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03: Convergence and Complexity in Technology

Breakout / Working Group
english language

Our experience in today s world is characterized by increasing complexity. The strong dynamics of our globalized and mechanized society and its fragmentation make it increasingly hard to control. An ever growing amount of information coming faster and faster forms the basis of individual decisions triggering global developments in societies. Without knowing the long-term consequences, we are profoundly changing natural global processes, and we become more and more aware that these changes are irreversible and unpredictable. We are all part of the system, we are changing and are being changed at the same time.
By focusing on concrete examples, like innovation systems, financial markets or climate change, we will discuss the chances of influencing these processes. The methods and results of complexity research enhance our understanding of these processes and show us when it becomes necessary to intervene, as for example in the case of convergence of research fields (bio- and nanotechnology) or technological applications (multifunctional electronic devices).
Will we be able to control these processes or is chaos the final consequence? Is technology part of the problem or its solution? This working group will try to answer those questions and deals with complexity in industry and society. The results may decisively influence political and entrepreneurial strategic processes.


Director and Professor, Center for Environmental Systems Research, University of Kassel Abstract
Earth Systems Modeling: Simulating the Complexity of the Earth System

When scientists first began modeling the environment, they focused on relatively small scale problems  water quality degradation in small stretches of rivers, the build-up of air pollutants in the atmosphere over cities, or the diffusion of contaminants in the soils below landfills. While much work on this scale remains to be done, the attention of many environmental modelers has shifted to a bigger scale, indeed the largest scale possible for environmental modeling  the earth system itself. The development of earth system modeling has been driven by factors such as scientific curiosity, the urgency to understand climate change processes, and the  globalization of environmental issues such as water availability and biodiversity. Many different factors have made it possible to simulate the fantastic complexity of the earth system. A key development has been the flood of earth observation data recently made available from satellites, remote sensing platforms and large-scale field campaigns. Another less apparent but equally important technological development has been the  democratization of computer power which has made considerable computational capacity affordable and available to smaller and medium-sized research groups in laboratories and universities around the world. Hence, the field of earth system modeling has been able to tap a much larger pool of scientific talent than found in the large research institutions. While science and technology have already made key contributions to the development of earth systems models, the open question is how they can help overcome the considerable hurdles now hindering further model development  How can disparate forms of knowledge coming from different scientific disciplines be interpreted in earth system models? How can the functioning of the human system and its influence on the global environment be incorporated in models on the global scale? How can earth system models better represent couplings between different parts of the earth system that lead to important feedbacks, thresholds, and surprises in the earth system? New applications of cybernetics, modeling techniques, and programming know-how can be the key to overcoming these barriers.
Professor of Philosophy, University of Paris-Sorbonne (Paris IV); Director, Department of Cognitive Studies, Ecole Normale Supérieure (ENS), Paris Abstract
A complex technology by and for a complex mind

Although all four factors (nano-bio-info-cogno: NBIC) of the convergence equation are essential, there is a principal axis around which the whole process revolves, and which has brought about its emergence in the first place. The invention of the computer has not only led to the key enabling technology for all of the tools of our age, including of course contemporary science of which the nano and bio sciences are a part; it has also given the information and cognitive sciences a  secular arm without which they would have remained essentially divided and weak. The twist is that reciprocally, the computer can be regarded as the direct progeny of the budding info-cogno sciences. The axis of revolution, then, is the historical and conceptual thread drawn between two informational-cognitive systems: the human mind, produced by nature, and the computer, produced by the human mind.
In the half-century since the computer came out of military labs, a major effort has gone into improv-ing the fit  or thickening the thread between the computer as a tool and the mind as a user. This has involved a gradual broadening of the problem situation, with an ever deeper probing of the mind s inner resources, of its material base, of the body of which it is an organic part, of the social and material environment in which the embodied mind operates.
The challenge for the coming half-century is becoming clear: the place of the solitary, localized, steady, turn-off able computer in the previous set-up is being displaced, at an ever accelerating pace, by an all-encompassing, permanent and dynamic techno sphere, whose most visible manifes-tations today are the internet and economic globalization, but which will come to include the multiple dimensions involved in the NBIC convergence. Just as we have learned to live with the computer and to improve the synergies between individual computers and individual people, we now have to learn to live with, or rather within, a cogno-info-bio-nano techno sphere, and to channel and enhance the synergies or symbiosis of the human species and this techno sphere. This is not however a sim-ple case of improving, according to traditional ergonomic methods, tool and tool-use. The talk will try and bring out some of the complexities of the co-evolutionary process which is getting underway, leaning on an inspection of what has already been taking place in the simpler framework of a society of people and computers. Our thinking here must not be restricted to is-s and may-s. It must include consideration of ought-s and should-s.
Scientific Officer, DG CONNECT, European Commission, Brussels Abstract
Harnessing complexity in socio-technological systems and in the environment

