Tenberge, C., & Fechner, S. (n.d.). Artificial Intelligence
in primary science and technology education with a focus on implementation of
AI in learning context – Results of a Scoping Review. Pupils’ Attitudes Towards
Technology (PATT), Norrköping, Sweden.
bibtex: '@inproceedings{Osnabrügge_Tenberge_Fechner, title={Artificial Intelligence
in primary science and technology education with a focus on implementation of
AI in learning context – Results of a Scoping Review}, author={Osnabrügge, Malin
and Tenberge, Claudia and Fechner, Sabine} }'
chicago: Osnabrügge, Malin, Claudia Tenberge, and Sabine Fechner. “Artificial Intelligence
in Primary Science and Technology Education with a Focus on Implementation of
AI in Learning Context – Results of a Scoping Review,” n.d.
ieee: M. Osnabrügge, C. Tenberge, and S. Fechner, “Artificial Intelligence in primary
science and technology education with a focus on implementation of AI in learning
context – Results of a Scoping Review,” presented at the Pupils’ Attitudes Towards
Technology (PATT), Norrköping, Sweden.
mla: Osnabrügge, Malin, et al. Artificial Intelligence in Primary Science and
Technology Education with a Focus on Implementation of AI in Learning Context
– Results of a Scoping Review.
short: 'M. Osnabrügge, C. Tenberge, S. Fechner, in: n.d.'
conference:
end_date: 2026-06-18
location: Norrköping, Sweden
name: Pupils' Attitudes Towards Technology (PATT)
start_date: 2026-06-15
date_created: 2025-12-04T14:12:38Z
date_updated: 2025-12-13T23:56:03Z
department:
- _id: '386'
- _id: '588'
- _id: '33'
keyword:
- Artificial intelligence
- primary education
- science and technology education
language:
- iso: eng
publication_status: draft
quality_controlled: '1'
status: public
title: Artificial Intelligence in primary science and technology education with a
focus on implementation of AI in learning context – Results of a Scoping Review
type: conference
user_id: '54823'
year: '2026'
...
---
_id: '58076'
abstract:
- lang: eng
text: This paper presents the concept of Information Circularity Assistance, which
provides decision support in the early stages of product creation for Circular
Economy. Engineers in strategic product planning need to proactively predict the
quantity, quality, and timing of secondary materials and returned components.
For example, products with high recycled content will only be economically sustainable
if the material is actually available in the future product life. Our assumption
is that Information Circularity Assistance enables decision makers to incorporate
insights from extreme data – high-volume, high-velocity, heterogeneous and distributed
data from the product life – into product creation through intelligent Digital
Twins. Artificial Intelligence can help to derive sustainable actions in favor
of circular products by processing extreme data and enriching it with expert knowledge.
The research contributes in three key dimensions. First, a comprehensive literature
review is conducted. This review covers concepts of intelligence in Scenario-Technique
for strategic product planning, Digital Twin-based analysis of extreme data and
relevant technologies from Data Science and Artificial Intelligence. In all areas,
the state of the art and emerging trends are identified. Secondly, the study identifies
information needs along the steps of the Scenario-Technique and information offerings
based on Digital Twins. The concept of Information Circularity Assistance results
from the coupling of these demands and offerings, extending the Scenario-Technique
beyond traditional expert-based methods. Third, we extend existing Digital Twin
methods used in circularity and discuss the deployment of Data Science and Artificial
Intelligence algorithms within the product creation process. Our approach uses
extreme data to provide a strategic advantage in optimizing product life cycle
planning, which is illustrated by two sample applications. The aim is to provide
Information Circularity Assistance that will support experienced product planners,
developers, and decision makers in the future.
alternative_title:
- Utilizing Artificial Intelligence, Scenario-Technique and Digital Twins to solve
challenges of product creation for Circular Economy
article_type: original
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Michael
full_name: Weyrich, Michael
last_name: Weyrich
- first_name: Jens
full_name: Pottebaum, Jens
id: '405'
last_name: Pottebaum
orcid: http://orcid.org/0000-0001-8778-2989
- first_name: Simon
full_name: Kamm, Simon
last_name: Kamm
citation:
ama: Gräßler I, Weyrich M, Pottebaum J, Kamm S. Information Circularity Assistance
based on extreme data. at - Automatisierungstechnik. 2025;73(1):3-21. doi:10.1515/auto-2024-0039
apa: Gräßler, I., Weyrich, M., Pottebaum, J., & Kamm, S. (2025). Information
Circularity Assistance based on extreme data. At - Automatisierungstechnik,
73(1), 3–21. https://doi.org/10.1515/auto-2024-0039
bibtex: '@article{Gräßler_Weyrich_Pottebaum_Kamm_2025, title={Information Circularity
Assistance based on extreme data}, volume={73}, DOI={10.1515/auto-2024-0039},
number={1}, journal={at - Automatisierungstechnik}, publisher={Walter de Gruyter
GmbH}, author={Gräßler, Iris and Weyrich, Michael and Pottebaum, Jens and Kamm,
Simon}, year={2025}, pages={3–21} }'
chicago: 'Gräßler, Iris, Michael Weyrich, Jens Pottebaum, and Simon Kamm. “Information
Circularity Assistance Based on Extreme Data.” At - Automatisierungstechnik
73, no. 1 (2025): 3–21. https://doi.org/10.1515/auto-2024-0039.'
ieee: 'I. Gräßler, M. Weyrich, J. Pottebaum, and S. Kamm, “Information Circularity
Assistance based on extreme data,” at - Automatisierungstechnik, vol. 73,
no. 1, pp. 3–21, 2025, doi: 10.1515/auto-2024-0039.'
mla: Gräßler, Iris, et al. “Information Circularity Assistance Based on Extreme
Data.” At - Automatisierungstechnik, vol. 73, no. 1, Walter de Gruyter
GmbH, 2025, pp. 3–21, doi:10.1515/auto-2024-0039.
short: I. Gräßler, M. Weyrich, J. Pottebaum, S. Kamm, At - Automatisierungstechnik
73 (2025) 3–21.
date_created: 2025-01-07T13:30:45Z
date_updated: 2025-02-15T09:41:54Z
department:
- _id: '152'
doi: 10.1515/auto-2024-0039
intvolume: ' 73'
issue: '1'
keyword:
- Scenario-Technique
- Artificial Intelligence
- Digital Twin
- Large Language Models
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
page: 3-21
publication: at - Automatisierungstechnik
publication_identifier:
issn:
- 0178-2312
- 2196-677X
publication_status: published
publisher: Walter de Gruyter GmbH
quality_controlled: '1'
status: public
title: Information Circularity Assistance based on extreme data
type: journal_article
user_id: '405'
volume: 73
year: '2025'
...
---
_id: '58650'
abstract:
- lang: eng
text: 'Technical systems are characterized by increasing interdisciplinarity, complexity
and networking. A product and its corresponding production systems require interdisciplinary
multi-objective optimization. Sustainability and recyclability demands increase
said complexity. The efficiency of previously established engineering methods
is reaching its limits, which can only be overcome by systematic integration of
extreme data. The aim of "hybrid decision support" is as follows: Data science
and artificial intelligence should be used to supplement human capabilities in
conjunction with existing heuristics, methods, modeling and simulation to increase
the efficiency of product creation.'
alternative_title:
- Hybride Entscheidungsunterstützung in der Produktentstehung - Mit Data Science und
Künstlicher Intelligenz die Leistungsfähigkeit erhöhen
article_type: original
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Jens
full_name: Pottebaum, Jens
id: '405'
last_name: Pottebaum
orcid: http://orcid.org/0000-0001-8778-2989
- first_name: Peter
full_name: Nyhuis, Peter
last_name: Nyhuis
- first_name: Rainer
full_name: Stark, Rainer
last_name: Stark
- first_name: Klaus-Dieter
full_name: Thoben, Klaus-Dieter
last_name: Thoben
- first_name: Petra
full_name: Wiederkehr, Petra
last_name: Wiederkehr
citation:
ama: Gräßler I, Pottebaum J, Nyhuis P, Stark R, Thoben K-D, Wiederkehr P. Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence. Industry 40 Science. 2025;2025(1). doi:10.30844/i4sd.25.1.18
apa: Gräßler, I., Pottebaum, J., Nyhuis, P., Stark, R., Thoben, K.-D., & Wiederkehr,
P. (2025). Hybrid Decision Support in Product Creation - Improving performance
with data science and artificial intelligence. Industry 4.0 Science, 2025(1).
