Publications

Lopez-Lopez, D., Rodriguez-Serrano, J. A., & Corlu, C. G. (2026). Descriptive Analytics and Data Visualization. In F. Xhafa (Ed.), Springer Handbook of Data Engineering (1 ed.). (Springer Handbook of Data Engineering; No. 1). Springer.  

Dávila, J. F., Casabayó, M., & Rayburn, S. W. (2026). Let's get physical! Instagram engagement and vanity in young people. Media International Australia, 199(1), 132-150. More info

Mickel, J., De-Arteaga, M., Leqi, L., & Tian, K. (2026). More of the Same: Persistent Representational Harms Under Increased Representation. Advances in Neural Information Processing Systems, 38, 1-39.

Unceta, I., Subías-Beltrán, P. & Pujol, O. The epistemic debt of generative AI. Nat Mach Intell (2026). More info

M. Abad, I. Unceta, J. Nin, Adapting deployed AI systems under operational constraints: A review of differential replication methods, Machine Learning with Applications, Volume:25, pages:100948, 2026.

F. Salas, J. Nin, Fast hierarchical risk parity methods for portfolio selection, Annals of Operations Research, in press, 2026.

B. Salbanya, J. Nin, R. Gras, Harmonized geospatial data to evaluate the Electric Distribution Networks in the US Northeast, Scientific Data, Volume: 13, issue: 3, pages: 147, 2026.

F. Salas, J. Nin, Risk Mitigation through Noise Reduction in Hierarchical Portfolio Selection, Expert Systems with Applications, Volume 299, Part D, pages: 130304, Elsevier, 2026.

Neumann, T., De-Arteaga, M. & Fazelpour, S. (2026). Should You Use LLMs to Simulate Opinions? Quality Checks for Early-Stage Deliberation. In Koenig, S., Jenkins, C. & Taylor, M. E. (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence (46th ed., pp. 39070-39079). Association for the Advancement of Artificial Intelligence. More info

Gupta, S., De-Arteaga, M. & Lease, M. (2026). Fairness-Aware Multi-Group Target Detection in Online Discussion. ACM FAccT 2026 - Proceedings of the 9th annual ACM Conference on Fairness, Accountability, and Transparency (pp. 7769-7792). Association for Computing Machinery. More info

Abuasaker, W., Sánchez, M., Nguyen, J., & Agell, N. et al. (2025). A Comparative Analysis of European Media Coverage of the Israel–Gaza War Using Hesitant Fuzzy Linguistic Term Sets. Machine Learning and Knowledge Extraction, 7(1), Article 8. More info

Ginès i Fabrellas, A., Almirall, E., Álvarez Cuesta, H., & Avogaro, M., Unceta Mendieta, I., et al. (2025). Algoritmos, Inteligencia Artificial y relación laboral. (1 ed.) Aranzadi.

Subias, P., Unceta Mendieta, I., De Lecuona, I., & Pujol, O. (2025). Asking the Right Questions: A Governance Approach to Uphold Human Autonomy in Artificial Intelligence. AI and Society, 1-25. More info   

Zacharaki, K., Prat-i-Pubill, Q., Nguyen, J., & Agell, N. et al. (2025). Comparing food waste interests and environmental concerns in young adults: A qualitative reasoning approach. Cognitive Systems Research, 89, Article 101318. More info 

Fronte, P., Agell, N., Torrens, M., & Mesa, D. (2025). Criteria definition for digital requirements using hesitant fuzzy linguistic terms sets: an application to the automotive industry. Annals of Operations Research, 353(1), 147-169. Article 110803. More info 

Carrasco-Farré, C., Grimaldi, D., Torrens, M., & Longobuco, E. (2025). Social Identity Theory and Algorithmic Bias: Ingroup and Outgroup Acrophily in Recommender Systems. Journal of Management Information Systems, 42(4), 1017-1054. More info 

Simonsohn, U., Montealegre, A., & Evangelidis, I. (2025). Stimulus Sampling Reimagined: Designing Experiments with Mix-and-Match, Analyzing Results with Stimulus Plots. Journal of Personality and Social Psychology, 129(1), 71-90. More info   

F. Salas Molina, A. Isus, and J. Nin, Una extensión de los modelos de paridad de riesgo jerárquicos para integrar los retornos esperados, XLI National Congress of Statistics and Operations Research (SEIO), 2025.

