References
- Ajelli, M., Gonçalves, B., Balcan, D., Colizza, V., Hu, H., Ramasco, J. J., Merler, S., and Vespignani, A. (2010), “Comparing Large-scale Computational Approaches to Epidemic Modeling: Agent-based Versus Structured Metapopulation Models,” BMC Infectious Diseases, 10, 190,1–13. DOI: https://doi.org/10.1186/1471-2334-10-190.
- Axelrod, R. (1980), “Effective Choice in the Prisoner’s Dilemma,” Journal of Conflict Resolution, 24, 3–25. DOI: https://doi.org/10.1177/002200278002400101.
- Banks, D., and Olszewski, R. (1997), “Estimating Local Dimensionality,” in Proceedings of the Statistical Computing Section of the American Statistical Association, ASA.
- Bayarri, M. J., Berger, J. O., Paulo, R., Sacks, J., Cafeo, J. A., Cavendish, J., Lin, C.-H., and Tu, J. (2007), “A Framework for Validation of Computer Models,” Technometrics, 49, 138–154. DOI: https://doi.org/10.1198/004017007000000092.
- Bearman, P. S., Moody, J., and Stovel, K. (2004), “Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks,” American Journal of Sociology, 110, 44–91. DOI: https://doi.org/10.1086/386272.
- Berger, J. O., Fienberg, S. E., Raftery, A. E., and Robert, C. P. (2010), “Incoherent Phylogeographic Inference,” Proceedings of the National Academy of Sciences, 107, pp. E157–E157.
- Berlekamp, E. R., Conway, J. H., and Guy, R. K. (2004), Winning Ways for Your Mathematical Plays (Vol. 4), Oxford, UK: AK Peters/CRC Press.
- Bobashev, G. V., Goedecke, D. M., Yu, F., and Epstein, J. M. (2007), “A Hybrid Epidemic Model: Combining the Advantages of Agent-based and Equation-based Approaches,” in 2007 Winter Simulation Conference, 1532–1537. IEEE. DOI: https://doi.org/10.1109/WSC.2007.4419767.
- Brauer, F. (2005), “The Kermack–McKendrick Epidemic Model Revisited,” Mathematical Biosciences, 198, 119–131. DOI: https://doi.org/10.1016/j.mbs.2005.07.006.
- Broms, K. M., Hooten, M. B., Johnson, D. S., Altwegg, R., and Conquest, L. L. (2016), “Dynamic Occupancy Models for Explicit Colonization Processes,” Ecology, 97, 194–204. DOI: https://doi.org/10.1890/15-0416.1.
- Chen, X., and Zhan, F. B. (2014), “Agent-based Modeling and Simulation of Urban Evacuation: Relative Effectiveness of Simultaneous and Staged Evacuation Strategies,” Agent-Based Modeling and Simulation, 78–96.
- Cioppa, T. M., Lucas, T. W., and Sanchez, S. M. (2004), “Military Applications of Agent-based Simulations,” in Proceedings of the 2004 Winter Simulation Conference, 2004 (Vol. 1). IEEE, Thousand Oaks CA: Sage.
- Couzin, I. D., Krause, J., Franks, N. R., and Levin, S. A. (2005), “Effective Leadership and Decision-making in Animal Groups on the Move,” Nature, 433, 513–516. DOI: https://doi.org/10.1038/nature03236.
- Dray, A., Mazerolle, L., Perez, P., and Ritter, A. (2008), “Drug Law Enforcement in an Agent-based Model: Simulating the Disruption to Street-level Drug Markets,” in Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, eds. L. Liu and J. Eck, 352–371. Hershey, PA: IGI Global.
- Eliasson, G., and Taymaz, E. (1992), “The Limits of Policy Making: An analysis of the Consequences of Boundedly Rational Government Using the Swedish Micro-to-macro Model (MOSES),” Technical report, IUI Working Paper.
- Epstein, J. M., and Axtell, R. (1996), Growing Artificial Societies: Social Science from the Bottom Up, Cambridge, MA: Brookings Institution Press.
- Farah, F., Birrel, P., Conti, S., Agnes, D. D. (2014), “Bayesian Emulation and Calibration of a Dynamic Epidemic Model for A/H1N1 Influenza,” Journal of the American Statistical Association, 109, 1398–1411. DOI: https://doi.org/10.1080/01621459.2014.934453.
