Zeitschriftenartikel: „Complexity“

1

Chow, Robert. "Complexity of Complexin." Biophysical Journal 106, no. 2 (January 2014): 11a. http://dx.doi.org/10.1016/j.bpj.2013.11.110.

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2

BLOWS, M. W. "Complexity for complexity's sake?" Journal of Evolutionary Biology 20, no. 1 (January 2007): 39–44. http://dx.doi.org/10.1111/j.1420-9101.2006.01241.x.

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3

Shoemaker, Jessica. "Complexity's Shadow: American Indian Property, Sovereignty, and the Future." Michigan Law Review, no. 115.4 (2017): 487. http://dx.doi.org/10.36644/mlr.115.4.complexity.

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This Article offers a new perspective on the challenges of the modern American Indian land tenure system. While some property theorists have renewed focus on isolated aspects of Indian land tenure, including the historic inequities of colonial takings of Indian lands, this Article argues that the complexity of today’s federally imposed reservation property system does much of the same colonizing work that historic Indian land policies—from allotment to removal to termination—did overtly. But now, these inequities are largely overshadowed by the daunting complexity of the whole land tenure structure. This Article introduces a new taxonomy of complexity in American Indian land tenure and explores in particular how the recent trend of hypercategorizing property and sovereignty interests into ever-more granular and interacting jurisdictional variables has exacerbated development and self-governance challenges in Indian country. This structural complexity serves no adequate purpose for Indian landowners or Indian nations and, instead, creates perverse incentives to grow the federal oversight role. Complexity begets complexity, and this has created a self-perpetuating and inefficient cycle of federal control. Stepping back and reviewing Indian land tenure in its entirety—as a whole complex, dynamic, and ultimately adaptable system—allows the introduction of new, and potentially fruitful, management techniques borrowed from social and ecological sciences. Top-down Indian land reforms have consistently intensified complexity’s costs. This Article explores how emphasizing grassroots experimentation and local flexibility instead can create critical space for more radical, reservation-by-reservation transformations of local property systems into the future.

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4

Keune, Hans. "Critical complexity in environmental health practice: simplify and complexify." Environmental Health 11, Suppl 1 (2012): S19. http://dx.doi.org/10.1186/1476-069x-11-s1-s19.

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5

Pöltner, P., and T. Grechenig. "Organic Finance Framework: Aligning Financing Complexity with Organisational Complexity (for Innovative Companies)." International Journal of Trade, Economics and Finance 11, no. 6 (December 2020): 156–62. http://dx.doi.org/10.18178/ijtef.2020.11.6.682.

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The organic finance framework is a new tool for managing the challenges of corporate financing. This framework is especially useful for small and medium-sized enterprises in the time of a crisis, such as the COVID-19 pandemic. At its core, the framework forces a rethink of the manner in which companies initiate their financing approach. In contrast to finding potential external sources of finance, the organic finance framework starts by looking at the relevant stakeholders of the company. Alternative financing methods, such as crowdfunding and crowdinvesting, have demonstrated that companies can work with potential future customers at an early stage in the company lifecycle to finance the development of an offering. Thus, the organic finance framework presents a global structural visualisation of the corporate financing domain that can help business owners to better align the lifecycle of a company with its funding sources.

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6

Read, Dwight, and Claes Andersson. "Cultural complexity and complexity evolution." Adaptive Behavior 28, no. 5 (January 20, 2019): 329–58. http://dx.doi.org/10.1177/1059712318822298.

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We review issues stemming from current models regarding the drivers of cultural complexity and cultural evolution. We disagree with the implication of the treadmill model, based on dual-inheritance theory, that population size is the driver of cultural complexity. The treadmill model reduces the evolution of artifact complexity, measured by the number of parts, to the statistical fact that individuals with high skills are more likely to be found in a larger population than in a smaller population. However, for the treadmill model to operate as claimed, implausibly high skill levels must be assumed. Contrary to the treadmill model, the risk hypothesis for the complexity of artifacts relates the number of parts to increased functional efficiency of implements. Empirically, all data on hunter-gatherer artifact complexity support the risk hypothesis and reject the treadmill model. Still, there are conditions under which increased technological complexity relates to increased population size, but the dependency does not occur in the manner expressed in the treadmill model. Instead, it relates to population size when the support system for the technology requires a large population size. If anything, anthropology and ecology suggest that cultural complexity generates high population density rather than the other way around.

