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OPEN FORUM
Symbiotic technology for creating social innovation 30 yearsin the future
Shinichi Doi • Keiji Yamada
Received: 9 March 2010 / Accepted: 11 September 2010 / Published online: 4 December 2010
� Springer-Verlag London Limited 2010
Abstract This paper discusses a way to create social
innovation around 2040. With such innovation, social
restrictions that are regarded as being inevitable in the
current society can be eliminated. First, it is necessary to
determine how to approach the innovation. Symbiotic
technology is one of the promising technologies for
achieving social innovation. It is the fusion of scientific
technology and socio-technology. Its elemental technolo-
gies are classified into two categories: technologies for
converging the real and cyber worlds and those for inte-
grating hetero-systems. This paper describes examples of
those technical categories and introduces the challenges of
the first step toward social innovation.
Keywords Open innovation � Social innovation �Symbiotic technology � Intellectual productivity � Emotion
1 Introduction
As the think tank Club of Rome indicated in 1970
(Meadows et al. 1972), global society is in a chaotic situ-
ation since the beginning of the 21st century. Economic
activities and markets are becoming increasingly global-
ized and global networks such as international
transportation and the Internet are covering the world like
strata. Therefore, an accident in one region immediately
impacts other regions thousands of miles away.
Global networks complicate causal relations of all
affairs on the earth, and they produce such a chaotic situ-
ation in traditional society that we cannot understand
behaviors of global economics or predict change in global
society. In order to create a sustainable and dependable
society over evolving global networks, we need to re-
design our society to be suitable for the development of
technologies. For this purpose, we introduce the method of
backcasting.
First, we review social trends and problems of the cur-
rent society and state our goal as ‘‘to achieve social inno-
vation and true information revolution by developing
technologies to remove the current limitations and solve
the problems.’’ Then, we draw pictures of the future society
that will appear after the problems are solved. This paper
proposes a way to create social innovation to approach
these problems. Then, it describes symbiotic technology,
which is a fusion of scientific technologies and socio-
technologies. After introducing a review of state-of-the-art
symbiotic technology, our current trials are described, in
which the activities of an entire research laboratory are
comprehensively monitored and used for collaborative
researches on innovation of intellectual productivity.
2 Social innovation
2.1 Social trends and current issues
Since the end of the eighteenth century, the industrial
revolution, called ‘‘second wave’’ by Toffler (1980), has
increased business efficiency and materialistic fulfillment.
S. Doi (&) � K. Yamada
C&C Innovation Research Laboratories,
NEC Corporation,
8916-47, Takayama-cho, Ikoma-shi,
Nara 630-0101, Japan
e-mail: [email protected]
URL: http://www.nec.co.jp/rd/en/Overview/soshiki/ccinov/
index.html
K. Yamada
e-mail: [email protected]
123
AI & Soc (2011) 26:197–204
DOI 10.1007/s00146-010-0308-0
Newly established hierarchical organizations such as
business enterprises and bureaucracy and widely spread
networks such as transportation and telecommunication
systems have introduced mass production, mass distribu-
tion, and mass consumption systems in the industrial field
and brought us drastic changes on our lives and social
structures. In Fig. 1, we can see the development of society
since then from the viewpoints of network spread and
market size. For example, the sum of GDPs around the
world has grown 70 times in the last 200 years.
Toffler also described in his book (Toffler 1980) that
many countries had moved into the post-industrial era,
called ‘‘third wave’’ at that time, where information and
knowledge play dominant roles. As he predicted, infor-
mation communication technologies, such as the Internet,
PCs, and mobiles, achieved rapid development during the
last decade of the twentieth century and are providing us
with advancement of information society and globalization
and making the layered organization flat. From then, we
have been enjoying the huge amount of benefits of the
technologies.
