FuturICT Austria

What is FuturICT?

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FuturICT Austria/Slovenia: Support Document


The FuturICT flagship proposal intends to unify hundreds of the best scientists in Europe in a 10 year 1 billion EUR program to explore social life on earth and everything it relates to. The FuturICT flagship proposal will produce historic breakthroughs and provide powerful new ways to manage challenges that make the modern world so difficult to predict, including the financial crisis. http://www.futurict.eu

FuturICT Austria/Slovenia

The Austria/Slovenia hub of FuturICT is constituted as a regional support team for the Flagship proposal. It involves a number of groups and institutions in the areas of ICT, Complexity Science and quantitative social science. In its constitutional meeting on July 1, 2011 it was stressed that the identification of concrete research visions on a regional level must be a primary goal at this stage, preferably in a bottom up manner. FuturICT Austria/Slovenia will play the coordination role for potential funding distributed within the region through FuturICT, including the handling of reporting, validation, call writing etc. It will represent the regional interests within the appropriate international steering committees of FuturICT.

Identifying excellence in ICT, Social- & Complexity Science in Austria/Slovenia

We identify the following specific strengths in the Austrian/Slovenian research landscape that directly relate to the aims and goals of FuturICT.

Themes in ICT

Modern ICT (Information and Communication Technology) has developed a vision where the "computer" is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situative services originating in a digital world, which are perceived through the physical world. It is expected that services with explicit user input and output will be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. The nature and appearance of computing devices will change to be hidden in the fabric of everyday life, invisibly networked, and omnipresent. Applications and services will have to be greatly based on the notions of context and knowledge, and will have to cope with highly dynamic environments and changing resources. "Context" refers to any information describing the situation of an entity, like a person, a thing or a place. "Knowledge" refers to the memorized experience items aggregated across individuals in a society, like fact items, problem solving competencies or procedural knowledge. Interaction with such ICT rich environments will presumably be more implicit, at the periphery of human attention, rather than explicit, i.e. at the focus of attention.

  1. Socially Aware ICT (JKU/OFAI) A yet underexplored impact of modern ICT (like e.g. pervasive and ubiquitous computing) relates to services exploiting the "social context" of individuals towards the provision of quality-of-life technologies that aim at the wellbeing of individuals and the welfare of societies. This position paper therefore is concerned with the intersection of social behavior and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, omnipresent and participative technologies. An explosive growth of social computing applications such as blogs, email, instant messaging, social networking (Facebook, MySpace, Twitter, LinkedIn etc.), wikis and social bookmarking is observed, profoundly impacting social behavior and life style of human beings while at the same time pushing the boundaries of ICT simultaneously. We aim to investigate interface technologies for one important phenomenon in humans, namely that of "social awareness". We aim at providing human environment interfaces, which allow individuals and groups to sense, explore and understand their social context.

  2. Socio-Emotional Processes (OFAI) The study of collective emotional processes in Internet-based interactions provides rich evidence of their influence on the creation, formation and breaking up of online communities. The growing impact of these spontaneously evolving online communities -- in which emotional factors often play a crucial role, affecting events in the offline world and influencing opinion formation processes -- is widely acknowledged. Especially, the last years provided numerous examples of their eminence in the fields of politics, sports, entertainment, culture, economics and on tight coupling between collective emotions expressed in the Internet and their impact on the events reaching far beyond the online world. Thus it is indispensable for the creation of socially intelligent systems to go far beyond the current state-of-the-art and to be able to detect and categorize a complex set of semantic, pragmatic, stylistic and sentiment oriented dimensions of messages representing and predicting the complex behavior of users in the Internet, the process of formation of collective emotions and their multifaceted impact on user preferences, decisions and actions in the online and offline world. Socially Adaptive Interactive Tools significantly contribute to the creation of sustainable, socio-inspired ICT systems that socially adapt to users, their respective context, and to individual and collective needs.