A key question at the start of the 21st century is  How to design policies that are economically, ecologically and ethically viable? I will argue that an operational answer to this question in areas such as innovation, sustainable development, and handling of large scale techno-social systems must be grounded in a  Science for complex systems .

Urgent and far-reaching decision need to be made for systems that are value-laden multi-level  systems of systems that are not predictable in a conventional scientific sense. Complex systems science acknowledges that a variety of natural, societal and technological systems follow fundamental laws that cannot be reduced to laws governing their components - system behaviour  emerges . It bridges the gap between the individual and the collective: from genes to organisms to ecosystems, from atoms to materials to products, from notebooks to the Internet, from citizens to society and thus addresses issues directly pertinent to policy making.

Complex systems science would not have emerged without ICT. ICT combines unprecedented  in vivo knowledge about systems - sensor networks in business, micro arrays in genomics, demographic real time data for social systems - with unprecedented level of realism and accuracy for modelling of systems. In particular, the capacity to link models and data at different levels of aggregation (micro-macro) will stimulate integrated modelling approaches to help decision makers better harness system complexity and to quantify and risk-assess the dangers of massive disjuncture in highly interlinked systems -both natural and anthropocentric, e.g. Internet society, transport, and climate change.

As a result of the entanglement of ICT with human, business and societal structures, up-to-now unknown types of social interaction, social structures and business models emerged: large scale service economy, P2P business like EBay, online communities, see for instance blogging and political activism, ambient infrastructures for disaster management.... There is a risk involved in getting systems highly connected via ICT and we must make informed decisions on whether the transformational role of ICT is good or bad. Complex Systems Science will allow a better understanding of the complex environment in which engineered systems exist, e.g. social and psychological dimension, regulation, ethics, markets and a better understanding of the design process for such systems which is often itself a multilevel complex human system.
Professor of Economics, University of Modena and Reggio Emilia, Italy, External Professor, Santa Fe Institute (USA) Abstract
Innovations are hard to forecast, and it is even harder to design policies that are likely to foster and nurture innovation as the basis of economic growth and development. The main reason for this is the ontological uncertainty that surrounds the development of new artifacts: in particular, when the new artifacts aren t just  better, faster, cheaper versions of existing products, but have the potential to deliver new kinds of functionality. New attributions of functionality have to be conceptualized, instantiated in artifact systems, and shared among enough people so that new agent roles can emerge that manage all the activities involved in producing, installing, exchanging, maintaining the artifacts and systems that people need to exploit the new functionality. During the processes through which these things happen, new entities, both agents and artifacts, are constantly entering the scene; new classifications of existing entities affect who relates to whom about what; new criteria of value change the way the new functionalities are perceived and artifacts that deliver them are chosen. In such contexts, reliable probability forecasts of possible outcomes aren t possible: the very terms in which these outcomes need to be expressed don t exist, rather they are under construction. So innovation policy must dispense with the policy-making tools that rely on a priori evaluation of expected economic benefit deriving from contemplated policy initiatives.
In the talk, I argue that a new set of tools must be forged to formulate and implement policies to create innovation-based policies for local development in an increasingly globalized economy. These tools will have to be based on concepts from the emerging sciences of complexity. In particular, I will focus on two key ideas and their policy implications: Lane and Maxfield s theory of generative relationships, which suggests that it may make more sense to create conditions under which competence networks with high generative potential can self-organize, than to try to construct pathways to achieve pre-conceived final results; and Edelman s concept of degeneracy of structure-function relationships in evolving systems, which challenges the idea that policy-makers should seek the  best solution to a perceived problem  or even necessarily aim for coherency among the solutions they decide to implement.
Deputy Director General and Deputy Chief Executive Officer, IIASA - International Institute for Applied Systems Analysis, Laxenburg; Emeritus Professor of Energy Economics, Vienna University of Technology, Vienna Abstract
Innovation and technology diffusion require both that opportunities are perceived and that the entrepreneurial spirit (both public and private) exists to pursue them. Because technological progress is a result of human ingenuity, it is also a human-made resource that is renewable and cumulative - as long as it is nurtured. But this nurture has a price. Innovation, especially the commercialization of novel technologies and processes, requires continual investments of effort and money in research, development, and demonstration (RD&D). Technology diffusion, in turn, depends on both RD&D, learning by doing and learning by using. Technology investments associated with diffusion are often an order of magnitude larger compared to the related RD&D efforts.

