Prediction and explanation in social systems

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Estimating the reproducibility of psychological science

Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515

False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567

Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340

Computational social science

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365

Limits of Predictability in Human Mobility

Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234

The link‐prediction problem for social networks

Liben‐Nowell, D., & Kleinberg, J. M. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,063

Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market

Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,231

The Boosting Approach to Machine Learning: An Overview

Schapire, R. E. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_9 · Cited by 2,101

Everyone's an influencer: quantifying influence on twitter

Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689

Vorhersagen und Entscheiden: Predictive Policing in PolizeiorganisationenPreprint

Egbert, S., Esposito, E., & Heimstädt, M. (2022) · doi:10.31235/osf.io/r3kbu · Cited by 3

Human versus non-human agents who make predictions of service delivery: An examination of user responses to agents’ view of the future

Söderlund, M. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104638 · Cited by 2

Luck and Predictability in the Life Course

van de Rijt, A., Bernardi, F., Foley, W., & Sage, L. (2026) · Annual Review of Sociology · doi:10.1146/annurev-soc-031324-115112 · Cited by 2

SOCIAL NETWORKS AS REFLECTION OF ACADEMIC PERFORMANCE

Maydangalieva, Z. A., sveta1108@inbox.ru, Doszhanova, S. E., Abisheva, N. M., Nazarova, G. J., & gulnoz.nazarova@gmail.com (2018) · BULLETIN OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN ( THE BULLETIN) · doi:10.32014/2018.2518-1467.32 · Cited by 2

Social bias insights concern judgments rather than real-world decisions

Białek, M., & Grossmann, I. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000728 · Cited by 2

What kind of prediction? Evaluating different facets of prediction in agent-based social simulation

Anzola, D., & García‐Díaz, C. (2022) · International Journal of Social Research Methodology · doi:10.1080/13645579.2022.2137919 · Cited by 2

Artificial Intelligence and Judging

Niblett, A. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780192898579.013.46 · Cited by 2

CauseRuDi: Explaining Behavior Sequence Models by Causal Statistics Generation and Rule Distillation

Zhang, Y., Xiong, Y., Sun, Y., Jin, Y., Shan, C., Lu, T., Song, H., & Sun, S. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3487625 · Cited by 2

Exploration potential sepsis–ferroptosis mechanisms through the use of CETSA technology and network pharmacology

Shen, Y., Luo, B., Zhang, Q., Li, H., Hu, Y. C., & Chen, M. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-95451-7 · Cited by 2

Tools Of Causal Inference: Review and Prospects

Bespala, O. M. (2020) · Control Systems and Computers · doi:10.15407/csc.2020.05.052 · Cited by 2

Putting an Explanatory Understanding into a Predictive Perspective: An Exemplary Study on School Track Enrollment

Helbling, L. A., Tomasik, M. J., & Moser, U. (2022) · Frontiers in Education · doi:10.3389/feduc.2021.793447 · Cited by 2

SleepLess: personalized sleep monitoring using smartphones and semi-supervised learning

Mammen, P. M., Zakaria, C., & Shenoy, P. J. (2023) · CSI Transactions on ICT · doi:10.1007/s40012-023-00389-8 · Cited by 2

Machine Learning, Abduction, and Computational Ethnography

Brandt, P. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.21 · Cited by 2

Conceptualizing and testing the vicious cycle in child protective services: The critical role played by child maltreatment fatalities

Camasso, M. J., & Jagannathan, R. (2019) · Children and Youth Services Review · doi:10.1016/j.childyouth.2019.05.024 · Cited by 2

Social Convos: A New Approach to Modeling Information Diffusion in Social Media

Katsios, G., Sa, N., & Strzalkowski, T. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-20454-9_3 · Cited by 2

Geographical Entity Community Mining Based on Spatial and Semantic Association

Yan, M., Jing, N., Zhong, Z., & Wu, Y. (2019) · International Conference on Computer Science and Application Engineering (CSAE) · doi:10.1145/3331453.3361652 · Cited by 2

Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community

Glenski, M., & Volkova, S. (2021) · doi:10.18653/v1/2021.cinlp-1.7 · Cited by 2

Science|Environment|Health: An Introduction

Zeyer, A., & Kyburz‐Graber, R. (2021) · Contributions from science education research · doi:10.1007/978-3-030-75297-2_1 · Cited by 2

Analytical bi-level multi-local-world complex network model on fresh agricultural products supply chain