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Goldreich, Oded, Rafail Ostrovsky, and Erez Petrank. "Computational Complexity and Knowledge Complexity." SIAM Journal on Computing 27, no. 4 (August 1998): 1116–41. http://dx.doi.org/10.1137/s0097539795280524.

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8

LINIAL, NATI, and ADI SHRAIBMAN. "Learning Complexity vs Communication Complexity." Combinatorics, Probability and Computing 18, no. 1-2 (March 2009): 227–45. http://dx.doi.org/10.1017/s0963548308009656.

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This paper has two main focal points. We first consider an important class of machine learning algorithms: large margin classifiers, such as Support Vector Machines. The notion of margin complexity quantifies the extent to which a given class of functions can be learned by large margin classifiers. We prove that up to a small multiplicative constant, margin complexity is equal to the inverse of discrepancy. This establishes a strong tie between seemingly very different notions from two distinct areas.In the same way that matrix rigidity is related to rank, we introduce the notion of rigidity of margin complexity. We prove that sign matrices with small margin complexity rigidity are very rare. This leads to the question of proving lower bounds on the rigidity of margin complexity. Quite surprisingly, this question turns out to be closely related to basic open problems in communication complexity, e.g., whether PSPACE can be separated from the polynomial hierarchy in communication complexity.Communication is a key ingredient in many types of learning. This explains the relations between the field of learning theory and that of communication complexity [6, l0, 16, 26]. The results of this paper constitute another link in this rich web of relations. These new results have already been applied toward the solution of several open problems in communication complexity [18, 20, 29].

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Lachish, Oded, Ilan Newman, and Asaf Shapira. "Space Complexity Vs. Query Complexity." computational complexity 17, no. 1 (April 2008): 70–93. http://dx.doi.org/10.1007/s00037-008-0239-z.

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10

Iván Tarride, Mario. "The complexity of measuring complexity." Kybernetes 42, no. 2 (February 2013): 174–84. http://dx.doi.org/10.1108/03684921311310558.

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11

Barton, C. Michael. "Complexity, Social Complexity, and Modeling." Journal of Archaeological Method and Theory 21, no. 2 (October 26, 2013): 306–24. http://dx.doi.org/10.1007/s10816-013-9187-2.

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12

Crano, Ricky D. "A Context for Complexism: Between Neoliberal Social Thought and Algorithmic Art." Open Cultural Studies 2, no. 1 (October 1, 2018): 341–52. http://dx.doi.org/10.1515/culture-2018-0031.

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Abstract Among the many genres of visual art to emerge in the wake of computerisation, the subset of generative or algorithmic art known as complexism seems uniquely keyed to the social and technological mainsprings of everyday life in the twenty-first century. Complexism typically deploys computer algorithms to demonstrate how complex phenomena can emerge through the reiterative enactment of simple rulesets. The light and sound installations and the videos that complexist artists produce, alongside the discourses surrounding the works, stand out as singularly contemporary, not necessarily for their exploitation of now-ubiquitous telematic tools and techniques, but for their deep commitment to the trailblazing problems, methods, and hypotheses set out by the new science of complexity. Practitioners of and commentators on complexism (the work and writings of Philip Galanter feature most prominently here) persistently invoke this booming interdisciplinary field of complexity research. Against this trend, I argue that for all the leverage the tools and terms of complexity science supply to complexist art, the concept of complexity itself remains surprisingly vague and shorn of any historical sensibility. One preliminary aim of this essay is to bring more theoretical rigour to the artists’ use of this concept by beginning to fill in the missing backstory. From there, I move to complicate this genealogy by introducing a somewhat controversial figure-the social theorist, political economist, and legal philosopher Friedrich Hayek, who had posited similar problems concerning the emergence and maintenance of complex, self-organized systems as early as the 1930s, and whose theoretical solutions to these problems were instrumental to what historians and sociologists have subsequently described as capitalism’s late “neoliberal turn.”