Technological change is a complex process that is associated with many deep uncertainties. Generally, it is not possible to forecast future technological  winners or  losers . The very fact that it is virtually impossible to anticipate specific future technological change to any degree of specificity is what interests researchers and innovators. Discovering new possibilities and demonstrating unanticipated possibilities is often what attracts the curiosity of researchers and innovators. Thus, the risk and opportunity are joint features of technological change rendering the process inherently unpredictable. This is an important reason why programs directed at promoting technology diffusion need to consider a range of alternative developments rather than attempting to dictate a particular direction of change or  pick a winner .

One of our research results based on modeling uncertainty and convergence of technology clusters illustrates some of the features of fundamental technological change. We generated a large ensemble of scenarios for an energy system that includes endogenous learning and spillovers among related technologies. However, both learning and spillovers were assumed to be uncertain. Both were characterized by frequency distributions (derived from historical analogues). At each time step, the uncertainty is resolved but prevails for the future periods. One of our conclusions of this cumulative learning under uncertainty was that technology clusters with stronger convergence would have lower cumulative investments compared with those associated with a wider portfolio of technology options and systems. However, technology clusters that were associated with lower costs had a much lower  likelihood of occurrence .

This gives a new twist to the idea that innovation policies should support a wide portfolio of options. While this is surely a sound strategy, it is important to support the possible convergence of different innovative technologies into new technology clusters. Through innovation spill-overs and other benefits from convergence, such clusters would tend to be associated with lower overall long-run investment costs and perhaps even some reduction of associated uncertainties. These benefits are likely to be an order of magnitude larger compared to the cost of the associated RD&D efforts that are necessary for enabling such processes of change. This is one important area where shorter-term public and private RD&D decisions may have very long-term consequences for technology diffusion and clustering reaching well into the next half of this century. A wider, all encompassing technology portfolio might be associated with similar benefits but would require significantly larger future technology investments.
Strategisches Risikomanagement, Bank Austria Creditanstalt AG, Wien Abstract
Financial Markets: Complexity and Randomness

For complex systems probability theory turns out to provide the framework for a most efficient description: A well known example is the
theory of thermodynamics as a description of multiparticle systems. In this case a deterministic description is possible in principle, however
inaccessible because of the complexity of the system.

The situation is different in the case of quantum mechanics: Here a deterministic description of observable quantities is impossible also from the theoretical point of view: The reason is the relation between observer and observed system.

Also the behaviour of financial markets is most efficiently described in terms of stochastic processes. From some point of view, one could
see this stochastic behaviour as a result of the interactions between a huge number of market participants, resembling the situation in multiparticle systems. But there is theoretical evidence, that - like in quantum mechanics - a deterministic description is impossible rather than inaccessible only: Market participants are human individuals using their knowledge about the behaviour of the market to take profit from it. Any theory allowing deterministic predictions of the market behaviour would trigger a change in the behaviour of market participants, as they would try to turn these predictions into profit. It is obvious that this change in behaviour would invalidate the predictions of the theory as not all market participants can have profits at the same time: In any trading activity the profit of one partner is associated with a loss for the other partner.

The question arises: If the theory of financial markets does not provide any prediction that allows to make profits, is it useless?
The answer is no, for two reasons: Firstly, it helps the market to be fair towards its clients by giving traders the tools for the
calculation of arbitrage free prices, and secondly, stochastic models for the market behaviour allow the market participants to manage their risks.

