Bill Rand

Jayalath, C., Gunaratne, C., Rand, W., Seneviratne, C., & Garibay, I. (2023). A GENERALIZATION OF THRESHOLD-BASED AND PROBABILITY-BASED MODELS OF INFORMATION DIFFUSION. ADVANCES IN COMPLEX SYSTEMS, 26(02). https://doi.org/10.1142/S0219525923500054

Weishampel, A., Staicu, A.-M., & Rand, W. (2023). Classification of social media users with generalized functional data analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 179. https://doi.org/10.1016/j.csda.2022.107647

Epstein, J. M., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (2023). Special Section on "Inverse Generative Social Science": Guest Editors? Statement. JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 26(2). https://doi.org/10.18564/jasss.5085

Romero, E., Chica, M., Damas, S., & Rand, W. (2023). [Review of Two decades of agent-based modeling in marketing: a bibliometric analysis]. PROGRESS IN ARTIFICIAL INTELLIGENCE, 12(3), 213–229. https://doi.org/10.1007/s13748-023-00303-y

Garibay, O. O., Yousefi, N., Aslett, K., Baggio, J., Hemberg, E., Jayalath, C., … Garibay, I. (2022). Entropy-Based Characterization of Influence Pathways in Traditional and Social Media. 2022 IEEE 8TH INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING, CIC, pp. 38–44. https://doi.org/10.1109/CIC56439.2022.00016

Gunaratne, C., De, D., Thakur, G., Senevirathna, C., Rand, W., Smyth, M., & Lipscomb, M. (2022). Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media. SOCIAL, CULTURAL, AND BEHAVIORAL MODELING (SBP-BRIMS 2022), Vol. 13558, pp. 24–34. https://doi.org/10.1007/978-3-031-17114-7_3

Overgoor, G., Rand, W., Dolen, W., & Mazloom, M. (2022). Simplicity is not key: Understanding firm-generated social media images and consumer liking. INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING, 39(3), 639–655. https://doi.org/10.1016/j.ijresmar.2021.12.005

Rand, W., & Stummer, C. (2021). Agent-based modeling of new product market diffusion: an overview of strengths and criticisms. ANNALS OF OPERATIONS RESEARCH, 305(1-2), 425–447. https://doi.org/10.1007/s10479-021-03944-1

Westmattelmann, D., Grotenhermen, J.-G., Sprenger, M., Rand, W., & Schewe, G. (2021). Apart we ride together: The motivations behind users of mixed-reality sports. JOURNAL OF BUSINESS RESEARCH, 134, 316–328. https://doi.org/10.1016/j.jbusres.2021.05.044

Gunaratne, C., Rand, W., & Garibay, I. (2021). Inferring mechanisms of response prioritization on social media under information overload. SCIENTIFIC REPORTS, 11(1). https://doi.org/10.1038/s41598-020-79897-5

Senevirathna, C., Gunaratne, C., Rand, W., Jayalath, C., & Garibay, I. (2021). Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media. ENTROPY, 23(2). https://doi.org/10.3390/e23020160

Rust, R. T., Rand, W., Huang, M.-H., Stephen, A. T., Brooks, G., & Chabuk, T. (2021). Real-Time Brand Reputation Tracking Using Social Media. JOURNAL OF MARKETING, 85(4), 21–43. https://doi.org/10.1177/0022242921995173

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks. (2020). ArXiv. Retrieved from https://publons.com/wos-op/publon/48244600/

Gopal, A., Karmegam, S. R., Koka, B. R., & Rand, W. M. (2020). Is the Grass Greener? On the Strategic Implications of Moving Along the Value Chain for IT Service Providers. INFORMATION SYSTEMS RESEARCH, 31(1), 148–175. https://doi.org/10.1287/isre.2019.0879

Gunaratne, C., Baral, N., Rand, W., Garibay, I., Jayalath, C., & Senevirathna, C. (2020). The effects of information overload on online conversation dynamics. COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY, 26(2), 255–276. https://doi.org/10.1007/s10588-020-09314-9

Roxburgh, N., Guan, D., Shin, K. J., Rand, W., Managi, S., Lovelace, R., & Meng, J. (2019). Characterising climate change discourse on social media during extreme weather events. GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS, 54, 50–60. https://doi.org/10.1016/j.gloenvcha.2018.11.004