https://doi.org/10.30844/i4sd.25.1.18
bibtex: '@article{Gräßler_Pottebaum_Nyhuis_Stark_Thoben_Wiederkehr_2025, title={Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence}, volume={2025}, DOI={10.30844/i4sd.25.1.18},
number={1}, journal={Industry 4.0 Science}, publisher={GITO mbH Verlag}, author={Gräßler,
Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter
and Wiederkehr, Petra}, year={2025} }'
chicago: Gräßler, Iris, Jens Pottebaum, Peter Nyhuis, Rainer Stark, Klaus-Dieter
Thoben, and Petra Wiederkehr. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science
2025, no. 1 (2025). https://doi.org/10.30844/i4sd.25.1.18.
ieee: 'I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, and P. Wiederkehr,
“Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence,” Industry 4.0 Science, vol. 2025,
no. 1, 2025, doi: 10.30844/i4sd.25.1.18.'
mla: Gräßler, Iris, et al. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science,
vol. 2025, no. 1, GITO mbH Verlag, 2025, doi:10.30844/i4sd.25.1.18.
short: I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, P. Wiederkehr,
Industry 4.0 Science 2025 (2025).
date_created: 2025-02-15T09:31:30Z
date_updated: 2025-02-15T09:40:52Z
department:
- _id: '152'
doi: 10.30844/i4sd.25.1.18
intvolume: ' 2025'
issue: '1'
keyword:
- AI
- artificial intelligence
- Data Science
- decision support
- extreme data
- Künstliche Intelligenz
- product creation
- product development
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Industry 4.0 Science
publication_identifier:
issn:
- 2942-6170
publication_status: published
publisher: GITO mbH Verlag
quality_controlled: '1'
status: public
title: Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence
type: journal_article
user_id: '405'
volume: 2025
year: '2025'
...
---
_id: '62920'
author:
- first_name: Marvin Lee
full_name: Fox, Marvin Lee
id: '80773'
last_name: Fox
- first_name: Hendrik
full_name: Peeters, Hendrik
id: '49942'
last_name: Peeters
orcid: https://orcid.org/ 0000-0002-7143-3781
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: 'Fox ML, Peeters H, Fechner S. KI-Einsatz durch Lernende im Erkenntnisgewinnungsprozess
- ein Review. In: GDCP Jahrestagung. ; 2025.'
apa: Fox, M. L., Peeters, H., & Fechner, S. (2025). KI-Einsatz durch Lernende
im Erkenntnisgewinnungsprozess - ein Review. GDCP Jahrestagung. GDCP Jahrestagung,
Frankfurt.
bibtex: '@inproceedings{Fox_Peeters_Fechner_2025, title={KI-Einsatz durch Lernende
im Erkenntnisgewinnungsprozess - ein Review}, booktitle={GDCP Jahrestagung}, author={Fox,
Marvin Lee and Peeters, Hendrik and Fechner, Sabine}, year={2025} }'
chicago: Fox, Marvin Lee, Hendrik Peeters, and Sabine Fechner. “KI-Einsatz Durch
Lernende Im Erkenntnisgewinnungsprozess - Ein Review.” In GDCP Jahrestagung,
2025.
ieee: M. L. Fox, H. Peeters, and S. Fechner, “KI-Einsatz durch Lernende im Erkenntnisgewinnungsprozess
- ein Review,” presented at the GDCP Jahrestagung, Frankfurt, 2025.
mla: Fox, Marvin Lee, et al. “KI-Einsatz Durch Lernende Im Erkenntnisgewinnungsprozess
- Ein Review.” GDCP Jahrestagung, 2025.
short: 'M.L. Fox, H. Peeters, S. Fechner, in: GDCP Jahrestagung, 2025.'
conference:
end_date: 2025-09-11
location: Frankfurt
name: GDCP Jahrestagung
start_date: 2025-09-08
date_created: 2025-12-05T12:53:09Z
date_updated: 2025-12-05T13:05:59Z
department:
- _id: '386'
- _id: '33'
keyword:
- Artificial intelligence
- education
- chemistry
language:
- iso: eng
publication: GDCP Jahrestagung
status: public
title: KI-Einsatz durch Lernende im Erkenntnisgewinnungsprozess - ein Review
type: conference_abstract
user_id: '54823'
year: '2025'
...
---
_id: '63053'
author:
- first_name: Carlos
full_name: Hernández, Carlos
last_name: Hernández
- first_name: Angel E.
full_name: Rodriguez-Fernandez, Angel E.
last_name: Rodriguez-Fernandez
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Oliver
full_name: Cuate, Oliver
last_name: Cuate
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
citation:
ama: Hernández C, Rodriguez-Fernandez AE, Schäpermeier L, Cuate O, Trautmann H,
Schütze O. An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization. IEEE Transactions on Evolutionary
Computation. Published online 2025:1-1. doi:10.1109/TEVC.2025.3637276
apa: Hernández, C., Rodriguez-Fernandez, A. E., Schäpermeier, L., Cuate, O., Trautmann,
H., & Schütze, O. (2025). An Evolutionary Approach for the Computation of
∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization. IEEE
Transactions on Evolutionary Computation, 1–1. https://doi.org/10.1109/TEVC.2025.3637276
bibtex: '@article{Hernández_Rodriguez-Fernandez_Schäpermeier_Cuate_Trautmann_Schütze_2025,
title={An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization}, DOI={10.1109/TEVC.2025.3637276},
journal={IEEE Transactions on Evolutionary Computation}, author={Hernández, Carlos
and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver
and Trautmann, Heike and Schütze, Oliver}, year={2025}, pages={1–1} }'
chicago: Hernández, Carlos, Angel E. Rodriguez-Fernandez, Lennart Schäpermeier,
Oliver Cuate, Heike Trautmann, and Oliver Schütze. “An Evolutionary Approach for
the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal
Optimization.” IEEE Transactions on Evolutionary Computation, 2025, 1–1.
https://doi.org/10.1109/TEVC.2025.3637276.
ieee: 'C. Hernández, A. E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann,
and O. Schütze, “An Evolutionary Approach for the Computation of ∈-Locally Optimal
Solutions for Multi-Objective Multimodal Optimization,” IEEE Transactions on
Evolutionary Computation, pp. 1–1, 2025, doi: 10.1109/TEVC.2025.3637276.'
mla: Hernández, Carlos, et al. “An Evolutionary Approach for the Computation of
∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization.” IEEE
Transactions on Evolutionary Computation, 2025, pp. 1–1, doi:10.1109/TEVC.2025.3637276.
short: C. Hernández, A.E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann,
O. Schütze, IEEE Transactions on Evolutionary Computation (2025) 1–1.
date_created: 2025-12-12T06:13:06Z
date_updated: 2025-12-12T06:13:51Z
department:
- _id: '819'
doi: 10.1109/TEVC.2025.3637276
keyword:
- Optimization
- Evolutionary computation
- Hands
- Proposals
- Convergence
- Computational efficiency
- Artificial intelligence
- Accuracy
- Approximation algorithms
- Aerospace electronics
- Multi-objective optimization
- evolutionary algorithms
- nearly optimal solutions
- multimodal optimization
- archiving
- continuation
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization
type: journal_article
user_id: '15504'
year: '2025'
...
