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12 - Poor Mayapan
- from Part IV - THE LATE POSTCLASSIC TO HISTORICAL PERIODS
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- By Clifford T. Brown, Florida Atlantic University, April A. Watson, Florida Atlantic University, Ashley Gravlin-Beman, Florida Atlantic University, Larry S. Liebovitch, City University of New York
- Edited by Geoffrey E. Braswell, University of California, San Diego
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- Book:
- The Ancient Maya of Mexico
- Published by:
- Acumen Publishing
- Published online:
- 05 April 2014
- Print publication:
- 30 April 2012, pp 306-324
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Summary
Abstract
The material culture of Mayapan (ca. A.D. 1250–1400), the last great capital city of the northern Maya lowlands, has often been described as “decadent.” Such descriptions, however, are highly subjective. In this chapter, we consider poverty and wealth at Mayapan from a perspective based in modern economics. We find that, as in modern societies, wealth (as measured by house size) at Mayapan fits a Pareto distribution. Nevertheless, compared to two Classic-period sites in Mexico—Palenque and Sayil—the distribution of wealth was more equal at Mayapan, suggesting that economic inequality was less extreme at the Postclassic city. One cause for the decadent material culture of Mayapan, therefore, was that the city was impoverished when compared to its Classic predecessors.
In this essay we analyze the magnitude and distribution of wealth at Mayapan and explore the implications of our findings for the general interpretation of the economy, society, and culture of that city. Mayapan, Yucatan, Mexico, is the largest and most important Maya archaeological site dating to the Late Postclassic period, and therefore inspires a lot of curiosity among archaeologists. Their interest is piqued because, founded by the legendary Kukulcan, Mayapan was the political capital of the largest and most powerful Maya state of its period. Because of its size and power, Mayapan also served as the social and cultural capital of the northern lowlands at the same time. Because it was a late prehistoric site, Mayapan was discussed in many historical chronicles from the early colonial period, and so we possess unusually detailed information about it.
1 - Introduction to Nonlinear Dynamics and Complexity
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- By Stephen J. Guastello, Marquette University, Larry S. Liebovitch, Florida Atlantic University
- Edited by Stephen J. Guastello, Marquette University, Wisconsin, Matthijs Koopmans, David Pincus, Chapman University, California
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- Book:
- Chaos and Complexity in Psychology
- Published online:
- 18 December 2013
- Print publication:
- 10 November 2008, pp 1-40
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Summary
Elephants and Horses
Things change. Sometimes they change gradually, sometimes dramatically. The prevailing concept of change in psychology consists of only one form of change, linear change, which is simply undifferentiated, and with the assumption that outcomes are proportional to inputs in a straightforward manner. The overreliance among psychologists and others on the general linear model as a statistical tool for depicting change has only served to reinforce this monochrome conceptualization of change. Perhaps the most significant deviations from the concept of linear change are the concepts of equilibrium and randomness. For most intents and purposes, the concept of equilibrium has been used to describe places or times when change stops occurring. Randomness suggests that the changes are unpredictable and not explicable by any known concepts or predictors.
Nonlinear dynamical systems (NDS) theory significantly enriches our capability to conceptualize change, and it provides a rich array of constructs that describe many types of change. The concept of equilibrium is no longer specific enough to describe either the change or the events that surround the point where change stops. The new constructs are the attractors, bifurcations, chaos, fractals, self-organization, and catastrophes. As this chapter explains, each of these constructs contains several more, including those associated with the “complexity” of a system. Importantly, change is not proportional to inputs. Large inputs sometimes produce small results, and a small input at the right time can produce a dramatic result.
Psychology is not the first science to break out of the linear rut.