Liu, Y., Xu, S., Liu, J., & Zhuang, J. (2022) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20221501.6353 · Cited by 2

AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice

Adiga, A., Lewis, B. L., Levin, S. A., MARATHE, M. V., Poor, H. V., Ravi, S. S., Rosenkrantz, D. J., Stearns, R. E., Venkatramanan, S., Vullikanti, A. K. S., et al. (2022) · Springer eBooks · doi:10.1007/978-3-031-08506-2_9 · Cited by 2

The wisdom of the few: Predicting collective success from individual behaviorPreprint

Mariani, M. S., Gimenez, Y., Brea, J., Minnoni, M., Algesheimer, R., & Tessone, C. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04777 · Cited by 2

Citations or dollars? Early signals of a firm's research successPreprint

Xu, S., Mariani, M. S., Lü, L., Napolitano, L., Pugliese, E., & Zaccaria, A. (2021) · Research Square · doi:10.21203/rs.3.rs-1068232/v1 · Cited by 2

Guest Editorial Special Issue on Advanced Cognitive Computing for Data-Driven Computational Social Systems

Wang, W. W., Sato, T., Piuri, V., Aloqaily, M., & Yu, K. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3219608 · Cited by 2

The Perks and Perils of Machine Learning in Business and Economic ResearchPreprint

Dudda, T. L., & Hornuf, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4981802 · Cited by 2

Dynamics of collective minds in online communitiesPreprint

Ha, S., Olsson, H., Jakšić, K., Pellert, M., & Galešić, M. (2025) · Research Square · doi:10.21203/rs.3.rs-6423149/v1 · Cited by 2

Enterprise Architecture-Based Project Assurance Model for the Proof-of-Concept of AI Service Systems

Takeuchi, H. (2021) · International Journal of Systems and Software Security and Protection · doi:10.4018/ijsssp.2021010104 · Cited by 1

Clarifying Military Advantages and Risks of AI Applications via a Scenario

Janssens, L. A. W. (2025) · Data science, machine intelligence, and law · doi:10.1007/978-3-032-02762-7_11 · Cited by 1

Predictors of Cultural Intelligence: Automated Machine Learning vs. PLS-SEM

Taras, V., Rickley, M., Alon, I., Dong, L.-Z., & Malmin, H. (2025) · Journal of International Management · doi:10.1016/j.intman.2025.101290 · Cited by 1

Regression-based multisource conditional domain adaptation for policy outcome prediction

Qi, C., Zhu, C., Wu, L., Lan, W., Zheng, D., & Lv, F. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108341 · Cited by 1

Crossing the digital threshold: An interpretable machine learning model of life satisfaction for older adults in China

Xilatu, D., Li, D., Song, Q., & Zhang, K. (2025) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.70104 · Cited by 1

The Limits of Predicting Individual-Level Longevity: Insights From the U.S. Health and Retirement Study

Badolato, L., Decter-Frain, A., Irons, N. J., Miranda, M. L., Walk, E., Zhalieva, E., Alexander, M. J., Basellini, U., & Zagheni, E. (2026) · Demography · doi:10.1215/00703370-12464628 · Cited by 2

Computing Grounded Theory: Algorithm-Based Approach to Deriving Theories

Chen, Y., Chen, Z., Ma, W., & Ju, G. (2026) · Springer eBooks · doi:10.1007/978-981-95-6465-1_6 · Cited by 1

SocLitGen: An LLM-assisted framework for automated literature review in social sciences

Hu, S., Wei, Y., & Sun, S. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104885 · Cited by 1

Method for Calculating Geographic Entity Relevance Based on Spatial and Semantic Association

Yan, M., Zhong, Z., Jing, N., & Wu, Y. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2019.00076 · Cited by 1

On the causal relation between users' real-world activities and their affective processes

Falavarjani, S. A. M., Ebrahim Bagheri, A., Chou, S. Y. Z., Jovanović, J., & Ghorbani, A. A. (2019) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3341161.3342918 · Cited by 1

Refactoring of Object-oriented Package Structure Based on Complex Network

Huang, Y., Chen, Y., Tang, Z., Jiang, N., & Chen, L.-Y. (2022) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2022-164 · Cited by 1

Exploring Causality between Public Opinion and the Death Penalty Using Granger Testing

Tatro, K., & Oliver, J. R. (2023) · Advances in Applied Sociology · doi:10.4236/aasoci.2023.136027 · Cited by 1