---
_id: '62921'
author:
- first_name: Marvin Lee
full_name: Fox, Marvin Lee
id: '80773'
last_name: Fox
- first_name: Hendrik
full_name: Peeters, Hendrik
id: '49942'
last_name: Peeters
orcid: https://orcid.org/ 0000-0002-7143-3781
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: 'Fox ML, Peeters H, Fechner S. How can students be supported by ChatGPT as
a tutor in hands-on chemistry education? In: Conference of The European Science
Education Research Association (ESERA). ; 2025.'
apa: Fox, M. L., Peeters, H., & Fechner, S. (2025). How can students be supported
by ChatGPT as a tutor in hands-on chemistry education? Conference of The European
Science Education Research Association (ESERA). ESERA conference, Copenhagen,
Denmark.
bibtex: '@inproceedings{Fox_Peeters_Fechner_2025, title={How can students be supported
by ChatGPT as a tutor in hands-on chemistry education?}, booktitle={Conference
of The European Science Education Research Association (ESERA)}, author={Fox,
Marvin Lee and Peeters, Hendrik and Fechner, Sabine}, year={2025} }'
chicago: Fox, Marvin Lee, Hendrik Peeters, and Sabine Fechner. “How Can Students
Be Supported by ChatGPT as a Tutor in Hands-on Chemistry Education?” In Conference
of The European Science Education Research Association (ESERA), 2025.
ieee: M. L. Fox, H. Peeters, and S. Fechner, “How can students be supported by ChatGPT
as a tutor in hands-on chemistry education?,” presented at the ESERA conference,
Copenhagen, Denmark, 2025.
mla: Fox, Marvin Lee, et al. “How Can Students Be Supported by ChatGPT as a Tutor
in Hands-on Chemistry Education?” Conference of The European Science Education
Research Association (ESERA), 2025.
short: 'M.L. Fox, H. Peeters, S. Fechner, in: Conference of The European Science
Education Research Association (ESERA), 2025.'
conference:
end_date: 2025-09-29
location: Copenhagen, Denmark
name: ESERA conference
start_date: 2025-09-25
date_created: 2025-12-05T12:57:51Z
date_updated: 2025-12-13T23:54:59Z
department:
- _id: '386'
- _id: '33'
keyword:
- Artificial intelligence
- education
- chemistry
language:
- iso: eng
publication: Conference of The European Science Education Research Association (ESERA)
quality_controlled: '1'
status: public
title: How can students be supported by ChatGPT as a tutor in hands-on chemistry education?
type: conference_abstract
user_id: '54823'
year: '2025'
...
---
_id: '53213'
article_number: '101027'
author:
- first_name: Arman
full_name: Amiri, Arman
last_name: Amiri
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Hosein
full_name: Arman, Hosein
last_name: Arman
citation:
ama: Amiri A, Tavana M, Arman H. An Integrated Fuzzy Analytic Network Process and
Fuzzy Regression Method for Bitcoin Price Prediction. Internet of Things.
2024;25. doi:10.1016/j.iot.2023.101027
apa: Amiri, A., Tavana, M., & Arman, H. (2024). An Integrated Fuzzy Analytic
Network Process and Fuzzy Regression Method for Bitcoin Price Prediction. Internet
of Things, 25, Article 101027. https://doi.org/10.1016/j.iot.2023.101027
bibtex: '@article{Amiri_Tavana_Arman_2024, title={An Integrated Fuzzy Analytic Network
Process and Fuzzy Regression Method for Bitcoin Price Prediction}, volume={25},
DOI={10.1016/j.iot.2023.101027},
number={101027}, journal={Internet of Things}, publisher={Elsevier BV}, author={Amiri,
Arman and Tavana, Madjid and Arman, Hosein}, year={2024} }'
chicago: Amiri, Arman, Madjid Tavana, and Hosein Arman. “An Integrated Fuzzy Analytic
Network Process and Fuzzy Regression Method for Bitcoin Price Prediction.” Internet
of Things 25 (2024). https://doi.org/10.1016/j.iot.2023.101027.
ieee: 'A. Amiri, M. Tavana, and H. Arman, “An Integrated Fuzzy Analytic Network
Process and Fuzzy Regression Method for Bitcoin Price Prediction,” Internet
of Things, vol. 25, Art. no. 101027, 2024, doi: 10.1016/j.iot.2023.101027.'
mla: Amiri, Arman, et al. “An Integrated Fuzzy Analytic Network Process and Fuzzy
Regression Method for Bitcoin Price Prediction.” Internet of Things, vol.
25, 101027, Elsevier BV, 2024, doi:10.1016/j.iot.2023.101027.
short: A. Amiri, M. Tavana, H. Arman, Internet of Things 25 (2024).
date_created: 2024-04-04T13:34:26Z
date_updated: 2024-04-15T13:08:17Z
department:
- _id: '277'
doi: 10.1016/j.iot.2023.101027
intvolume: ' 25'
keyword:
- Management of Technology and Innovation
- Artificial Intelligence
- Computer Science Applications
- Hardware and Architecture
- Engineering (miscellaneous)
- Information Systems
- Computer Science (miscellaneous)
- Software
language:
- iso: eng
publication: Internet of Things
publication_identifier:
issn:
- 2542-6605
publication_status: published
publisher: Elsevier BV
status: public
title: An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for
Bitcoin Price Prediction
type: journal_article
user_id: '51811'
volume: 25
year: '2024'
...
---
_id: '56166'
abstract:
- lang: eng
text: Developing Intelligent Technical Systems (ITS) involves a complex process
encompassing planning, analysis, design, production, and maintenance. Model-Based
Systems Engineering (MBSE) is a key methodology for systematic systems engineering.
Designing models for ITS requires harmonious interaction of various elements,
posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we
generated a dataset for modeling, using prompt engineering on large language models.
The generated artifacts can aid engineers in MBSE design or serve as synthetic
training data for AI assistants.
author:
- first_name: Pranav Jayant
full_name: Kulkarni, Pranav Jayant
id: '86782'
last_name: Kulkarni
- first_name: Denis
full_name: Tissen, Denis
id: '44458'
last_name: Tissen
- first_name: Ruslan
full_name: Bernijazov, Ruslan
id: '36312'
last_name: Bernijazov
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Kulkarni PJ, Tissen D, Bernijazov R, Dumitrescu R. Towards Automated Design:
Automatically Generating Modeling Elements with Prompt Engineering and Generative
Artificial Intelligence. In: Malmqvist J, Candi M, Saemundsson R, Bystrom F, Isaksson
O, eds. DS 130: Proceedings of NordDesign 2024. ; 2024:617-625. doi:10.35199/NORDDESIGN2024.66'
apa: 'Kulkarni, P. J., Tissen, D., Bernijazov, R., & Dumitrescu, R. (2024).
Towards Automated Design: Automatically Generating Modeling Elements with Prompt
Engineering and Generative Artificial Intelligence. In J. Malmqvist, M. Candi,
R. Saemundsson, F. Bystrom, & O. Isaksson (Eds.), DS 130: Proceedings of
NordDesign 2024 (pp. 617–625). https://doi.org/10.35199/NORDDESIGN2024.66'
bibtex: '@inproceedings{Kulkarni_Tissen_Bernijazov_Dumitrescu_2024, title={Towards
Automated Design: Automatically Generating Modeling Elements with Prompt Engineering
and Generative Artificial Intelligence}, DOI={10.35199/NORDDESIGN2024.66},
booktitle={DS 130: Proceedings of NordDesign 2024}, author={Kulkarni, Pranav Jayant
and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}, editor={Malmqvist,
J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}, year={2024},
pages={617–625} }'
chicago: 'Kulkarni, Pranav Jayant, Denis Tissen, Ruslan Bernijazov, and Roman Dumitrescu.
“Towards Automated Design: Automatically Generating Modeling Elements with Prompt
Engineering and Generative Artificial Intelligence.” In DS 130: Proceedings
of NordDesign 2024, edited by J. Malmqvist, M. Candi, R. Saemundsson, F. Bystrom,
and O. Isaksson, 617–25, 2024. https://doi.org/10.35199/NORDDESIGN2024.66.'
ieee: 'P. J. Kulkarni, D. Tissen, R. Bernijazov, and R. Dumitrescu, “Towards Automated
Design: Automatically Generating Modeling Elements with Prompt Engineering and
Generative Artificial Intelligence,” in DS 130: Proceedings of NordDesign 2024,
Reykjavik, 2024, pp. 617–625, doi: 10.35199/NORDDESIGN2024.66.'
mla: 'Kulkarni, Pranav Jayant, et al. “Towards Automated Design: Automatically Generating
Modeling Elements with Prompt Engineering and Generative Artificial Intelligence.”
DS 130: Proceedings of NordDesign 2024, edited by J. Malmqvist et al.,
2024, pp. 617–25, doi:10.35199/NORDDESIGN2024.66.'
short: 'P.J. Kulkarni, D. Tissen, R. Bernijazov, R. Dumitrescu, in: J. Malmqvist,
M. Candi, R. Saemundsson, F. Bystrom, O. Isaksson (Eds.), DS 130: Proceedings
of NordDesign 2024, 2024, pp. 617–625.'
conference:
end_date: 2024-08-14
location: Reykjavik
name: NordDesign Conference 2024
start_date: 2024-08-12
date_created: 2024-09-17T09:56:43Z
date_updated: 2024-09-17T09:57:07Z
doi: 10.35199/NORDDESIGN2024.66
editor:
- first_name: J.
full_name: Malmqvist, J.
last_name: Malmqvist
- first_name: M.
full_name: Candi, M.
last_name: Candi
- first_name: R.
full_name: Saemundsson, R.
last_name: Saemundsson
- first_name: F.
full_name: Bystrom, F.
last_name: Bystrom
- first_name: O.
full_name: Isaksson, O.
last_name: Isaksson
keyword:
- Data Driven Design
- Design Automation
- Systems Engineering (SE)
- Artificial Intelligence (AI)
language:
- iso: eng
page: 617-625
publication: 'DS 130: Proceedings of NordDesign 2024'
publication_identifier:
unknown:
- 978-1-912254-21-7
publication_status: epub_ahead
related_material:
link:
- relation: confirmation
url: https://www.designsociety.org/publication/47658/Towards+Automated+Design%3A+Automatically+Generating+Modeling+Elements+with+Prompt+Engineering+and+Generative+Artificial+Intelligence
status: public
title: 'Towards Automated Design: Automatically Generating Modeling Elements with
Prompt Engineering and Generative Artificial Intelligence'
type: conference
user_id: '86782'
year: '2024'
...