In these days, however, the negative effects of the
advancement of information society, industrialization, and
globalization have become conspicuous. Figure 1 also
shows the issues happening recently and to happen in near
future. Especially after the beginning of the 21st century,
human beings are facing more and more problems brought
by the technologies, such as global financial crisis, envi-
ronment and energy issues, and aging of the society and
information explosion. The idea of family and the rela-
tionship between the individuals and community and
society are also changing. Current advancement of
information society is only promoting industrialization and
has not solved the problems. In other words, we are facing
the paradoxical situation, where information communica-
tion technology, which should be one of the best means to
resolve these problems, even prevents us from resolving by
providing too much information to understand the situa-
tion. In Table 1, we can see other problems predicted to
occur in the near future and the limitations necessary to be
removed for solving the problems but believed to be
impossible to be removed. Now is the time to achieve true
information revolution that resolves the fundamental cau-
ses of these problems. The most important goal of our
activities is to maintain or increase quality of life during
the changes. We should not simply endure, but should
positively innovate in our society.
2.2 Strategy for creating future societies
In the last chapter, we have described the current status of
the information society, where many problems are still
unsolved and even created. True future society will come
only after overcoming all the problems. For this purpose,
the advancement of information communication technolo-
gies alone is not enough but the social innovation and the
change in the various social systems and people’s lifestyles
are required. Therefore, our goal is to achieve social
innovation and true information revolution by developing
technologies to remove the current limitations and solve
the problems.
To clarify what kind of technologies are essential for
this purpose, we employed the backcasting method
(Robinson 1982). First, we draw three pictures of the future
Spread of netw
ork
Renaissance
Enlargement of MarketOligopoly
Colonial policy
Market openingin Eastern Europe
Growth of BRICs
Growth of Africa and Asia
Pre-moderntrade
Monopoly
Polygopoly
18001400
Interaction
Dive
rsific
atio
n
Informationrevolution
2000
1800
2000
Netw
ork of trust
Global C
apitalism IndustrializationAdvancementof information
society
-
Globalfinancial
crisis
Environmental problem Multi
-polarization
Socialliquidation
Loss oflocality
Open system
Industrial revolution
Change of social structure
Aged society
Energy netw
ork
Printing
Global
transportationTelecomnetw
ork
Spread of netw
ork
RenaissanceEnlargement of Market
Oligopoly
Colonial policyPre-moderntrade
Monopoly18001400
Interaction
Dive
rsific
atio
n
2000
1800
2000
Netw
ork of trust
Global C
apitalism Industrialization -
Globalfinancial
crisis
-
Industrial revolution
Change of social structure
Aged society
Energy netw
ork
Printing
Global
transportationTelecomnetw
ork
Differences inwealth and poverty
Fig. 1 Social trends and issues
to happen in near future
198 AI & Soc (2011) 26:197–204
123
society that will appear after the limitations listed in the
Table 1 are removed, then design the road map to achieve
the three scenes.
Figure 2a indicates a new daily life created by release
from limited places and by correcting the misunderstanding
that all of the old people are less active. There, family
members will live independently, but they will peacefully
communicate with one another and carefully watch over
one another.
Figure 2b illustrates the future work style. From any-
where, individuals will contribute to a project they select,
and each will be able to work in the way he or she likes. A
job that was previously accomplished by one person will be
divided into partial jobs executed by many people. Old
people and disabled people will be able to perform frag-
mental jobs. Partial executions will be integrated into a big
achievement. This is a new type of work sharing, and we
call such a community a co-creation community.
Figure 2c describes the image of the future society
where everyone actively participates in society to solve the
problems through collaboration. With support of informa-
tion communication technologies, every people can con-
sider the global problems such as environmental problem
as their own problems and express their opinions over-
coming linguistic, cultural, spatial, and temporal barriers.
In order to remove the current limitations and realize the
above-mentioned future societies with the development of
scientific technology, we need to plan the progress of
socio-technology, which includes design of social systems
and design of a way to change the sense of values in the
society. Therefore, we need to integrate many kinds of
expertise such as economics, social science, psychology,
medical science, culture, and media design. Furthermore,
we have to evaluate whether the new social system pro-
posed can be accepted by as many people in the near future
as possible. We also need to collaborate with venture
companies, residents in local areas, local governments,
non-profit organizations, and so on.
