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Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.
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April 2016 | Volume 3 | Article 141
PERSPECTIVE
published: 11 April 2016
doi: 10.3389/frobt.2016.00014
Frontiers in Robotics and AI
| www.frontiersin.org
Edited by:
Carlos Gershenson,
Universidad Nacional Autónoma de
México, Mexico
Reviewed by:
Mahendra Piraveenan,
University of Sydney, Australia
Matjaž Perc,
University of Maribor, Slovenia
*Correspondence:
Heiko Hamann
heiko.hamann@uni-paderborn.de
Specialty section:
This article was submitted to
Computational Intelligence,
a section of the journal
Frontiers in Robotics and AI
Received: 30January2016
Accepted: 14March2016
Published: 11April2016
Citation:
HamannH, KhalufY, BotevJ,
Divband SooratiM, FerranteE,
KosakO, MontanierJ-M,
MostaghimS, RedpathR, TimmisJ,
VeenstraF, WahbyM and ZamudaA
(2016) Hybrid Societies: Challenges
and Perspectives in the Design
of Collective Behavior in
Self-organizing Systems.
Front. Robot. AI 3:14.
doi: 10.3389/frobt.2016.00014
Hybrid Societies: Challenges
and Perspectives in the Design
of Collective Behavior in
Self-organizing Systems
Heiko Hamann
1
* , Yara Khaluf
2
, Jean Botev
3
, Mohammad Divband Soorati
1
,
Eliseo Ferrante
4
, Oliver Kosak
5
, Jean-Marc Montanier
6
, Sanaz Mostaghim
7
,
Richard Redpath
8
, Jon Timmis
8
, Frank Veenstra
9
, Mostafa Wahby
1
and Aleš Zamuda
10
1
Department of Computer Science, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany,
2
iMinds, Ghent
University, Ghent, Belgium,
3
Computer Science and Communications Research Unit, Faculty of Science, Technology and
Communication, University of Luxembourg, Luxembourg City, Luxembourg,
4
Laboratory of Socioecology and Social
Evolution, Department of Biology, KU Leuven, Leuven, Belgium,
5
Institute for Software & Systems Engineering, University of
Augsburg, Augsburg, Germany,
6
Department of Computer Applications in Science & Engineering, Barcelona
Supercomputing Center (BSC), Barcelona, Spain,
7
Faculty of Computer Science, Otto von Guericke University Magdeburg,
Magdeburg, Germany,
8
Department of Electronics, University of York, York, UK,
9
Robotics, Evolution and Art Laboratory,
IT-University of Copenhagen, Copenhagen, Denmark,
10
Computer Architecture and Languages Laboratory, Faculty of
Electrical Engineering and Computer Science, Institute of Computer Science, University of Maribor, Maribor, Slovenia
Hybrid societies are self-organizing, collective systems, which are composed of different
components, for example, natural and artificial parts (bio-hybrid) or human beings inter-
acting with and through technical systems (socio-technical). Many different disciplines
investigate methods and systems closely related to the design of hybrid societies.
A stronger collaboration between these disciplines could allow for re-use of methods and
create significant synergies. We identify three main areas of challenges in the design of
self-organizing hybrid societies. First, we identify the formalization challenge. There is an
urgent need for a generic model that allows a description and comparison of collective
hybrid societies. Second, we identify the system design challenge. Starting from the
formal specification of the system, we need to develop an integrated design process.
Third, we identify the challenge of interdisciplinarity. Current research on self-organizing
hybrid societies stretches over many different fields and hence requires the re-use and
synthesis of methods at intersections between disciplines. We then conclude by pre-
senting our perspective for future approaches with high potential in this area.
Keywords: hybrid society, bio-hybrid, distributed, collective, self-organization, design, interdisciplinarity
1. INTRODUCTION
is paper originates from a small international workshop on “Methods for Self-Organizing
Distributed Systems” that was held in Laubusch, Germany, during October 2015. We name sev-
eral challenges and give our perspectives for the eld of hybrid societies [cf. Eiben (2014) and
Prokopenko (2014)]. In general, hybrid societies are made of dierent components instead of hav-
ing a homogeneous identity. We call them “societies” because the components possess individual
agency and interact persistently. Such societies can be comprised both natural and articial agents
FIGURE 1 | Overview of design challenges in hybrid societies: primary
challenge A–formalization, primary challenge B–system design, and
primary challenge C–interdisciplinarity.
2
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | www.frontiersin.org April 2016 | Volume 3 | Article 14
(Baxter and Sommerville, 2010; Halloy etal., 2013; Schmickl etal.,
2013; Hamann etal., 2015) or dierent types of articial agents
only (Dorigo etal., 2013). We focus on self-organizing collective
hybrid societies that are characterized by multiple interactions
of agents, positive and negative feedback processes, and uctua-
tions (Ashby, 1947; Bonabeau etal., 1999; Camazine etal., 2001;
Omicini and Viroli, 2011; Heylighen, 2016). Oen, these systems
show collective behavior indicated by the emergence of global
spatial and/or temporal patterns (Serugendo etal., 2006; Attanasi
et al., 2014; Popkin, 2016). Furthermore, hybrid societies are
describable on a microscopic level, the level of an individual agent,
and a macroscopic level, the level of the whole society (Schelling,
1978; Alexander etal., 1987; Schillo etal., 2000; Hamann etal.,
2014). We want to design and determine the articial part of these
systems, although the articial subpopulation is in contact with a
natural subpopulation in hybrid societies.
Typical examples of hybrid societies are investigated in the pro-
ject ASSISI|
bf
(Schmickl etal., 2013) where robots closely interact
either with groups of bees or sh. Such systems require dierent
approaches than those developed for multi-agent systems because
they are heterogeneous and while the robots are variably program-
mable the biological agents (bees and sh) have a determined
behavior. e ASSISI|
bf
system heavily relies on social aspects
because the robots need to learn the “social language” (Schmickl
etal., 2013) of bees/sh to trigger desired behaviors. It is a hybrid
system because the robot–animal interaction is not only in one
way but also the animals determine the systems further devel-
opment. Similarly, we have hybrid societies in socio-technical
systems where human beings closely interact with technological
artifacts (Baxter and Sommerville, 2010; Smirnov et al., 2014;
DOrsogna and Perc, 2015; Helbing etal., 2015).