---
_id: '53073'
abstract:
- lang: eng
text: While shallow decision trees may be interpretable, larger ensemble models
like gradient-boosted trees, which often set the state of the art in machine learning
problems involving tabular data, still remain black box models. As a remedy, the
Shapley value (SV) is a well-known concept in explainable artificial intelligence
(XAI) research for quantifying additive feature attributions of predictions. The
model-specific TreeSHAP methodology solves the exponential complexity for retrieving
exact SVs from tree-based models. Expanding beyond individual feature attribution,
Shapley interactions reveal the impact of intricate feature interactions of any
order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order
additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ
is supported by a mathematical framework that exploits polynomial arithmetic to
compute the interaction scores in a single recursive traversal of the tree, akin
to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and
explore interactions on well-established benchmark datasets.
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: 'Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient
Computation of Any-Order Shapley Interactions for Tree Ensembles. In: Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI). Vol 38. ; 2024:14388-14396.
doi:10.1609/aaai.v38i13.29352'
apa: 'Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond
TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 38(13),
14388–14396. https://doi.org/10.1609/aaai.v38i13.29352'
bibtex: '@inproceedings{Muschalik_Fumagalli_Hammer_Huellermeier_2024, title={Beyond
TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles},
volume={38}, DOI={10.1609/aaai.v38i13.29352},
number={13}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara
and Huellermeier, Eyke}, year={2024}, pages={14388–14396} }'
chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier.
“Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for
Tree Ensembles.” In Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), 38:14388–96, 2024. https://doi.org/10.1609/aaai.v38i13.29352.'
ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP:
Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” in
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024,
vol. 38, no. 13, pp. 14388–14396, doi: 10.1609/aaai.v38i13.29352.'
mla: 'Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order
Shapley Interactions for Tree Ensembles.” Proceedings of the AAAI Conference
on Artificial Intelligence (AAAI), vol. 38, no. 13, 2024, pp. 14388–96, doi:10.1609/aaai.v38i13.29352.'
short: 'M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 14388–14396.'
date_created: 2024-03-27T14:50:04Z
date_updated: 2025-09-11T16:20:11Z
department:
- _id: '660'
doi: 10.1609/aaai.v38i13.29352
intvolume: ' 38'
issue: '13'
keyword:
- Explainable Artificial Intelligence
language:
- iso: eng
page: 14388-14396
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
publication: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
publication_identifier:
issn:
- 2374-3468
- 2159-5399
publication_status: published
status: public
title: 'Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for
Tree Ensembles'
type: conference
user_id: '93420'
volume: 38
year: '2024'
...
---
_id: '48290'
abstract:
- lang: eng
text: AbstractIdentifying, classifying, and analyzing
arguments in legal discourse has been a prominent area of research since the inception
of the argument mining field. However, there has been a major discrepancy between
the way natural language processing (NLP) researchers model and annotate arguments
in court decisions and the way legal experts understand and analyze legal argumentation.
While computational approaches typically simplify arguments into generic premises
and claims, arguments in legal research usually exhibit a rich typology that is
important for gaining insights into the particular case and applications of law
in general. We address this problem and make several substantial contributions
to move the field forward. First, we design a new annotation scheme for legal
arguments in proceedings of the European Court of Human Rights (ECHR) that is
deeply rooted in the theory and practice of legal argumentation research. Second,
we compile and annotate a large corpus of 373 court decisions (2.3M tokens and
15k annotated argument spans). Finally, we train an argument mining model that
outperforms state-of-the-art models in the legal NLP domain and provide a thorough
expert-based evaluation. All datasets and source codes are available under open
lincenses at https://github.com/trusthlt/mining-legal-arguments.
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Daniel
full_name: Faber, Daniel
last_name: Faber
- first_name: Nicola
full_name: Recchia, Nicola
last_name: Recchia
- first_name: Sebastian
full_name: Bretthauer, Sebastian
last_name: Bretthauer
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
- first_name: Indra
full_name: Spiecker genannt Döhmann, Indra
last_name: Spiecker genannt Döhmann
- first_name: Christoph
full_name: Burchard, Christoph
last_name: Burchard
citation:
ama: Habernal I, Faber D, Recchia N, et al. Mining legal arguments in court decisions.
Artificial Intelligence and Law. Published online 2023. doi:10.1007/s10506-023-09361-y
apa: Habernal, I., Faber, D., Recchia, N., Bretthauer, S., Gurevych, I., Spiecker
genannt Döhmann, I., & Burchard, C. (2023). Mining legal arguments in court
decisions. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-023-09361-y
bibtex: '@article{Habernal_Faber_Recchia_Bretthauer_Gurevych_Spiecker genannt Döhmann_Burchard_2023,
title={Mining legal arguments in court decisions}, DOI={10.1007/s10506-023-09361-y},
journal={Artificial Intelligence and Law}, publisher={Springer Science and Business
Media LLC}, author={Habernal, Ivan and Faber, Daniel and Recchia, Nicola and Bretthauer,
Sebastian and Gurevych, Iryna and Spiecker genannt Döhmann, Indra and Burchard,
Christoph}, year={2023} }'
chicago: Habernal, Ivan, Daniel Faber, Nicola Recchia, Sebastian Bretthauer, Iryna
Gurevych, Indra Spiecker genannt Döhmann, and Christoph Burchard. “Mining Legal
Arguments in Court Decisions.” Artificial Intelligence and Law, 2023. https://doi.org/10.1007/s10506-023-09361-y.
ieee: 'I. Habernal et al., “Mining legal arguments in court decisions,” Artificial
Intelligence and Law, 2023, doi: 10.1007/s10506-023-09361-y.'
mla: Habernal, Ivan, et al. “Mining Legal Arguments in Court Decisions.” Artificial
Intelligence and Law, Springer Science and Business Media LLC, 2023, doi:10.1007/s10506-023-09361-y.
short: I. Habernal, D. Faber, N. Recchia, S. Bretthauer, I. Gurevych, I. Spiecker
genannt Döhmann, C. Burchard, Artificial Intelligence and Law (2023).
date_created: 2023-10-19T08:23:39Z
date_updated: 2023-10-19T12:10:02Z
department:
- _id: '34'
- _id: '820'
doi: 10.1007/s10506-023-09361-y
keyword:
- Law
- Artificial Intelligence
language:
- iso: eng
publication: Artificial Intelligence and Law
publication_identifier:
issn:
- 0924-8463
- 1572-8382
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Mining legal arguments in court decisions
type: journal_article
user_id: '15504'
year: '2023'
...
---
_id: '48777'
abstract:
- lang: eng
text: AbstractExplainable artificial intelligence
has mainly focused on static learning scenarios so far. We are interested in dynamic
scenarios where data is sampled progressively, and learning is done in an incremental
rather than a batch mode. We seek efficient incremental algorithms for computing
feature importance (FI). Permutation feature importance (PFI) is a well-established
model-agnostic measure to obtain global FI based on feature marginalization of
absent features. We propose an efficient, model-agnostic algorithm called iPFI
to estimate this measure incrementally and under dynamic modeling conditions including
concept drift. We prove theoretical guarantees on the approximation quality in
terms of expectation and variance. To validate our theoretical findings and the
efficacy of our approaches in incremental scenarios dealing with streaming data
rather than traditional batch settings, we conduct multiple experimental studies
on benchmark data with and without concept drift.
author:
- first_name: Fabian
full_name: Fumagalli, Fabian
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. Incremental permutation
feature importance (iPFI): towards online explanations on data streams. Machine
Learning. Published online 2023. doi:10.1007/s10994-023-06385-y'
apa: 'Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental
permutation feature importance (iPFI): towards online explanations on data streams.
