792,882 research outputs found
Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network
We introduce an easily computable topological measure which locates the
effective crossover between segregation and integration in a modular network.
Segregation corresponds to the degree of network modularity, while integration
is expressed in terms of the algebraic connectivity of an associated
hyper-graph. The rigorous treatment of the simplified case of cliques of equal
size that are gradually rewired until they become completely merged, allows us
to show that this topological crossover can be made to coincide with a
dynamical crossover from cluster to global synchronization of a system of
coupled phase oscillators. The dynamical crossover is signaled by a peak in the
product of the measures of intra-cluster and global synchronization, which we
propose as a dynamical measure of complexity. This quantity is much easier to
compute than the entropy (of the average frequencies of the oscillators), and
displays a behavior which closely mimics that of the dynamical complexity index
based on the latter. The proposed toplogical measure simultaneously provides
information on the dynamical behavior, sheds light on the interplay between
modularity vs total integration and shows how this affects the capability of
the network to perform both local and distributed dynamical tasks
Web 2.0-based Collaborative Multicriteria Spatial Decision Support System: A Case Study of Human-Computer Interaction Patterns
The integration of GIS and Multicriteria Decision Analysis (MCDA) capabilities into the Web 2.0 platform offers an effective Multicriteria Spatial Decision Support System (MC-SDSS) with which to involve the public, or a particular group of individuals, in collaborative spatial decision making. Understanding how decision makers acquire and integrate decision-related information within the Web 2.0-based collaborative MC-SDSS has been one of the major concerns of MC-SDSS designers for a long time. This study focuses on examining human-computer interaction patterns (information acquisition behavior) within the Web 2.0-based MC-SDSS environment. It reports the results of an experimental study that investigated the effects of task complexity, information aids, and decision modes on information acquisition metrics and their relations. The research involved three major steps: (1) developing a Web 2.0-based analytic-deliberative MC-SDSS for parking site selection in Tehran, Iran to analyze human-computer interaction patterns, (2) conducting experiments using this system and collecting the human-computer interaction data, and (3) analyzing the log data to detect the human-computer interaction patterns (information acquisition metrics).
Using task complexity, decision aid, and decision mode as the independent factors, and the information acquisition metrics as the dependent variables, the study adopted a repeated-measures experimental design (or within-subjects design) to test the relevant hypotheses. Task complexity was manipulated in terms of the number of alternatives and attributes at four levels. At each level of task complexity, the participants carried out the decision making process in two different GIS-MCDA modes: individual and group modes. The decision information was conveyed to participants through common map and decision table information structures. The map and table were used, respectively, for the exploration of the geographic (or decision) and criterion outcome spaces.
The study employed a process-tracing method to directly monitor and record the decision makers’ activities during the experiments. The data on the decision makers’ activities were recorded as Web-based event logs using a database logging technique. Concerningiv
task complexity effects, the results of the study suggest that an increase in task complexity results in a decrease in the proportion of information searched and proportion of attribute ranges searched, as well as an increase in the variability of information searched per attribute. This finding implies that as task complexity increases decision makers use a more non-compensatory strategy. Regarding the decision mode effects, it was found that the two decision modes are significantly different in terms of: (1) the proportion of information search, (2) the proportion of attribute ranges examined, (3) the variability of information search per attribute, (4) the total time spent acquiring the information in the decision table, and (5) the average time spent acquiring each piece of information. Regarding the effect of the information aids (map and decision table) on the information acquisition behavior, the findings suggest that, in both of the decision modes, there is a significant difference between information acquisition using the map and decision table. The results show that decision participants have a higher number of moves and spend more time on the decision table than map.