We identify three common, primary challenges in the design
of hybrid societies (see Figure1). Each is discussed in detail, sup-
plemented by additional secondary challenges, and we give our
perspective on future approaches with high potential.
2. PRIMARY CHALLENGE A:
FORMALIZATION OF
HYBRID SOCIETIES
e analysis of hybrid societies using tools of mathematics
and computer science is essential to gain deep insights into the
dynamics and prominent principles of hybrid systems. Besides
allowing for predictions, the formal approach also guides ones
thoughts when designing hybrid societies. e formalization of
hybrid societies is the precondition to move from formal speci-
cations to an integrated design process.
2.1. Purpose of Formalization
From our experience in work with collective hybrid societies, we
have the strong belief that our eld of research requires a tremen-
dous eort to develop a generic model. Hence, a grand challenge
of the design of collective behavior in hybrid societies is to develop
an appropriate generic formalization. A truly generic formal model
would overcome the diversity of methods and models in the eld.
If not completely generic, we would at least require a methodology
that allows to model a large range of dierent collective hybrid
societies. e purpose of a generic model is to understand the
desired system and to gain deep insights. Formalization is neces-
sary to achieve a good understanding of a systems inner dynamics
and, if possible, to predict its outcome. With the optimal model,
we could predict future behaviors and eects of hybrid societies.
Such a model would permit to analyze a wide variety of collective
systems, enable rigorous mathematical comparisons, and help to
understand potential problems in system design before realiza-
tion in simulation, and hardware was achieved.
2.2. Requirements and Actions for a
Formalization
e formalization approach should be generic and applicable in
many domains sharing essential system features. e develop-
ment of such modeling techniques requires, however, to unify
methods, concepts, and denitions from many dierent elds.
It requires a high degree of integration, knowledge about each of
these domains, and a high convertibility of the model. First steps
toward a unied methodology have been made, for example, in
the elds of socio-technical systems (Baxter and Sommerville,
2010; Jones etal., 2013; Schöttl and Lindemann, 2015) and swarm
robotics (Lerman et al., 2005; Brambilla et al., 2013). Models
originating from natural sciences are limited in representing
typical abilities of agents and also modeling the emergence of
self-organizing artifacts is challenging (see Sec.2.6). A generic
framework reecting domain-specic characteristics while
accurately capturing the evolution and dynamics of collective
behavior, both on the micro- and macroscopic level, needs to be
established.
2.3. Secondary Challenge: Diversity
of Methods
Depending on the system modeled, as well as the type of questions
asked, multiple approaches have been developed ranging from
purely mathematical equations to spatial multi-agent systems.
TABLE 1 | Diversity of methods for the formalization of hybrid societies.
Physics Biology/swarm intelligence Engineering Computer science Networks
Spontaneous magnetization,
laser theory (Yang, 1952;
Haken, 1971)
Animal groups (Okubo, 1986; Buhl
etal., 2006; Edelstein-Keshet, 2006)
Swarm robotics (Martinoli etal.,
2004; Winfield etal., 2005; Prorok
etal., 2011; Brambilla etal., 2013)
Amorphous computing
(Abelson etal., 2000)
Scale-free random networks
(Barabási and Albert, 1999;
Barabási etal., 1999)
Percolation, diffusion-limited
aggregation (Witten and
Sander, 1981; Grimmett, 1999)
Swarm intelligence (Bonabeau
etal., 1999; Dorigo and Caro, 1999;
Kennedy and Eberhart, 2001)
Sensor/actuator networks (Beal
and Bachrach, 2006; Correll etal.,
2006)
World-embedded
computation (Payton etal.,
2001; Stepney, 2007)
Temporal networks (Holme
and Saramäki, 2012)
Self-driven particles (Vicsek
etal., 1995)
Opinion dynamics (Schelling,
1978; Galam and Moscovici, 1991;
Hegselmann and Krause, 2002)
Distributed robotics (Weiß, 1996;
Parker, 2000; Stone and Veloso,
2000)
Natural computation
(Castro, 2007)
Ad hoc networks (Bettstetter,
2004), network simulations
(McCanne etal., 1997)
In physics, a major achievement is the macroscopic description of many-particle systems with multiple stochastic interactions. In mathematical biology and swarm intelligence, a
relatively high variety of non-linear agent behaviors is integrated in macroscopic models. In engineering, methodologies to design appropriate microscopic behaviors have been
defined. Computer science provides appropriate programing paradigms, which help to find a general access to hybrid societies by the computation paradigm. In network theory, an
outstanding achievement is the generality of results concerning complex networks, which can serve as a role model here. General network models helped to detect basic principles
that have applications across many fields (Barabási and Albert, 1999).
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e total amount of modeling and investigation techniques for
homogeneous and heterogeneous collective systems is huge and
spans elds such as collective animal behavior, statistical physics,
network theory, control theory, opinion dynamics, and diverse
subelds of computer science. In order to give a little, incomplete
overview, we cite only a few of these, see Table 1. Despite the
strict column-wise presentation of methods, there exist already
approaches that combine several methods from dierent elds,
such as the combination of game theory with networks (Perc and
Szolnoki, 2010), percolation and networks (Piraveenan et al.,
2013a), and hybrid systems with temporal networks (Boerkoel and
Durfee, 2013). Furthermore, the eld of evolutionary game theory
investigates hybrid societies, especially the interaction of agents
also with reference to collective behavior and self-organization
(Perc and Szolnoki, 2010; Perc and Grigolini, 2013). However, the
developed models oen abstract away proximate mechanisms,
that is, the behavioral rules that generate the spatio-temporal
dynamics of collective systems (André, 2014). Partially due to
the extreme diversity of methods, it is dicult to compare hybrid
societies or their models. A generic, formal modeling approach of
collective hybrid societies would help to overcome that problem.
2.4. Secondary Challenge: System
Complexity
Another challenge is the complexity of hybrid societies due to
self-organization that contains by denition a multitude of locally
interacting agents. Local interactions between agents create
dynamic environments, which are complex to model. e agents
operate locally but can trigger emergent global patterns; we have
dierent types of agents, and they oen live in dynamic environ-
ments, which are challenging to model.