Machine Learning. https://doi.org/10.1007/s10994-023-06385-y'
bibtex: '@article{Fumagalli_Muschalik_Hüllermeier_Hammer_2023, title={Incremental
permutation feature importance (iPFI): towards online explanations on data streams},
DOI={10.1007/s10994-023-06385-y},
journal={Machine Learning}, publisher={Springer Science and Business Media LLC},
author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and
Hammer, Barbara}, year={2023} }'
chicago: 'Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, and Barbara
Hammer. “Incremental Permutation Feature Importance (IPFI): Towards Online Explanations
on Data Streams.” Machine Learning, 2023. https://doi.org/10.1007/s10994-023-06385-y.'
ieee: 'F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “Incremental permutation
feature importance (iPFI): towards online explanations on data streams,” Machine
Learning, 2023, doi: 10.1007/s10994-023-06385-y.'
mla: 'Fumagalli, Fabian, et al. “Incremental Permutation Feature Importance (IPFI):
Towards Online Explanations on Data Streams.” Machine Learning, Springer
Science and Business Media LLC, 2023, doi:10.1007/s10994-023-06385-y.'
short: F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, Machine Learning (2023).
date_created: 2023-11-10T14:15:36Z
date_updated: 2023-11-10T14:24:27Z
department:
- _id: '424'
- _id: '660'
doi: 10.1007/s10994-023-06385-y
keyword:
- Artificial Intelligence
- Software
language:
- iso: eng
publication: Machine Learning
publication_identifier:
issn:
- 0885-6125
- 1573-0565
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: 'Incremental permutation feature importance (iPFI): towards online explanations
on data streams'
type: journal_article
user_id: '55908'
year: '2023'
...
---
_id: '44639'
article_number: '100258'
author:
- first_name: Julia Amelie
full_name: Hoppe, Julia Amelie
id: '73093'
last_name: Hoppe
- first_name: Outi
full_name: Tuisku, Outi
last_name: Tuisku
- first_name: Rose-Marie
full_name: Johansson-Pajala, Rose-Marie
last_name: Johansson-Pajala
- first_name: Satu
full_name: Pekkarinen, Satu
last_name: Pekkarinen
- first_name: Lea
full_name: Hennala, Lea
last_name: Hennala
- first_name: Christine
full_name: Gustafsson, Christine
last_name: Gustafsson
- first_name: Helinä
full_name: Melkas, Helinä
last_name: Melkas
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
citation:
ama: Hoppe JA, Tuisku O, Johansson-Pajala R-M, et al. When do individuals choose
care robots over a human caregiver? Insights from a laboratory experiment on choices
under uncertainty. Computers in Human Behavior Reports. 2023;9. doi:10.1016/j.chbr.2022.100258
apa: Hoppe, J. A., Tuisku, O., Johansson-Pajala, R.-M., Pekkarinen, S., Hennala,
L., Gustafsson, C., Melkas, H., & Thommes, K. (2023). When do individuals
choose care robots over a human caregiver? Insights from a laboratory experiment
on choices under uncertainty. Computers in Human Behavior Reports, 9,
Article 100258. https://doi.org/10.1016/j.chbr.2022.100258
bibtex: '@article{Hoppe_Tuisku_Johansson-Pajala_Pekkarinen_Hennala_Gustafsson_Melkas_Thommes_2023,
title={When do individuals choose care robots over a human caregiver? Insights
from a laboratory experiment on choices under uncertainty}, volume={9}, DOI={10.1016/j.chbr.2022.100258},
number={100258}, journal={Computers in Human Behavior Reports}, publisher={Elsevier
BV}, author={Hoppe, Julia Amelie and Tuisku, Outi and Johansson-Pajala, Rose-Marie
and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä
and Thommes, Kirsten}, year={2023} }'
chicago: Hoppe, Julia Amelie, Outi Tuisku, Rose-Marie Johansson-Pajala, Satu Pekkarinen,
Lea Hennala, Christine Gustafsson, Helinä Melkas, and Kirsten Thommes. “When Do
Individuals Choose Care Robots over a Human Caregiver? Insights from a Laboratory
Experiment on Choices under Uncertainty.” Computers in Human Behavior Reports
9 (2023). https://doi.org/10.1016/j.chbr.2022.100258.
ieee: 'J. A. Hoppe et al., “When do individuals choose care robots over a
human caregiver? Insights from a laboratory experiment on choices under uncertainty,”
Computers in Human Behavior Reports, vol. 9, Art. no. 100258, 2023, doi:
10.1016/j.chbr.2022.100258.'
mla: Hoppe, Julia Amelie, et al. “When Do Individuals Choose Care Robots over a
Human Caregiver? Insights from a Laboratory Experiment on Choices under Uncertainty.”
Computers in Human Behavior Reports, vol. 9, 100258, Elsevier BV, 2023,
doi:10.1016/j.chbr.2022.100258.
short: J.A. Hoppe, O. Tuisku, R.-M. Johansson-Pajala, S. Pekkarinen, L. Hennala,
C. Gustafsson, H. Melkas, K. Thommes, Computers in Human Behavior Reports 9 (2023).
date_created: 2023-05-08T12:29:18Z
date_updated: 2023-12-06T09:16:42Z
department:
- _id: '178'
- _id: '184'
doi: 10.1016/j.chbr.2022.100258
intvolume: ' 9'
keyword:
- Artificial Intelligence
- Cognitive Neuroscience
- Computer Science Applications
- Human-Computer Interaction
- Applied Psychology
- Neuroscience (miscellaneous)
language:
- iso: eng
project:
- _id: '46'
grant_number: 16SV7954
name: 'ORIENT: Use of care robots in welfare services: New models for effective
orientation'
publication: Computers in Human Behavior Reports
publication_identifier:
issn:
- 2451-9588
publication_status: published
publisher: Elsevier BV
status: public
title: When do individuals choose care robots over a human caregiver? Insights from
a laboratory experiment on choices under uncertainty
type: journal_article
user_id: '42933'
volume: 9
year: '2023'
...
---
_id: '49516'
abstract:
- lang: eng
text: In this article, we present RISE—a Robotics
Integration and Scenario-Management
Extensible-Architecture—for designing human–robot dialogs
and conducting Human–Robot Interaction (HRI) studies.
In current HRI research, interdisciplinarity in the creation and implementation
of interaction studies is becoming increasingly important. In addition, there
is a lack of reproducibility of the research results. With the presented open-source
architecture, we aim to address these two topics. Therefore, we discuss the advantages
and disadvantages of various existing tools from different sub-fields within robotics.
Requirements for an architecture can be derived from this overview of the literature,
which 1) supports interdisciplinary research, 2) allows reproducibility of the
research, and 3) is accessible to other researchers in the field of HRI. With
our architecture, we tackle these requirements by providing a Graphical
User Interface which explains the robot behavior and allows introspection
into the current state of the dialog. Additionally, it offers controlling possibilities
to easily conduct Wizard of Oz studies. To achieve
transparency, the dialog is modeled explicitly, and the robot behavior can be
configured. Furthermore, the modular architecture offers an interface for external
features and sensors and is expandable to new robots and modalities.
article_type: original
author:
- first_name: André
full_name: Groß, André
last_name: Groß
- first_name: Christian
full_name: Schütze, Christian
last_name: Schütze
- first_name: Mara
full_name: Brandt, Mara
last_name: Brandt
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
- first_name: Birte
full_name: Richter, Birte
last_name: Richter
citation:
ama: 'Groß A, Schütze C, Brandt M, Wrede B, Richter B. RISE: an open-source architecture
for interdisciplinary and reproducible human–robot interaction research. Frontiers
in Robotics and AI. 2023;10. doi:10.3389/frobt.2023.1245501'
apa: 'Groß, A., Schütze, C., Brandt, M., Wrede, B., & Richter, B. (2023). RISE:
an open-source architecture for interdisciplinary and reproducible human–robot
interaction research. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1245501'
bibtex: '@article{Groß_Schütze_Brandt_Wrede_Richter_2023, title={RISE: an open-source
architecture for interdisciplinary and reproducible human–robot interaction research},
volume={10}, DOI={10.3389/frobt.2023.1245501},
journal={Frontiers in Robotics and AI}, publisher={Frontiers Media SA}, author={Groß,
André and Schütze, Christian and Brandt, Mara and Wrede, Britta and Richter, Birte},
year={2023} }'
chicago: 'Groß, André, Christian Schütze, Mara Brandt, Britta Wrede, and Birte Richter.
