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Introducing the oscillations based paradigm : the simulation of agents and social systems
Plikynas D., Springer International Publishing, New York, NY, 2016. 325 pp. Type: Book (978-3-319390-39-0)
Date Reviewed: Sep 20 2017

OSIMAS is based on three conceptual models: the oscillating agent model (OAM), the wave-like interaction mechanism (WIM), and the pervasive information field (PIF). This book has been prepared and produced as one of the results of the OSIMAS research project, “Creation of oscillations-based social simulation paradigm” (http://osimas.ksu.lt/). Being extremely controversial and challenging, it presents a new so-called oscillations-based paradigm. The author presents a highly innovative and original approach to the field of social multiagent system (MAS) simulation.

Apart from the presentation of conceptual theoretical notions, specific proofs of concepts are presented as validation frameworks that encompass human brain electroencephalography (EEG) signal dynamics. The book represents a mix of interdisciplinary and multidisciplinary contemporary achievements. Emergent research results in domains like artificial intelligence, multiagent systems, quantum physics, neuroscience, and social networks are intertwined and interrelated.

It is definitely an ambitious work, and potential readers should be ready to cross disciplinary boundaries and make relationships that are somewhat counterintuitive. Being innovative and even imaginative, the book might be from time to time rather difficult to read; some readers will need to consult additional resources for deeper explanations. On the other hand, for enthusiastic readers, the book offers novel methodological approaches to study the physical nature of social cognition and behavior.

This pioneering book consists of 11 chapters, all of them related to the OSIMAS paradigm, presented in a chronological way. However, some chapters can be read separately as they have their own line of thought.

The main aim of the book is briefly presented in the first chapter.

Chapter 2 focuses on the essence of information-based economic systems and social networks. Attention is then given to the use of multiagent platforms in order to simulate some economic systems.

At the beginning of chapter 3, a review is given of a range of multidisciplinary theories. Fundamental principles of coordination and self-organization as bases of quantum, cellular, and social theories are used to deeply explain the OSIMAS simulation paradigm.

In chapter 4, oscillating agent modeling based on neuroscience is presented. EEG-based experimental validation of the biologically inspired OSIMAS paradigm is realized by implementation of social agents that mimic human behavior.

Chapter 5 describes a newly created model called the coupled oscillator energy exchange model (COEEM). This model is validated through a simulation of human brain EEG signals. The proposed oscillating agent model brings new insights in EEG signal prognostication, a specific direction of neuroscience.

Chapter 6 describes a simulation of agents’ mind states based on quantum theory, a conceptually innovative modeling approach. The dynamics of basic human mind states are modeled in artificial agent societies using unique sets of brainwaves.

Chapter 7 is devoted to the OAM simulation setup. Results obtained through extensive experiments on the dynamics of different mental states and mind states are graphically presented and deeply explained.

Chapter 8 is devoted to the specific OAM-based multiagent systems. Such systems support collective mind-brain states, and simulations performed with such systems can help in understanding and explaining different social phenomena: social circadian rhythms, spatial features-based clustering, and so on.

Improved cellular mechanism-based models for economic and financial simulations represented as populations of agents are considered in chapter 9. The main idea was to study excitation information propagation in artificial neuron-based agent societies.

Chapter 10 is more oriented to possible real applications of the presented innovative modeling approaches, especially for the simulation of automated investment strategies. Specific representations of multidimensional time series are observed and tested in several types of investment strategies and activities. Some of the principles introduced in previous chapters are integrated in the large-scale artificial intelligence-based MASs.

Chapter 11 presents brief concluding remarks.

Appendices A, B, C, and D relate OSIMAS approaches to other similar theories and models.

This book offers intriguing approaches and material that could be challenging for a wide range of readers: researchers and practitioners in multidisciplinary research areas, including social scientists. Ambitious and extraordinary students, especially PhD students, could find a lot of unusual topics for definitively unconventional research.

Some readers will probably be disappointed and feel frustrated because the book is full of demanding theories that require an extensive mathematics and physics background.

Throughout the book, there are many difficult mathematical formulas, as well as many high-quality and illustrative pictures, diagrams, and tables. An extensive list of references is provided at the end of the book, which gives readers many opportunities for additional reading. The physical and technical quality of the book is very good.

Reviewer:  M. Ivanović Review #: CR145554 (1711-0717)
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