This is the first step in the road map. The cycle of design
of social systems, technology development, and evaluation
Table 1 Examples of limitations to be removed for solving predicted problem
Problems predicted Limitations to be removed
Daily life Increase in number of old people living alone Daily life restricted to limited places
Increase in social welfare costs Older people are less active
Work Company competence weakening caused by
liquidation of human resources
Much tacit knowledge in an organization
Know-how collects in workers’ brains
Society and
community
Indifference to social affairs Individual behavior does not influence society
Aggravation of global environment Individuals cannot sense gradual change of global environment
Fig. 2 Examples of the ideal daily life people will have in the new
society. a Independent daily life: Family members live independently
while communicating peacefully with each other. b Work suitable to
individuals: Individuals can contribute to projects in their own way
from anywhere. c Global citizens: Everyone can understand social
situations around him/her, exchange opinions to solve common
problems, and act together
AI & Soc (2011) 26:197–204 199
123
of the social systems proposed will be repeated until the
goal of the social innovation is achieved.
3 Symbiotic technology
3.1 Integration of scientific technologies and socio-
technologies
In order to create a social innovation, scientific technology
should be integrated with socio-technology to become
symbiotic technology. Then, the symbiotic technology can
be classified in two dimensions (Fig. 3).
One axis corresponds to the convergence of the real
world and the cyber world. The digitization of telecom-
munication was achieved a decade ago. Now, the digiti-
zation of the real world is getting more popular through the
use of semiconductor sensors. Technologies for utilizing
sensor data will be developed further to improve the real
world. Real situations are sensed, integrated, and processed
in the cyber world. A huge amount of information and
many opinions are exchanged there. Then, the results are
fed back into the real world.
The other dimension is the integration of hetero-type
systems. So far, elements of the same kind are integrated
into a large system like a computer network or a power
management system. However, in the future, systems of
different types should be controlled together as one huge
system. Human beings are the most important element in
society, and human networks cover society in addition to
energy power networks and traffic networks. Their
behaviors should change in order to improve society,
smooth all the traffic in a city, and keep daily life secure.
Therefore, we need to study further the integration of
hetero-systems such as social dynamics, open co-creation,
and mutual understanding with the convergence of the real
world and the cyber world.
3.2 Convergence of real and cyber worlds
Development of technologies regarding the convergence of
the real world and the cyber world can be viewed in terms
of two dimensions. In Fig. 4, research subjects and appli-
cation areas of the following related studies are positioned
regarding these two dimensions. One dimension is the
change of characteristics that are used for the convergence.
Currently, physical characteristics are sensed. At the next
stage, characteristics of human behaviors or social behav-
iors are used in terms of biological sensors and/or analysis
of data sensed physically (Patel et al. 2008). After this
stage, human emotions will be sensed and utilized. For
example, people will be persuaded to improve their
behaviors in order to improve the society or improve their
own health conditions (Fogg 2003). Furthermore, if human
creativity can be measured, the productivity of intellectual
works will be able to be improved drastically (Carroll et al.
2009; Miwa and Ishii 2004).
The vertical axis denotes the size of a party. The origin
corresponds to an individual person and an individual
object. A technology of sensor networks is available for
sensing a wider area and a larger party. One of its appli-
cations is intelligent agriculture (Farkas 2003; Hashimoto
Humanbeing
Society
Energy
Information Environment
Objects Economics
Integration of hetero-systems
Digitization oftelecommunication
The convergence ofthe real world and the cyber world
Onlinecomputer
Nation-widecomputing system
Digitization of the real world
Social dynamics
Open co-creation
Mutual understandingSpontaneous behavior
Intellectual productivity
Activation of eldersPeaceful communication
Cloud computing
Ubiquitous computing
Supply chain managementBusiness process management
Informatizationof energy
The new generationnetwork
Networked robotsWearable systems
Online bank systemOnline Store computer
Mobile ECInformation services of
position sensing
Integration of hetero-systems
Social dynamics
Open co-creation
Mutual understandingSpontaneous behavior
Intellectual productivity
Activation of eldersPeaceful communication
Cloud computing
Ubiquitous computing
Supply chain managementBusiness process management
Informatizationof energy
The new generationnetwork
Networked robotsWearable systems
Online bank systemOnline Store computer
Mobile ECInformation services of
position sensing
Fig. 3 Development toward symbiotic technology
200 AI & Soc (2011) 26:197–204
123
et al. 2001). The safety, quality, and amount of agricultural
products are improved by using sensed data.