- Frias-Martinez, E., Williamson, G., and Frias-Martinez, V. (2011), An Agent-based Model of Epidemic Spread Using Human Mobility and Social Network Information, 57–64. IEEE.
- Gardner, M. (1970), “Mathematical Games-The Fantastic Combinations of John Conway’s New Solitaire Game, Life, 1970,” Scientific American, October, pp. 120–123.
- Gilbert, N. (2019), Agent-Based Models, Thousand Oaks, CA: Sage Publications, Incorporated.
- Gramacy, R. B. (2020), Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences, Boca Raton: CRC Press.
- Gramacy, R. B., and Lee, H. K. H. (2008), “Bayesian Treed Gaussian Process Models With an Application to Computer Modeling,” Journal of the American Statistical Association, 103,: 1119–11130. DOI: https://doi.org/10.1198/016214508000000689.
- Griffeath, D. (1993), “Frank Spitzer’s Pioneering Work on Interacting Particle Systems,” The Annals of Probability, 21, 608–621. DOI: https://doi.org/10.1214/aop/1176989258.
- Heard, D. (2014), “Statistical Inference Utilizing Agent Based Models” (doctoral dissertation). Duke University.
- Heard, D., Bobashev, G. V., and Morris, R. J. (2014), “Reducing the Complexity of an Agent-based Local Heroin Market Model,” PLoS One, 9, e102263. DOI: https://doi.org/10.1371/journal.pone.0102263.
- Hefley, T. J., and Hooten, M. B. (2016), “Hierarchical Species Distribution Models,” Current Landscape Ecology Reports, 1, 87–97. DOI: https://doi.org/10.1007/s40823-016-0008-7.
- Hefley, T. J., Hooten, M. B., Russell, R. E., Walsh, D. P., and Powell, J. A. (2017), “When Mechanism Matters: Bayesian Forecasting Using Models of Ecological Diffusion,” Ecology Letters, 20, 640–650. DOI: https://doi.org/10.1111/ele.12763.
- Higdon, D., Gattiker, J., Williams, B., and Rightley, M. (2008), “Computer Model Calibration Using High-dimensional Output,” Journal of the American Statistical Association, 103, 570–583. DOI: https://doi.org/10.1198/016214507000000888.
- Hoeting, J. A., Leecaster, M., and Bowden, D. (2000), “An Improved Model for Spatially Correlated Binary Responses,” Journal of Agricultural, Biological, and Environmental Statistics, 5, 102–114. DOI: https://doi.org/10.2307/1400634.
- Hoffer, L. D., Bobashev, G., and Morris, R. J. (2009), “Researching a Local Heroin Market as a Complex Adaptive System,” American Journal of Community Psychology, 44, 273. DOI: https://doi.org/10.1007/s10464-009-9268-2.
- Hooten, M., Wikle, C., and Schwob, M. (2020), “Statistical Implementations of Agent-based Demographic Models,” International Statistical Review, 88, 441–461. DOI: https://doi.org/10.1111/insr.12399.
- Hooten, M. B., Anderson, J., and Waller, L. A. (2010 a) , “Assessing North American Influenza Dynamics With a Statistical SIRS Model,” Spatial and Spatio-Temporal Epidemiology, 1, 177–185. DOI: https://doi.org/10.1016/j.sste.2010.03.003.
- Hooten, M. B., Buderman, F. E., Brost, B. M., Hanks, E. M., and Ivan, J. S. (2016), “Hierarchical Animal Movement Models for Population-level Inference,” Environmetrics, 27, 322–333. DOI: https://doi.org/10.1002/env.2402.
- Hooten, M. B., Johnson, D. S., Hanks, E. M., and Lowry, J. H . (2010b) , “Agent-based Inference for Animal Movement and Selection,” Journal of Agricultural, Biological and Environmental Statistics, 15, 523–538. DOI: https://doi.org/10.1007/s13253-010-0038-2.
- Hooten, M. B., Johnson, D. S., McClintock, B. T., and Morales, J. M. (2017), Animal Movement: Statistical Models for Telemetry Data, Boca Raton, FL: CRC Press.
- Hooten, M. B., and Wikle, C. K. (2008), “A Hierarchical Bayesian Non-linear Spatio-temporal Model for the Spread of Invasive Species With Application to the Eurasian Collared-Dove,” Environmental and Ecological Statistics, 15, 59–70. DOI: https://doi.org/10.1007/s10651-007-0040-1.
