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  1. An Iterative Information-Theoretic Approach to the Detection of Structures in Complex Systems.Marco Villani, Laura Sani, Riccardo Pecori, Michele Amoretti, Andrea Roli, Monica Mordonini, Roberto Serra & Stefano Cagnoni - 2018 - Complexity 2018:1-15.
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  • Complexity of networks (Reprise).Russell K. Standish - 2012 - Complexity 17 (3):50-61.
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  • Emergence and self‐organization in partially ordered sets.Sergio Pissanetzky - 2011 - Complexity 17 (2):19-38.
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  • Entropic Movement Complexity Reflects Subjective Creativity Rankings of Visualized Hand Motion Trajectories.Zhen Peng & Daniel A. Braun - 2015 - Frontiers in Psychology 6.
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  • Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences.Zhen Peng, Tim Genewein & Daniel A. Braun - 2014 - Frontiers in Human Neuroscience 8.
  • Modeling networked systems using the topologically distributed bounded rationality framework.Dharshana Kasthurirathna, Mahendra Piraveenan & Shahadat Uddin - 2016 - Complexity 21 (S2):123-137.
  • The sigma profile: A formal tool to study organization and its evolution at multiple scales.Carlos Gershenson - 2011 - Complexity 16 (5):37-44.
    The σ profile is presented as a tool to analyze the organization of systems at different scales, and how this organization changes in time. Describing structures at different scales as goal‐oriented agents, one can define σ ∈ [0,1] (satisfaction) as the degree to which the goals of each agent at each scale have been met. σ reflects the organization degree at that scale. The σ profile of a system shows the satisfaction at different scales, with the possibility to study their (...)
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  • Harnessing the complexity of education with information technology.Carlos Gershenson - 2015 - Complexity 20 (5):13-16.
  • Complexity and information: Measuring emergence, self‐organization, and homeostasis at multiple scales.Carlos Gershenson & Nelson Fernández - 2013 - Complexity 18 (2):29-44.
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  • Calculating entropy at different scales among diverse communication systems.Gerardo Febres & Klaus Jaffé - 2016 - Complexity 21 (S1):330-353.
  • Complexity measurement of natural and artificial languages.Gerardo Febres, Klaus Jaffé & Carlos Gershenson - 2015 - Complexity 20 (6):25-48.
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  • The complexity of partition tasks.Fernando Eesponda, Matías Vera-Cruz, Jorge Tarrasó & Marco Morales - 2010 - Complexity 16 (1):56-64.
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  • The “weight” of models and complexity.Jing Du - 2016 - Complexity 21 (3):21-35.
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  • A definition of information, the arrow of information, and its relationship to life.Stirling A. Colgate & Hans Ziock - 2011 - Complexity 16 (5):54-62.
  • Models and people: An alternative view of the emergent properties of computational models.Fabio Boschetti - 2016 - Complexity 21 (6):202-213.
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  • Scaling in structural complexity.Valerio De Biagi & Bernardino Chiaia - 2014 - Complexity 20 (1):57-63.
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  • Defining emergence: Learning from flock behavior.Manuel Berrondo & Mario Sandoval - 2016 - Complexity 21 (S1):69-78.
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  • The world as evolving information.Dr Carlos Gershenson - unknown
    This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents (...)
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