As probes, it is possible to use cars with sensors to
monitor the traffic load on a road and other conditions of a
city (Axhausen 2006; Boltze 2003; Boltze et al. 2008). By
using these data, we can construct safer and smoother roads
and can reduce energy consumption by transportation in a
city by changing users’ behaviors.
Along the vertical axis, finally we need to deal with the
holistic social system. For example, it is important to
maintain social dependability by monitoring social activi-
ties, re-designing the social infrastructure, and guiding
social behaviors (Avizienis et al. 2001; Dorf 2001). Though
the smart grid controls a power network that includes many
kinds of power sources (Massoud Amin and Wollenberg
2005), it will be evolved so as to deal with behaviors of
consumers.
At mid-size organizations, business process manage-
ment (BPM) has been tried by sensing elemental activities
in work flows (van der Aalst et al. 2003; Grigori et al.
2004). Complicated business processes can be demystified
and their quality can be optimized after analysis, predic-
tion, monitoring, and control. Communities of Practice
(CoP) can be achieved to manage knowledge flows on
networks of business work flows (Allee 2000; Khosla et al.
2009). It provides sharing of knowledge, sharing of best
practices, utilization of past experiences, knowledge gen-
eration for innovation, and networks of experts. Further-
more, knowledge can be embedded in products, services,
and processes to produce better business situations.
The wisdom of crowds is expected to produce much
more intelligence in the holistic society (Surowiecki 2007;
Tapscott and Williams 2006). For promoting it, the
situations of partial societies should be monitored and their
intellectual activities should increase. Basically open
communities for intellectual activities should not be con-
trolled, but individuals in the communities may be emo-
tionally motivated so that their spontaneous behaviors will
be induced.
3.3 Integration of hetero-systems
Networks such as human communication, transportation,
energy supply, and national infrastructures are growing
(see the bottom row in Fig. 5). They are becoming entan-
gled with one another and influence social activities to such
an extent that a local failure will be immediately spread
globally and damage other systems (Massoud Amin 2002).
On the contrary, they should be designed depending on
fundamental demands for social activities that are listed in
the top row of Fig. 5. However, it is extremely difficult to
design a system in relation to others. Therefore, it is nec-
essary to develop a new technology for generating a huge-
scale system by integrating systems of various types. The
mid-row of Fig. 5 indicates elements for managing a huge-
scale integrated system. Basically, the integrated system
has the characteristics of complex dynamics and strong
interactions among partial systems.
There are some ways to analyze structures of entangled
networks. One is to analyze the social system layer by layer
when it has latent layered structures (Levi-Strauss 2006;
Granovetter 2005). There is another way to investigate the
causal relations of the elements in the system (Pearl 2000).
After the analysis, the system should be modeled and its
dynamics can be calculated by simulation technologies
such as system dynamics and multi-agent techniques
Holisticsocial system
Individualseach objects
Physical characteristics
Emotional characteristicsIntellectual characteristics
Behavioralcharacteristics
Sensornetwork
Intelligentagriculture
BPMSCM
Emotionalsensing
Behavioralsensing
Wisdom ofcrowds
Socialdependability Smart Grid
CoPs
ITS
Physicalsensor Biological
sensing
PersuasionBehavior induction
Probe car
Visualization ofbusiness situation
Social monitoringMonitoring of
intellectual activity
Creativity
Organizationcommunity
Fig. 4 Examples of studies on
convergence of real world and
cyber world
AI & Soc (2011) 26:197–204 201
123
(Boccara 2004; Gilbert and Troitzsch 2005; Tesfatson and
Judd 2006). Then, the behaviors of the system can be
estimated and predicted. In particular, when the system
includes human beings like a social system, its behavior is
not easy to estimate because their emotions have many
effects on social behavior. However, many studies have
begun to challenge this issue for unraveling mysterious
economic and cultural behaviors (Nau and Wilkenfeld
2008; Kahneman 2003). In behavioral economics, several
approaches for management have been tried by using case-
based decision theory, emotion, neuroscience, and proce-
dural rationality (Camerer and Loewenstein 2004).
