Distributed sensing and swarm sensing

Is this is a real field separate from all the things that looks similar to it? e.g.probability collectives (are they a thing?) and the nature-inspired algorithms people get disturbingly enthusiastic about (ant colonies, particle swarms, that one based on choirs…), and reliability engineering (Byzantine generals etc), …and quorum sensing? How about that?

Although this looks a little bit like collective decisions, I am thinking here of more design-oriented questions. When we say “multi agent systems” there is usually a presumption that the individual agents are fairly simple, not whole human beings. Simpler still, distributed statistics is about algorithms that approximate or approach statistics in a distributed fashion. Special case, flocking. The barriers betwixt these are permeable.

Links to those themes:

Achlioptas, Dimitris, Aaron Clauset, David Kempe, and Cristopher Moore. 2005. “On the Bias of Traceroute Sampling: Or, Power-Law Degree Distributions in Regular Graphs.” In Proceedings of the Thirty-Seventh Annual ACM Symposium on Theory of Computing, 694–703. STOC ’05. New York, NY, USA: ACM. https://doi.org/10.1145/1060590.1060693.

Akyildiz, Ian F., Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci. 2002. “A Survey on Sensor Networks.” Communications Magazine, IEEE 40 (8): 102–14. https://doi.org/10.1109/MCOM.2002.1024422.

Bianchi, P., and J. Jakubowicz. 2013. “Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization.” IEEE Transactions on Automatic Control 58 (2): 391–405. https://doi.org/10.1109/TAC.2012.2209984.

Bieniawski, Stefan, and David H. Wolpert. 2004. “Adaptive, Distributed Control of Constrained Multi-Agent Systems.” In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3, 4:1230–1. IEEE Computer Society. https://ti.arc.nasa.gov/m/profile/dhw/papers/7.pdf.

Codenotti, Bruno, and Kasturi Varadarajan. 2004. “Efficient Computation of Equilibrium Prices for Markets with Leontief Utilities.” In ICALP, 371–82. Springer. https://doi.org/10.1007/978-3-540-27836-8_33.

Degroot, Morris H. 1974. “Reaching a Consensus.” Journal of the American Statistical Association 69 (345): 118–21. https://doi.org/10.1080/01621459.1974.10480137.

Deng, Xiaotie, Christos Papadimitriou, and Shmuel Safra. 2002. “On the Complexity of Equilibria.” In Proceedings of the Thiry-Fourth Annual ACM Symposium on Theory of Computing, 67–71. STOC ’02. New York, NY, USA: ACM. https://doi.org/10.1145/509907.509920.

Freeman, R. A., Peng Yang, and K. M. Lynch. 2006. “Stability and Convergence Properties of Dynamic Average Consensus Estimators.” In 2006 45th IEEE Conference on Decision and Control, 338–43. San Diego, CA, USA: IEEE. https://doi.org/10.1109/CDC.2006.377078.

Galesic, Mirta, Daniel Barkoczi, and Konstantinos Katsikopoulos. 2018. “Smaller Crowds Outperform Larger Crowds and Individuals in Realistic Task Conditions.” Decision 5 (1): 1–15. https://doi.org/10.1037/dec0000059.

Hong, Lu, and Scott E. Page. 2004. “Groups of Diverse Problem Solvers Can Outperform Groups of High-Ability Problem Solvers.” Proceedings of the National Academy of Sciences 101 (46): 16385–9. https://doi.org/10.1073/pnas.0403723101.

Lalitha, Anusha, Tara Javidi, and Anand Sarwate. 2014. “Social Learning and Distributed Hypothesis Testing,” October. http://arxiv.org/abs/1410.4307.

Mann, Richard P., and Dirk Helbing. 2016. “Minorities Report: Optimal Incentives for Collective Intelligence,” November. http://arxiv.org/abs/1611.03899.

Mateo, David, Nikolaj Horsevad, Vahid Hassani, Mohammadreza Chamanbaz, and Roland Bouffanais. 2019. “Optimal Network Topology for Responsive Collective Behavior.” Science Advances 5 (4). https://doi.org/10.1126/sciadv.aau0999.

Navlakha, Saket, and Ziv Bar-Joseph. 2014. “Distributed Information Processing in Biological and Computational Systems.” Communications of the ACM 58 (1): 94–102. https://doi.org/10.1145/2678280.

Olfati-Saber, R. 2005. “Distributed Kalman Filter with Embedded Consensus Filters.” In 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC ’05, 8179–84. Seville, Spain: IEEE. https://doi.org/10.1109/CDC.2005.1583486.

Olfati-Saber, R. 2006. “Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory.” Automatic Control, IEEE Transactions on 51 (3): 401–20.

Olfati-Saber, R., J. A. Fax, and R. M. Murray. 2007. “Consensus and Cooperation in Networked Multi-Agent Systems.” Proceedings of the IEEE 95 (1): 215–33. https://doi.org/10.1109/JPROC.2006.887293.

Ren, W, and R W Beard. 2005. “Consensus Seeking in Multiagent Systems Under Dynamically Changing Interaction Topologies.” Automatic Control, IEEE Transactions on 50 (5): 655–61.

Samuelson, Larry. 2001. “Analogies, Adaptation, and Anomalies.” Journal of Economic Theory 97 (2): 320–66. https://doi.org/10.1006/jeth.2000.2754.

Spanos, Demetri P., Reza Olfati-Saber, and Richard M. Murray. 2005. “Dynamic Consensus on Mobile Networks.” In IFAC World Congress, 1–6. Citeseer.

Stiglitz, Joseph E. 2006. “The Contributions of the Economics of Information to Twentieth Century Economics.” The Quarterly Journal of Economics 115 (4). https://doi.org/10.1162/003355300555015.

Tumer, Kagan, and David H Wolpert. 2004. “Coordination in Large Collectives- Chapter 1.” In.

Wolpert, David H. 2006. “Advances in Distributed Optimization Using Probability Collectives.” Advances in Complex Systems 9.

Wolpert, David H, Stefan R Bieniawski, and Dev G Rajnarayan. 2011. “Probability Collectives in Optimization.”

Wolpert, David H, and John W Lawson. 2002. “Designing Agent Collectives for Systems with Markovian Dynamics.” In, 1066–73. https://doi.org/10.1145/545056.545074.

Wolpert, David H., and Kagan Tumer. 1999. “An Introduction to Collective Intelligence,” August. http://arxiv.org/abs/cs/9908014.

Wolpert, David H, Kevin R Wheeler, and Kagan Tumer. 1999. “General Principles of Learning-Based Multi-Agent Systems.” In, 77–83. https://doi.org/10.1145/301136.301167.

———. 2000. “Collective Intelligence for Control of Distributed Dynamical Systems.” EPL (Europhysics Letters) 49: 708. https://doi.org/10.1209/epl/i2000-00208-x.

Ye, Yinyu. 2008. “A Path to the Arrow–Debreu Competitive Market Equilibrium.” Mathematical Programming 111 (1-2): 315–48. https://doi.org/10.1007/s10107-006-0065-5.

Zhang, Rui, and Quanyan Zhu. 2017. “Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries,” October. http://arxiv.org/abs/1710.04677.