For example, a diculty specic to self-organization is to
link the model that describes the global behavior of the system
to the model that describes the behavior of the individuals.
Dening the so-called micro–macro link is a fundamental issue
in both directions (Schelling, 1978; Hamann and Wörn, 2008).
Macro-to-micro means that a certain global behavior is required;
however, the respective individual behaviors are unknown.
Micro-to-macro is the challenge of predicting the macro-behavior
for a given micro-behavior. Particular internal states of these
agents may be essential, e.g., the internal energy levels are
crucial especially in ying agents (e.g., quadrocopters) or forest
ecosystems (Zamuda and Brest, 2013). e formal approach has
to address these internal states and model their dynamics. Local
and global correlations between these internal states add another
challenge.
In summary, we have the dynamics of the internal states and
local interactions of individual agents on the one side and the
overall dynamics of the global system on the other side. e chal-
lenge is to nd the link between these two sides, which is key to
understand and formalize hybrid societies.
2.5. Shortcomings in the State of the Art
e vast number of methods of hybrid societies comes with
individual shortcomings. We discuss only a few that may serve
as representative examples. e methods of formal specica-
tion from the eld of soware engineering [e.g., see Hoare
(1978) and Jackson (2006)] are challenged by the number of
interacting entities and their local interactions because the
size of state space grows with the size of a collective (Brambilla
etal., 2014). When this is coupled with the complexity of the
dynamic environments that we typically expect these agents to
exist in, we rapidly nd ourselves in need of novel techniques
to model and explain the dynamics of our systems. Concise
mathematical descriptions of systems, such as methods from
chemistry (van Kampen, 1992), are typically incapable to
model complex agent-to-agent interactions, especially in the
case where spatiality plays a central role (Ohkubo etal., 2008).
Computational models oen require rather strong abstractions
for the sake of run-time eciency. Agent-based models typi-
cally require an increased number of parameters with increas-
ing system complexity which challenges their signicance
(Mayer etal., 2010).
2.6. Our Perspective and Approaches
Engineered hybrid societies are complex, and therefore it is
dicult to develop de novo novel mathematical formalisms.
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A common option is to use frameworks that were developed for
natural systems to formalize articial systems when they share
key features. In general, two aspects are formalized: (1) the
behavioral mechanisms themselves (at microscopic or macro-
scopic level) and (2) the process that leads to these mechanisms
(e.g., evolution in natural systems, machine learning in articial
systems).
Chemistry and statistical physics provide formal, mechanistic
descriptions of hybrid systems. ey are the disciplines that
inspired, for example within swarm robotics, the most commonly
used modeling frameworks (Brambilla etal., 2013), such as the
master equation approach from chemistry (Martinoli etal., 1999)
and use of Fokker–Planck and Langevin equations from statisti-
cal physics (Hamann and Wörn, 2008). However, the main chal-
lenge consists in going beyond the typical assumptions of these
approaches that are intrinsic for large numbers of components
[“Avogadro-large,” cf. Beni (2005)] and lack capabilities to model
cognition and communication. Hence, collaborations with physi-
cists and theoretical chemists could help to extend these models,
to account for smaller system sizes, and to model cognition, and
to explicit communication.
Less attention has been paid to the formalization of processes
leading to self-organization as done in theoretical evolutionary
biology and machine learning. In the rst case, evolutionary
game theory (Nowak, 2006) with innite (e.g., dierential
equations) and nite (e.g., birth–death processes) populations
provides promising approaches but is limited to the evolution of
nite discrete strategies, rather than continuous behavioral traits.
Reinforcement learning is a framework suited for single-agent
systems (Kaelbling et al., 1996) and in some cases collective
systems (Wolpert and Tumer, 1999). In multi-agent settings,
machine learning struggles with the combinatorial explosion
of possibilities, which is usually approached with sophisticated
methods that reduce the search space (Matarić, 1997). To the best
of our knowledge, machine learning techniques have never been
extended to hybrid societies.
3. PRIMARY CHALLENGE B: SYSTEM
DESIGN OF HYBRID SOCIETIES
Even if we assume that we have a formal specication of our
hybrid society already, then the actual system design is still a
big challenge. We would like to dene an integrated process that
implements the step from a specication of a self-organizing
collective system to the actual real-world system and its deploy-
ment in the eld. In addition, we have to consider typical
requirements for engineered systems, such as safety, reliability,
and stability. Also note that we consciously take an engineer-
ing perspective on hybrid societies, hence assuming that such
self-organizing collective systems can actually be designed.
is hypothesis is in line with assumptions made in standard
approaches, such as swarm robotics (Martinoli, 1999; Brambilla
etal., 2013). However, one can also take the perspective that
self-organizing systems can at most be guided but not fully
determined (Prokopenko, 2009).
3.1. Requirements and Actions
for System Design
Moving from a specication of a hybrid society to a veried
implementation on actual hardware remains dicult. Dealing
with issues such as time, non-determinism, and scale presents
signicant challenges to formal methods. Hybrid societies can be
designed with a smaller eort for pre-specied environments but
for real-world implementations quality characteristics have to be
determined (Mahendra Rajah etal., 2005; Levi and Kernbach,
2010; Brambilla etal., 2013). Formal methods help to develop
tools that ensure system properties, a level of safety, and guaran-
teed safe soware from specication to implementation.
e design for reliability and stability needs to be addressed
before we are able to deploy many hybrid societies in the real
world. e stochasticity and the autonomy present in such sys-
tems make assuring reliability a dicult task. erefore, develop-
ing such systems needs to provide evaluation tools that allows for
measuring those aspects in a representative way.
3.2. Secondary Challenge: Stochasticity,
Uncertainty, Unpredictability
Most real-world environments show a high degree of stochastic-
ity, which makes it challenging to deploy hybrid societies in real-
world applications. We need methodologies to deal with known
uncertainties but also to deal with unforeseen uncertainties. For
collective behaviors, we are missing a general model that could be
used to verify the system against the expected behaviors. In addi-
tion, there might be even unpredictable behaviors [cf., emergent
behavior Matarić (1993) and Bedau (2002)] that prevent us from
assuring that the system never leaves the set of safe states.