- Hooten, M. B., and Wikle, C. K. (2010), “Statistical Agent-based Models for Discrete Spatio-temporal Systems,” Journal of the American Statistical Association, 105, 236–248. DOI: https://doi.org/10.1198/jasa.2009.tm09036.
- Hunter, E., Mac Namee, B., and Kelleher, J. (2018), “An Open-data-driven Agent-based Model to Simulate Infectious Disease Outbreaks,” PLoS One, 13, e0208775. DOI: https://doi.org/10.1371/journal.pone.0208775.
- Johnson, D. S., Conn, P. B., Hooten, M. B., Ray, J. C., and Pond, B. A. (2013), “Spatial Occupancy Models for Large Data Sets,” Ecology, 94, 801–808. DOI: https://doi.org/10.1890/12-0564.1.
- Kennedy, M. C., and O’Hagan, A. (2001), “Bayesian Calibration of Computer Models,” Journal of the Royal Statistical Society, Series B, 63, 425–464. DOI: https://doi.org/10.1111/1467-9868.00294.
- King, R., Morgan, B., Gimenez, O., and Brooks, S. (2009), Bayesian Analysis for Population Ecology, Boca Raton, FL: CRC Press.
- Klabunde, A., and Willekens, F. (2016), “Decision-making in Agent-based Models of Migration: State of the Art and Challenges,” European Journal of Population, 32, 73–97. DOI: https://doi.org/10.1007/s10680-015-9362-0.
- Li, X., and Liu, X. (2008), “Embedding Sustainable Development Strategies in Agent-based Models for Use as a Planning Tool,” International Journal of Geographical Information Science, 22, 21–45. DOI: https://doi.org/10.1080/13658810701228686.
- Lu, X., Williams, P. J., Hooten, M. B., Powell, J. A., Womble, J. N., and Bower, M. R. (2019), “Nonlinear Reaction–diffusion Process Models Improve Inference for Population Dynamics,” Environmetrics, 31, e2604. DOI: https://doi.org/10.1002/env.2604.
- MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., and Langtimm, C. A. (2002), “Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One,” Ecology, 83, 2248–2255. DOI: https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD.2.0.CO;2]
- Merler, S., Ajelli, M., Fumanelli, L., Gomes, M. F., Piontti, A. P. y., Rossi, L., Chao, D. L. I., Longini, Jr, M., Halloran, M. E., and Vespignani, A. (2015), “Spatiotemporal Spread of the 2014 Outbreak of Ebola Virus Disease in Liberia and the Effectiveness of Non-pharmaceutical Interventions: A Computational Modelling Analysis,” The Lancet Infectious Diseases, 15, 204–211. DOI: https://doi.org/10.1016/S1473-3099(14)71074-6.
- Pelechano, N., and Malkawi, A. (2008), “Evacuation Simulation Models: Challenges in Modeling High Rise Building Evacuation With Cellular Automata Approaches,” Automation in Construction, 17, 377–385.
- Perez, L., and Dragicevic, S. (2009), “An Agent-based Approach for Modeling Dynamics of Contagious Disease Spread,” International Journal of Health Geographics, 8, 50. DOI: https://doi.org/10.1186/1476-072X-8-50.
- Plummer, M., et al. (2003), “JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling,” in Proceedings of the 3rd International Workshop on Distributed Statistical Computing (Vol. 124). Berlin, Germany: Springer. pp. 1–10.
- Robert, C. P., Cornuet, J.-M., Marin, J.-M., and Pillai, N. S. (2011), “Lack of Confidence in Approximate Bayesian Computation Model Choice,” Proceedings of the National Academy of Sciences, 108, 15112–15117. DOI: https://doi.org/10.1073/pnas.1102900108.
- Romano, D. M., Lomax, L., and Richmond, P. (2009), “NARCSim an agent-based Illegal Drug Market Simulation,” in 2009 International IEEE Consumer Electronics Society’s Games Innovations Conference, Thousand Oaks, CA: Sage. pp. 101–108. IEEE.
- Rubin, D. B. (1984), “Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician,” The Annals of Statistics, 12, 1151–1172. DOI: https://doi.org/10.1214/aos/1176346785.
- Russell, J. C., Hanks, E. M., Modlmeier, A. P., and Hughes, D. P.(2017), “Modeling Collective Animal Movement Through Interactions in Behavioral States,” Journal of Agricultural, Biological and Environmental Statistics, 22, 313–334. DOI: https://doi.org/10.1007/s13253-017-0296-3.
