To deepen the analysis and understanding of human behaviour by using artificial societies, a credible modelling of agents and their decision processes is needed. Such a deep analysis can be informed by the psychological knowledge about processes and triggers of specific behaviours, given the interaction between an agent’s environment and some individual preference set. This requires a framework capable of handling a high number of deep, heterogeneous agents, together with their individual networks.

In this presentation, a software framework is presented that aims at providing psychological plausibility to the modelling of so-called citizen agents. It fills the gap between (flat) agent frameworks without any built-in psychological foundations on the one hand and full-fledged (deep) cognitive architectures on the other hand, the latter being computationally too intensive to be of any use in larger scale problems. The framework provides prefabricated components of an agent’s decision process like perception, memory, different modes of decision making, basic learning algorithms, or social influence. Each of these components is based on appropriate psychological empirical results or theories.

Using this framework, together with specifically gathered data, an agent’s interaction with other agents, technology, or the natural environment can be investigated. Examples include energy use, innovation adoption, or helper networks. Besides the emerging macro phenomena, their micro foundation lying in the individual preferences and processes are important data produced by the framework. Individual, local perception of the technical or physical environment and the information of the individual social network are crucial in determining an agent’s behaviour.

To model larger amounts of social data, with a higher number of agents together with their networks, the sociological concept of lifestyles can be used as a classifying element, and as a bridge to upscale the individual data to the agent set.

The advent of the web (доставка цветов Москва) has led to an ever expanding ocean of data whose outlines are hard to discern but its effects are easy to feel. It reflects a societal change that is both global and daunting at the same time. Global because it encompasses content created by people seeking attention and information from all over the world without much awareness of privacy issues. And daunting since it is hard to discern what to pay attention to when confronted with such flood of content.

This talk will describe the effects that the attention economy has on the production and consumption of big data and the opportunities that big data offers about learning the behavior of large groups of people and the design of novel socially attentive systems. It will also address the questions that big data poses about privacy and how markets for private data can restore a sense of control to most users of the web.

What can we learn from the way society is organized? What are the underlying principles? How can we use them to design new technological systems?

Along the lines of these guiding questions, I will start with models of pedestrians and crowds, and the applications in logistics and traffic light control they have inspired.

It will be shown that self-organization is a wide-spread principle underlying the emergence of social coordination, cooperation, and social norms. These phenomena can now be dynamically modeled based on evolutionary principles, which are transferable to ICT systems as well. I will also discuss, how and why the wrong kinds of system designs can lead to breakdowns of traffic flows, cooperation, or financial markets. It will be argued that the theory of complex systems can help to provide an explanatory understanding of desirable and undesirable cascading effects and guidelines for the design of socially interactive systems (such as the 'ecosystem' of financial trading algorithms).