The k‐systems glitch: granulation of predictor variables

The k‐systems glitch: granulation of predictor variables Ecosystem behavior is complex and may be controlled by many factors that change in space and time. Consequently, when exploring system functions such as ecosystem “health”, scientists often measure dozens of variables and attempt to model the behavior of interest using combinations of variables and their potential interactions. This methodology, using parametric or nonparametric models, is often flawed because ecosystems are controlled by events , not variables, and events are comprised of (often tiny) pieces of variable combinations (states and substates). Most events are controlled by relatively few variables (≤4) that may be modulated by several others, thereby creating event distributions rather than point estimates. These event distributions may be thought of as comprising a set of fuzzy rules that could be used to drive simulation models. The problem with traditional approaches to modeling is that predictor variables are dealt with in total, except for interactions, which themselves must be static. In reality, the “low” piece of one variable may influence a particular event differently than another, depending on how pieces of other variables are shaping the event, as demonstrated by the k‐systems state model of algal productivity. A swamp restoration example is used to demonstrate the changing faces of predictor variables with respect to influence on the system function, depending on particular states. The k‐systems analysis can be useful in finding potent events, even when region size is very small. However, small region sizes are the result of using many variables and/or many states and substates, which creates a high probability of extracting falsely‐potent events by chance alone. Furthermore, current methods of granulating predictor variables are inappropriate because the information in the predictor variables rather than that of the system function is used to form clusters. What is needed is an iterative algorithm that granulates the predictor variables based on the information in the system function. In most ecological scenarios, few predictor variables could be granulated to two or three categories with little loss of predictive potential. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

The k‐systems glitch: granulation of predictor variables

Kybernetes, Volume 33 (5/6): 11 – Jun 1, 2004

Loading next page...
 
/lp/emerald-publishing/the-k-systems-glitch-granulation-of-predictor-variables-0o5wXOXIHE
Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
0368-492X
DOI
10.1108/03684920410534001
Publisher site
See Article on Publisher Site

Abstract

Ecosystem behavior is complex and may be controlled by many factors that change in space and time. Consequently, when exploring system functions such as ecosystem “health”, scientists often measure dozens of variables and attempt to model the behavior of interest using combinations of variables and their potential interactions. This methodology, using parametric or nonparametric models, is often flawed because ecosystems are controlled by events , not variables, and events are comprised of (often tiny) pieces of variable combinations (states and substates). Most events are controlled by relatively few variables (≤4) that may be modulated by several others, thereby creating event distributions rather than point estimates. These event distributions may be thought of as comprising a set of fuzzy rules that could be used to drive simulation models. The problem with traditional approaches to modeling is that predictor variables are dealt with in total, except for interactions, which themselves must be static. In reality, the “low” piece of one variable may influence a particular event differently than another, depending on how pieces of other variables are shaping the event, as demonstrated by the k‐systems state model of algal productivity. A swamp restoration example is used to demonstrate the changing faces of predictor variables with respect to influence on the system function, depending on particular states. The k‐systems analysis can be useful in finding potent events, even when region size is very small. However, small region sizes are the result of using many variables and/or many states and substates, which creates a high probability of extracting falsely‐potent events by chance alone. Furthermore, current methods of granulating predictor variables are inappropriate because the information in the predictor variables rather than that of the system function is used to form clusters. What is needed is an iterative algorithm that granulates the predictor variables based on the information in the system function. In most ecological scenarios, few predictor variables could be granulated to two or three categories with little loss of predictive potential.

Journal

KybernetesEmerald Publishing

Published: Jun 1, 2004

Keywords: Cybernetics; Cluster analysis; Data reduction; Dynamics

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off