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13

Fink, B. Raymond. "Complexity." Science 231, no. 4736 (January 24, 1986): 319. http://dx.doi.org/10.1126/science.231.4736.319.c.

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14

Levy, Ellen K. "Complexity." Leonardo 27, no. 1 (1994): 75. http://dx.doi.org/10.2307/1575955.

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15

Gómez-Hernández, J. Jaime. "Complexity." Ground Water 44, no. 6 Understanding (November 2006): 782–85. http://dx.doi.org/10.1111/j.1745-6584.2006.00222.x.

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16

SILVERT, WILLIAM. "COMPLEXITY." Journal of Biological Systems 04, no. 04 (December 1996): 585–91. http://dx.doi.org/10.1142/s0218339096000375.

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Complexity is a property of models, not of systems. It is shown that the complexity of a system is not a well-defined quantity, and that the complexity implicit in a model is connected to the amount of information about the system (in an information-theoretic sense) that the model is able to process.

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17

Standish, Russell K. "Complexity." International Journal of Signs and Semiotic Systems 3, no. 1 (January 2014): 27–45. http://dx.doi.org/10.4018/ijsss.2014010103.

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The term complexity is used informally both as a quality and as a quantity. As a quality, complexity has something to do with our ability to understand a system or object—people understand simple systems, but not complex ones. On another level, complexity is used as a quantity, when people talk about something being more complicated than another. In this article, the author explores the formalisation of both meanings of complexity, which happened during the latter half of the twentieth century.

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18

Smith, Brian D. "Complexity." Journal of Medical Marketing 9, no. 3 (July 2009): 185–86. http://dx.doi.org/10.1057/jmm.2009.28.

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19

Galanter, Philip, Ellen K. Levy, Manuel A. Báez, Jonathan Callan, Remo Campopiano, Guy Marsden, Jonathan Schull, et al. "Complexity." Leonardo 36, no. 4 (August 2003): 259–67. http://dx.doi.org/10.1162/002409403322258583.

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20

Davie, George. "Complexity." Annals of Probability 29, no. 4 (October 2001): 1426–34. http://dx.doi.org/10.1214/aop/1015345756.

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21

Keil, Ode R. "Complexity." Journal of Clinical Engineering 27, no. 2 (2002): 83–84. http://dx.doi.org/10.1097/00004669-200202720-00003.

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22

Benı́tez-Bribiesca, Luis. "Complexity." Archives of Medical Research 31, no. 1 (January 2000): 1–2. http://dx.doi.org/10.1016/s0188-4409(99)00086-7.

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23

Trabesinger, Andreas. "Complexity." Nature Physics 8, no. 1 (December 22, 2011): 13. http://dx.doi.org/10.1038/nphys2198.

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24

FINK, B. R. "Complexity." Science 231, no. 4736 (January 24, 1986): 319. http://dx.doi.org/10.1126/science.231.4736.319-b.

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25

Sporns, Olaf. "Complexity." Scholarpedia 2, no. 10 (2007): 1623. http://dx.doi.org/10.4249/scholarpedia.1623.

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26

Urry, John. "Complexity." Theory, Culture & Society 23, no. 2-3 (May 2006): 111–15. http://dx.doi.org/10.1177/0263276406062818.

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The term ‘complexity’ has recently sprung into the physical and social sciences, humanities and semi-popular writings. ‘Complexity’ practices are constituted as something of a self-organizing global network that is spreading ‘complexity’ notions around the globe. There is a new ‘structure of feeling’ that complexity approaches both signify and enhance. Such an emergent structure of feeling involves a greater sense of contingent openness to people, corporations and societies, of the unpredictability of outcomes in time–space, of a charity towards objects and nature, of the diverse and non-linear changes in relationships, households and persons, and of the sheer increase in the hyper-complexity of products, technologies and socialities.

