Evolution of Collective Computational Abilities of (Pre)Historic Societies - Resources

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A Special Issue was published on February 10, 2022 as a result of this workshop, available here.



Bibliographic Info Link
H. Bhatia, G. Norgard, V. Pascucci, P. Bremer, The Helmholtz-Hodge Decomposition — A Survey. IEEE Trans. Vis. Comput. Graph. 19, 1386–1404 (2013). Media:Bhatia2014natural.helmholtz.hodge.decomp.pdf
H. Bhatia, V. Pascucci, P. T. Bremer, The natural Helmholtz-Hodge decomposition for open-boundary flow analysis. IEEE Trans. Vis. Comput. Graph. 20, 1566–1578 (2014). Media:Bhatia2013helmholtz.hodge.decomp.survey.pdf
R. H. Coase, The Nature of the Firm. Economica. 4, 386–405 (1937). Media:Coase1937nature.of.the.firm.pdf
A. Frishman, P. Ronceray, Learning Force Fields from Stochastic Trajectories. Phys. Rev. X. 10, 21009 (2020). Media:Frishman2020force.fields.limits.pdf
J. L. Garcia-Palacios, Introduction to the theory of stochastic processes and Brownian motion problems. arXiv Prepr. condmat/0701242, 1–104 (2007). Media:Garcia.palacios2007intro.stochastic.processes.pdf
G. A. Johnson, in Social Archeology: Beyond Subsistence and Dating, C. L. Redman, W. T. Langhorne, M. J. Berman, N. M. Versaggi, E. V. Curtin, J. C. Wanser, Eds. (Academic Press, New York, 1978), pp. 87–105. Media:Johnson1978InformationSources.pdf
G. A. Johnson, Decision-Making Organization and Pastoral Nomad Camp Size. Hum. Ecol. 11, 175–199 (1983). Media:Johnson1984scalarstress.pdf
K. J. Meier, J. Bohte, Span of Control and Public Organizations: Implementing Luther Gulick’s Research Design. Public Adm. Rev. 63, 61–70 (2003). Media:Meier2003span.control.pdf
J. Shin, M. H. Price, D. H. Wolpert, H. Shimao, B. Tracey, T. A. Kohler, Scale and information-processing thresholds in Holocene social evolution. Nat. Commun. 11 (2020), doi:10.1038/s41467-020-16035-9. Media:Shin2020_scaleinformationthresholds.pdf
M. E. Smith. Metadata on cross‐cultural/temporal variation in civic building types by society. Prepared for the Working group, “Evolution of collective computational abilities of (pre)historic societies,” at the Santa Fe Institute, Nov 2‐4, 2020. Media:Smith-CivicArchitecture-MetaData.pdf
Strang, A. Applications of the Helmholtz-Hodge Decomposition to Networks and Random Processes. (Case Western Reserve University, 2020). Media:Strang2020hemholtzdecomp.pdf
G. Sugihara, R. May, H. Ye, C. H. Hsieh, E. Deyle, M. Fogarty, S. Munch, Detecting causality in complex ecosystems. Science (80-. ). 338, 496–500 (2012). Media:Sugihara2012causality.pdf Media:Sugihara2012causality_sup.pdf
Y. Tong, S. Lombeyda, A. N. Hirani, M. Desbrun, Discrete multiscale vector field decomposition. ACM SIGGRAPH 2003 Pap. SIGGRAPH ’03, 445–452 (2003). Media:Tong2012discrete.multiscale.vector.field.decomp.pdf
P. Turchin, T. E. Currie, H. Whitehouse, P. François et al. Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization (2017), vol. 115. Media:Turchin2017SESHAT.pdf
C. Yildiz, M. Heinonen, J. Intosalmi, H. Mannerstrom, H. Lahdesmaki, Learning stochastic differential equations with Gaussian processes without gradient matching. IEEE Int. Work. Mach. Learn. Signal Process. MLSP. 2018-September (2018), doi:10.1109/MLSP.2018.8516991. Media:Yildiz2018learning.stoch.diff.eqs.pdf