N. Agell, and J. Nin, Capturing Player Influence in Football Matches, in Proceedings of the 27th Int. Conf. of the Catalan Association for Artificial Intelligence (CCIA), ISBN 978-1-64368-618-9, pages 123-132, IOS Press, 2025  

Torrens, M. (2024). El "machine learning" en la ciberseguridad. Harvard Deusto Management & Innovation, (61), 28-31.

Dávila, J. F., & Casabayó, M. (2024). Instagram paths to materialism in young people: social comparison and identification with influencers. Behaviour and Information Technology. More info 

Simonsohn, U. (2024). Interacting With Curves: How to Validly Test and Probe Interactions in the Real (Nonlinear) World. Advances in Methods and Practices in Psychological Science, 7(1), Article 25152459231207787. More info

Unceta Mendieta, I., Salbanya Rovira, B., Coll, J., & Villaret, M. et al. (2024). Optimizing resource allocation in home care services using MaxSAT. Cognitive Systems Research, 88, Article 101291. More info

Rodriguez-Serrano, J. A. (2024). Prototype-based learning for real estate valuation: a machine learning model that explains prices. Annals of Operations Research. More info 

B. Salbanyà, C. Carrasco-Farré, J. Nin, Structure Matters: Assessing the Statistical Significance of Organizational Network Topologies. PLOS ONE 19(10): e0309005. 2024. More info

W. Abuasaker, N. Agell, J. Nguyen, N. Agell, J. Nin, M. Sánchez and F. J. Ruiz, Using qualitative reasoning to compare media coverage of Israel-Gaza war, Proc. of the 37th Int. Workshop on Qualitative Reasoning: co-located with the European Conference on Artificial Intelligence (ECAI), 2024.

P. Fronte, N. Agell, M. Torrens, J. Nin, T. Garcia, A Data-Driven Approach to Digital Requirements Prioritization in Automotive Service Operations: Addressing Subjectivity and Improving Decision-Making, EURO working group on Multiple Criteria Decision Aiding (MCDA), 2024.

C. Carrasco-Farre, and J. Nin, Mimicking the truth: How legitimacy shapes the topology of mimetic isomorphism between misinformation and reliable news sources, 18th Organization Studies Workshop Organization, Organizing, and Politics: Disciplinary Traditions and Possible Futures, 2024.

Statuto, N., Unceta, I., Nin, J., & Pujol, O. (2023). A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers. Journal of Machine Learning Research, 24. More info

Ding, B., Ferrás-Hernández, X., & Agell, N. (2023). Combining lean and agile manufacturing competitive advantages through Industry 4.0 technologies: an integrative approach. Production Planning and Control, 34(5), 442-458. More info 

Unceta Mendieta, I. (2023). Notas para un aprendizaje automático justo. In A. Ginès i Fabrellas (Ed.), Algoritmos, Inteligencia Artificial y relación laboral (1 ed., pp. 81-111). Aranzadi.  

Abuasaker, W., Nguyen, J., Ruiz, F. J., & Sánchez, M., Agell, N. et al. (2023). Perceptual maps to aggregate assessments from different rating profiles: A hesitant fuzzy linguistic approach. Applied Soft Computing Journal, 147, Article 110803. More info   

Fernández, Ó., Vandendriessche, M., Saz Carranza, A., & Agell, N. et al. (2023). The impact of Russia’s 2022 invasion of Ukraine on public perceptions of EU security and defence integration: a big data analysis. Journal of European Integration, 45(3), 463-485. More info 

R. Tous, L. Igual, and J. Nin, Human Pose Completion in Partial Body Camera Shots, Journal of Experimental & Theoretical Artificial Intelligence (TETA), volume: 0, issue: 0, pages: 1-11, Taylor & Francis, 2023.

J. Nin, and E. Tom\'{a}s, Default propagation in customer-supplier networks: An agent-based model approach to the isolation of companies in default, Journal of Ambient Intelligence and Humanized Computing, volume: 14, issue: 11, pages: 15097-15108, Springer, 2023.