The results can be applied to manage and optimize
complex systems (Granovetter 2005; Figueira et al. 2005).
A transportation control (Axhausen 2006) and a smart
power grid (Massoud Amin and Wollenberg 2005) are
examples of their actual applications. Furthermore, risks in
a huge-scale system can be analyzed to make it dependable
and sustainable (Vose 2008; Dorf 2001).
4 Trials for social intelligence design
This section describes examples of the trials that are con-
ducted in the authors’ research group.
4.1 Current trials on behavior monitoring
for intellectual productivity of CoPs
As one of the trials for achieving convergence of the real
and cyber worlds, we study how to evaluate and improve
intellectual productivity. Here, the sum of intellectual
productivity over a community of practice (CoP) is as
important as individual intellectual productivity.
How to measure intellectual productivity is investigated
by various kinds of experts in management science, eco-
nomics, psychology, sociology, sensor technology, and so
on. In order to share and mutually utilize the research
results, we developed a system sensing behaviors of
researchers and installed it in our research laboratories.
This sensor system can record all intellectual activities in
the real and cyber laboratories by using:
1. a multi-camera system covering the entire area and
capturing the gestures of all researchers,
2. a multi-microphone system that records all
conversations,
3. Position sensing of researchers,
4. a book sensing system where researchers read books,
5. Logging of personal computer usage, which records
screens, accessed files, and tasks,
6. a mental measurement system such as stress scales,
electroencephalographs, and an infra-red camera as a
thermometer.
We have already recorded all of these data for two and a
half years and use these data for open collaboration. Many
researchers come to our laboratories to analyze these data
for their own studies. They perform interdisciplinary
studies to exchange and consider their findings with others.
One example is a structure analysis of CoPs (Shime
et al. 2008). In this topic, records of voice communication,
e-mails, meetings, and information distribution over the
authors’ laboratories are analyzed so that the embedded
structures are extracted. Extracted relations in our labora-
tories are shown in Fig. 6. The result indicates that their
structures change according to the purpose of a place. It
was found from other research topics that an individual job
can be automatically segmented into fragments. The rela-
tions among them and the importance of a fragmental job
can be measured. Some of them should be promoted and
some should be improved or reduced. Because the intel-
lectual productivity depends on a worker’s personality, the
environment of the work place, and mental stress on him or
her, a worker’s personality will be used for management of
CoPs to increase its productivity.
In the next step, the relation between the amount of
outputs and researchers’ activities will be investigated in
detail. Furthermore, this structure analysis will be applied
Security & safety(Sickness, accidents, disaster,
environmental problems)
Economical productivity
Food, clothing, and shelter
Management of huge-scale integrated system
Structure &networkanalysis
Estimation
Prediction
Riskanalysis
Modeling
Simulation
ControlManagementOptimization
CommunicationMobility
(transportation, Physical distribution)
Energy supplynetwork
Nationalinfrastructure
-scale integrated system
Fig. 5 Relations of hetero-
systems
202 AI & Soc (2011) 26:197–204
123
to design of a larger community and an open community
for creative works.
4.2 Current trials on analysis of interdependency
for dynamic system analysis
As an example of research on hetero-system integration,
interdependencies of social events are analyzed (Perrin
et al. 2008). In this trial, sentences on web pages are
analyzed. In a web page, an event as a social activity is
described and its relation with another event is often
referred to as in, ‘‘Higher crude oil prices are due to a
hurricane in Gulf of Mexico’’ and ‘‘Mass transportation is
usually more available in urban areas, helping reduce
automobile use and thus pollution.’’ A natural language
analysis can extract expressions about quantitative change
or qualitative change with words such as ‘‘go up,’’
‘‘increase,’’ and ‘‘higher.’’ The method we proposed can
automatically extract and collect such expressions that
imply relations between social events. The relation network
can be generated by connecting extracted events. Figure 7
shows an example of the extracted network, which indi-
cates a network around ‘‘industrial development’’ and
‘‘economic growth.’’ There are many relation paths. One is
a positive path through ‘‘economic development.’’ Another
is a negative path through ‘‘climate change’’ and ‘‘increase
of hurricanes.’’