3.3. Secondary Challenge: Dynamic
Environments, Run-Time Decisions,
and Open Systems
Related to the above complex problems, we also face the chal-
lenge of dynamic environments that require non-trivial run-time
decisions of our system. Run-time decisions and coupling the
collective hybrid society with other systems at run-time require
new methodologies. Especially systems with high requirements
for robustness operating in dynamic environments have to be
able to appropriately self-adapt their behaviors and organization
structure (e.g., topology). e required time for non-productive
reorganization and adaptation processes should be minimal.
If we allow dynamic changes of the system size, that is, we have
an open system, then we need to tackle the challenge of scalability
at runtime as well. is adds additional uncertainties introduced
by added or removed system components. ese changes need to
be balanced by the system at run-time to establish a stable and
robust system behavior. We oen face diculties when attempt-
ing to make guarantees about the behaviors of our systems and
in the scenarios when existing techniques can be used they oen
model a xed number of agents, making our proofs meaningless
as the size of our collective changes dynamically.
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3.4. Secondary Challenge: Design of
Feedbacks for Self-Organization and
User Feedback
Natural collective systems exhibit dierent features that are
remarkable, such as exibility, adaptability, and robustness. To
achieve these through self-organization, they resort to positive
and negative feedback mechanisms, the ability to amplify and
weaken local individual decisions. e careful design of appropri-
ate feedback processes requires special attention and sophisticated
design methods. Besides behavioral feedbacks, collective systems
also rely on certain network topologies and network properties,
such as power-law degree distributions (scale-free networks),
that increase the systems robustness to the loss of connections
(Albert etal., 2000; Crucitti etal., 2003; Piraveenan etal., 2013b).
Another feature is that of scale-free correlations (Cavagna
etal., 2010), which is the ability of collective systems to inu-
ence far-away neighbors independently of the system size, by
still resorting to local interactions only. Besides research on
modulating positive feedback (Valentini etal., 2014), the negative
feedback and scale-free correlations have received little attention
yet and are challenging.
A notable quality of deployed systems is user behavior feeding
back steadily into the system. is inevitably entails risks such
as collusion, free-riding, or other exploitative and destabiliz-
ing actions. e additional challenges, for example in terms of
robustness and reliability, therefore need to be considered and
rmly rooted in the system design.
3.5. Our Perspective and Approaches
Once deployed in the eld, bugs are likely to appear in ways
unforeseen by the formalization process. is limitation of the
formalization task is termed reality gap in robotics and has been
studied in recent years. Solutions range from the restriction of the
search space (Koos etal., 2013; Cully etal., 2015) to the design
of behaviors during the deployment of the system (Watson etal.,
2002; Bredeche and Montanier, 2010). e design of a hybrid-
society system could benet from these approaches.
In order to allow our system to adapt to changes in its dynamic
environment, it requires a sucient degree of freedom enabling
it to self-optimize and to show reliable behavior. We need to
allow for methods of self-repair (Ismail and Timmis, 2010) and
self-sustainability (Bredeche and Montanier, 2010), which adds
even more complexity to the system and increases the challenge
of system design. Incorporating the capability for autonomous
reasoning (Anshakov and Gergely, 2010) certainly improves the
system but at rst it increases its complexity.
4. PRIMARY CHALLENGE C:
INTERDISCIPLINARITY IN HYBRID
SOCIETY RESEARCH
As the reliance on knowledge gained from other scientic disci-
plines grows, so too does the need for researchers from all elds
to be prepared to learn from the insights and techniques of oth-
ers. e investigated problems are becoming too complex to stay
within the scope of a single discipline, and hence, interdisciplinary
research is becoming more popular (Helbing etal., 2015). Hybrid
societies are an inherently interdisciplinary problem domain,
thus the inclusion of ndings from various disciplines is essential
for their structural and algorithmic design [e.g., combination of
results from plant science, robotics, and architecture (Hamann
et al., 2015)]. Interdisciplinarity is crucial to produce a valid
model of a system observed in another discipline, or to take inspi-
ration from another discipline in the design of systems. From an
engineering perspective, being inspired by biology, chemistry,
and sociology is becoming common place. However, engaging
in a meaningful way with another discipline can be challenging
and oen, not fruitful in part because an approach remains rather
supercial where an extra eort with additional overhead would
have been required.
4.1. Requirements and Actions
for Interdisciplinarity
Engineering has much to oer to the life sciences, but benets of
engagement must be bi-lateral, so that all disciplines benet from
the collaboration. In particular, the contribution of computer
science should go beyond that of a mere service to life sciences
but instead establish a bidirectional interaction that also scien-
tically enriches computer science. For example in the context of
bio-hybrid societies, modeling and simulation can be an eective
vehicle for collaborations between computer scientists (e.g.,
multi-agent simulations) and biologists (e.g., behavioral models),
with computational models being useful to help understand chal-
lenges in behavioral biology, yet providing a formal background
and inspiration to the creation of an articial system, for example
based on behavioral models of animals (Schmickl and Hamann,
2011) or growth models of plants (Zamuda and Brest, 2013;
Hamann etal., 2015). We should try to get inspiration from biol-
ogy and sociology while lending our skill sets to the understand-
ing of other elds. However, interdisciplinary research in hybrid
societies has proven to be challenging.
4.2. Secondary Challenge:
Common Language
Despite our best will to ensure interdisciplinarity, it remains
dicult to achieve in practice. ese diculties stem from the
disparity in vocabulary, the dierent methodologies used, and a
general lack of understanding of the way of thinking and the tools
available on each side. Time is needed to develop an interdiscipli-
nary collaboration. A common language needs to be developed so
that deep and meaningful collaborations are possible.
4.3. Secondary Challenge: Integration
of Methods
Once a simple mutual understanding of the available methods
and present problems is obtained, it is tempting to merely transfer
a method from one eld to the other and to directly apply it to a
particular problem. However, mastering the complex problems at
hand and lastingly improving these systems goes beyond applying
existing results but requires true interdisciplinary collaboration.