“RISE: An Open-Source Architecture for Interdisciplinary and Reproducible Human–Robot
Interaction Research.” Frontiers in Robotics and AI 10 (2023). https://doi.org/10.3389/frobt.2023.1245501.'
ieee: 'A. Groß, C. Schütze, M. Brandt, B. Wrede, and B. Richter, “RISE: an open-source
architecture for interdisciplinary and reproducible human–robot interaction research,”
Frontiers in Robotics and AI, vol. 10, 2023, doi: 10.3389/frobt.2023.1245501.'
mla: 'Groß, André, et al. “RISE: An Open-Source Architecture for Interdisciplinary
and Reproducible Human–Robot Interaction Research.” Frontiers in Robotics and
AI, vol. 10, Frontiers Media SA, 2023, doi:10.3389/frobt.2023.1245501.'
short: A. Groß, C. Schütze, M. Brandt, B. Wrede, B. Richter, Frontiers in Robotics
and AI 10 (2023).
date_created: 2023-12-07T09:17:09Z
date_updated: 2023-12-07T12:09:41Z
ddc:
- '000'
doi: 10.3389/frobt.2023.1245501
file:
- access_level: closed
content_type: application/pdf
creator: angross
date_created: 2023-12-07T09:18:55Z
date_updated: 2023-12-07T09:18:55Z
file_id: '49517'
file_name: frobt-10-1245501.pdf
file_size: 40679118
relation: main_file
success: 1
file_date_updated: 2023-12-07T09:18:55Z
has_accepted_license: '1'
intvolume: ' 10'
keyword:
- Artificial Intelligence
- Computer Science Applications
language:
- iso: eng
project:
- _id: '109'
grant_number: '438445824'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '113'
name: 'TRR 318 - A3: TRR 318 - Subproject A3'
- _id: '115'
grant_number: '438445824'
name: 'TRR 318 - A05: TRR 318 - Echtzeitmessung der Aufmerksamkeit im Mensch-Roboter-Erklärdialog
(Teilprojekt A05)'
publication: Frontiers in Robotics and AI
publication_identifier:
issn:
- 2296-9144
publication_status: published
publisher: Frontiers Media SA
status: public
title: 'RISE: an open-source architecture for interdisciplinary and reproducible human–robot
interaction research'
type: journal_article
user_id: '93405'
volume: 10
year: '2023'
...
---
_id: '50121'
abstract:
- lang: eng
text: Many researchers and practitioners see artificial intelligence as a game changer
compared to classical statistical models. However, some software providers engage
in “AI washing”, relabeling solutions that use simple statistical models as AI
systems. By contrast, research on algorithm aversion unsystematically varied the
labels for advisors and treated labels such as "artificial intelligence" and "statistical
model" synonymously. This study investigates the effect of individual labels on
users' actual advice utilization behavior. Through two incentivized online within-subjects
experiments on regression tasks, we find that labeling human advisors with labels
that suggest higher expertise leads to an increase in advice-taking, even though
the content of the advice remains the same. In contrast, our results do not suggest
such an expert effect for advice-taking from algorithms, despite differences in
self-reported perception. These findings challenge the effectiveness of framing
intelligent systems as AI-based systems and have important implications for both
research and practice.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
citation:
ama: 'Leffrang D. AI Washing: The Framing Effect of Labels on Algorithmic Advice
Utilization. In: International Conference on Information Systems. ; 2023.'
apa: 'Leffrang, D. (2023). AI Washing: The Framing Effect of Labels on Algorithmic
Advice Utilization. International Conference on Information Systems, 10.'
bibtex: '@inproceedings{Leffrang_2023, title={AI Washing: The Framing Effect of
Labels on Algorithmic Advice Utilization}, number={10}, booktitle={International
Conference on Information Systems}, author={Leffrang, Dirk}, year={2023} }'
chicago: 'Leffrang, Dirk. “AI Washing: The Framing Effect of Labels on Algorithmic
Advice Utilization.” In International Conference on Information Systems,
2023.'
ieee: 'D. Leffrang, “AI Washing: The Framing Effect of Labels on Algorithmic Advice
Utilization,” in International Conference on Information Systems, Hyderabad,
India, 2023, no. 10.'
mla: 'Leffrang, Dirk. “AI Washing: The Framing Effect of Labels on Algorithmic Advice
Utilization.” International Conference on Information Systems, no. 10,
2023.'
short: 'D. Leffrang, in: International Conference on Information Systems, 2023.'
conference:
location: Hyderabad, India
name: International Conference on Information Systems (ICIS)
date_created: 2024-01-03T09:54:00Z
date_updated: 2024-01-10T09:53:41Z
department:
- _id: '196'
issue: '10'
keyword:
- Artificial Intelligence
- Algorithm Appreciation
- Framing
- Advice-taking
- Expertise
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/10
publication: International Conference on Information Systems
status: public
title: 'AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization'
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '53301'
article_number: '120986'
author:
- first_name: Solveig
full_name: Vieluf, Solveig
last_name: Vieluf
- first_name: Tanuj
full_name: Hasija, Tanuj
id: '43497'
last_name: Hasija
- first_name: Maurice
full_name: Kuschel, Maurice
id: '56070'
last_name: Kuschel
- first_name: Claus
full_name: Reinsberger, Claus
id: '48978'
last_name: Reinsberger
- first_name: Tobias
full_name: Loddenkemper, Tobias
last_name: Loddenkemper
citation:
ama: Vieluf S, Hasija T, Kuschel M, Reinsberger C, Loddenkemper T. Developing a
deep canonical correlation-based technique for seizure prediction. Expert Systems
with Applications. 2023;234. doi:10.1016/j.eswa.2023.120986
apa: Vieluf, S., Hasija, T., Kuschel, M., Reinsberger, C., & Loddenkemper, T.
(2023). Developing a deep canonical correlation-based technique for seizure prediction.
Expert Systems with Applications, 234, Article 120986. https://doi.org/10.1016/j.eswa.2023.120986
bibtex: '@article{Vieluf_Hasija_Kuschel_Reinsberger_Loddenkemper_2023, title={Developing
a deep canonical correlation-based technique for seizure prediction}, volume={234},
DOI={10.1016/j.eswa.2023.120986},
number={120986}, journal={Expert Systems with Applications}, publisher={Elsevier
BV}, author={Vieluf, Solveig and Hasija, Tanuj and Kuschel, Maurice and Reinsberger,
Claus and Loddenkemper, Tobias}, year={2023} }'
chicago: Vieluf, Solveig, Tanuj Hasija, Maurice Kuschel, Claus Reinsberger, and
Tobias Loddenkemper. “Developing a Deep Canonical Correlation-Based Technique
for Seizure Prediction.” Expert Systems with Applications 234 (2023). https://doi.org/10.1016/j.eswa.2023.120986.
ieee: 'S. Vieluf, T. Hasija, M. Kuschel, C. Reinsberger, and T. Loddenkemper, “Developing
a deep canonical correlation-based technique for seizure prediction,” Expert
Systems with Applications, vol. 234, Art. no. 120986, 2023, doi: 10.1016/j.eswa.2023.120986.'
mla: Vieluf, Solveig, et al. “Developing a Deep Canonical Correlation-Based Technique
for Seizure Prediction.” Expert Systems with Applications, vol. 234, 120986,
Elsevier BV, 2023, doi:10.1016/j.eswa.2023.120986.
short: S. Vieluf, T. Hasija, M. Kuschel, C. Reinsberger, T. Loddenkemper, Expert
Systems with Applications 234 (2023).
date_created: 2024-04-05T14:37:06Z
date_updated: 2024-04-05T14:49:56Z
department:
- _id: '263'
doi: 10.1016/j.eswa.2023.120986
intvolume: ' 234'
keyword:
- Artificial Intelligence
- Computer Science Applications
- General Engineering
language:
- iso: eng
publication: Expert Systems with Applications
publication_identifier:
issn:
- 0957-4174
publication_status: published
publisher: Elsevier BV
status: public
title: Developing a deep canonical correlation-based technique for seizure prediction
type: journal_article
user_id: '56070'
volume: 234
year: '2023'
...
