The ‘Teacher' Pole

2010 ◽  
pp. 127-139 ◽  
Author(s):  
Jean-Paul Narcy-Combes
Keyword(s):  
Complex Systems ◽  
Learning Process

The teacher is one of the poles of our model. As is often the case in complex systems, it is difficult to observe one of the poles in isolation, since there is constant interaction between them. Chapters on the language pole, the learner poles, as well as the reflection on the learning process have reduced our description of the teacher pole to what is directly relevant to the person.The reader can initially try and see how he or she would answer the questions and compare with what is described in the chapter. A synthetic table summarizing the content of the chapter will conclude the chapter, followed by a figure illustrating the place of the teacher in the cycle. Anticipating the figure and discussing how different the anticipation is from the position defended in the book may prove a worthwhile way of reading the following pages.

Trust Learning and Estimation

2017 ◽  
pp. 317-330
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Learning Process ◽  
Learning Algorithm ◽  
Estimation Algorithm ◽  
Decision Making Process ◽  
Self Organizing Map ◽  
The Core ◽  
Self Organizing

Predict uncertainty is critic in decision making process, especially for the complex systems. This chapter aims to discuss the theory involved in Self-Organizing Map (SOM) and its learning process, SOM based Trust Learning Algorithm (STL), SOM based Trust Estimation Algorithm (STL) as well as features of generated trust patterns. Several patterns are discussed within context. Both algorithms and how they are processed have been described in detail. It is found that SOM based Trust Estimation algorithm is the core algorithm that help agent make trustworthy or untrustworthy decisions.

Supporting the Comprehension of Complex Systems with Video Narratives

2010 ◽  
pp. 412-431
Author(s):  
Weiqin Chen ◽  
Nils Magnus Djupvik
Keyword(s):  
Pilot Study ◽  
General Population ◽  
Complex Systems ◽  
Learning Process ◽  
School Students ◽  
Policy Makers ◽  
Complex Dynamic System ◽  
Simulation Tools ◽  
Video Clips ◽  
Extended Training

Complex systems are difficult to understand, and without extended training and experience, people tend to misperceive these systems. Although current simulation tools illustrate what is happening in complex systems, they lack the means to represent the narrative aspects of the exhibited behaviours, in order to provide an account for the behaviours. The goal of this research is to provide visualizations of complex dynamic system behaviours with multimedia, focusing on video narratives, and to study the implications and added values of the video clips. The target users are primarily university students in System Dynamics. The method could also be of value both to lower level school students as well as to policy makers and general population who must deal with challenging complex problems. A pilot study was conducted and the findings confirmed our prior expectations; namely, that providing the users with video clips facilitates their learning process.

Uncertainty as a monster in the science–policy interface: four coping strategies

2005 ◽  
Vol 52 (6) ◽  
pp. 87-92 ◽  
Author(s):  
Jeroen van der Sluijs
Keyword(s):  
Complex Systems ◽  
Coping Strategies ◽  
Science Policy ◽  
Learning Process ◽  
Scientific Community ◽  
The Other ◽  
The Past ◽  
Science Policy Interface ◽  
Complex Environmental Problems ◽  
Policy And Science

Using the metaphor of monsters, an analysis is made of the different ways in which the scientific community responds to uncertainties that are hard to tame. A monster is understood as a phenomenon that at the same moment fits into two categories that were considered to be mutually excluding, such as knowledge versus ignorance, objective versus subjective, facts versus values, prediction versus speculation, science versus policy. Four styles of coping with monsters in the science–policy interface can be distinguished with different degrees of tolerance towards the abnormal: monster-exorcism, monster-adaptation, monster-embracement, and monster-assimilation. Each of these responses can be observed in the learning process over the past decades and current practices of coping with uncertainties in the science policy interface on complex environmental problems. We might see this ongoing learning process of the scientific community of coping with complex systems as a dialectic process where one strategy tends to dominate the field until its limitations and shortcomings are recognized, followed by a rise of one of the other strategies. We now seem to find ourselves in a phase with growing focus on monster assimilation placing uncertainty at the heart of the science–policy and science–society interfaces.

scholarly journals THE CONCEPTUAL STRUCTURE OF PHYSICS TEXTBOOKS FOR SECONDARY SCHOOLS

2016 ◽  
Vol 4 ◽  
pp. 563-568
Author(s):  
Petra Králiková ◽  
Aba Teleki
Keyword(s):  
Secondary Schools ◽  
Complex Systems ◽  
Power Law ◽  
Learning Process ◽  
Real Life ◽  
Conceptual Structure ◽  
Invariant Distribution ◽  
Scale Invariant ◽  
Zipf's Law ◽  
Power Law Distributions

Textbooks are an essential part of the learning process, therefore they need to be written in a way that is easy to understand. In real life, we often come across complex systems with scale invariant (power law) distributions, which display a surprising degree of tolerance against errors, i.e. degree of robustness. We are confident that knowledge organized in this manner is better for usage in textbooks and promotes easier learning as content would be more intelligible. Initially, we talk about the evolution of some networks, and then we deal with the differences between Poisson and scale invariant distribution in real networks. In conclusion, we are looking for connection between scale invariant distribution and Zipf’s law.

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