  3. Socio-Technical Fabric (JKU) Ensembles of digital artifacts (appliances, tools and everyday objects with integrated electronics and communication capabilities) as compounds of huge numbers constitute a future generation of socially interactive ICT, to which we refer to as Socio-Technical Fabric, weaving social and technological phenomena into the 'fabric of technology-rich societies'. Indications of evidence for such large scale, complex, technology rich societal settings are facts like 1012 -1013 "things" or "goods" being traded in (electronic) markets today, 109 personal computer nodes and 109 mobile phones on the internet, 108 cars or 108 digital cameras with sophisticated embedded electronics - even for internet access on the go etc. Today's megacities approach sizes of 107 citizens. Already today some 108 users are registered on Facebook, 108 videos have been uploaded to YouTube, like 107 music titles have been labeled on last.fm, etc. Our research is thus going away from single user or small user group ICT research issues, and is now heading more towards complex socio-technical systems, i.e. large scale to very large scale deployments of ICT and the respective societal concerns.

  4. Socio-Technical ICT Foundations (JKU/OFAI) Issues arising from the creation, development, deployment and use of ICT at the societal level spawn a whole new research space, demanding foundational underpinning right from the beginning. We refer to the ICT grounded on societal considerations as socio-inspired ICT. From both theoretical and technological perspectives, socio-inspired ICT moves beyond social information processing, towards emphasizing social intelligence. Among the challenges are issues of (i) modeling and analyzing social behavior facilitated with modern ICT, (ii) the provision access opportunities and participative technologies, (iii) the reality mining of societal change induced by omnipresent ICT, (iv) the establishment of social norm and individual respect, as well as (v) the means of collective choice and society controlled welfare, e.g. by capturing human social dynamics, and by creating artificial social agents and generating and managing actionable social knowledge.

  5. Self-organizing networked embedded systems (KLA) are an important enabler for building large complex embedded systems, which are robust, scalable, and adaptive. The general economic advantages of such an approach will be shorter-time-to-market, reduced system cost, and lower maintenance cost. Such systems will penetrate society via different applications based on basic research results on complex and self-organizing systems. Examples are algorithms for coordinating robots (cooperative search missions for micro-copters, surveillance tasks, carpet cleaning robots) and self-organizing algorithms for robust sensor networks (e.g. air quality monitoring, water monitoring, structural health monitoring, health monitoring, agricultural environment monitoring, energy efficiency monitoring, mine monitoring).

  6. Smart Grid (KLA). The transformation of our energy system to a complex interacting system of all users (generators and consumers) promises a more efficient and robust provision of energy. To optimize the operation of the system, all its users (generators and consumers) will be integrated into a smart grid, where local interactions support balancing energy consumption and production based on demand and availability. Therefore, it is necessary to define interaction protocols, predict network effects and elaborate a model of the users' behavior. This topic connects traditional engineering research with complex systems and social systems.

  7. Linked Data (SRFG/KMT/OFAI). To harvest and integrate the data from heterogeneous sources (e.g. data pools, the social web), a unified method of accessing data in different schemas is needed. Such a method is evolving in the Linked Data initiative, and various data pools are already published according to their guidelines, e.g. DBPedia, GeoNames, and Facebook. However, there are many open problems evolving with the deployment of Linked Data on a large scale. Specifically, issues of data extraction, data quality, real-time data, data provenance, data reliability and trustworthiness need to be addressed to allow deriving reliable information from the web of data. Such deployment will however be necessary to achieve the overall goals of the FutureICT project. The KMT group at Salzburg Research would investigate the following research issues:

    • How to generate and make accessible as Linked Data ("triplify") data from heterogeneous information sources, e.g. sensors, databases, textual content, media content? How to extract "social information", e.g. sentiments, interactions between people?
    • How to measure and ensure data quality along the data value chain (e.g. from sensor raw data to published abstracted data, from human-readable content via information extraction, face recognition in images or videos etc.)
    • How to track the provenance of data, i.e. from which sources does the data come from on which decisions are based, how reliable are these sources? How reliable will the derived information be based on the reliability of the source datasets?

    This work is complemented with OFAI's longstanding expertise in the development and application of natural language processing and understanding tools being employed for ontology creation and population, as well as for quality and reliabiltiy assessment.

