×

Complementary variety: when can cooperation in uncertain environments outperform competitive selection? (English) Zbl 1373.92090

Summary: Evolving biological and socioeconomic populations can sometimes increase their growth rate by cooperatively redistributing resources among their members. In unchanging environments, this simply comes down to reallocating resources to fitter types. In uncertain and fluctuating environments, cooperation cannot always outperform blind competitive selection. When can it? The conditions depend on the particular shape of the fitness landscape. The article derives a single measure that quantifies by how much an intervention in stochastic environments can possibly outperform the blind forces of natural selection. It is a multivariate and multilevel measure that essentially quantifies the amount of complementary variety between different population types and environmental states. The more complementary the fitness of types in different environmental states, the proportionally larger the potential benefit of strategic cooperation over competitive selection. With complementary variety, holding population shares constant will always outperform natural and market selection (including bet-hedging, portfolio management, and stochastic switching). The result can be used both to determine the acceptable cost of learning the details of a fitness landscape and to design multilevel classification systems of population types and environmental states that maximize population growth. Two empirical cases are explored, one from the evolving economy and the other one from migrating birds.

MSC:

92D15 Problems related to evolution
92D25 Population dynamics (general)
91D10 Models of societies, social and urban evolution
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Darwin, C., On the Origin of Species by Means of Natural Selection; Or, The Preservation of Favoured Races in the Struggle for Life, (1861)
[2] Kelly, J., A new interpretation of information rate, The Bell System Technical Journal, 35, 917-926, (1956)
[3] Latané, H. A., Criteria for choice among risky ventures, Journal of Political Economy, 67, 2, 144-155, (1959)
[4] Algoet, P. H.; Cover, T. M., Asymptotic optimality and asymptotic equipartition properties of log-optimum investment, The Annals of Probability, 16, 2, 876-898, (1988) · Zbl 0642.90016
[5] Blume, L. E.; Easley, D., Economic natural selection, Economics Letters, 42, 2-3, 281-289, (1993)
[6] Keynes, The General Theory of Employment Interest and Money, (1936), London, UK: Palgrave Macmillan, London, UK
[7] Wilson, D. J., Fiscal spending jobs multipliers: evidence from the 2009 American recovery and reinvestment act, American Economic Journal: Economic Policy, 4, 3, 251-282, (2012)
[8] Cohen, D., Optimizing reproduction in a randomly varying environment, Journal of Theoretical Biology, 12, 1, 119-129, (1966)
[9] Haccou, P.; Iwasa, Y., Optimal mixed strategies in stochastic environments, Theoretical Population Biology, 47, 2, 212-243, (1995) · Zbl 0823.92015
[10] Donaldson-Matasci, M. C.; Lachmann, M.; Bergstrom, C. T., Phenotypic diversity as an adaptation to environmental uncertainty, Evolutionary Ecology Research, 10, 4, 493-515, (2008)
[11] Kussell, E.; Leibler, S., Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments, Science, 309, 5743, 2075-2078, (2005)
[12] Salathé, M.; Cleve, J. V.; Feldman, M. W.; Salathé, M., Evolution of stochastic switching rates in asymmetric fitness landscapes, Genetics, 182, 4, 1159-1164, (2009)
[13] Levins, R., Theory of fitness in a heterogeneous environment. I. the fitness set and adaptive function, The American Naturalist, 96, 891, 361-373, (1962)
[14] Bergstrom, C. T.; Lachmann, M., Shannon information and biological fitness, Proceedings of the IEEE Workshop on Information Theory
[15] Donaldson-Matasci, M. C.; Bergstrom, C. T.; Lachmann, M., The fitness value of information, Oikos, 119, 2, 219-230, (2010)
[16] Rivoire, O.; Leibler, S., The value of information for populations in varying environments, Journal of Statistical Physics, 142, 6, 1124-1166, (2011) · Zbl 1216.92052
[17] Cherkashin, D.; Farmer, J. D.; Lloyd, S., The reality game, Journal of Economic Dynamics and Control, 33, 5, 1091-1105, (2009) · Zbl 1170.91309
[18] Permuter, H. H.; Kim, Y.-H.; Weissman, T., Interpretations of directed information in portfolio theory, data compression, and hypothesis testing, IEEE Transactions on Information Theory, 57, 6, 3248-3259, (2011) · Zbl 1365.94128
[19] Levins, R., Evolution in Changing Environments: Some Theoretical Explorations, (1968), Princeton University Press