Providing a broad set of insightful tools, only highly integrated
research on novel systems leads to a meaningful design method
6
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | www.frontiersin.org April 2016 | Volume 3 | Article 14
for hybrid societies. Prime examples of successful integration
of methods are the integration of robots and sh (Marras and
Porri, 2012) and the automatic analysis of social networks in
honeybees (Wario etal., 2015). Again, establishing such a deep
understanding of the other eld requires time.
4.4. Secondary Challenge: Interdisciplinarity
in a Mono-Disciplinary World
Despite the success of interdisciplinary research and a lot of hype
and lip service in favor of interdisciplinarity, realities still look
dierent. Many institutions and traditions in research are still
forming tiny mono-disciplinary worlds. Hence, there is a chal-
lenge for individual researchers to fulll their own disciplines
requirements in terms of measures of success.
4.5. Our Perspective and Approaches
A probably obvious solution is to enable the human factor and to
form small, strongly linked teams that work interdisciplinarily. In
addition, interdisciplinary researchers should receive an elabo-
rate training for the eld they are collaborating with. en the
methods that are used to design solutions for dierent problems
should transgress disciplinary bounds, in order to allow re-use of
methods across elds of research.
Similarly to the situation when travelers have to adapt to local
customs, all involved parties need to compromise. e common
vocabulary needs to be found and the various perspectives and
the dierent knowledge need to be understood. Only then one
can start to discover where and how both sides can benet from
each other or how they can join forces to design novel methods
for hybrid societies.
5. CONCLUSION
We have identied three primary challenges of designing hybrid
societies: formalization, system design, and interdisciplinarity.
All of them require a lot of attention and a major eort to be
overcome. However, a generic formalization approach and
ecient interdisciplinary collaborations shall create synergies
and enable us to re-use methods at intersections between
disciplines. An appropriate system design approach would
enable us to quickly deploy safe, reliable, and stable systems
in hardware.
AUTHOR CONTRIBUTIONS
HH and YK wrote the paper and organized the overall writing
process. All other authors contributed about equally to the writ-
ing process.
ACKNOWLEDGMENTS
is work was partially supported by the European Unions
Horizon 2020 research and innovation program under the
FET grant agreement “ora robotica,” no. 640959 and the ERC
Advanced Grant EPNet (340828). EF acknowledges support
from the Fund for Scientic Research (FWO), Flanders, Belgium.
RR acknowledges support from EPSRC and the Department of
Electronics, University of York, UK. AZ acknowledges sup-
port from the Slovenian Research Agency (ARRS, programme
P2-0041: Computer Systems, Methodologies, and Intelligent
Services).
REFERENCES
Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight, T., etal. (2000).
Amorphous computing. Commun. ACM 43, 74–82. doi:10.1145/332833.332842
Albert, R., Jeong, H., and Barabási, A.-L. (2000). Error and attack tolerance of
complex networks. Nature 406, 378–382. doi:10.1038/35019019
Alexander, J. C., Giesen, B., Münch, R., and Smelser, N. J. (eds) (1987). e Micro-
Macro Link. Berkeley, CA: University of California Press.
André, J.-B. (2014). Mechanistic constraints and the unlikely evolution of recipro-
cal cooperation.
J. Evol. Biol. 27, 784–795. doi:10.1111/jeb.12351
Anshakov, O. M., and Gergely, T. (2010). Cognitive Reasoning: A Formal Approach.
Berlin, Heidelberg: Springer Science & Business Media.
Ashby, W. (1947). Principles of the self-organizing dynamic system.
J. Gen. Psychol.
37, 125–128. doi:10.1080/00221309.1947.9918144
Attanasi, A., Cavagna, A., Del Castello, L., Giardina, I., Grigera, T. S., Jelić, A.,
etal. (2014). Information transfer and behavioural inertia in starling ocks. Nat.
Phys. 10, 691–696. doi:10.1038/nphys3035
Barabási, A.-L., and Albert, R. (1999). Emergence of scaling in random networks.
Science 286, 509. doi:10.1126/science.286.5439.509
Barabási, A.-L., Albert, R., and Jeong, H. (1999). Mean-eld theory for
scale-free random networks. Physica A 272, 173–187. doi:10.1016/S0378-
4371(99)00291-5
Baxter, G., and Sommerville, I. (2010). Socio-technical systems: from design
methods to systems engineering. Interact. Comput. 23, 4–17. doi:10.1016/j.
intcom.2010.07.003
Beal, J., and Bachrach, J. (2006). Infrastructure for engineered emergence on
sensor/actuator networks. IEEE Intell. Syst. 21, 10–19. doi:10.1109/MIS.
2006.29
Bedau, M. A. (2002). Downward causation and the autonomy of weak emergence.
Principia 6, 5–50.
Beni, G. (2005). “From swarm intelligence to swarm robotics,” in Swarm
Robotics–SAB 2004 International Workshop, Volume 3342 of LNCS, eds E. Şahin
and W. M. Spears (Santa Monica, CA: Springer-Verlag), 1–9.
Bettstetter, C. (2004). On the connectivity of Ad Hoc networks.
Comput. J. 47,
432–447. doi:10.1093/comjnl/47.4.432
Boerkoel, J. C. Jr., and Durfee, E. H. (2013). Distributed reasoning for multiagent
simple temporal problems. J. Artif. Intell. Res. 47, 95–156. doi:10.1613/jair.3840
Bonabeau, E., Dorigo, M., and eraulaz, G. (1999). Swarm Intelligence: From
Natural to Articial Systems. New York, NY: Oxford Univ. Press.
Brambilla, M., Dorigo, M., and Birattari, M. (2014). Formal Methods for the Design
and Analysis of Robot Swarms. Ph.D. thesis, Universite Libre de Bruxelles,
Berlin, Heidelberg.
Brambilla, M., Ferrante, E., Birattari, M., and Dorigo, M. (2013). Swarm robotics:
a review from the swarm engineering perspective. Swarm Intell. 7, 1–41.
doi:10.1007/s11721-012-0075-2
Bredeche, N., and Montanier, J.-M. (2010). “Environment-driven embodied
evolution in a population of autonomous agents,” in Parallel Problem Solving
from Nature, PPSN XI, eds R. Schaefer, C. Cotta, J. Kołodziej, and G. Rudolph
(Berlin, Heidelberg: Springer), 290–299.