  8. Trust in ICT (SRFG/ANC). The penetration of ICT systems in people's daily lives is already in an advanced state and it is still progressing. "Smart" technologies are becoming present in every private and economic domain. However, trust in such technologies is often a limiting factor in the exploitation of innovations, and trustful data sources are a strong requirement to reach the FuturICT objectives. Approved reliability starting on the sensor level as well as transparency in the complete data collection and processing path are the keys to provide trustable data source infrastructures. We therefore see the necessity to assess and quantify the reliability of the data and if possible improve it e.g. by correlation of multiple data sources. Therefore, the following research issues will be targeted:

    • How to ensure trust in data, e.g. via digital signatures, approved datasets, networks of trust, reputation systems?
    • How to process and publish highly distributed real-time data, e.g. sensor data, as Linked Data (which is currently mostly static)?
    • How to deal with inconsistencies and errors in the data sets and their transmissions, e.g. how to keep the error small in case some sources are unreliable or temporarily unavailable?

Themes in the Social Sciences

Complexity in the Social Sciences - the macro-micro-macro link complexity and complexity science have become widely accepted frameworks because they provide concepts needed to investigate and understand systems composed from heterogeneous and interacting entities. Often systems are considered complex if some degree of unintended order is achieved as the result of interaction. In contrast to neoclassical concepts relying on an invisible hand ensuring that social systems converge toward a stable equilibrium, complexity science is the science of out-of-equilibrium systems. It takes into account individual and possibly conflicting goals and intentions and explains emergent properties arising from local interactions rather than well-advised interventions of a central planner. It is often criticised that social sciences suffer from a poor level of precision in the theoretical construction and a statistical modeling that is insufficiently theory-driven. Partially this is due to the inadequateness of the methodological toolbox to answer relevant questions. Empirical approaches typically assume a constant association of cause and effect. In real social systems information is transmitted via social networks imposing spatial and temporal constraints on social interactions. Individual behavioural decisions are the outcome of interactions with the partner, friends, colleagues and relatives. The social structure determines the timing and the topology of interpersonal communication and the institutional structure determines the non-personal environment. The institutional structure mostly limits the individuals' ability, feasibility and acceptability to pursue a certain type of behaviour. The inclusion of agent based modelling and systematic and comparative investigations offers new possibilities to develop cognitive valid behavioural theories and to speculate on the consequences of alterative micro-macro feedbacks in order to explain observed patterns.

  1. Modeling complex innovation systems (AIT/F&PD) The link between innovation in organizations and the performance of economies is a major unresolved issue in innovation economics. To address the issue, we will develop and implement an agent-based model of innovation that can serve as a computational laboratory for simulating innovation arising from knowledge production and exchange processes. The model will comprise both theoretical underpinnings - from knowledge economics, industrial economics, and social networks - and solid empirical validation to ensure applicability to specific sectoral and spatial contexts.

  2. Self-organization in oligopolistic (technology) markets shaping environmental innovation trajectories (AIT/F&PD) It is difficult to predict technology development and innovation in an oligopolistic market setting. The interdependence of decisions of the key market players usually leads to complex game-theoretic situations that have ambiguous outcomes. Agent-based modeling is one way to analyze such oligopolistic systems. We will apply agent based modeling to the field of environmental technologies. The research question is how oligopolistic settings shape the trajectories of environmental innovation processes, particularly focusing on critical bifurcation points and lock-ins that are suboptimal regarding the eventual effects on the natural environment.

  3. Analyses of the systemic feedback from self-organizing entities on infrastructure (AIT/F&PD) Interactive simulation has been used to support analyses of feedback-driven systemic changes. Using the concept of multi-agent system modeling combined with an interactive stakeholder involvement, we will explore the systemic feedback on infrastructure. These analyses will be established on different time and spatial resolutions accounting for the spatial as well as the time-dependent constraints shaping the system behavior. We will include analyses of spatio-temporal access patterns of selected infrastructure facilities, including transportation hubs (railway stations, airports …), shopping areas and recreation areas, applying analyses on GSM mobile device tracking data for spatial movement behavior to access those infrastructures.

