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020 _a9789401704175
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024 7 _a10.1007/978-94-017-0417-5
_2doi
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_2bicssc
072 7 _aMAT034000
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082 0 4 _a515.625
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100 1 _aSedaghat, H.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aNonlinear Difference Equations
_h[electronic resource] :
_bTheory with Applications to Social Science Models /
_cby H. Sedaghat.
250 _a1st ed. 2003.
264 1 _aDordrecht :
_bSpringer Netherlands :
_bImprint: Springer,
_c2003.
300 _aXV, 388 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMathematical Modelling: Theory and Applications,
_x1386-2960 ;
_v15
505 0 _aI Theory -- 1. Preliminaries -- 2. Dynamics on the Real Line -- 3. Vector Difference Equations -- 4. Higher Order Scalar Difference Equations -- II Applications to Social Science Models -- 5. Chaos and Stability in Some Models -- 6. Additional Models.
520 _aIt is generally acknowledged that deterministic formulations of dy­ namical phenomena in the social sciences need to be treated differently from similar formulations in the natural sciences. Social science phe­ nomena typically defy precise measurements or data collection that are comparable in accuracy and detail to those in the natural sciences. Con­ sequently, a deterministic model is rarely expected to yield a precise description of the actual phenomenon being modelled. Nevertheless, as may be inferred from a study of the models discussed in this book, the qualitative analysis of deterministic models has an important role to play in understanding the fundamental mechanisms behind social sci­ ence phenomena. The reach of such analysis extends far beyond tech­ nical clarifications of classical theories that were generally expressed in imprecise literary prose. The inherent lack of precise knowledge in the social sciences is a fun­ damental trait that must be distinguished from "uncertainty. " For in­ stance, in mathematically modelling the stock market, uncertainty is a prime and indispensable component of a model. Indeed, in the stock market, the rules are specifically designed to make prediction impossible or at least very difficult. On the other hand, understanding concepts such as the "business cycle" involves economic and social mechanisms that are very different from the rules of the stock market. Here, far from seeking unpredictability, the intention of the modeller is a scientific one, i. e.
650 0 _aDifference equations.
650 0 _aFunctional equations.
650 0 _aGlobal analysis (Mathematics).
650 0 _aManifolds (Mathematics).
650 0 _aEconomic theory.
650 0 _aMicroeconomics.
650 0 _aSocial sciences.
650 1 4 _aDifference and Functional Equations.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M12031
650 2 4 _aGlobal Analysis and Analysis on Manifolds.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M12082
650 2 4 _aEconomic Theory/Quantitative Economics/Mathematical Methods.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W29000
650 2 4 _aMicroeconomics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W31000
650 2 4 _aSocial Sciences, general.
_0https://scigraph.springernature.com/ontologies/product-market-codes/X00000
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789048162154
776 0 8 _iPrinted edition:
_z9781402011160
776 0 8 _iPrinted edition:
_z9789401704182
830 0 _aMathematical Modelling: Theory and Applications,
_x1386-2960 ;
_v15
856 4 0 _uhttps://s443-doi-org.br.lsproxy.net/10.1007/978-94-017-0417-5
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