Buhl, J., Sumpter, D. J. T., Couzin, I. D., Hale, J. J., Despland, E., Miller, E. R., etal.
(2006). From disorder to order in marching locusts. Science 312, 1402–1406.
doi:10.1126/science.1125142
Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., eraulaz, G., and
Bonabeau, E. (2001). Self-Organizing Biological Systems. Princeton, NJ:
Princeton University Press.
Castro, L. (2007). Fundamentals of natural computing: an overview. Phys. Life Rev.
4, 1–36. doi:10.1016/j.plrev.2006.10.002
Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., etal.
(2010). Scale-free correlations in starling ocks. Proc. Natl. Acad. Sci. 107,
11865–11870. doi:10.1073/pnas.1005766107
7
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | www.frontiersin.org April 2016 | Volume 3 | Article 14
Correll, N., Cianci, C., Raemy, X., and Martinoli, A. (2006). “Self-organized embed-
ded sensor/actuator networks for “smart” turbines,” in IEEE/RSJ International
Conference on Intelligent Robots and Systems Workshop on Network Robot
System: Toward Intelligent Robotic Systems Integrated with Environments (Los
Alamitos, CA: IEEE Press).
Crucitti, P., Latora, V., Marchiori, M., and Rapisarda, A. (2003). Eciency of scale-
free networks: error and attack tolerance. Physica A 320, 622–642. doi:10.1016/
S0378-4371(02)01545-5
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015). Robots that can adapt
like animals. Nature 521, 503–507. doi:10.1038/nature14422
Dorigo, M., and Caro, G. D. (1999). “Ant colony optimization: a new meta-heuristic,
in Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99),
eds P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala
(Piscataway, NJ: IEEE Press), 1470–1477.
Dorigo, M., Floreano, D., Gambardella, L. M., Mondada, F., Nol, S., Baaboura, T.,
etal. (2013). Swarmanoid: a novel concept for the study of heterogeneous
robotic swarms. IEEE Robot. Autom. Mag. 20, 60–71. doi:10.1109/MRA.2013.
2252996
DOrsogna, M. R., and Perc, M. (2015). Statistical physics of crime: a review. Phys.
Life Rev. 12, 1–21. doi:10.1016/j.plrev.2014.11.001
Edelstein-Keshet, L. (2001). “Mathematical models of swarming and social aggre-
gation,” in Proceedings of the 2001 International Symposium on Nonlinear eory
and Its Applications (Miyagi), 1–7.
Eiben, A. (2014). Grand challenges for evolutionary robotics. Front. Robot. AI 1:4.
doi:10.3389/frobt.2014.00004
Galam, S., and Moscovici, S. (1991). Towards a theory of collective phenomena:
consensus and attitude changes in groups.
Eur. J. Soc. Psychol. 21, 49–74.
doi:10.1002/ejsp.2420210105
Grimmett, G. (1999). Percolation, Volume 321 of Grundlehren der mathematischen
Wissenschaen. Berlin: Springer.
Haken, H. (1971). Synergetik–Die Lehre vom Zusammenwirken. Umsch. Wiss.
Tech. 6, 191–195.
Halloy, J., Mondada, F., Kernbach, S., and Schmickl, T. (2013). “Towards bio-
hybrid systems made of social animals and robots,” in Biomimetic and Biohybrid
Systems, Volume 8064 of LNCS, eds N. F. Lepora, A. Mura, H. G. Krapp,
P. F. Verschure, and T. J. Prescott (Berlin, Heidelberg: Springer), 384–386.
Hamann, H., Valentini, G., Khaluf, Y., and Dorigo, M. (2014). “Derivation of a
micro-macro link for collective decision-making systems: Uncover network
features based on dri measurements,” in e International Conference on
Parallel Problem Solving from Nature (PPSN 2014), Volume 8672 of LNCS,
ed. T. Bartz-Beielstein (Berlin, Heidelberg: Springer), 181–190.
Hamann, H., Wahby, M., Schmickl, T., Zahadat, P., Hofstadler, D., Støy, K., etal.
(2015). “Flora robotica–mixed societies of symbiotic robot-plant bio-hybrids,
in Proceedings of IEEE Symposium on Computational Intelligence (IEEE SSCI
2015) (Piscataway, NJ: IEEE), 1102–1109.
Hamann, H., and Wörn, H. (2008). A framework of space-time continuous models
for algorithm design in swarm robotics. Swarm Intell. 2, 209–239. doi:10.1007/
s11721-008-0015-3
Hegselmann, R., and Krause, U. (2002). Opinion dynamics and bounded con-
dence models, analysis, and simulation. Journal of Artical Societies and Social
Simulation 5, 1–24.
Helbing, D., Brockmann, D., Chadefaux, T., Donnay, K., Blanke, U., Woolley-
Meza, O., et al. (2015). Saving human lives: what complexity science and
information systems can contribute.
J. Stat. Phys. 158, 735–781. doi:10.1007/
s10955-014-1024-9
Heylighen, F. (2016). Stigmergy as a universal coordination mechanism I: denition
and components. Cogn. Syst. Res. 38, 4–13. doi:10.1016/j.cogsys.2015.12.002
Hoare, C. A. R. (1978). Communicating sequential processes. Commun. ACM 21,
666–677. doi:10.1145/359576.359585
Holme, P., and Saramäki, J. (2012). Temporal networks. Phys. Rep. 519, 97–125.
doi:10.1016/j.physrep.2012.03.001
Ismail, A. R., and Timmis, J. (2010). “Towards self-healing swarm robotic systems
inspired by granuloma formation,” in Engineering of Complex Computer Systems
(ICECCS), 2010 15th IEEE International Conference (Piscataway, NJ: IEEE),
313–314.
Jackson, D. (2006). Soware Abstractions: Logic, Language, and Analysis.
Cambridge: e MIT Press.
Jones, A. J. I., Artikis, A., and Pitt, J. (2013). e design of intelligent socio-
technical systems. Artif. Intell. Rev. 39, 5–20. doi:10.1007/s10462-012-9387-2
Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996). Reinforcement learning:
a survey.