- Scharf, H. R., Hooten, M. B., Fosdick, B. K., Johnson, D. S., London, J. M., Durban, J. W. (2016), “Dynamic Social Networks Based on Movement,” The Annals of Applied Statistics, 10, 2182–2202. DOI: https://doi.org/10.1214/16-AOAS970.
- Scharf, H. R., Hooten, M. B., Johnson, D. S., and Durban, J. W. (2018), “Process Convolution Approaches for Modeling Interacting Trajectories,” Environmetrics, 29, e2487. DOI: https://doi.org/10.1002/env.2487.
- Siettos, C., Anastassopoulou, C., Russo, L., Grigoras, C., and Mylonakis, E. (2015), “Modeling the 2014 Ebola Virus Epidemic–agent-based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone,” PLoS Currents, 7.
- Simmonds, J., Gómez, J. A., and Ledezma, A. (2019), “The Role of Agent-based Modeling and Multi-agent Systems in Flood-based Hydrological Problems: A Brief Review,” Journal of Water and Climate Change, 11, 1580–1602. DOI: https://doi.org/10.2166/wcc.2019.108.
- Smith, D. L., Lucey, B., Waller, L. A., Childs, J. E., and Real, L. A. (2002), “Predicting the Spatial Dynamics of Rabies Epidemics on Heterogeneous Landscapes,” Proceedings of the National Academy of Sciences, 99, 3668–3672. DOI: https://doi.org/10.1073/pnas.042400799.
- Smith, R. (2013), Uncertainty Quantification: Theory, Implementation, and Applications (Vol. 12). Philadelphia, PA: SIAM.
- Snijders, T. A. (1996), “Stochastic Actor-oriented Models for Network Change,” Journal of Mathematical Sociology, 21, 149–172. DOI: https://doi.org/10.1080/0022250X.1996.9990178.
- Strandburg-Peshkin, A., Farine, D. R., Couzin, I. D., and Crofoot, M. C. (2015), “Shared Decision-making Drives Collective Movement in Wild Baboons,” Science, 348, 1358–1361. DOI: https://doi.org/10.1126/science.aaa5099.
- Tavaré, S., Balding, D. J., Griffiths, R. C., and Donnelly, P. (1997), “Inferring Coalescence Times from DNA Sequence Data,” Genetics, 145, 505–518. DOI: https://doi.org/10.1093/genetics/145.2.505.
- Trenti, M., and Hut, P. (2008), “N-body Simulations (Gravitational),” Scholarpedia, 3, 3930. DOI: https://doi.org/10.4249/scholarpedia.3930.
- Turchin, P. (1998), Quantitative Analysis of Movement, New York, NY: Sinauer Associates.
- Venkatramanan, S., Lewis, B., Chen, J., Higdon, D., Vullikanti, A., and Marathe, M. (2018), “Using Data-driven Agent-based Models for Forecasting Emerging Infectious Diseases,” Epidemics, 22, 43– 49. DOI: https://doi.org/10.1016/j.epidem.2017.02.010.
- Von Neumann, J., Burks, A. W. et al. (1966), “Theory of Self-reproducing Automata,” IEEE Transactions on Neural Networks, 5, 3–14.
- Wikle, C., and Hooten, M. (2015), “Hierarchical Agent-based Spatio-temporal Dynamic Models for Discrete Valued Data,” in Handbook of Discrete-Valued Time Series, chapter 16. Boca Raton, FL: Chapman and Hall/CRC Press.
- Wikle, C. K. (2003), “Hierarchical Bayesian Models for Predicting the Spread of Ecological Processes,” Ecology, 84, 1382–1394. DOI: https://doi.org/10.1890/0012-9658(2003)084[1382:HBMFPT.2.0.CO;2]
- Williams, P. J., Hooten, M. B., Womble, J. N., Esslinger, G. G., Bower, M. R., and Hefley, T. J. (2017), “An Integrated Data Model to Estimate Spatiotemporal Occupancy, Abundance, and Colonization Dynamics,” Ecology, 98, 328–336. DOI: https://doi.org/10.1002/ecy.1643.
- Zheng, X., Zhong, T., and Liu, M. (2009), “Modeling Crowd Evacuation of a Building Based on Seven Methodological Approaches,” Building and Environment, 44, 437–445. DOI: https://doi.org/10.1016/j.buildenv.2008.04.002.