[20] Hens, T.; Schenk-Hoppe, K. R., Evolutionary finance: introduction to the special issue, Journal of Mathematical Economics, 41, 1-2, 1-5, (2005)
[21] Cover, T. M.; Thomas, J. A., Elements of Information Theory, (2006), Hoboken, NJ, USA: Wiley-Interscience, Hoboken, NJ, USA · Zbl 1140.94001
[22] Feenstra, R. C.; Lipsey, R. E.; Deng, H.; Ma, A. C.; Mo, H., World Trade Flows, 1962–2000, (2005), National Bureau of Economic Research
[23] Hausmann, R.; Hidalgo, C. A.; Bustos, S.; Coscia, M.; Chung, S.; Jimenez, J.; Yildirim, A.; Yildirim, M. A., The Atlas of Economic Complexity: Mapping Paths to Prosperity, (2011), Harvard University Center for International Development, MIT Media Lab.
[24] UNSD, UN COMTRADE database (International Merchandise Trade Statistics (IMTS)), (2012), New York, NY, USA: United Nations Statistics Division, New York, NY, USA
[25] Pardieck, K. L.; Ziolkowski, J. D.; Hudson, M. A. R., North American Breeding Bird Survey Dataset 1966–2014, (2015), Patuxent Wildlife Research Center
[26] Hug, L. A.; Baker, B. J.; Anantharaman, K., A new view of the tree of life, Nature Microbiology, 1, 5, (2016)
[27] Guerrero, O. A.; Axtell, R. L., Employment Growth through Labor Flow Networks, PLoS ONE, 8, 5, (2013)
[28] Platt, M., Animal cognition: monkey meteorology, Current Biology, 16, 12, R464-R466, (2006)
[29] Hilbert, M., Big data for development: a review of promises and challenges, Development Policy Review, 34, 1, 135-174, (2016)
[30] Hilbert, M., The more you know, the more you can grow: an information theoretic approach to growth in the information age, Entropy, 19, 2, 82, (2017)
[31] Shannon, C. E., A mathematical theory of communication, The Bell System Technical Journal, 27, 379-423, (1948) · Zbl 1154.94303
[32] Fisher, R. A., The Genetical Theory of Natural Selection, (1930), Oxford, UK: Clarendon Press, Oxford, UK · JFM 56.1106.13
[33] Ewens, W. J., An interpretation and proof of the fundamental theorem of natural selection, Theoretical Population Biology. An International Journal, 36, 2, 167-180, (1989) · Zbl 0702.92012
[34] Frank, S. A., The price equation, fisher’s fundamental theorem, kin selection, and causal analysis, Evolution, 51, 6, 1712-1729, (1997)
[35] Price, G. R., Fisher’s “fundamental theorem“ made clear, Annals of Human Genetics, 36, 2, 129-140, (1972) · Zbl 0241.92011
[36] Crow, J. F.; Kimura, M., An introduction to population genetics theory, (1970), Hoboken, NJ, USA: Blackburn Press, Hoboken, NJ, USA · Zbl 0246.92003
[37] Kimura, M., Natural selection as the process of accumulating genetic information in adaptive evolution, Genetical Research, 2, 1, 127-140, (1961)
[38] Kullback, S.; Leibler, R. A., On Information and Sufficiency, The Annals of Mathematical Statistics, 22, 1, 79-86, (1951) · Zbl 0042.38403
[39] Akin, E., The Geometry of Population Genetics, (1979), Berlin, Germany: Springer, Berlin, Germany · Zbl 0437.92016
[40] Fujiwara, A.; Amari, S., Gradient systems in view of information geometry, Physica D: Nonlinear Phenomena, 80, 3, 317-327, (1995) · Zbl 0883.53020
[41] Harper, M., The Replicator Equation as an Inference Dynamic
[42] Sato, Y.; Akiyama, E.; Crutchfield, J. P., Stability and diversity in collective adaptation, Physica D: Nonlinear Phenomena, 210, 1-2, 21-57, (2005) · Zbl 1149.91305
[43] Frank, S. A., Natural selection. IV. The Price equation, Journal of Evolutionary Biology, 25, 6, 1002-1019, (2012)
[44] Frank, S. A., Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory, Journal of Evolutionary Biology, 25, 12, 2377-2396, (2012)
[45] Frank, S. A., Natural selection maximizes fisher information, Journal of Evolutionary Biology, 22, 2, 231-244, (2009)
[46] Hilbert, M., An information theoretic decomposition of fitness: engineering the communication channels of nature and society, Social Science Research Network, (2015)
[47] Price, G. R., Selection and Covariance, Nature, 227, 5257, 520-521, (1970)
[48] Price, G. R., Extension of covariance selection mathematics, Annals of Human Genetics, 35, 4, 485-490, (1972)
[49] UNSD, National Classifications: 870 Classifications in 154 Countries, (2014), New York, NY, USA
[50] Mafessoni, F., Limits in selection, from populations to cognition [Ph.D. thesis], (2015), Leipzig university
[51] Stearns, S. C., Daniel Bernoulli (1738): Evolution and economics under risk, Journal of Biosciences, 25, 3, 221-228, (2000)
[52] de Mazancourt, C.; Dieckmann, U., Trade-Off Geometries and Frequency-Dependent Selection, The American Naturalist, 164, 6, 765-778, (2004)
[53] Rueffler, C.; Van Dooren, T. J. M.; Metz, J. A. J., Adaptive walks on changing landscapes: levins’ approach extended, Theoretical Population Biology, 65, 2, 165-178, (2004) · Zbl 1106.92057
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.