In a somewhat simplified picture, there is a trade off between the - from some point of view unwanted - stochastic fluctuations
of the markets inevitably generated by trading activities and the ability of the markets to provide fair prices to its clients. The efficient market optimally balances these two. The negative effects of the stochastic fluctuations can be controlled by appropriate risk management.

With respect to risk management a stochastic description is most efficient for small fluctuations around some equilibrium state. Doubts have been raised, whether individual risk management by market participants can help to guarantee such a stability of the markets. In particular, in combination with highly developed IT infrastructure individual risk management could spoil the stability of the entire market, if all participants using similar risk management tools react on some small fluctuation in precisely the same way. I am more optimistic here: Different market participants have different incentives. In technical terms this different incentives can be partly expressed in terms of different investment horizons. It is this broad spectrum of time horizons - broadened by the use of highly developed IT tools - that stabilizes the markets.
Head of Center for Innovation Systems and Policy, AIT Austrian Institute of Technology GmbH, Vienna Abstract
Insights from research in complexity have over the past three decades increasingly influenced our thinking about processes of technological change and innovation. They also helped understand the possibilities and limitations of exerting influence on these processes by means of targeted political actions. However, when it comes to providing indications for action, complexity-inspired research does not seem to deliver  strong and clearcut advice to decision-makers. Although this can be interpreted as a weakness, it should rather be seen as a realistic recognition of the inherent limitations of political action and steering of change processes. To be effective, policy must be attentive to other aspects and dimension than in conventional decision-making. Matters of framework-setting and timing of action become crucial, as do monitoring and adaptive strategic behaviour.
Against this background, some key insights from complexity research for political decision-making will be elaborated in this presentation, together with some concrete lessons and examples for how to tackle complexity in technology and innovation policy.
Deputy Director General for Innovation and Telecommunication; Head of Directorate for Innovation, Austrian Federal Ministry of Transport, Innovation and Technology, Vienna Abstract Chair
Convergence and complexity in technology

Simplicity lost: The world we live in today is increasingly perceived as complex and dynamic. We are confronted with an enormous speed of change and a high degree of fragmentation in our globalised society. We have access to huge and still growing amounts of information, which can become a problem itself rather than opening up ways to solution. Not withstanding we have to act in this complex environment. From our everyday experience, we know that the effects of our actions often were not intended, as we may have failed to take into account all the influential factors. Moreover we ourselves are part of the system. We shape our environment and are shaped by it. In all these cases  some of them we will analyse in this working group  the reduction of complexity, though being the classical methodology in science and technology development, cannot be the best solution. We have to think of new methods and tools to manage the increasing degree of complexity in our society.
Technology is  a means to fulfil a human purpose . We develop and use technology, thus changing our natural and social environment, which might require another change of technology, resulting in an irreversible, complex process. There are several dimensions within this complex process, two of which shall be tackled here: First, the (social, natural, or economic) environment itself is changing in a complex way. Second, the  evolution of technology is considered as a complex process being influenced by science, industry, and society, which can be referred to as the convergence of technologies, like in the current  nano-bio-info-cogno -convergence. Technology policy, seen as public intervention in this complex process, must be aware of these interrelations and complexities.
This working group discusses complexity concepts and methods that have been developed in different scientific disciplines like cognitive science, natural sciences, social sciences, economics and finance. It will discuss the lessons for the policy maker dealing with science and technology, while taking into account the complex structure and evolution of technology. When and by which means is it thus recommendable and feasible for the state to intervene into technology development and convergence?
Geschäftsfeld Technologiepolitik, ARC systems research GmbH, Seibersdorf Coordination

Dr. Joseph ALCAMO

Director and Professor, Center for Environmental Systems Research, University of Kassel

 Ph.D., Civil-Environmental Engineering, University of California, Davis; atmospheric science and systems analysis
1975-1981 Project Engineer, Hydroscience Inc., USA
1982-1991 Research Scholar, International Institute of Applied Systems Analysis, Austria. Co-developer of the RAINS model and of the methodology of integrated modeling of the environment.
1992-1996 Project Leader, National Institute of the Environment, The Netherlands. Led development of IMAGE 2 Model, the first spatially-resolved integrated model of global change.
since 1996 Director and Professor, Center for Environmental Systems Research, University of Kassel. Leading the development of earth system models of the global water cycle and global land use change. Developing new methods for environmental scenario analysis.