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27

Gough, David. "Complexity." Evidence & Policy: A Journal of Research, Debate and Practice 8, no. 4 (November 1, 2012): 415–16. http://dx.doi.org/10.1332/174426412x660151.

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28

Fontana, Walter, and Susan Ballati. "Complexity." Complexity 4, no. 3 (January 1999): 14–16. http://dx.doi.org/10.1002/(sici)1099-0526(199901/02)4:3<14::aid-cplx3>3.0.co;2-o.

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29

Euchner, Jim. "Complexity." Research-Technology Management 67, no. 1 (January 2, 2024): 12–14. http://dx.doi.org/10.1080/08956308.2023.2280480.

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30

Leabu, Mircea, and Valentin Muresan. "Preface: Life's Complexity, Complexity of Ethics." Ethics in Biology, Engineering and Medicine: An International Journal 6, no. 1-2 (2015): 45–47. http://dx.doi.org/10.1615/ethicsbiologyengmed.2015016125.

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31

Turner, Barbara J., and Leona Cuttler. "The Complexity of Measuring Clinical Complexity." Annals of Internal Medicine 155, no. 12 (December 20, 2011): 851. http://dx.doi.org/10.7326/0003-4819-155-12-201112200-00009.

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32

Knor, Martin, and Riste Škrekovski. "Wiener Complexity versus the Eccentric Complexity." Mathematics 9, no. 1 (December 31, 2020): 79. http://dx.doi.org/10.3390/math9010079.

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Let wG(u) be the sum of distances from u to all the other vertices of G. The Wiener complexity, CW(G), is the number of different values of wG(u) in G, and the eccentric complexity, Cec(G), is the number of different eccentricities in G. In this paper, we prove that for every integer c there are infinitely many graphs G such that CW(G)−Cec(G)=c. Moreover, we prove this statement using graphs with the smallest possible cyclomatic number. That is, if c≥0 we prove this statement using trees, and if c<0 we prove it using unicyclic graphs. Further, we prove that Cec(G)≤2CW(G)−1 if G is a unicyclic graph. In our proofs we use that the function wG(u) is convex on paths consisting of bridges. This property also promptly implies the already known bound for trees Cec(G)≤CW(G). Finally, we answer in positive an open question by finding infinitely many graphs G with diameter 3 such that Cec(G)<CW(G).

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Machta, J. "Natural complexity, computational complexity and depth." Chaos: An Interdisciplinary Journal of Nonlinear Science 21, no. 3 (September 2011): 037111. http://dx.doi.org/10.1063/1.3634009.

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34

Freeberg, Todd M. "Social Complexity Can Drive Vocal Complexity." Psychological Science 17, no. 7 (July 2006): 557–61. http://dx.doi.org/10.1111/j.1467-9280.2006.01743.x.

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Wang, M. Z. "Linear complexity profiles and jump complexity." Information Processing Letters 61, no. 3 (February 1997): 165–68. http://dx.doi.org/10.1016/s0020-0190(97)00004-5.

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36

Wilson, T., T. Holt, and T. Greenhalgh. "Complexity science: Complexity and clinical care." BMJ 323, no. 7314 (September 22, 2001): 685–88. http://dx.doi.org/10.1136/bmj.323.7314.685.

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37

Kjos-Hanssen, Bjørn. "On the Complexity of Automatic Complexity." Theory of Computing Systems 61, no. 4 (July 6, 2017): 1427–39. http://dx.doi.org/10.1007/s00224-017-9795-4.

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38

Xu, Kexiang, Aleksandar Ilić, Vesna Iršič, Sandi Klavžar, and Huimin Li. "Comparing Wiener complexity with eccentric complexity." Discrete Applied Mathematics 290 (February 2021): 7–16. http://dx.doi.org/10.1016/j.dam.2020.11.020.