Burghardt, K., Rand, W., & Girvan, M. (2019). Inferring models of opinion dynamics from aggregated jury data. PLOS ONE, 14(7). https://doi.org/10.1371/journal.pone.0218312

Overgoor, G., Chica, M., Rand, W., & Weishampel, A. (2019). Letting the Computers Take Over: Using AI to Solve Marketing Problems. CALIFORNIA MANAGEMENT REVIEW, 61(4), 156–185. https://doi.org/10.1177/0008125619859318

Rand, W., Rust, R. T., & Kim, M. (2018). Complex systems: marketing’s new frontier. AMS Review, 8(3-4), 111–127. https://doi.org/10.1007/s13162-018-0122-2

Darmon, D., Rand, W., & Girvan, M. (2018). Computational landscape of user behavior on social media. PHYSICAL REVIEW E, 98(6). https://doi.org/10.1103/PhysRevE.98.062306

Smith, E. B., & Rand, W. (2018). Simulating Macro-Level Effects from Micro-Level Observations. MANAGEMENT SCIENCE, 64(11), 5405–5421. https://doi.org/10.1287/mnsc.2017.2877

Chica, M., & Rand, W. (2017). Building Agent-Based Decision Support Systems for Word-of-Mouth Programs: A Freemium Application. JOURNAL OF MARKETING RESEARCH, 54(5), 752–767. https://doi.org/10.1509/jmr.15.0443

Verhoef, P. C., Stephen, A. T., Kannan, P. K., Luo, X., Abhishek, V., Andrews, M., … Zhang, Y. (2017). Consumer Connectivity in a Complex, Technology-enabled, and Mobile-oriented World with Smart Products. JOURNAL OF INTERACTIVE MARKETING, 40, 1–8. https://doi.org/10.1016/j.intmar.2017.06.001

Burghardt, K., Alsina, E. F., Girvan, M., Rand, W., & Lerman, K. (2017). The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange. PLOS ONE, 12(3). https://doi.org/10.1371/journal.pone.0173610

Brand Buzz in the Echoverse. (2016). Journal of Marketing. https://doi.org/10.1509/JM.15.0033

Breaking into new Data-Spaces: Infrastructure for Open Community Science. (2016). ACM Conference on Computer Supported Cooperative Work and Social Computing. https://doi.org/10.1145/2818052.2855512

Competing opinions and stubborness: Connecting models to data. (2016). Physical Review E. https://doi.org/10.1103/PHYSREVE.93.032305

Yoo, E., Rand, W., Eftekhar, M., & Rabinovich, E. (2016). Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. JOURNAL OF OPERATIONS MANAGEMENT, 45, 123–133. https://doi.org/10.1016/j.jom.2016.05.007

The simple rules of a complex world: William Rand and Roland Rust. (2016). European Journal of Marketing. https://doi.org/10.1108/EJM-02-2016-0109

Hsu, S.-C., Weng, K.-W., Cui, Q., & Rand, W. (2016). Understanding the complexity of project team member selection through agent-based modeling. International Journal of Project Management, 34(1), 82–93. https://doi.org/10.1016/J.IJPROMAN.2015.10.001

An Agent-Based Model of Urgent Diffusion in Social Media. (2015). Journal of Artificial Societies and Social Simulation. Retrieved from https://publons.com/wos-op/publon/56056143/

Forecasting High Tide: Predicting Times of Elevated Activity in Online Social Media. (2015). IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). https://doi.org/10.1145/2808797.2809392

The future applications of agent-based modeling in marketing. (2014). Routledge Companion to the Future of Marketing. https://doi.org/10.4324/9780203103036.CH20

Stonedahl, F., & Rand, W. (2014). When Does Simulated Data Match Real Data? In Advances in Computational Social Science (pp. 297–313). https://doi.org/10.1007/978-4-431-54847-8_19

Automatic Crowdsourcing-Based Classification of Marketing Messaging on Twitter. (2013). ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM). https://doi.org/10.1109/SOCIALCOM.2013.155

Does Love Change on Twitter? The Dynamics of Topical Conversations in Microblogging. (2013). ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM). https://doi.org/10.1109/SOCIALCOM.2013.19

Improving Prelaunch Diffusion Forecasts: Using Synthetic Networks as Simulated Priors. (2013). Journal of Marketing Research. https://doi.org/10.1509/JMR.11.0508