Dynamics of events in the networks can be simulated by
system dynamics methods and a multi-agent simulation.
That analysis method will provide the items included in the
simulation and the parameters for simulators, though it is
difficult to give enough items and parameters to simulators
with manual works.
This technique will enable the society to be well
designed based not only on the knowledge of experts of a
limited number but also on the knowledge of a large
number of ordinary people.
5 Concluding remarks
This paper describes the design of social innovation and the
way to trigger innovation. Symbiotic technology is one
of the most promising technologies for creating social
innovation. It consists of the technology category for the
real-cyber convergence and that for the integration of
hetero-systems. This paper describes examples of those
technical categories and the challenges of the first step
toward social innovation. We introduce our current trials,
in which the activities of an entire research laboratory are
comprehensively monitored and used for collaborative
researches on innovation of intellectual productivity. In
addition, as one of the trials for achieving convergence of
the real and cyber worlds, we have recently started studies
on social dynamics understanding by analyzing real and
cyber human communications including conversation and
e-mails (Itaya et al. 2010; Yoshinaga et al. 2010).
These will be accomplished by interdisciplinary col-
laboration with experts in economics, sociology, psychol-
ogy, health science, media design, and system science.
Therefore, a new management of research is necessary and
symbiotic technology can be examined in management to
(a) Meeting space (b) Refreshment space (c) Around own desks
X
Y Z
Group X
Group Y Group Z
X
Y Z
Group X
Group Y Group Z
X
Y Z
Group X
Group Y Group Z
Fig. 6 Examples of embedded structures depending on positions
Fig. 7 Extracted example of relation network of social events
AI & Soc (2011) 26:197–204 203
123
improve intellectual activities of interdisciplinary collabo-
rative research. In the future, we will strive to develop an
integrated system based on symbiotic technology and
evaluate it in real environments such as local communities
for improving their natural environments, open communi-
ties for creative works, and communities of old people for
global business.
Acknowledgments The authors appreciate meticulous comments
by Dr. Kunieda and Mr. Kawai.
References
Allee V (2000) Knowledge networks and communities of practice.
OD Practitioner 32(4)
Avizienis A, Laprie JC, Randell B (2001) Fundamental concepts of
dependability. Technical report 01145, LAAS-CNRS, Toulouse,
France
Axhausen KW (2006) Social factors in future travel: an assessment.
Intell Transp Syst IEE Proc 153(2):156–166
Boccara N (2004) Modeling complex systems. Springer, New York
Boltze M (2003) A German example for a public private partnership
in transport research. IATSS Res 27(1):19–32
Boltze M, Fellendorf M, Kuwahara M, Wolfermann A (2008) Traffic
management—the key to more efficiency in road traffic. In:
Proceedings of the international WS congress traffic transport
2030—visions, concepts, technologies, Feb 2008, pp 35–42
Camerer CF, Loewenstein G (2004) Behavioral economics: past,
present, future. In: Advances in behavioral economics, chap 1.