---
_id: '53220'
article_number: '100786'
author:
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Arash
full_name: Khalili Nasr, Arash
last_name: Khalili Nasr
- first_name: Alireza Barati
full_name: Ahmadabadi, Alireza Barati
last_name: Ahmadabadi
- first_name: Alireza Shamekhi
full_name: Amiri, Alireza Shamekhi
last_name: Amiri
- first_name: Hassan
full_name: Mina, Hassan
last_name: Mina
citation:
ama: Tavana M, Khalili Nasr A, Ahmadabadi AB, Amiri AS, Mina H. An interval multi-criteria
decision-making model for evaluating blockchain-IoT technology in supply chain
networks. Internet of Things. 2023;22. doi:10.1016/j.iot.2023.100786
apa: Tavana, M., Khalili Nasr, A., Ahmadabadi, A. B., Amiri, A. S., & Mina,
H. (2023). An interval multi-criteria decision-making model for evaluating blockchain-IoT
technology in supply chain networks. Internet of Things, 22, Article
100786. https://doi.org/10.1016/j.iot.2023.100786
bibtex: '@article{Tavana_Khalili Nasr_Ahmadabadi_Amiri_Mina_2023, title={An interval
multi-criteria decision-making model for evaluating blockchain-IoT technology
in supply chain networks}, volume={22}, DOI={10.1016/j.iot.2023.100786},
number={100786}, journal={Internet of Things}, publisher={Elsevier BV}, author={Tavana,
Madjid and Khalili Nasr, Arash and Ahmadabadi, Alireza Barati and Amiri, Alireza
Shamekhi and Mina, Hassan}, year={2023} }'
chicago: Tavana, Madjid, Arash Khalili Nasr, Alireza Barati Ahmadabadi, Alireza
Shamekhi Amiri, and Hassan Mina. “An Interval Multi-Criteria Decision-Making Model
for Evaluating Blockchain-IoT Technology in Supply Chain Networks.” Internet
of Things 22 (2023). https://doi.org/10.1016/j.iot.2023.100786.
ieee: 'M. Tavana, A. Khalili Nasr, A. B. Ahmadabadi, A. S. Amiri, and H. Mina, “An
interval multi-criteria decision-making model for evaluating blockchain-IoT technology
in supply chain networks,” Internet of Things, vol. 22, Art. no. 100786,
2023, doi: 10.1016/j.iot.2023.100786.'
mla: Tavana, Madjid, et al. “An Interval Multi-Criteria Decision-Making Model for
Evaluating Blockchain-IoT Technology in Supply Chain Networks.” Internet of
Things, vol. 22, 100786, Elsevier BV, 2023, doi:10.1016/j.iot.2023.100786.
short: M. Tavana, A. Khalili Nasr, A.B. Ahmadabadi, A.S. Amiri, H. Mina, Internet
of Things 22 (2023).
date_created: 2024-04-04T13:49:53Z
date_updated: 2024-04-15T13:10:41Z
department:
- _id: '277'
doi: 10.1016/j.iot.2023.100786
intvolume: ' 22'
keyword:
- Management of Technology and Innovation
- Artificial Intelligence
- Computer Science Applications
- Hardware and Architecture
- Engineering (miscellaneous)
- Information Systems
- Computer Science (miscellaneous)
- Software
language:
- iso: eng
publication: Internet of Things
publication_identifier:
issn:
- 2542-6605
publication_status: published
publisher: Elsevier BV
status: public
title: An interval multi-criteria decision-making model for evaluating blockchain-IoT
technology in supply chain networks
type: journal_article
user_id: '51811'
volume: 22
year: '2023'
...
---
_id: '53218'
article_number: '119902'
author:
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Mehdi
full_name: Soltanifar, Mehdi
last_name: Soltanifar
- first_name: Francisco J.
full_name: Santos-Arteaga, Francisco J.
last_name: Santos-Arteaga
- first_name: Hamid
full_name: Sharafi, Hamid
last_name: Sharafi
citation:
ama: 'Tavana M, Soltanifar M, Santos-Arteaga FJ, Sharafi H. Analytic hierarchy process
and data envelopment analysis: A match made in heaven. Expert Systems with
Applications. 2023;223. doi:10.1016/j.eswa.2023.119902'
apa: 'Tavana, M., Soltanifar, M., Santos-Arteaga, F. J., & Sharafi, H. (2023).
Analytic hierarchy process and data envelopment analysis: A match made in heaven.
Expert Systems with Applications, 223, Article 119902. https://doi.org/10.1016/j.eswa.2023.119902'
bibtex: '@article{Tavana_Soltanifar_Santos-Arteaga_Sharafi_2023, title={Analytic
hierarchy process and data envelopment analysis: A match made in heaven}, volume={223},
DOI={10.1016/j.eswa.2023.119902},
number={119902}, journal={Expert Systems with Applications}, publisher={Elsevier
BV}, author={Tavana, Madjid and Soltanifar, Mehdi and Santos-Arteaga, Francisco
J. and Sharafi, Hamid}, year={2023} }'
chicago: 'Tavana, Madjid, Mehdi Soltanifar, Francisco J. Santos-Arteaga, and Hamid
Sharafi. “Analytic Hierarchy Process and Data Envelopment Analysis: A Match Made
in Heaven.” Expert Systems with Applications 223 (2023). https://doi.org/10.1016/j.eswa.2023.119902.'
ieee: 'M. Tavana, M. Soltanifar, F. J. Santos-Arteaga, and H. Sharafi, “Analytic
hierarchy process and data envelopment analysis: A match made in heaven,” Expert
Systems with Applications, vol. 223, Art. no. 119902, 2023, doi: 10.1016/j.eswa.2023.119902.'
mla: 'Tavana, Madjid, et al. “Analytic Hierarchy Process and Data Envelopment Analysis:
A Match Made in Heaven.” Expert Systems with Applications, vol. 223, 119902,
Elsevier BV, 2023, doi:10.1016/j.eswa.2023.119902.'
short: M. Tavana, M. Soltanifar, F.J. Santos-Arteaga, H. Sharafi, Expert Systems
with Applications 223 (2023).
date_created: 2024-04-04T13:47:25Z
date_updated: 2024-04-15T13:08:50Z
department:
- _id: '277'
doi: 10.1016/j.eswa.2023.119902
intvolume: ' 223'
keyword:
- Artificial Intelligence
- Computer Science Applications
- General Engineering
language:
- iso: eng
publication: Expert Systems with Applications
publication_identifier:
issn:
- 0957-4174
publication_status: published
publisher: Elsevier BV
status: public
title: 'Analytic hierarchy process and data envelopment analysis: A match made in
heaven'
type: journal_article
user_id: '51811'
volume: 223
year: '2023'
...
---
_id: '53229'
author:
- first_name: Francisco J.
full_name: Santos-Arteaga, Francisco J.
last_name: Santos-Arteaga
- first_name: Debora
full_name: Di Caprio, Debora
last_name: Di Caprio
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Emilio Cerda
full_name: Tena, Emilio Cerda
last_name: Tena
citation:
ama: Santos-Arteaga FJ, Di Caprio D, Tavana M, Tena EC. A Credibility and Strategic
Behavior Approach in Hesitant Multiple Criteria Decision-Making With Application
to Sustainable Transportation. IEEE Transactions on Fuzzy Systems. 2023;31(2):460-474.
doi:10.1109/tfuzz.2022.3188875
apa: Santos-Arteaga, F. J., Di Caprio, D., Tavana, M., & Tena, E. C. (2023).
A Credibility and Strategic Behavior Approach in Hesitant Multiple Criteria Decision-Making
With Application to Sustainable Transportation. IEEE Transactions on Fuzzy
Systems, 31(2), 460–474. https://doi.org/10.1109/tfuzz.2022.3188875
bibtex: '@article{Santos-Arteaga_Di Caprio_Tavana_Tena_2023, title={A Credibility
and Strategic Behavior Approach in Hesitant Multiple Criteria Decision-Making
With Application to Sustainable Transportation}, volume={31}, DOI={10.1109/tfuzz.2022.3188875},
number={2}, journal={IEEE Transactions on Fuzzy Systems}, publisher={Institute
of Electrical and Electronics Engineers (IEEE)}, author={Santos-Arteaga, Francisco
J. and Di Caprio, Debora and Tavana, Madjid and Tena, Emilio Cerda}, year={2023},
pages={460–474} }'
chicago: 'Santos-Arteaga, Francisco J., Debora Di Caprio, Madjid Tavana, and Emilio
Cerda Tena. “A Credibility and Strategic Behavior Approach in Hesitant Multiple
Criteria Decision-Making With Application to Sustainable Transportation.” IEEE
Transactions on Fuzzy Systems 31, no. 2 (2023): 460–74. https://doi.org/10.1109/tfuzz.2022.3188875.'