M. Nuñez, and J. Nin, Revisiting online anonymization algorithms to ensure location privacy Journal of Ambient Intelligence and Humanized Computing, volume: 14, issue: 11, pages: 15097–15108, Springer, 2023.

B. Salbanyà, I. Unceta, and J. Nin, Resource Allocation in Home Care Services Using Reinforcement Learning, in Proceedings of the 25th Int. Conf. of the Catalan Association for Artificial Intelligence (CCIA), ISBN 978-1-64368-448-2, pages 173-182, IOS Press, 2023

Torrens, M., & Tabakovic, A. (2022). A Banking Platform to Leverage Data Driven Marketing with Machine Learning. Entropy, 24(3), 1-22. Article 347. More info 

Riu, D., Casabayó, M., Sayeras Maspera, J., & Rovira Llobera, X., Agell, N. et al. (2022). A new method to assess how curricula prepare students for the workplace in higher education. Educational Review, 74(2), 207-225. More info 

Zamora, J., Peña, M. A., Torrens, M., & Ortiz, E. et al. (2022). El gran reto de la ciberseguridad: ¿Está tu empresa protegida? Harvard Deusto Business Review, Julio 2022(324), 30-53.

Afsordegan, A., Del Vasto-Terrientes, L., Valls, A., & Agell, N. et al. (2022). Finding the most sustainable wind farm sites with a hierarchical outranking decision aiding method. Annals of Operations Research, 312(2), 1307-1335. More info 

 A Saz-Carranza, M Vandendriessche, J Nguyen, N Agell. The EU’s interactions with formal intergovernmental organizations: a big data analysis of news media. Journal of European Integration, 2022.

Nguyen, J. T. Van, Armisen-Morell, A., Agell, N., & Saz Carranza, A. (2022). Comparing global news sentiment using hesitant linguistic terms. International Journal of Intelligent Systems, 37.

David Riu, Monica Casabayo, Josep M. Sayeras, Xari Rovira, Nuria Agell. A new method to assess how curricula prepare students for the workplace in higher education (2021).
Educational Review, 73, pp. 1-19
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Ding, B., Ferrás-Hernández, X. & Agell, N. (2021). Combining lean and agile manufacturing competitive advantages through Industry 4.0 technologies: An integrative approach. Production Planning & Control: The Management of Operations, 8 (1), pp. 1-17.
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Porro Martorell, O., Agell, N., Sánchez Soler, M. & Ruiz Vegas, F. (2021). A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: An application to assess entrepreneurial competencies in secondary schools. Applied Soft Computing, 111 (3), pp. 107662.
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Simmons, J. P., Nelson, L. & Simonsohn, U. (2021). Pre-registration is a game changer. But, like random assignment, it is neither necessary nor sufficient for credible science. Journal of Consumer Psychology, 31 (1), pp. 177-180.
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Simmons, J. P., Nelson, L. & Simonsohn, U. (2021). Pre-registration: Why and how. Journal of Consumer Psychology, 31 (1), pp. 151-162.
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Unceta Mendieta, Irene (2021). Adapting by copying: Towards a sustainable machine learning. Unpublished doctoral dissertation. Universitat de Barcelona (UB).
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Unceta Mendieta, Irene, Nin, J. & Pujol Vila, O. (2021). Differential replication for credit scoring in regulated environments. Entropy, 23 (4), pp. 407-424.
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Angulo Bahón, C., Falomir, I. Z., Anguita, D., Agell, N. & Cambria, E. (2020). Bridging cognitive models and recommender systems. Cognitive Computation, 12 (2), pp. 426-427.
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Dávila Blázquez, J. & Casabayó, M. (2020, May). The vanity behind Instagram: The hidden 'likes' to narcissism and histrionic personality. [Paper presentation]. EMAC 2020, Budapest.
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Casabayó, M. (2020). Multistakeholder marketing strategies: How to navigate an increasingly polarized world.
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Porro Martorell, O., Pardo-Bosch, F., Agell, N. & Sánchez Soler, M. (2020). Understanding location decisions of energy multinational enterprises within the European smart cities' context: An integrated AHP and Extended Fuzzy Linguistic TOPSIS Method. Energies, 13 (10)2415.
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Mislavsky, R., Dietvorst, B. J. & Simonsohn, U. (2020). Critical condition: People only object to corporate experiments if they disapprove of a condition. Marketing Science, 39 (6), pp. 1092-1104.
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Jennifer Nguyen, Albert Armisen, Germán Sánchez-Hernández, Mònica Casabayó, Núria Agell (2020). An OWA-based hierarchical clustering approach to understanding users’ lifestyles. Knowledge-Based Systems, 2020, 190.
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Nguyen, J., Montserrat Adell, J., Agell, N., Sánchez Soler, M. & Ruiz, FJ. (2020). Fusing hotel ratings and reviews with hesitant terms and consensus measures. Neural Computing and Applications, 32 (19), pp. 15301-15311.
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Sábada, T., Sanmiguel, P., Casabayó, M., Gallo, Í., Luis Bassa, C., Carreras, F. & Moreno De los Ríos, P. (2020). Sumando ideas 'Marketing' de 'Influencers': ¿Tiene realmente el impacto esperado? Harvard Deusto Business Review, (302), pp. 32-35.
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Simonsohn, U., Simmons, J. P. & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4 (11), pp. 1208-1214.
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Torrens, M., Cortés, U., Valogianni, K., Valor, J., et al. (2020). Los retos éticos de la inteligencia artificial. Harvard Deusto Business Review.