Urban Anthropocene-Socio-Ecology Planning Resilience

Vargas-Hernández, J. G., & Vargas-González, O. C. (2023) · Advances in environmental engineering and green technologies book series · doi:10.4018/978-1-6684-9034-1.ch008 · Cited by 1

Dimensions of predictive success

Syrjänen, P. (2024) · The British Journal for the Philosophy of Science · doi:10.1086/733193 · Cited by 1

Introduction: Computational Social Science and the Study of Political Communication

Theocharis, Y., & Jungherr, A. M. (2024) · Routledge eBooks · doi:10.4324/9781003546726-1 · Cited by 1

Towards a performance characteristic curve for model evaluation: An application in information diffusion prediction

Xie, W., Wang, X., Michalski, R., & Jia, T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126482 · Cited by 1

Reduce-Then-Predict or Simultaneous Reduce-and-Predict? Data-Driven Sparse Modeling for Improving R&D Efficiency

Hsiao, P.-C., Chou, Y., & Chuang, H. H. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3577580 · Cited by 1

Airline Choice

Shrivastava, A., Paul, P. J. D., & Sharma, J. (2019) · International Journal of Business Analytics · doi:10.4018/ijban.2020040103 · Cited by 1

Towards a Methodology for Field Work in Computational Creativity

y Pérez, R. P., & Ackerman, M. (2020) · New Generation Computing · doi:10.1007/s00354-020-00105-z · Cited by 1

Incomplete Gamma Integrals for Deep Cascade Prediction Using Content, Network, and Exogenous Signals

Dutta, S., Mittal, S., Das, D., Chakrabarti, S., & Chakraborty, T. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3174206 · Cited by 2

Causal and predictive modeling in computational social science

Engel, U. (2021) · Routledge eBooks · doi:10.4324/9781003024583-10 · Cited by 5

A Policy Framework Towards the Use of Artificial Intelligence by Public Institutions

Patnaik, P. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9609-8.ch002 · Cited by 1

Existing Implications and Relationship Between Anthropology and Anthropocene Urban Socio-Ecology Planning Resilience

Vargas-Hernández, J. G., Rodríguez-Maillard, C. A., & Vargas-González, O. C. (2023) · Advances in environmental engineering and green technologies book series · doi:10.4018/978-1-6684-6366-6.ch016 · Cited by 1

The Anthropology and Anthropocene Urban Socio-Ecology Resilience Approach to Resilience Planning and Green Innovations

Vargas-Hernández, J. G., Rodríguez-Maillard, C. A., & Vargas-González, O. C. (2023) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-6684-9503-2.ch008 · Cited by 1

Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge TransferPreprint

Luo, C., Chen, Z., Tang, L., Shrivastava, A., & Li, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.07867 · Cited by 1

Discriminative Modeling of Social Influence for Prediction and Explanation in Event Cascades.Preprint

Soni, S., Ramirez, S. L., & Eisenstein, J. (2018) · arXiv (Cornell University) · Cited by 1

Interdependence and Superordinate Goals: The Revenge of the DualistsPreprint

Lawless, W. F. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3179029 · Cited by 1

Application of Artificial Intelligence In Smart City

Law, J. W. (2021) · Cited by 1

Semana del Doctorado

AA., V. (2023) · doi:10.15366/9788483448977 · Cited by 2

From ideal experiments to ideal research designs (IDRs): What they are and why we should use them morePreprint

Bauer, P. C., & Landesvatter, C. (2023) · doi:10.31219/osf.io/4dahm · Cited by 1

Data mining in social science research

Shu, X., Ye, Y., & Pan, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26629-4.00104-0 · Cited by 1

Accelerating progress in the social sciences: the potential of benchmarksPreprint

Pankowska, P., Mendrik, A. M., Emery, T., & Garcia‐Bernardo, J. (2023) · doi:10.31235/osf.io/ekfxy · Cited by 1

Gaining and Losing Influence in Online Conversation

Sharma, A., & Strzalkowski, T. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2kq2b2ateein

The Mathematics of Interdependence for Superordinate Decision-Making with Teams

Lawless, W. F. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-99698-1_11

Assessing the predictive value of the UK-CDI for early identification of developmental language delay

Just, J. (2019) · Lincoln Repository (University of Lincoln) · doi:10.24385/lincoln.24325987

Analyzing WeChat Diffusion Cascade: Pattern Discovery and Prediction

Lv, R., Zang, C., Chan, W. K. V., & Zhu, W. (2019) · Springer proceedings in business and economics · doi:10.1007/978-3-030-30967-1_34

Politik als komplexes System: Bemerkungen zum Prozess der Digitalisierung, zur Anwendung von Heuristiken und zur Frage des Gemeinwohls

Scobel, G. (2019) · Zeitschrift für Politikwissenschaft · doi:10.1007/s41358-019-00190-7

Do We Need a Critical Evaluation of the Role of Mathematics in Data Science?