Media, Aggregators, and the Link Economy: Strategic Hyperlink Formation in Content Networks. (2013). Management Science. https://doi.org/10.1287/MNSC.2013.1710

Predictability of User Behavior in Social Media: Bottom-Up v. Top-Down Modeling. (2013). ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM). https://doi.org/10.1109/SOCIALCOM.2013.22

SWITCHING BEHAVIOR IN ONLINE AUCTIONS: EMPIRICAL OBSERVATIONS AND PREDICTIVE IMPLICATIONS. (2013). Winter Simulation Conference. Retrieved from https://publons.com/wos-op/publon/56056141/

Agent-based modeling in marketing: Guidelines for rigor. (2011). International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2011.04.002

THE SHAKY LADDER HYPERPLANE-DEFINED FUNCTIONS AND CLASSIC DYNAMIC PROBLEMS. (2010). International Journal of Computational Intelligence and Applications. https://doi.org/10.1142/S1469026810002756

Zellner, M. L., Riolo, R. L., Rand, W., Brown, D. G., Page, S. E., & Fernandez, L. E. (2010). The Problem with Zoning: Nonlinear Effects of Interactions between Location Preferences and Externalities on Land Use and Utility. Environment and Planning B: Planning and Design, 37(3), 408–428. https://doi.org/10.1068/b35053

PARTICIPATORY SIMULATION AS A TOOL FOR AGENT-BASED SIMULATION. (2009). International Conference on Agents and Artificial Intelligence (ICAART). Retrieved from https://publons.com/wos-op/publon/56056148/

Zellner, M. L., Page, S. E., Rand, W., Brown, D. G., Robinson, D. T., Nassauer, J., & Low, B. (2009). The emergence of zoning policy games in exurban jurisdictions: Informing collective action theory. Land Use Policy, 26(2), 356–367. https://doi.org/10.1016/j.landusepol.2008.04.004

Brown, D. G., Robinson, D. T., An, L., Nassauer, J. I., Zellner, M., Rand, W., … Wang, Z. (2008). Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system. Geoforum, 39(2), 805–818. https://doi.org/10.1016/j.geoforum.2007.02.010

Wang, J., Dam, G., Yildirim, S., Rand, W., Wilensky, U., & Houk, J. C. (2008). Reciprocity between the cerebellum and the cerebral cortex: Nonlinear dynamics in microscopic modules for generating voluntary motor commands. Complexity, 14(2), 29–45. https://doi.org/10.1002/cplx.20241

Alharbi, A., Rand, W., & Riolo, R. (2007). Understanding the Semantics of the Genetic Algorithm in Dynamic Environments. In Lecture Notes in Computer Science (pp. 657–667). https://doi.org/10.1007/978-3-540-71805-5_72

Rand, W., & Riolo, R. (2006). The Effect of Building Block Construction on the Behavior of the GA in Dynamic Environments: A Case Study Using the Shaky Ladder Hyperplane-Defined Functions. In Lecture Notes in Computer Science (pp. 776–787). https://doi.org/10.1007/11732242_75

Path dependence and the validation of agent‐based spatial models of land use. (2005). International Journal of Geographical Information Science. https://doi.org/10.1080/13658810410001713399

Rand, W., & Riolo, R. (2005). Shaky Ladders, Hyperplane-Defined Functions and Genetic Algorithms: Systematic Controlled Observation in Dynamic Environments. In Lecture Notes in Computer Science (pp. 600–609). https://doi.org/10.1007/978-3-540-32003-6_63

Brown, D. G., Riolo, R., Robinson, D. T., North, M., & Rand, W. (2005). Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographical Systems, 7(1), 25–47. https://doi.org/10.1007/S10109-005-0148-5

The problem with a self-adaptative mutation rate in some environments - A case study using the Shaky Ladder Hyperplane-Defined Functions. (2005). Genetic and Evolutionary Computation Conference. Retrieved from https://publons.com/wos-op/publon/56056147/

Brown, D., Page, S., Riolo, R., & Rand, W. (2004). Agent-based and analytical modeling to evaluate the effectiveness of greenbelts. Environmental Modelling & Software, 19(12), 1097–1109. https://doi.org/10.1016/j.envsoft.2003.11.012