The study presented in this dissertation has implications for formulating behavioral theories in the spatial decision context and practical implications for the development of MC-SDSS. Specifically, the findings provide a new perspective on the use of decision support aids, and important clues for designers to develop an appropriate user-centered Web-based collaborative MC-SDSS. The study’s implications can advance public participatory planning and allow for more informed and democratic land-use allocation decisions
Understanding and using comparative healthcare information; the effect of the amount of information and consumer characteristics and skills
<p>Abstract</p> <p>Background</p> <p>Consumers are increasingly exposed to comparative healthcare information (information about the quality of different healthcare providers). Partly because of its complexity, the use of this information has been limited. The objective of this study was to examine how the amount of presented information influences the comprehension and use of comparative healthcare information when important consumer characteristics and skills are taken into account.</p> <p>Methods</p> <p>In this randomized controlled experiment, comparative information on total hip or knee surgery was used as a test case. An online survey was distributed among 800 members of the NIVEL Insurants Panel and 76 hip- or knee surgery patients. Participants were assigned to one of four subgroups, who were shown 3, 7, 11 or 15 quality aspects of three hospitals. We conducted Kruskall-Wallis tests, Chi-square tests and hierarchical multiple linear regression analyses to examine relationships between the amount of information and consumer characteristics and skills (literacy, numeracy, active choice behaviour) on one hand, and outcome measures related to effectively using information (comprehension, perceived usefulness of information, hospital choice, ease of making a choice) on the other hand.</p> <p>Results</p> <p>414 people (47%) participated. Regression analysis showed that the amount of information slightly influenced the comprehension and the perceived usefulness of comparative healthcare information. It did not affect consumers’ hospital choice and ease of making this choice. Consumer characteristics (especially age) and skills (especially literacy) were the most important factors affecting the comprehension of information and the ease of making a hospital choice. For the perceived usefulness of comparative information, active choice behaviour was the most influencing factor.</p> <p>Conclusion</p> <p>The effects of the amount of information were not unambiguous. It remains unclear what the ideal amount of quality information to be presented would be. Reducing the amount of information will probably not automatically result in more effective use of comparative healthcare information by consumers. More important, consumer characteristics and skills appeared to be more influential factors contributing to information comprehension and use. Consequently, we would suggest that more emphasis on improving consumers’ skills is needed to enhance the use of comparative healthcare information.</p
Effective complexity of stationary process realizations
The concept of effective complexity of an object as the minimal description
length of its regularities has been initiated by Gell-Mann and Lloyd. The
regularities are modeled by means of ensembles, that is probability
distributions on finite binary strings. In our previous paper we propose a
definition of effective complexity in precise terms of algorithmic information
theory. Here we investigate the effective complexity of binary strings
generated by stationary, in general not computable, processes. We show that
under not too strong conditions long typical process realizations are
effectively simple. Our results become most transparent in the context of
coarse effective complexity which is a modification of the original notion of
effective complexity that uses less parameters in its definition. A similar
modification of the related concept of sophistication has been suggested by
Antunes and Fortnow.Comment: 14 pages, no figure
Effective Complexity and its Relation to Logical Depth
Effective complexity measures the information content of the regularities of
an object. It has been introduced by M. Gell-Mann and S. Lloyd to avoid some of
the disadvantages of Kolmogorov complexity, also known as algorithmic
information content. In this paper, we give a precise formal definition of
effective complexity and rigorous proofs of its basic properties. In
particular, we show that incompressible binary strings are effectively simple,
and we prove the existence of strings that have effective complexity close to
their lengths. Furthermore, we show that effective complexity is related to
Bennett's logical depth: If the effective complexity of a string exceeds a
certain explicit threshold then that string must have astronomically large
depth; otherwise, the depth can be arbitrarily small.Comment: 14 pages, 2 figure
On Macroscopic Complexity and Perceptual Coding
The theoretical limits of 'lossy' data compression algorithms are considered.
The complexity of an object as seen by a macroscopic observer is the size of
the perceptual code which discards all information that can be lost without
altering the perception of the specified observer. The complexity of this
macroscopically observed state is the simplest description of any microstate
comprising that macrostate. Inference and pattern recognition based on
macrostate rather than microstate complexities will take advantage of the
complexity of the macroscopic observer to ignore irrelevant noise
Predictability, complexity and learning
We define {\em predictive information} as the mutual
information between the past and the future of a time series. Three
qualitatively different behaviors are found in the limit of large observation
times : can remain finite, grow logarithmically, or grow
as a fractional power law. If the time series allows us to learn a model with a
finite number of parameters, then grows logarithmically with
a coefficient that counts the dimensionality of the model space. In contrast,
power--law growth is associated, for example, with the learning of infinite
parameter (or nonparametric) models such as continuous functions with
smoothness constraints. There are connections between the predictive
information and measures of complexity that have been defined both in learning
theory and in the analysis of physical systems through statistical mechanics
and dynamical systems theory. Further, in the same way that entropy provides
the unique measure of available information consistent with some simple and
plausible conditions, we argue that the divergent part of
provides the unique measure for the complexity of dynamics underlying a time
series. Finally, we discuss how these ideas may be useful in different problems
in physics, statistics, and biology.Comment: 53 pages, 3 figures, 98 references, LaTeX2
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