Dr. Daniel ANDLER

Professor of Philosophy, University of Paris-Sorbonne (Paris IV); Director, Department of Cognitive Studies, Ecole Normale Supérieure (ENS), Paris

1967 Assistant, Dpt. of mathematics, Faculté des sciences d'Orsay
1973 Université Paris 7
1974 Chargé d'enseignement, Université Paris-Nord
1976 maître assistant, Université Paris 7
1985 maître de conférences, Université Paris 7
1989 professeur, Dpt. of philosophy, Université de Lille III
1993 Université de Paris X-Nanterre
1999 Université de Paris-Sorbonne (Paris IV)
2001-2005 Founder and director, Dpt. of cognitive studies, Ecole normale supérieure

Ph.D. Ralph DUM

Scientific Officer, DG CONNECT, European Commission, Brussels

 M.Sc., Ph.D. in Physics, University of Innsbruck
1990-1994 Ph.D. Thesis and Research Assistant at Joint Institute of Laboratory Astrophysics, Colorado
1994-2000 Researcher, Ecole Normale Superieure, Paris
since 2001 Scientific Officer, European Commission, Brussels

PhD David P. LANE

Professor of Economics, University of Modena and Reggio Emilia, Italy, External Professor, Santa Fe Institute (USA)

1975-1994 Professor of Statistics, University of Minnesota
1981-1983 Visiting Professor, Department of Mathematics, Duke University
1986-1987 Visiting Professor, Department of Epidemiology, McGill University
1990 Visiting Professor, Statistics, Bocconi University
since 1992 External Professor, Santa Fe Institute
1992-2003 Professor of Statistics, University of Modena and Reggio Emilia
2003-2006 Professor of Economics, University of Modena and Reggio Emilia


Deputy Director General and Deputy Chief Executive Officer, IIASA - International Institute for Applied Systems Analysis, Laxenburg; Emeritus Professor of Energy Economics, Vienna University of Technology, Vienna

1973-1984 Research Scholar, Energy Systems Program, IIASA
1984-1986 Research Scholar, Science and Technology Program, IIASA
1986-1991 Leader, The Dynamics of Technological Project, IIASA
1986-1991 Acting Leader, Technology, Economy and Society Program, IIASA
1991-2000 Leader, Environmentally Compatible Energy Strategies Project, IIASA
1993-1995 Convening Lead Author, Second Assessment Report IPCC - Intergovernmental Panel on Climate Change
1993-1998 Director, Global Energy Perspectives, WEC - World Energy Council
1993-2003 Guest Professor, Technical University of Graz
1997-2000 Coordinating Lead Author, Special Report on Emissions Scenarios, IPCC
1998-2000 Convening Lead Author, WEA - World Energy Assessment
1998-2001 Lead Author, Third Assessment Report of the IPCC
2003-2005 Coordinating Lead Author, MEA - Millennium Ecosystem Assessment
2003-2007 Co-Leader, Greenhouse Gas Initiative Program, IIASA
2000-2008 Leader, Transitions to New Technologies Program, IIASA
since 2003 University Professor of Energy Economics, VUT - Vienna University of Technology
since 2005 Director, GEA - Global Energy Assessment
since 2009 Deputy Director, IIASA
since 2010 Lead Author, Fifth Assessment Report, IPCC


Strategisches Risikomanagement, Bank Austria Creditanstalt AG, Wien

 After finishing his PhD in theoretical physics at the Technical University of Vienna in June 1986 he held research positions at physics institutes of the Technical University in Karlsruhe and the Queen Mary College in London.
1990-1995 worked as assistant professor at the Institute for Theoretical Physics of the Technical University of Vienna.
 Gives lectures on theoretical physics as well as on financial risk management at Universities in Vienna. He is a regular speaker at scientific and commercial conferences on strategic risk management.
1995 risk management department of Creditanstalt in Vienna, where he developed a Value at Risk based market risk management system, which was successfully implemented in the entire Bank Austria-Creditanstalt group.