  4. Investigating the dynamics of European R&D networks (AIT/F&PD) Research & Development (R&D) networks are widely considered to be crucial elements for successful innovation, and, thereby, for the economic competitiveness of organizations, regions and countries. Thus, the analysis of the dynamics of R&D networks is of central importance for enhancing our understanding on how such networks modify knowledge diffusion in complex innovation systems. For the European case, recent empirical studies have considered the evolution of R&D networks by focusing on their structure at different points in time. An extension to include richer dynamics may significantly further our understanding, providing into the evolution of R&D networks structures insight at the micro- and the macro levels. We will use systematic information from the EUPRO database, constructed and maintained by AIT, to capture the large-scale pan-European network of actors performing joint R&D. We will employ methods from (spatial) econometrics, including dynamic spatial panel data models, as well as methods from the statistical physics of complex networks, developing these further as necessary for the investigation of European R&D network dynamics.

  5. Complexity of social interactions (TU Wien) During the last decade a new sub-discipline of socio-economics has been developed which avoids the core paradigm of equilibrium theory and market forces. Nobody´s initiation into some good or bad habit is created in a Robinson Crusoe manner rather than by social interactions. The individual addiction to consume alcohol, to smoke, to use illicit drugs etc. crucially depends on the prevalence in a reference group. In a nutshell, social interactions mean that the individual behavior is strongly influenced by the macro-level (compare Tom Schelling´s crucial idea of micro-motives and macro-behavior). While it is well known that the inherent non-linearities of such socio-economic interactions generate complex behavior, including multiple equilibra, persistent limit cycles and chaos, further research is needed in the creation and solution of economic models. In particular, the economics of crime, and, more generally, of "deviant" behavior provides a field of applications par excellence. To mention only a few key topics: corruption, illicit drug consumption, violence, counter-terrorism etc.

Themes of Complexity Science

  1. Systemic risk in economic systems (MUV) Starting on a decade background in agent based modeling and experience with institutions and policy makers, we design regulation mechanisms for financial markets. Aim is to increase systemic stability within an ever-evolving system. These models are based on new developments of co-evolutionary dynamics and fed with massive data from the real economy, the later posing nontrivial ICT issues.

  2. Artificial worlds - understanding collective socio-economic behavior (MUV) Human collective behavior is poorly understood. In particular experimental data on a multi-relational level is hardly available. Our aim is to use complete information of human (artificial) societies such as available in massive multiplayer online games to experimentally understand human behavior, herding behavior in particular. We plan to design role playing games as large scale human behavior laboratories. We aim to understand the minimum multi-relational data structures, necessary to make testable predictions in real world societies. Collecting behavioral electronic fingerprints poses highly non-trivial ICT challenges.

  3. Multi-relational data analysis in mass-health issues (MUV) Western public health systems become increasingly expensive, some even non-financeable. The availability of almost complete nation-wide data records of all medical treatments allows for systemic efficiency analysis which allows to base strategic decisions on fully rational and transparent grounds. These datasets further allow to design early warning and trend identification systems in public health, improving institutional preparedness to act. These analyses rest heavily on recent developments in network theory of time-varying graphs, community detection in particular.

  4. Theory of Complex Adaptive Systems (MUV) We continuously work on methodological progress of quantitatively understanding complex adaptive systems, especially the statistics of correlated and strongly interacting systems, network theory and multi-relational analysis.

  5. Evolution of complex systems (KLA, MUV) In order to design complex systems, we employ evolutionary algorithms to find the micro-behavior rules, which lead the overall system to the intended macro behavior. This approach is especially important for future networked systems, which are becoming more and more complex, leading to a breakdown of traditional methods for design, implementation and maintenance.

  6. Complex network dynamics driven by node and link properties (AIT/F&PD) The last decade has witnessed tremendous advances in understanding the structure and dynamics of networks, deducing important system properties based purely on the relations encoded in the network links. We anticipate that the next decade will manifest an equally great focus on the impact of characteristics of the system elements corresponding to the network nodes and links, with interest in both how those characteristics evince dynamic changes in the network structure and how relations in the network influence the system constituents. We will explore the interplay of node and link properties with dynamical changes in the network structure, with particular focus on development of the network community structure and of those nodes providing a unifying global backbone to the network. This will be applied to understanding politically induced changes in European collaboration networks.