Allo, P. (2019) · Digital ethics lab yearbook · doi:10.1007/978-3-030-17152-0_3

Technochimp: An African Savannah Survivor Looking for Solutions in the Modern World

Dodds, W. (2019) · Springer eBooks · doi:10.1007/978-3-030-30410-2_11

Personalized Market Basket Prediction With Temporal Annotated Recurring Sequence

null, M. S. S., & null, M. C. S. (2020) · International Journal of Computer Techniques · doi:10.29126/23942231/ijct-v7i3p1

Count Regression and Machine Learning Approach for Zero-Inflated Over-Dispersed Count Data. Application to Micro-Retail Distribution and Urban Form

Araldi, A., Venerandi, A., & Fusco, G. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58811-3_40

Networked Miscommunication: The Relationship Between Communication Networks, Misunderstandings, and Organizational Performance

Meluso, J. (2020) · Deep Blue (University of Michigan)

Estimating individual-level average treatment effects: Challenges, modeling approaches, and practical applications

Talisa, V. B., & Chang, C. H. (2020) · Handbook of statistics · doi:10.1016/bs.host.2020.09.001

The simple regularities in the dynamics of online news impact

Medo, M., Mariani, M. S., & Lü, L. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00140-w

Determining Appropriate Social Media Sites for Knowledge Sharing

Gyamfi, A. (2021) · Advances in knowledge acquisition, transfer, and management book series/Advances in knowledge acquisition, transfer and management book series · doi:10.4018/978-1-7998-6792-0.ch002

Predicting Pasture and Forest Landowner Intention to Create Early Successional Habitat

Meister, H. M., Valdez, R. X., Martin, D. R., & Bulluck, L. P. (2021) · Journal of Wildlife Management · doi:10.1002/jwmg.22109

A Machine Learning Tool to Determine State of Mind and EmotionPreprint

Jamisola, R. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.03444 · Cited by 1

Pre-training of Temporal Convolutional Neural Networks for Popularity Prediction.Preprint

Cao, Q., Shen, H., Liu, Y., Gao, J., & Cheng, X. (2021) · arXiv (Cornell University)

Theoretical Overview

Anderson, E. N. (2019) · World-systems evolution and global futures · doi:10.1007/978-3-030-16870-4_1

Structural Shockwaves: Consecutive Crises and Professional Mobility Among Judges and ProsecutorsPreprint

Pârvulescu, R. A. (2020) · doi:10.31235/osf.io/hg9tq

Maskinlæring som politologisk værktøj

Bach, A., Svejgaard, J., & Hjort, F. (2019) · Politica · doi:10.7146/politica.v51i2.131144

Modular Analysis on Increasing Chinese Economy Based on Complex Network

Feng, P., Fan, Y., Ke, X., & Wu, Y. (2022) · International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) · doi:10.1109/icceai55464.2022.00060

Anthropocene, Urban, and Antho-Socio-Ecology Planning Resilience

Vargas-Hernández, J. G., Castañeda-Burciaga, S., & Vargas-González, O. C. (2023) · Practice, progress, and proficiency in sustainability · doi:10.4018/978-1-6684-8879-9.ch001

In or out?

Fernandes, C. A. D. M. (2023) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.768055223

A FRONTEIRA ALGORÍTMICA NAS MÍDIAS SOCIAIS: ALGUMAS REFLEXÕES SOBRE A ESPACIALIDADE EM UM ESPAÇO TECNOCÊNTRICO

BRANDAO, M. B. S. (2023) · doi:10.17771/pucrio.acad.64548

Network perspective on complex sociotechnical systems: insights for design and policy decisions

Cao, Q. (2023) · Digital Repository Service (Northeastern University) · doi:10.17760/d20487164

Predicting the Self with Generative AIPreprint

Jones, J. J. (2024) · doi:10.31235/osf.io/eh9sk

Personalized Sleep Monitoring Using Smartphones and Semi-supervised Learning

Mammen, P. M., Zakaria, C., & Shenoy, P. J. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-59717-6_22