ieee: 'F. J. Santos-Arteaga, D. Di Caprio, M. Tavana, and E. C. Tena, “A Credibility
and Strategic Behavior Approach in Hesitant Multiple Criteria Decision-Making
With Application to Sustainable Transportation,” IEEE Transactions on Fuzzy
Systems, vol. 31, no. 2, pp. 460–474, 2023, doi: 10.1109/tfuzz.2022.3188875.'
mla: Santos-Arteaga, Francisco J., et al. “A Credibility and Strategic Behavior
Approach in Hesitant Multiple Criteria Decision-Making With Application to Sustainable
Transportation.” IEEE Transactions on Fuzzy Systems, vol. 31, no. 2, Institute
of Electrical and Electronics Engineers (IEEE), 2023, pp. 460–74, doi:10.1109/tfuzz.2022.3188875.
short: F.J. Santos-Arteaga, D. Di Caprio, M. Tavana, E.C. Tena, IEEE Transactions
on Fuzzy Systems 31 (2023) 460–474.
date_created: 2024-04-04T14:00:53Z
date_updated: 2024-04-15T13:15:07Z
department:
- _id: '277'
doi: 10.1109/tfuzz.2022.3188875
intvolume: ' 31'
issue: '2'
keyword:
- Applied Mathematics
- Artificial Intelligence
- Computational Theory and Mathematics
- Control and Systems Engineering
language:
- iso: eng
page: 460-474
publication: IEEE Transactions on Fuzzy Systems
publication_identifier:
issn:
- 1063-6706
- 1941-0034
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: A Credibility and Strategic Behavior Approach in Hesitant Multiple Criteria
Decision-Making With Application to Sustainable Transportation
type: journal_article
user_id: '51811'
volume: 31
year: '2023'
...
---
_id: '53228'
article_number: '106945'
author:
- first_name: Erfan Babaee
full_name: Tirkolaee, Erfan Babaee
last_name: Tirkolaee
- first_name: Ali Ebadi
full_name: Torkayesh, Ali Ebadi
last_name: Torkayesh
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Alireza
full_name: Goli, Alireza
last_name: Goli
- first_name: Vladimir
full_name: Simic, Vladimir
last_name: Simic
- first_name: Weiping
full_name: Ding, Weiping
last_name: Ding
citation:
ama: Tirkolaee EB, Torkayesh AE, Tavana M, Goli A, Simic V, Ding W. An integrated
decision support framework for resilient vaccine supply chain network design.
Engineering Applications of Artificial Intelligence. 2023;126. doi:10.1016/j.engappai.2023.106945
apa: Tirkolaee, E. B., Torkayesh, A. E., Tavana, M., Goli, A., Simic, V., &
Ding, W. (2023). An integrated decision support framework for resilient vaccine
supply chain network design. Engineering Applications of Artificial Intelligence,
126, Article 106945. https://doi.org/10.1016/j.engappai.2023.106945
bibtex: '@article{Tirkolaee_Torkayesh_Tavana_Goli_Simic_Ding_2023, title={An integrated
decision support framework for resilient vaccine supply chain network design},
volume={126}, DOI={10.1016/j.engappai.2023.106945},
number={106945}, journal={Engineering Applications of Artificial Intelligence},
publisher={Elsevier BV}, author={Tirkolaee, Erfan Babaee and Torkayesh, Ali Ebadi
and Tavana, Madjid and Goli, Alireza and Simic, Vladimir and Ding, Weiping}, year={2023}
}'
chicago: Tirkolaee, Erfan Babaee, Ali Ebadi Torkayesh, Madjid Tavana, Alireza Goli,
Vladimir Simic, and Weiping Ding. “An Integrated Decision Support Framework for
Resilient Vaccine Supply Chain Network Design.” Engineering Applications of
Artificial Intelligence 126 (2023). https://doi.org/10.1016/j.engappai.2023.106945.
ieee: 'E. B. Tirkolaee, A. E. Torkayesh, M. Tavana, A. Goli, V. Simic, and W. Ding,
“An integrated decision support framework for resilient vaccine supply chain network
design,” Engineering Applications of Artificial Intelligence, vol. 126,
Art. no. 106945, 2023, doi: 10.1016/j.engappai.2023.106945.'
mla: Tirkolaee, Erfan Babaee, et al. “An Integrated Decision Support Framework for
Resilient Vaccine Supply Chain Network Design.” Engineering Applications of
Artificial Intelligence, vol. 126, 106945, Elsevier BV, 2023, doi:10.1016/j.engappai.2023.106945.
short: E.B. Tirkolaee, A.E. Torkayesh, M. Tavana, A. Goli, V. Simic, W. Ding, Engineering
Applications of Artificial Intelligence 126 (2023).
date_created: 2024-04-04T13:59:47Z
date_updated: 2024-04-15T13:14:11Z
department:
- _id: '277'
doi: 10.1016/j.engappai.2023.106945
intvolume: ' 126'
keyword:
- Electrical and Electronic Engineering
- Artificial Intelligence
- Control and Systems Engineering
language:
- iso: eng
publication: Engineering Applications of Artificial Intelligence
publication_identifier:
issn:
- 0952-1976
publication_status: published
publisher: Elsevier BV
status: public
title: An integrated decision support framework for resilient vaccine supply chain
network design
type: journal_article
user_id: '51811'
volume: 126
year: '2023'
...
---
_id: '53230'
author:
- first_name: Hannan Amoozad
full_name: Mahdiraji, Hannan Amoozad
last_name: Mahdiraji
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Ali
full_name: Rezayar, Ali
last_name: Rezayar
citation:
ama: Mahdiraji HA, Tavana M, Rezayar A. A Game-Theoretic Framework for Analyzing
the Impact of Social Responsibility and Supply Chain Profitability. Cybernetics
and Systems. 2023;54(1):104-137. doi:10.1080/01969722.2022.2055402
apa: Mahdiraji, H. A., Tavana, M., & Rezayar, A. (2023). A Game-Theoretic Framework
for Analyzing the Impact of Social Responsibility and Supply Chain Profitability.
Cybernetics and Systems, 54(1), 104–137. https://doi.org/10.1080/01969722.2022.2055402
bibtex: '@article{Mahdiraji_Tavana_Rezayar_2023, title={A Game-Theoretic Framework
for Analyzing the Impact of Social Responsibility and Supply Chain Profitability},
volume={54}, DOI={10.1080/01969722.2022.2055402},
number={1}, journal={Cybernetics and Systems}, publisher={Informa UK Limited},
author={Mahdiraji, Hannan Amoozad and Tavana, Madjid and Rezayar, Ali}, year={2023},
pages={104–137} }'
chicago: 'Mahdiraji, Hannan Amoozad, Madjid Tavana, and Ali Rezayar. “A Game-Theoretic
Framework for Analyzing the Impact of Social Responsibility and Supply Chain Profitability.”
Cybernetics and Systems 54, no. 1 (2023): 104–37. https://doi.org/10.1080/01969722.2022.2055402.'
ieee: 'H. A. Mahdiraji, M. Tavana, and A. Rezayar, “A Game-Theoretic Framework for
Analyzing the Impact of Social Responsibility and Supply Chain Profitability,”
Cybernetics and Systems, vol. 54, no. 1, pp. 104–137, 2023, doi: 10.1080/01969722.2022.2055402.'
mla: Mahdiraji, Hannan Amoozad, et al. “A Game-Theoretic Framework for Analyzing
the Impact of Social Responsibility and Supply Chain Profitability.” Cybernetics
and Systems, vol. 54, no. 1, Informa UK Limited, 2023, pp. 104–37, doi:10.1080/01969722.2022.2055402.
short: H.A. Mahdiraji, M. Tavana, A. Rezayar, Cybernetics and Systems 54 (2023)
104–137.
date_created: 2024-04-04T14:01:48Z
date_updated: 2024-04-15T13:15:21Z
department:
- _id: '277'
doi: 10.1080/01969722.2022.2055402
intvolume: ' 54'
issue: '1'
keyword:
- Artificial Intelligence
- Information Systems
- Software
language:
- iso: eng
page: 104-137
publication: Cybernetics and Systems
publication_identifier:
issn:
- 0196-9722
- 1087-6553
publication_status: published
publisher: Informa UK Limited
status: public
title: A Game-Theoretic Framework for Analyzing the Impact of Social Responsibility
and Supply Chain Profitability
type: journal_article
user_id: '51811'
volume: 54
year: '2023'
...