This is the book of Abstracts from the 2018 Conference on Complex Systems held in Thessaloniki, Greece, 23-28 September, 2018.

Source: zenodo.org

In Rise of the Self-Replicators we delve into the deep history of thought about machines, AI and robots that can reproduce and evolve. Although these might seem like very modern concepts, we show that people were thinking about them as far back as the mid-1600s and that the discussion gathered pace in the 1800s following the British Industrial Revolution and the publication of Darwin’s On The Origin of Species.

Behind all of the work we discuss lie two central questions:

1. Is it possible to design robots and other machines that can reproduce and evolve just like biological organisms do?
2. And, if so, what are the implications: for the machines, for ourselves, for our environment, and for the future of life on Earth and elsewhere?

Source: www.tim-taylor.com

An up-to-date overview of the field of connectomics, introducing concepts and mechanisms underlying brain network change at different stages.
The human brain undergoes massive changes during its development, from early childhood and the teenage years to adulthood and old age. Across a wide range of species, from C. elegans and fruit flies to mice, monkeys, and humans, information about brain connectivity (connectomes) at different stages is now becoming available. New approaches in network neuroscience can be used to analyze the topological, spatial, and dynamical organization of such connectomes. In Changing Connectomes, Marcus Kaiser provides an up-to-date overview of the field of connectomics and introduces concepts and mechanisms underlying brain network changes during evolution and development.

Drawing on a range of results from experimental, clinical, and computational studies, Kaiser describes changes during healthy brain maturation and during brain network disorders (including such neurodevelopmental conditions as schizophrenia and depression), brain injury, and neurodegenerative disorders including dementia. He argues that brain stimulation is an area where understanding connectome development could help in assessing long-term effects of interventions. Changing Connectomes is a suitable starting point for researchers who are new to the field of connectomics, and also for researchers who are interested in the link between brain network organization and brain and cognitive development in health and disease. Matlab/Octave code examples available at the MIT Press website will allow computational neuroscience researchers to understand and extend the shown mechanisms of connectome development.

Source: www.amazon.com

With the recent developments in computing technologies and the thriving research scene in Complexity Science, economists and other social scientists have become aware of a more flexible and promising alternative for modelling socioeconomic systems; one that, in contrast with neoclassical economics, advocates for the realism of the assumptions, the importance of context and culture, the heterogeneity of agents (individuals or organisations), and the bounded rationality of individuals who behave and learn in multifaceted ways in uncertain environments. The book synthesises an extensive body of work in the field of social complexity and constructs a unifying framework that allows developing concrete applications to important socioeconomic problems. This one-of-a-kind textbook provides a comprehensive panorama for advanced undergraduates and graduate students who want to become familiar with a wide range of issues related to social complexity. It is also a pioneering text that can support professors who wish to learn techniques and produce research in this novel field.
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After reviewing the main concepts, premises and implications of complexity theory, the book frames this vision within the history of economic thought. Then, it articulates a meta-theory in which interdependent agents are embedded in a social context and whose collective and decentralised behaviour generates socio-economic phenomena. Such a framework builds on theories from evolutionary, institutional and behavioural economics, as well as analytical sociology. The book then reviews different computational tools for modelling complex adaptive systems, such as cellular automata, networks, and agent-based models. It elaborates on their analytical advantages in comparison to equation-based models, and how they can be calibrated/estimated and validated with empirical data. Finally, the book advocates for the practical use of these computational tools and makes a case for policy applications and the study of causal mechanisms.

Source: www.social-complexity.com

Source: www.amazon.com