Predictability of information spreading on online social networks

Meng, F., Xie, J., Ma, X., Wang, J., & Hu, Y. (2024) · International Journal of Modern Physics C · doi:10.1142/s0129183124501699

The Analytics Paradigm: Balancing Innovation and Ethics in a Data-Centric World

Choudhary, T. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241051034

Abduction in Real World Navigation. The Issue of Proxies in Recommender Systems

Hildebrandt, M. (2024) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-031-69300-7_21

Toplumsal Analizlerde Makine Öğrenmesi

Seymen, Ö. F. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub587.c2428

Fair machine learning models for disease prediction: In-depth interviews with key health expertsPreprint

Nghiem, N., & Tiatia, R. (2025) · medRxiv · doi:10.1101/2025.02.04.25321632

Intelligence artificielle en anesthésie-réanimation : quoi de neuf en 2025 ?

Elmaleh, Y. D., Guessous, K., & Delvaux, B. (2025) · Le Praticien en Anesthésie Réanimation · doi:10.1016/j.pratan.2025.04.009

Bridging Traditional Statistics and Machine-Learning Approaches in Psychology: Navigating Small Samples, Measurement Error, Non-independent Observations and Missing DataPreprint

Lavelle-Hill, R. E., Smith, G., & Murayama, K. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/6xt82_v1

Federated Learning for Smart Cities in Free Trade Area of China: Current Status, Application Cases, Challenges and Opportunities

Zuo, M. (2024) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3724154.3724309

Intelligence artificielle en anesthésie-réanimation : quoi de neuf en 2025 ?

Elmaleh, Y. D., Guessous, K., & Delvaux, B. (2025) · Le Praticien en Anesthésie Réanimation · doi:10.1016/j.pratan.2025.06.003

Machine confirming: Validating financial theories with transfer learning

Zhao, X., Li, Y., & Wang, T. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104485

Data Science and Decision-Making in Society: A Causal Perspective on Policy and Equity

Chadagonda, B. R. (2025) · European Modern Studies Journal · doi:10.59573/emsj.9(4).2025.37

UNDERSTANDING OVERFITTED INFERIOR EQUILIBRIUM EMERGENCE MECHANISMS INFLUENCED BY WISDOM OF CROWDS IN DEMAND AND SUPPLY DECISION-MAKING

Inagaki, K., & Hara, Y. (2025) · Japanese Journal of JSCE · doi:10.2208/jscejj.24-00191

Constrained Maximal Controllability of Complex Networks

Liu, Y., Ma, Z., Ding, J., & Li, X. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3635299

Applying machine learning to analyse the layouts of Qing Dynasty forestland agreements in Fujian

Chen, K., Liang, B., 孙, 浩., Liu, W., Li, Z., Yan, H., & Xie, M. (2025) · npj Heritage Science · doi:10.1038/s40494-025-02227-3

Additional file 2 of Egocentric network characteristics of people who inject drugs in the Chicago metro area and associations with hepatitis C virus and injection risk behavior

Mackesy-Amiti, M. E., Falk, J., Latkin, C., Kaufmann, M., Williams, L., & Boodram, B. (2022) · Figshare · doi:10.6084/m9.figshare.19976681

Additional file 1 of Egocentric network characteristics of people who inject drugs in the Chicago metro area and associations with hepatitis C virus and injection risk behavior

Mackesy-Amiti, M. E., Falk, J., Latkin, C., Kaufmann, M., Williams, L., & Boodram, B. (2022) · Figshare · doi:10.6084/m9.figshare.19976675

Additional file 1 of Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

Poux-Médard, G., Cobo-López, S., Duch, J., Guimerà, R., & Sales‐Pardo, M. (2021) · Figshare · doi:10.6084/m9.figshare.14615339

Predicting Social Systems – a Challenge

Edmonds, B. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3946569 · Cited by 3

Predicció en les ciències socials

Salmon, M. H. (2005) · Enrahonar An international journal of theoretical and practical reason · doi:10.5565/rev/enrahonar.359 · Cited by 1

Predictability and Explanation in the Social Sciences

MacIntyre, A. (1972) · SUNY Digital Repository Support (State University of New York System) · Cited by 3

Integrating explanation and prediction in computational social science

Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03659-0 · Cited by 371

Integrating Computer Prediction Methods in Social Science: A Comment on Hofman et al. (2021)

Breznau, N. (2022) · Social Science Computer Review · doi:10.1177/08944393211049776 · Cited by 12