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Systems
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Complex Adaptive Systems
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Complex Adaptive Systems

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (15 December 2017) | Viewed by 24618

Special Issue Editor

Prof. Dr. Mirsad Hadzikadic

E-Mail Website
Guest Editor
Complex Systems Institute, The University of North Carolina at Charlotte, Charlotte, NC 28213, USA
Interests: complex adaptive systems; agent-based modeling; complexity; validation; definitions of complexity; financial markets; social science modeling; economics; healthcare; human behavior; machine learning; data science

Special Issue Information

Dear Colleagues,

The world around us is getting more complex by the minute, exacerbated by the ever-changing dynamic connectivity patterns of the network of actors, organizations, states, policies, technologies, and interventions. Simulating and modelling such networks using the complex adaptive systems and agent-based modelling paradigms is critical to our ability to maintain, manage, sustain, evaluate, and improve systems.

However, we do not have the right tools for such a task. Simple platforms available today allow for rapid prototyping, but are inadequate for developing and deploying professional class solutions. The tools of the future will enable the end user to design, implement, run, analyze, test, evaluate, validate, and visualize the system and its outcomes.

However, before we can do that, we will have to develop theories that shed more light on the process of complex adaptive system definition, system evaluation, hierarchical system representation, dynamical change of the system over time, system’s representation as a dynamical network, sensitivity analysis, sensory inputs, near-real time simulation, and system verification and validation.

There are still no “killer-applications” of complex adaptive systems and agent-based modelling. There are also very few social, economic, or other policies that have been successfully implemented in the real world after being suggested by a complex adaptive system simulation. Why is that? What is it that is preventing complex adaptive systems to gain a wide-range adoption by the businesses and governments?

These and related questions are the topics of this Special Issue on “Complex Adaptive Systems”. Authors are invited to submit a paper on one or more of the areas described above. A sample, but not exhaustive, list of potential topics of interest includes:

  • Complex adaptive systems
  • Agent-based modelling and simulation
  • Evaluation, verification, and validation of agent-based models
  • Dynamical behavior of systems
  • Relationship between complex adaptive systems and network science
  • Hierarchical complex adaptive systems
  • Policy evaluations and recommendations
  • Sensitivity analysis
  • Visualization
  • Programming environments
  • Tools building
  • Automated design of agent-based models
  • Human-system interaction
  • Real-time simulations
  • Embedded complex systems
  • Implementations of complex adaptive systems in various application domains
  • Designing minimal systems
  • Big data and complex adaptive systems
  • Deep learning and complex adaptive systems
Prof. Dr. Mirsad Hadzikadic
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.



Keywords

  • complex adaptive systems
  • agent-based modeling
  • complexity
  • emergence
  • self-organization
  • nonlinearity
  • simulation
  • evaluation
  • validation
  • policy
  • system hierarchy
  • feedback
  • big data
  • deep learning

Published Papers (3 papers)

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Research

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16 pages, 1584 KiB  
Article
Innovation Emergence: Public Policies versus Actors’ Free Interaction
by Mauro Fazion Filho and Mauri L. Heerdt
Systems 2018, 6(2), 13; https://doi.org/10.3390/systems6020013 - 03 May 2018
Cited by 5 | Viewed by 7949
Abstract
The main argument of this work is that innovation flourishes and emerges in a creative environment where the actors interact freely, to the extent that this environment is a complex adaptive system. Public or institutional policies, trying to induce innovation, must be careful [...] Read more.
The main argument of this work is that innovation flourishes and emerges in a creative environment where the actors interact freely, to the extent that this environment is a complex adaptive system. Public or institutional policies, trying to induce innovation, must be careful to not stifle or interrupt the emergence of novelties in the path from creation and conception to market involvement. Our proposed model argues that innovation emerges wherever evolution, learning, mutation, and competition between individuals and firms are permitted, without restrictions or pre-defined paths to the market. We describe two cases of innovation by way of example: the first case shows how several—and sometimes anonymous—elements interact and compete in a typical environment of innovation, while the second case shows how continuous policies to foment innovation may create results to the contrary. In addition, we show technology clusters as cases where the emergence of innovation can be fostered by policies that observe the complex adaptive system characteristics. Full article
(This article belongs to the Special Issue Complex Adaptive Systems)
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2971 KiB  
Article
Social Systems: Resources and Strategies
by Pavel Brazhnikov
Systems 2017, 5(4), 51; https://doi.org/10.3390/systems5040051 - 15 Nov 2017
Cited by 2 | Viewed by 7069
Abstract
This theoretical article reviews the model describing processes in social systems based on the analysis of their resource base. Application of the system theory can help to explain why some systems are aimed at prevention of type I errors, while others seek to [...] Read more.
This theoretical article reviews the model describing processes in social systems based on the analysis of their resource base. Application of the system theory can help to explain why some systems are aimed at prevention of type I errors, while others seek to decrease the quantity of type II errors. Such differences are manifested in investment of resources either into deep interaction or into wide coverage. Some examples of such strategies in economic, market and production systems are provided in the article. The article introduces some provisions of the system theory in the context of the resource flows. The main indicators that are considered in this article are the characteristics of the sources of the exchanging flows of resources. Their relative frequency and quality are investigated; on the basis of which the most effective strategy of the system is derived; as a mechanism for redistribution of resources. The rigor of the system’s strategy depends on the magnitude of the difference in characteristics. It is explained how exactly it influences the exchange processes, that in reality systems do not interact simultaneously and one of the opposite resource flows is always delayed. It is shown how the system strategy depends on the risks linked with interactions. Also, there are grounds for the need to accumulate resources, including in the situation of their surplus. The model helps also explain shift of economic centers throughout history. Additionally, there is an analogy between systems strategies and the competitive strategies described by M. Porter and outsourcing versus integration. Full article
(This article belongs to the Special Issue Complex Adaptive Systems)
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Review

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17 pages, 1701 KiB  
Review
Floodplains and Complex Adaptive Systems—Perspectives on Connecting the Dots in Flood Risk Assessment with Coupled Component Models
by Andreas Paul Zischg
Systems 2018, 6(2), 9; https://doi.org/10.3390/systems6020009 - 05 Apr 2018
Cited by 16 | Viewed by 8743
Abstract
Floodplains, as seen from the flood risk management perspective, are composed of co-evolving natural and human systems. Both flood processes (that is, the hazard) and the values at risk (that is, settlements and infrastructure built in hazardous areas) are dynamically changing over time [...] Read more.
Floodplains, as seen from the flood risk management perspective, are composed of co-evolving natural and human systems. Both flood processes (that is, the hazard) and the values at risk (that is, settlements and infrastructure built in hazardous areas) are dynamically changing over time and influence each other. These changes influence future risk pathways. The co-evolution of all of these drivers for changes in flood risk could lead to emergent behavior. Hence, complexity theory and systems science can provide a sound theoretical framework for flood risk management in the 21st century. This review aims at providing an entry point for modelers in flood risk research to consider floodplains as complex adaptive systems. For the systems science community, the actual problems and approaches in the flood risk research community are summarized. Finally, an outlook is given on potential future coupled component modeling approaches that aims at bringing together both disciplines. Full article
(This article belongs to the Special Issue Complex Adaptive Systems)
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