Unceta Mendieta, Irene, Jordi Nin, Oriol Pujol (2020). Copying Machine Learning Classifiers. IEEE Access.

Unceta Mendieta, Irene, Nin, J., & Pujol, O. (2020). Environmental adaptation and differential replication in machine learning. Entropy.

Unceta Mendieta, Irene, Nin, J., & Pujol, O. (2020). Transactional compatible representations for high value client identification: A financial case study. Springer.

Unceta Mendieta, Irene, Nin, J., & Pujol, O. (2020). Sampling unknown decision functions to build classifier copies. Springer.

Unceta Mendieta, Irene, Jordi Nin, Oriol Pujol (2020). Risk mitigation in algorithmic accountability: The role of machine learning copies. PLOS ONE.

Porro Martorell, O., Agell, N. & Sánchez Soler, M. (2019). In Sabater-Mir, J., Torra, V., Aguiló, I. & González-Hidalgo, M. (Eds.). A fuzzy decision-aiding approach to implement sustainable marine itineraries. Artificial intelligence research and development, pp. 223-227. IOS Press.

Mislavsky, R., Dietvorst, B. J. & Simonsohn, U. (2019). The minimum mean paradox: A mechanical explanation for apparent experiment aversion. Proceedings of the National Academy of Sciences, 116 (48), pp. 23883-23884.

Nguyen, J., Montserrat Adell, J., Armisen-Morell, A., Torrens, M. & Agell, N. (2019). In Sabater-Mir, J., Torra, V., Aguiló, I. & González-Hidalgo, M. (Eds.). Measuring rating exigency: Identifying relevant consumer reviews. Artificial intelligence research and development, pp. 256-265. IOS Press.

Pardo-Bosch, F. & Torrens, M. (2019). In Aguilar, L. F. (Ed.), Cabrero, E., Pardo-Bosch, F., Muñoz, J., Torrens, M., Coronado, A. A. & Macías, D. A. La Inteligencia Artificial para impulsar los Objetivos de Desarrollo Sostenible. Hacia el gobierno digital en México: Conceptos y experiencias, pp. 41-54. Prometeo Editores SA.

Simonsohn, U., Vosgerau, J., Nelson, L. D. & Simmons, J. P. (2019). 99% impossible: A valid, or falsifiable, internal meta-analysis. Journal of Experimental Psychology: General, 148 (9), pp. 1628-1639.

Unceta Mendieta, Irene, Nin, J. & Pujol, O. (2019). In Sabater-Mir, J., Torra, V., Aguiló, I. & González-Hidalgo, M. From batch to online learning using copies. Artificial intelligence research and development, pp. 125-134. IOS Press.