Natural algorithms and influence systems

Algorithms offer a rich, expressive language for modelers of biological and social systems. They lay the grounds for numerical simulations and, crucially, provide a powerful framework for their analysis. The new area of natural algorithms may reprise in the life sciences the role differential equations have long played in the physical sciences. For this to happen, however, an "algorithmic calculus" is needed. We discuss what this program entails in the context of influence systems, a broad family of multiagent models arising in social dynamics.

[1]  Bernard Chazelle,et al.  The Dynamics of Influence Systems , 2012, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.

[2]  E. Seneta Non-negative Matrices and Markov Chains , 2008 .

[3]  Noga Alon,et al.  A Biological Solution to a Fundamental Distributed Computing Problem , 2011, Science.

[4]  Z. Bar-Joseph,et al.  Algorithms in nature: the convergence of systems biology and computational thinking , 2011, Molecular systems biology.

[5]  V. Isaeva Self-organization in biological systems , 2012, Biology Bulletin.

[6]  Valerie Isham,et al.  Non‐Negative Matrices and Markov Chains , 1983 .

[7]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[8]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[9]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[10]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

[11]  Felipe Cucker,et al.  Emergent Behavior in Flocks , 2007, IEEE Transactions on Automatic Control.

[12]  Thomas A. Henzinger,et al.  Biology as reactivity , 2011, Commun. ACM.

[13]  George E. Collins,et al.  Hauptvortrag: Quantifier elimination for real closed fields by cylindrical algebraic decomposition , 1975, Automata Theory and Formal Languages.

[14]  Rainer Hegselmann,et al.  Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology , 2006, J. Artif. Soc. Soc. Simul..

[15]  Bernard Chazelle,et al.  The Total s-Energy of a Multiagent System , 2010, SIAM J. Control. Optim..

[16]  Jan Lorenz,et al.  A stabilization theorem for dynamics of continuous opinions , 2005, 0708.2981.

[17]  Bernard Chazelle,et al.  The Convergence of Bird Flocking , 2009, JACM.

[18]  V. Blondel,et al.  Convergence of different linear and non-linear Vicsek models , 2006 .

[19]  J. Hutchinson Animal groups in three dimensions , 1999 .

[20]  Seif Haridi,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[21]  S. Strogatz From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators , 2000 .

[22]  Luc Moreau,et al.  Stability of multiagent systems with time-dependent communication links , 2005, IEEE Transactions on Automatic Control.

[23]  George E. Collins,et al.  Quantifier elimination for real closed fields by cylindrical algebraic decomposition , 1975 .

[24]  A. Winfree Biological rhythms and the behavior of populations of coupled oscillators. , 1967, Journal of theoretical biology.

[25]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[26]  Kurt Mehlhorn,et al.  Physarum can compute shortest paths , 2011, SODA.