From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

Consciousness, cognition, and the hierarchy of context: extending the global neuronal workspace model

We adapt an information theory analysis of interacting cognitive biological and social modules to the problem of the global neuronal workspace, the new standard neuroscience paradigm for consciousness. Tunable punctuation emerges in a natural way, suggesting the possibility of fitting appropriate phase transition power law, and away from transition, generalized Onsager relation expressions, to observational data on conscious reaction. The development can be extended in a straightforward manner to include psychosocial stress, culture, or other cognitive modules which constitute a structured, embedding hierarchy of contextual constraints acting at a slower rate than neuronal function itself. This produces a 'biopsychosociocultural' model of individual consciousness that, while otherwise quite close to the standard treatment, meets compelling philosophical and other objections to brain-only descriptions

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Null Models For Cultural And Social Evolution

Analogies between biological and cultural evolution date back to Darwin, yet the analogies have remained loose. Neutral evolution, known to be important in biology, has been proposed as a null model for cultural change, but has not developed into tests for selection on cultural features. Using inference in timeseries of alternative word forms and grammatical constructions, I demonstrate a cultural analog of natural selection on a background of netural evolution. Social evolution, on the other hand, implies selection in a social environment and therefore cannot be described with a neutral model. I propose a model of pure frequency-dependent selection as a generic null model for social evolution, and use the model to illustrate diverse effects of social selection. I derive a non-linear form of frequency-dependent selection from a mechanistic model of mate choice and show unintuitive consequences for evolutionary dynamics. I infer complex forms of frequency dependent selection—including positive and negative frequency-dependent selection at different frequencies—from data regarding the copying of baby names, the fashions of dog breeds, and the use of rare languages, and discuss the implications for cultural diversity