Dipl.-Ing. Dr. MA Matthias WEBER

Head of Center for Innovation Systems and Policy, AIT Austrian Institute of Technology GmbH, Vienna

1992 Dipl.-Ing. Verfahrenstechnik (Universität Stuttgart)
1993 MA Politikwissenschaften (Universität Stuttgart)
1993-2000 wissenschaftlicher Mitarbeiter und Forschungsgruppenleiter am Institute for Prospective Technological Studies IPTS der Europäischen Kommission in Sevilla (E) und Ispra (I)
  Forschungsaufenthalte an den Universitäten Manchester und Sydney
1998 Dr.rer.pol. Volkswirtschaftslehre (Universität Stuttgart)
seit 2001 Leiter Geschäftsfeld FTI-Politik, AIT
seit 2003 Lehrbeauftragter an der WU Wien
2005-2008 Co-Geschäftsführer der ARGE Innovationsorientierte nachhaltige Regionalentwicklung zwischen der Stadt Wien und AIT

Mag. Ingolf SCHÄDLER

Deputy Director General for Innovation and Telecommunication; Head of Directorate for Innovation, Austrian Federal Ministry of Transport, Innovation and Technology, Vienna

1978 Studienabschluss, Volkswirtschaft, Universität Wien
1978-1979 Studium, Internationale Politik, Paul H. Nitze School of Advanced International Studies, Johns Hopkins Universität, Bologna
1979-1980 Forschungsassistent, Wiener Institut für Entwicklungsfragen, Wien
1981 Eintritt in den öffentlichen Dienst, Referent, Bundeskanzleramt, Wien
1993 Leiter, Abteilung für Technologiepolitik und -programme, Bundesministerium für öffentliche Wirtschaft und Verkehr, Wien
2003 Leiter, Bereich Innovation; stellvertretender Sektionsleiter, Bundesministerium für Verkehr, Innovation und Technologie, Wien
2010 Übernahme des Vorsitzes, EU-Joint Programming Initiative Urban Europe

Technology Forum

show timetable


10:00 - 12:00Technology Brunch sponsored by Tiroler ZukunftsstiftungSocial
11:00 - 22:00Presentation CD-Laboratory "Biomechanics in skiing"Culture
13:00 - 13:30Welcome by the OrganisersPlenary
13:30 - 14:00Welcome StatementsPlenary
14:00 - 15:00OriginsPlenary
15:30 - 17:30Competition for TalentPlenary
19:00 - 20:15Research at the Cutting EdgePlenary
20:15 - 21:30Science and Research Models and Best PracticePlenary
21:30 - 23:30Evening Reception sponsored by Alcatel AustriaSocial


09:00 - 18:00Junior AlpbachBreakout
09:00 - 15:00Working Group 01: Science and Technology in Sport: Challenge for Industry and Benefit for PeopleBreakout
09:00 - 15:00Working Group 02: Technology Transfer: the Motor for Developing LocationsBreakout
09:00 - 15:00Working Group 03: Convergence and Complexity in TechnologyBreakout
09:00 - 15:00Working Group 04: Convergence and Excellence in ScienceBreakout
09:00 - 15:00Working Group 05: Innovative Telematics Systems in Intermodal TransportBreakout
09:00 - 15:00Working Group 06: Changes in Technology and Natural Sciences - Is Our Tertiary Education System Still Up to Date?Breakout
09:00 - 15:00Working Group 07: High-performance Materials from Nature as an Opportunity for Economic GrowthBreakout
09:00 - 15:00Working Group 08: The Reassuring HabitatBreakout
09:00 - 15:00Working Group 09: Energy Security - the Case of Hydro CarbonsBreakout
16:00 - 17:15Convergence and Complexity in Science and TechnologyPlenary
17:15 - 18:00Faith and SciencePlenary
19:00 - 20:00Atom and Eve - an Alpbach MinioperaCulture
20:00 - 23:30Reception sponsored by the Province of Lower AustriaSocial


09:00 - 10:00Energy and SecurityPlenary
10:00 - 10:30Alpbach 2006 - Resumée Junior AlpbachPlenary
11:00 - 12:30Science and DemocracyPlenary
12:30 - 13:30The Universe is a Strange PlacePlenary
13:30 - 14:30Farewell Reception sponsored by Microsoft AustriaSocial