Integrating simulation and signal processing in tracking complex social systems

Data that continuously track the dynamics of large populations have the potential to revolutionize how we study complex social systems. However, coping with massive, noisy, unstructured, and disparate data streams is not easy. In this paper, we describe a particle filter algorithm that integrates signal processing and simulation modeling to study complex social systems using massive, noisy, unstructured data. This integration enables researchers to specify and track the dynamics of real-world complex social systems by building a simulation model. To show the effectiveness of this algorithm, we infer city-scale traffic dynamics from the observed trajectories of a small number of probe vehicles uniformly sampled from the system. The results show that our model can not only track and predict human mobility, but also explain how traffic is generated through the movements of individual vehicles. The algorithm and its application point to a new way of bringing together modelers and data miners to turn the real world into a living lab.

[1]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[2]  Michael K. Molloy,et al.  Petri net , 2003 .

[3]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[4]  Sargur N. Srihari,et al.  Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model , 2016, NIPS.

[5]  Alex Pentland,et al.  Using the influence model to recognize functional roles in meetings , 2007, ICMI '07.

[6]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[7]  Chunming Qiao,et al.  Fine-Grained Location Extraction and Prediction with Little Known Data , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[8]  Marco Janssen,et al.  Diffusion dynamics in small-world networks with heterogeneous consumers , 2007, Comput. Math. Organ. Theory.

[9]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[10]  Michael Batty,et al.  Cities and complexity - understanding cities with cellular automata, agent-based models, and fractals , 2007 .

[11]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[12]  Darren J. Wilkinson.  Stochastic Modelling for Systems Biology , 2006 .

[13]  Chunming Qiao,et al.  Robust, Cost-Effective and Scalable Localization in Large Indoor Areas , 2014, GLOBECOM 2014.

[14]  R. Bales.  Interaction process analysis; a method for the study of small groups. , 2013 .

[15]  P. Moral.  Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications , 2004 .

[16]  YAN JIN,et al.  The virtual design team: A computational model of project organizations , 1996, Comput. Math. Organ. Theory.

[17]  P J Goss,et al.  Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Chandra R. Bhat,et al.  Integrating CEMDAP and MATSIM to Increase the Transferability of Transport Demand Models , 2015 .

[19]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[20]  Andrei. Borshchev,et al.  The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6 , 2013 .

[21]  Alex Pentland,et al.  Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems , 2012, IEEE Signal Processing Magazine.

[22]  Edoardo M. Airoldi,et al.  A Survey of Statistical Network Models , 2009, Found. Trends Mach. Learn..

[23]  Brian Litt,et al.  Parsing epileptic events using a Markov switching process model for correlated time series , 2013, ICML.

[24]  Joshua M. Epstein,et al.  Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity) , 2007 .

[25]  Alex Pentland,et al.  Modeling Influence Between Experts , 2007, Artifical Intelligence for Human Computing.

[26]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[27]  Venu Govindaraju,et al.  Virtual Networks and Poverty Analysis in Senegal , 2015, ArXiv.

[28]  Alex Pentland,et al.  Mapping Organizational Dynamics with Body Sensor Networks , 2012, 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks.

[29]  Alex Pentland,et al.  Honest Signals - How They Shape Our World , 2008 .

[30]  Zbigniew Smoreda,et al.  D4D-Senegal: The Second Mobile Phone Data for Development Challenge , 2014, ArXiv.

[31]  Katherine A. Heller,et al.  Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data , 2015, KDD.

[32]  P. Waddell.  UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning , 2002 .

[33]  Alex Pentland,et al.  Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  Daniel T Gillespie,et al.  Stochastic simulation of chemical kinetics. , 2007, Annual review of physical chemistry.

[35]  Stephen P. Borgatti,et al.  Identifying sets of key players in a social network , 2006, Comput. Math. Organ. Theory.

[36]  Winfried K. Grassmann.  Transient solutions in markovian queueing systems , 1977, Comput. Oper. Res..

[37]  Francis Heylighen,et al.  Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map , 1999, Comput. Math. Organ. Theory.

[38]  Andrew J. Evans,et al.  Dynamic calibration of agent-based models using data assimilation , 2016, Royal Society Open Science.

[39]  Alex Pentland,et al.  Modeling the co-evolution of behaviors and social relationships using mobile phone data , 2011, MUM.

[40]  Alex Pentland,et al.  Modeling Infection with Multi-agent Dynamics , 2012, SBP.