  7. Predictive identification of emerging areas in knowledge markets (AIT/F&PD) The accelerating pace at which knowledge is created, accumulated, and spread has profoundly intensified the dynamics of scientific and technological progress in knowledge markets. Showing strong competition and interdependencies as well as "knowledge spillovers," the identification and monitoring of new growth opportunities for entering knowledge markets has become increasingly complex through a richly heterogeneous landscape of many institutions and through the continuous adjustment of actors to changing markets conditions. To this end, we will develop new information-theoretic methods for "digital foot-printing" and time-dependent visualization with limited or no evidence for the evaluation of emerging areas. We build upon a hybrid approach of both semantic clustering techniques and generalized relational maps between "co-occurrences" based on historical, contemporary, and simulated future data for respective data categories of input, output, or outcome in knowledge markets. Ultimately, this will directly contribute to illuminating characteristic routes to emerging areas and foster analyzing and discussing institutions, technologies, and social regulations that can facilitate the efficient production and use of knowledge.

Specialty in the institutional setup and computing facilities

The Austria/Slovenia region finds itself in an ideal starting position for the intended FuturICT initiative due to a number of leading scientists and institutions with long standing traditions in complexity science, ICT and related fields. A particular strength is the diversity of research issues and at the same time the connectedness of scientists. There exist countless examples of scientific co-operations between the supporting institutions and the expressed willingness to grow together on project oriented science questions. A specialty of the Austria/Slovenia support team is the mix of academic (Medical University of Vienna, Josef Stefan Institute, University of Vienna, JKU, …), policy oriented (IIASA, Salzburg Research, …) and institutions with a strong focus on applications (JKU, Lakeside Labs …).

There exist two supercomputing facilities which could be interested in supporting the initiative (TO CONTACT).

The Institutions

Names of active supporters (PIs, coordinators, project leaders, scientists)

Christian Bettstetter, University of Klagenfurt and Lakeside Labs - christian.bettstetter@uni-klu.ac.at

Ulf Dieckmann (IIASA) - dieckmann@iiasa.ac.at

Wilfried Elmenreich, University of Klagenfurt - wilfried.elmenreich@aau.at

Gustav Feichtinger - gustav@eos.tuwien.ac.at

Thomas Fent (ÖAW), Vienna Institute of Demography of the Austrian Academy of Sciences, Wittgenstein Centre for Demography and Global Human Capital - thomas.fent@oeaw.ac.at

Alois Ferscha (JK Univ Linz) - ferscha@soft.uni-linz.ac.at

Steffen Fritz (IIASA) - fritz@iiasa.ac.at

Josef Fröhlich, AIT Austrian Institute of Technology GmbH, Foresight & Policy Development Department

Ulrich Hofmann - ulrich.hofmann@salzburgresearch.at

Brigitte Krenn (OFAI) - brigitte.krenn@ofai.at

Helga Nowotny (ERC,WWTF) - helga.nowotny@wwtf.at

Michael Obersteiner (IIASA) - michael.obersteiner@gmail.com

Sebastian Schaffert - sebastian.schaffert@salzburgresearch.at

Marcin Skowron (OFAI) marcin.skowron@ofai.at

Michael Stampfer (WWTF) - Michael.Stampfer@wwtf.at

Felix Strohmeier - felix.strohmeier@salzburgresearch.at

Bosiljka Tadic, Jožef Stefan Institute, Ljubljana

Stefan Thurner, Medical University of Vienna, IIASA - stefan.thurner@meduniwien.ac.at

Detlof von Winterfeld (IIASA) detlof@iiasa.ac.at

Steering Committee
Alois Ferscha
Stefan Thurner (speaker)
Bosiljka Tadic


Stefan Thurner
Section for Science of Complex Systems, CeMSIIS
Medical University of Vienna
Spitalgasse 23
A 1090 Vienna, Austria
+43 (0)1 40160 36251