J. Artif. Intell. Res. 4, 237–285.
Kennedy, J., and Eberhart, R. C. (2001). Swarm Intelligence. Burlington, MA:
Morgan Kaufmann.
Koos, S., Mouret, J.-B., and Doncieux, S. (2013). e transferability approach:
crossing the reality gap in evolutionary robotics. IEEE Trans. Evol. Comput. 17,
122–145. doi:10.1109/TEVC.2012.2185849
Lerman, K., Martinoli, A., and Galstyan, A. (2005). “A review of probabilistic
macroscopic models for swarm robotic systems,” in Swarm Robotics, eds
E. Sahin and W. M. Spears (Berlin, Heidelberg: Springer), 143–152.
Levi, P., and Kernbach, S. (eds) (2010). Symbiotic Multi-Robot Organisms: Reliability,
Adaptability, Evolution. Berlin, Heidelberg: Springer.
Mahendra Rajah, P., Prokopenko, M., Wang, P., and Price, D. (2005). “Towards adap-
tive clustering in self-monitoring multi-agent networks,” in Knowledge-Based
Intelligent Information and Engineering Systems: 9th International Conference,
KES 2005, Melbourne, Australia, September 14–16, 2005, Proceedings, Part
II, eds R. Khosla, R. J. Howlett, and L. C. Jain (Berlin, Heidelberg: Springer),
796–805.
Marras, S., and Porri, M. (2012). Fish and robots swimming together: attraction
towards the robot demands biomimetic locomotion. Journal of e Royal
Society Interface 9, 1856–1868. doi:10.1098/rsif.2012.0084
Martinoli, A. (1999). Swarm Intelligence in Autonomous Collective Robotics: From
Tools to the Analysis and Synthesis of Distributed Control Strategies. Ph.D. thesis,
Ecole Polytechnique Fédérale de Lausanne, Lausanne.
Martinoli, A., Easton, K., and Agassounon, W. (2004). Modeling swarm robotic
systems: a case study in collaborative distributed manipulation.
Int. J. Robot.
Res. 23, 415–436. doi:10.1177/0278364904042197
Martinoli, A., Ijspeert, A., and Mondada, F. (1999). Understanding collective
aggregation mechanisms: from probabilistic modelling to experiments with
real robots. Rob. Auton. Syst. 29, 51–63. doi:10.1016/S0921-8890(99)00038-X
Matarić, M. J. (1993). “Designing emergent behaviors: from local interactions to
collective intelligence,” in Proceedings of the Second Int. Conf. on From Animals
to Animats 2: simulation of adaptive behavior, eds J.-A. Meyer, H. L. Roitblat,
and S. W. Wilson (Cambridge, MA: MIT Press), 432–441.
Matarić, M. J. (1997). “Reinforcement learning in the multi-robot domain,” in
Robot Colonies, eds R. C. Arkin and G. Bekey (Berlin, Heidelberg: Springer),
73–83.
Mayer, J., Khairy, K., and Howard, J. (2010). Drawing an elephant with four com-
plex parameters.
Am. J. Phys. 78, 648–649. doi:10.1119/1.3254017
McCanne, S., Floyd, S., Fall, K., and Varadhan, K. (1997). Network Simulator–ns-2.
Availabe from: http://nsnam.sourceforge.net/wiki/index.php/Main_Page
Nowak, M. (2006). Evolutionary Dynamics. Cambridge, MA: Harvard University
Press.
Ohkubo, J., Shnerb, N., and Kessler, D. A. (2008). Transition phenomena induced
by internal noise and quasi-absorbing state.
J. Phys. Soc. Jap. 77, 044002.
doi:10.1143/JPSJ.77.044002
Okubo, A. (1986). Dynamical aspects of animal grouping: swarms, schools, ocks,
and herds. Adv. Biophys. 22, 1–94. doi:10.1016/0065-227X(86)90003-1
Omicini, A., and Viroli, M. (2011). Coordination models and languages: from par-
allel computing to self-organisation. Knowl. Eng. Rev. 26, 53–59. doi:10.1017/
S026988891000041X
Parker, L. E. (2000). “Current state of the art in distributed autonomous mobile
robotics,” in Distributed Autonomous Robotic Systems, Vol. 4. eds L. E. Parker,
G. Bekey, and J. Barhen (Berlin, Heidelberg: Springer), 3–12.
Payton, D., Daily, M., Estowski, R., Howard, M., and Lee, C. (2001). Pheromone
robotics. Auton. Robots 11, 319–324. doi:10.1023/A:1012411712038
Perc, M., and Grigolini, P. (2013). Collective behavior and evolutionary games–an
introduction. Chaos Solitons Fractals 56, 1–5. doi:10.1016/j.chaos.2013.06.002
Perc, M., and Szolnoki, A. (2010). Coevolutionary games – a mini review.
BioSystems 99, 109–125. doi:10.1016/j.biosystems.2009.10.003
Piraveenan, M., Prokopenko, M., and Hossain, L. (2013a). Percolation centrality:
quantifying graph-theoretic impact of nodes during percolation in networks.
PLoS ONE 8:e53095. doi:10.1371/journal.pone.0053095
Piraveenan, M., edchanamoorthy, G., Uddin, S., and Chung, K. S. K. (2013b).
Quantifying topological robustness of networks under sustained targeted
attacks. Soc. Netw. Anal. Min. 3, 939–952. doi:10.1007/s13278-013-0118-8
Popkin, G. (2016). e physics of life. Nature 529, 16–18. doi:10.1038/529016a
Prokopenko, M. (2009). Guided selforganization.
HFSP J. 3, 287–289.
doi:10.2976/1.3233933
8
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | www.frontiersin.org April 2016 | Volume 3 | Article 14
Prokopenko, M. (2014). Grand challenges for computational intelligence. Front.
Robot. AI 1:1–3. doi:10.3389/frobt.2014.00002
Prorok, A., Correll, N., and Martinoli, A. (2011). Multi-level spatial models
for swarm-robotic systems.
Int. J. Robot. Res. 30, 574–589. doi:10.1177/
0278364910399521
Schelling, T. C. (1978). Micromotives and macrobehavior. New York City, NY: WW
Norton & Company.