Princeton University Press, New York, pp 3–51
Carroll EA, Latulipe C, Fung R, Terry M (2009) Creativity factor
evaluation: towards a standardized survey metric for creativity
support. In: Proceedings of the 2009 conference on creativity and
cognition, pp 127–136
Dorf RC (2001) Technology, humans and society: toward a sustain-
able world. Academic Press, London
Farkas I (ed) (2003) Artificial intelligence in agriculture. Comput
Electron Agric 40(1–3) (special issue)
Figueira J, Greco S, Ehrgott M (eds) (2005) Multiple criteria decision
analysis: state of the art surveys. Springer, New York
Fogg BJ (2003) Persuasive technology—using computers to change
what we think and do. Morgan Kaufmann Publishers, San
Francisco
Gilbert N, Troitzsch KG (2005) Simulation for the social scientist,
2nd edn. Open University Press, Buckingham
Granovetter M (2005) The impact of social structure on economic
outcomes. J Econ Perspect 19(1):33–50
Grigori D, Casati D, Castellanos M, Dayal U, Sayal M, Shan MC
(2004) Business process intelligence. Comput Ind 53(3):321–343
Hashimoto Y, Murase H, Morimoto T, Torii T (2001) Intelligent
systems for agriculture in Japan. Control Syst Mag IEEE
21(5):71–85
Itaya S, Yoshinaga N, Davis P, Tanaka R, Konishi T, Doi S, Yamada
K (2010) Common features of short-time dynamics of e-mail
communication in work groups. The 7th Conference on the
Application of social network analysis, Zurich, September 2010
Kahneman D (2003) Maps of bounded rationality: psychology for
behavioral economics. Am Econ Rev 93(5):1449–1475
Khosla R, Chu MT, Doi S, Yamada S, Nishida T (2009) Knowledge
flow networks and communities of practice in business process
management. In: Handbook of research on complex dynamic
process management: techniques for adaptability in turbulent
environments. Business Science Reference, Hershey, pp 551–572
Levi-Strauss C (2006) Social structure. In: Anthropology in theory:
issues in epistemology, chap 13. Blackwell, Malden, pp 136–146
Massoud Amin S (2002) Modeling and control of complex interactive
networks. IEEE Control Syst Mag 22(1):22–27
Massoud Amin S, Wollenberg BF (2005) Toward a smart grid: power
delivery for the 21st century. IEEE Power Energy Mag
3(5):34–41
Meadows DH, Meadows DL, Randers J, Behrens WW III (1972) The
limits to growth. Universe Books, New York
Miwa K, Ishii N (2004) Cognitive approaches to creative activities.
J Jpn Soc Artif Intell 19(2):194–204 (in Japanese)
Nau D, Wilkenfeld J (2008) Computational cultural dynamics. Intell
Syst IEEE 23(4):18–19
Patel SN, Reynolds MS, Abowd GD (2008) Detecting human
movement by differential air pressure sensing in HVAC system
ductwork: an exploration in infrastructure mediated sensing. In:
Proceedings of Pervasive 2008, Sydney, pp 1–18
Pearl J (2000) Causality: models, reasoning, and inference. Cam-
bridge University Press, Cambridge
Perrin T, Kawai H, Kunieda K, Yamada K (2008) Global dynamics
network construction from the web. In: Proceedings of the 2008
international workshop on information-explosion and next
generation search
Robinson JB (1982) Energy backcasting: a proposed method of policy
analysis. Energy Policy 10(4):337–344
Shime T, Kawai H, Bojduj B, Kunieda K, Yamada K (2008)
Communication detection system considering areas, workshop
on groupware and network service, information processing
society, Japan, pp 115–120 (in Japanese)
Surowiecki J (2007) The wisdom of crowds: why the many are
smarter than the few and how collective wisdom shapes
business, economies, societies, and nations. Doubleday, New
York
Tapscott D, Williams AD (2006) Wikinomics: how mass collabora-
tion changes everything. Portfolio, USA
Tesfatson L, Judd KL (eds) (2006) Handbook of computational
economics: agent-based computational economics, vol 2. North-
Holland, Amsterdam
Toffler A (1980) The third wave. Bantam Books, ISBN 0-553-
24698-4
van der Aalst WMP, ter Hofstede AHM, Weske M (2003) Business
process management: a survey. International Conference on
Business Process Management, vol 2678 of LNCS, pp 1–12.
Springer-Verlag, Berlin
Vose D (2008) Risk analysis: a quantitative guide, 3rd edn. Wiley,
Chichester
Yoshinaga N, Itaya S, Davis P, Tanaka R, Konishi T, Doi S, Yamada
K (2010) Content propagation analysis of email communica-
tions. International workshop on computational social networks,
Toronto, Aug–Sept 2010
204 AI & Soc (2011) 26:197–204
123
