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.

Prof. Dr. Andreas Ernst achieved his psychology Diplom degree with a focus on psychology of knowledge/cognitive science in 1988. He finished his PhD—entitled "Social knowledge as basis of decisions in conflict situations"—in 1993. Until 1999 he was assistant professor at the Institute of Psychology, University of Freiburg i. Br. and in the same year he finished his postdoctoral lecture qualification under the title: "Information dilemmas and the use of natural resources". After another three years at the Institute of Psychology, University of Freiburg i.Br. he became full professor for Environmental Systems Analysis at the Center for Environmental Systems Research of the University of Kassel. He also heads the Graduate Program "Man-Environment-Systems" (ProMUS) and is the current president of the European Social Simulation Association (ESSA). Prof. Dr. Andreas Ernst is co-editor of the book series "Social Science Simulations" of the Metropolis-Verlag.

Personal Website

The advent of the web 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.

Bernardo A. Huberman is a Senior HP Fellow and director of the Social Computing Research Group at HP Labs, which focuses on methods for harvesting the collective intelligence of groups of people in order to realize greater value from the interaction between users and information. Huberman’s main research focus is on the relationship between local actions and the global behavior of large, distributed systems. Areas of exploration include distributed knowledge, social organizations and the economics of attention. Much of Huberman's research has concentrated on the World Wide Web, with an emphasis on the dynamics of its growth and use. This work helped uncover the nature of electronic markets, the detailed structure of the web and the laws governing the way people surf for information. One of the originators of the field of ecology of computation, Huberman recently published the book, "The Laws of the Web: Patterns in the Ecology of Information," with MIT Press.

Personal Website

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).

Dirk Helbing has worked as Managing Director of the Institute for Transport & Economics at TU Dresden and is now Professor of Sociology, in particular of Modeling and Simulation at ETH Zurich. Having studied physics and mathematics, he investigates complex social, economic, and transport systems with methods from statistical physics, individual-based models, and behavioral experiments. Helbing is well-known for the social force model, in particular its application to self-organization phenomena in pedestrian crowds. Besides the slower-is-faster effect, he introduced the freezing-by-heating effect and the phase diagram of congested traffic states. Recent work applies principles of collective intelligence and dynamics to the optimization of freeway and urban traffic flows. In game theory, Helbing proposed a microscopic foundation of evolutionary game theory and studied self-organized behavioral conventions early on. His current work develops socio-inspired technologies and investigates the role of success-driven motion for the establishment of cooperation among selfish individuals.

Personal Website