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39

Frank, Kenneth A., and Kim Bernstein. "“Complexity, Complexity, Complexity”: An Introduction to the Special Issue on Adoption." Psychoanalytic Perspectives 10, no. 1 (March 2013): 1–9. http://dx.doi.org/10.1080/1551806x.2013.768873.

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BAUWENS, B., and A. SHEN. "COMPLEXITY OF COMPLEXITY AND STRINGS WITH MAXIMAL PLAIN AND PREFIX KOLMOGOROV COMPLEXITY." Journal of Symbolic Logic 79, no. 2 (June 2014): 620–32. http://dx.doi.org/10.1017/jsl.2014.15.

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AbstractPéter Gács showed (Gács 1974) that for every n there exists a bit string x of length n whose plain complexity C(x) has almost maximal conditional complexity relative to x, i.e., $C\left( {C\left( x \right)|x} \right) \ge {\rm{log}}n - {\rm{log}}^{\left( 2 \right)} n - O\left( 1 \right)$ (Here ${\rm{log}}^{\left( 2 \right)} i = {\rm{loglog}}i$.) Following Elena Kalinina (Kalinina 2011), we provide a simple game-based proof of this result; modifying her argument, we get a better (and tight) bound ${\rm{log}}n - O\left( 1 \right)$ We also show the same bound for prefix-free complexity.Robert Solovay showed (Solovay 1975) that infinitely many strings x have maximal plain complexity but not maximal prefix complexity (among the strings of the same length): for some c there exist infinitely many x such that $|x| - C\left( x \right) \le c$ and $|x| + K\left( {|x|} \right) - K\left( x \right) \ge {\rm{log}}^{\left( 2 \right)} |x| - c{\rm{log}}^{\left( 3 \right)} |x|$ In fact, the results of Solovay and Gács are closely related. Using the result above, we provide a short proof for Solovay’s result. We also generalize it by showing that for some c and for all n there are strings x of length n with $n - C\left( x \right) \le c$ and $n + K\left( n \right) - K\left( x \right) \ge K\left( {K\left( n \right)|n} \right) - 3K\left( {K\left( {K\left( n \right)|n} \right)|n} \right) - c.$ We also prove a close upper bound $K\left( {K\left( n \right)|n} \right) + O\left( 1 \right)$Finally, we provide a direct game proof for Joseph Miller’s generalization (Miller 2006) of the same Solovay’s theorem: if a co-enumerable set (a set with c.e. complement) contains for every length a string of this length, then it contains infinitely many strings x such that$|x| + K\left( {|x|} \right) - K\left( x \right) \ge {\rm{log}}^{\left( 2 \right)} |x| - O\left( {{\rm{log}}^{\left( 3 \right)} |x|} \right).$

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Elvina, Elvina, and Oscar Karnalim. "Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner." ComTech: Computer, Mathematics and Engineering Applications 8, no. 1 (March 31, 2017): 21. http://dx.doi.org/10.21512/comtech.v8i1.3783.

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Based on the informal survey, learning algorithm time complexity in a theoretical manner can be rather difficult to understand. Therefore, this research proposed Complexitor, an educational tool for learning algorithm time complexity in a practical manner. Students could learn how to determine algorithm time complexity through the actual execution of algorithm implementation. They were only required to provide algorithm implementation (i.e. source code written on a particularprogramming language) and test cases to learn time complexity. After input was given, Complexitor generated execution sequence based on test cases and determine its time complexity through Pearson correlation. An algorithm time complexity with the highest correlation value toward execution sequence was assigned as its result. Based on the evaluation, it can be concluded this mechanism is quite effective for determining time complexity as long as the distribution of given input set is balanced.

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Natera, Jose Miguel, and Fulvio Castellacci. "Transformational complexity, systemic complexity and economic development." Research Policy 50, no. 7 (September 2021): 104275. http://dx.doi.org/10.1016/j.respol.2021.104275.