Schillo, M., Fischer, K., and Klein, C. T. (2000). “e micro-macro link in DAI and
sociology,” in Multi-Agent-Based Simulation: Second International Workshop,
Boston, MA, USA (MABS 2000), Volume 1979 of LNCS, eds S. Moss and
P. Davidsson (Berlin: Springer), 303–317.
Schmickl, T., Bogdan, S., Correia, L., Kernbach, S., Mondada, F., Bodi, M., et al.
(2013). “ASSISI: mixing animals with robots in a hybrid society,” in Biomimetic and
Biohybrid Systems, Volume 8064 of LNCS, eds N. F. Lepora, A. Mura, H. G. Krapp,
P. F. Verschure, and T. J. Prescott (Berlin, Heidelberg: Springer), 441–443.
Schmickl, T., and Hamann, H. (2011). “BEECLUST: A swarm algorithm derived
from honeybees,” in Bio-inspired Computing and Communication Networks, ed.
Y. Xiao (Boca Raton, FL: CRC Press), 95–137.
Schöttl, F., and Lindemann, U. (2015). Quantifying the complexity of socio-technical
systems–a generic, interdisciplinary approach. Proc. Comput. Sci. 44, 1–10.
doi:10.1016/j.procs.2015.03.019
Serugendo, G. D. M. S., Irit, M.-P., and Karageorgos, A. (2006). Self-organisation
and emergence in mas: an overview. Informatica 30, 45–54.
Smirnov, A., Levashova, T., Shilov, N., and Sandkuhl, K. (2014). “Ontology
for cyber-physical-social systems self-organisation,” in Open Innovations
Association (FRUCT16), 2014 16th Conference (Piscataway, N J: IEEE), 101–107.
Stepney, S. (2007). “Chapter 12: embodiment,” in In Silico Immunology, eds
D. Flower and J. Timmis (Berlin: Springer), 265–288.
Stone, P., and Veloso, M. (2000). Multiagent systems: a survey from a machine
learning perspective. Auton. Robots 8, 345–383. doi:10.1023/A:1008942012299
Valentini, G., Hamann, H., and Dorigo, M. (2014). “Self-organized collective
decision making: the weighted voter model,” in Proceedings of the 13th Int. Conf.
on Autonomous Agents and Multiagent Systems, AAMAS ’14, eds A. Lomuscio,
P. Scerri, A. Bazzan, and M. Huhns (Richland, SC: International Foundation for
Autonomous Agents and Multiagent Systems), 45–52.
van Kampen, N. G. (1992). Stochastic processes in physics and chemistry. Amsterdam:
Elsevier, 1.
Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., and Shochet, O. (1995). Novel
type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 6,
1226–1229. doi:10.1103/PhysRevLett.75.1226
Wario, F., Wild, B., Couvillon, M. J., Rojas, R., and Landgraf, T. (2015).
Automatic methods for long-term tracking and the detection and decoding
of communication dances in honeybees. Front. Ecol. Evol. 3:103. doi:10.3389/
fevo.2015.00103
Watson, R. A., Ficici, S. G., and Pollack, J. B. (2002). Embodied evolution: distrib-
uting an evolutionary algorithm in a population of robots. Rob. Auton. Syst. 39,
1–18. doi:10.1016/S0921-8890(02)00170-7
Weiß, G. (1996). “Adaptation and learning in multi-agent systems: some remarks
and a bibliography,” in Adaption and Learning in Multi-Agent Systems, Volume
1042 of LNCS, eds G. Weiß and S. Sen (New York City, NY: Springer), 1–21.
doi:10.1007/3-540-60923-7_16
Wineld, A. F. T., Sav, J., Fernández-Gago, M.-C., Dixon, C., and Fisher, M. (2005).
On formal specication of emergent behaviours in swarm robotic systems.
International Journal of Advanced Robotic Systems 2, 363–370. doi:10.5772/5769
Witten, J. T. A., and Sander, L. M. (1981). Diusion-limited aggregation, a
kinetic critical phenomenon. Phys. Rev. Lett. 47, 1400–1403. doi:10.1103/
PhysRevLett.47.1400
Wolpert, D. H., and Tumer, K. (1999). An Introduction to Collective Intelligence.
Technical Report. Handbook of Agent Technology. Moett Field, CA: AAAI.
Yang, C. N. (1952). e spontaneous magnetization of a two-dimensional Ising
model. Phys. Rev. 85, 808–816. doi:10.1103/PhysRev.85.808
Zamuda, A., and Brest, J. (2013). Environmental framework to visualize emergent
articial forest ecosystems. Inf. Sci. 220, 522–540. doi:10.1016/j.ins.2012.07.031
Conict of Interest Statement: e authors declare that the research was con-
ducted in the absence of any commercial or nancial relationships that could be
construed as a potential conict of interest.
e reviewer MP declared a shared aliation, though no other collaboration,
with one of the authors (AZ) to the handling Editor, who ensured that the process
nevertheless met the standards of a fair and objective review.
Copyright © 2016 Hamann, Khaluf, Botev, Divband Soorati, Ferrante, Kosak,
Montanier, Mostaghim, Redpath, Timmis, Veenstra, Wahby and Zamuda. is
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... Итак, гибридное общество [20] можно определить как самоорганизующуюся гетерогенную коллективную систему, которая состоит из переплетенности различных компонентов как минимум двух больших сфер: натурального и искусственного (биогибридная сфера), а также людей, взаимодействующих через техническое измерение (социотехническая сфера). ...
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The third edition of Van Kampen's standard work has been revised and updated. The main difference with the second edition is that the contrived application of the quantum master equation in section 6 of chapter XVII has been replaced with a satisfactory treatment of quantum fluctuations. Apart from that throughout the text corrections have been made and a number of references to later developments have been included. From the recent textbooks the following are the most relevant. C.W.Gardiner, Quantum Optics (Springer, Berlin 1991) D.T. Gillespie, Markov Processes (Academic Press, San Diego 1992) W.T. Coffey, Yu.P.Kalmykov, and J.T.Waldron, The Langevin Equation (2nd edition, World Scientific, 2004) * Comprehensive coverage of fluctuations and stochastic methods for describing them * A must for students and researchers in applied mathematics, physics and physical chemistry.
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