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43

Papin, Marielle. "Institutional complexity is complexity with an adjective." Complexity, Governance & Networks 6, no. 1 (February 15, 2021): 82. http://dx.doi.org/10.20377/cgn-101.

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A review of the studies on institutional complexity reveals that the many definitions of institutional complexity and related concepts share similarities with the understanding of complexity and complex systems of complexity science. Yet few publications on institutional complexity engage explicitly with complexity science. Most observers still confuse complicated and complex systems, for instance. Furthermore, the variety of definitions may create disarray regarding what institutional complexity and its related concepts are and what they imply. Highlighting the similarities between institutional complexity and complexity science in global governance, this think piece offers a conceptual and operational definition of institutional complexity using a complexity science lens. It highlights the attributes and properties of institutional complexity. It also presents the benefits of such an approach. Besides offering advantages in terms of concept clarification, this approach aims to engage theoretically, epistemologically, and methodologically with the complexity of global governance, as well as propose a way to answer remaining questions on this crucial topic.

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Yang, J., R. Lusk, and W. H. Li. "Organismal complexity, protein complexity, and gene duplicability." Proceedings of the National Academy of Sciences 100, no. 26 (December 5, 2003): 15661–65. http://dx.doi.org/10.1073/pnas.2536672100.

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45

Chambers, William V. "INTEGRATIVE COMPLEXITY, COGNITIVE COMPLEXITY AND IMPRESSION FORMATION." Social Behavior and Personality: an international journal 13, no. 1 (January 1, 1985): 27. http://dx.doi.org/10.2224/sbp.1985.13.1.27.

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Personal construct integrative complexity (I-C) refers to the assimilation of complex information into a system of impressions. Consistent with Kelly's (1955) theory of personal constructs, Chambers (1983; 1985) found I-C subjects tended to use a credulous approach to life and were better at resolving conflicting information in forming impressions. In similar research, Crockett et al. (1975) showed a measure of cognitive complexity (C-C) interacted with a credulous cognitive set to be predictive of conflict resolution. In the present study, I-C and C-C are compared, in interaction with cognitive set, as predictors of conflict resolution.

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Rodriguez-Toro, C. A., S. J. Tate, G. E. M. Jared, and K. G. Swift. "Complexity metrics for design (simplicity + simplicity = complexity)." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217, no. 5 (May 1, 2003): 721–25. http://dx.doi.org/10.1243/095440503322011461.

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This paper presents an introduction to concepts of complexity in support of assembly-oriented design, to guide the designer in creating a product with the most effective balance of manufacturing and assembly difficulty. The goal is to provide the designer with such information throughout the design process that an efficient design is produced in the first instance. In this paper, definitions and applications of the term ‘complexity’ are reviewed, and then definitions appropriate for the situation are selected. The metrics required for comparison of different complexity variants is discussed. Finally, a research agenda is presented for development of the proposed metrics within the Designers' Sandpit project, to make complexity in design a practically useful concept.

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Mier, Pablo, Lisanna Paladin, Stella Tamana, Sophia Petrosian, Borbála Hajdu-Soltész, Annika Urbanek, Aleksandra Gruca, et al. "Disentangling the complexity of low complexity proteins." Briefings in Bioinformatics 21, no. 2 (January 30, 2019): 458–72. http://dx.doi.org/10.1093/bib/bbz007.

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Abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

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48

Manson, Steven M. "Simplifying complexity: a review of complexity theory." Geoforum 32, no. 3 (August 2001): 405–14. http://dx.doi.org/10.1016/s0016-7185(00)00035-x.

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49

Kitahara, Kazuo. "Challange to Complexity; (1) Science of Complexity." Journal of the Institute of Television Engineers of Japan 50, no. 1 (1996): 60–67. http://dx.doi.org/10.3169/itej1978.50.60.

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Kunneman, Harry. "General Complexity, Ethical Complexity and Normative Professionalization." Foundations of Science 21, no. 2 (December 20, 2014): 449–53. http://dx.doi.org/10.1007/s10699-014-9407-6.

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