=Paper=
{{Paper
|id=Vol-1283/paper33
|storemode=property
|title=
Algorithmic Self-Governance and the
Design of Socio-Technical Systems
|pdfUrl=https://ceur-ws.org/Vol-1283/paper_33.pdf
|volume=Vol-1283
|dblpUrl=https://dblp.org/rec/conf/ecsi/PittBDNRR14
}}
==
Algorithmic Self-Governance and the
Design of Socio-Technical Systems==
https://ceur-ws.org/Vol-1283/paper_33.pdf
Algorithmic Self-Governance and the
Design of Socio-Technical Systems
Jeremy Pitt1 , Dı́dac Busquets1 , Ada Diaconescu2 , Andrzej Nowak3 ,
Agnieszka Rychwalska3 , and Magda Roszczyńska-Kurasińska3
1
Imperial College London, Exhibition Road, SW7 2BT, UK
2
Telecom ParisTech, 46 rue Barrault F-75634 Paris Cedex 13, France
3
University of Warsaw, ul. Stawki 5/7, 00-183 Warsaw, Poland
Abstract. The Digital Society is increasingly characterised by an ecosys-
tem of smart, socio-technical applications. Unlike biological ecosystems,
each application, and indeed the entire socio-technical ecosystem, is crit-
ically dependent on human-centred, mutually agreed, conventional rules
for its effective and efficient operation, and inter-operation. This pa-
per is concerned with exploring how to represent, reason with, and ex-
ploit these rules. In particular, it proposes the idea of algorithmic self-
governance, which interleaves dynamic social psychology, holonic systems
and self-organising electronic institutions, can provide a basis for de-
veloping socio-technical (eco)systems which empower solutions to large-
scale collective action problems. We conclude by suggesting that this
provides an innovative approach to the development of smart(er) cities.
Keywords: Socio-Technical Systems, Self-Organising Systems, Compu-
tational Social Intelligence, Electronic Institutions
1 Introduction
The Digital Society is increasingly characterised by an ecosystem of smart, socio-
technical applications. These applications are predicated on the interaction of
people and technology, and embedded in environments that are fully instru-
mented with devices and sensors, inter-connected (e.g. through both social and
sensor networks) and intelligent (interleaving both social (human) and compu-
tational intelligence. Examples include electricity generation, distribution and
storage, water management, and urban transportation, amongst others. The
unification of these individual examples as an ‘ecosystem’ is well exemplified by
the concept of smart cities.
Unlike biological ecosystems, each application, and indeed the entire socio-
technical ecosystem, is critically dependent on human-centred, mutually agreed,
conventional rules for its effective and efficient operation, and inter-operation.
There is a well-established understanding of the importance of such conventional
rules in the conduct of human affairs, especially when encapsulated by institu-
tions. This understanding is, perhaps, best epitomized by the pioneering work
2 Pitt, Busquets, Diaconescu, Nowak, Rychwalska and Roszczyńska-Kurasińska
of Nobel Laureate Elinor Ostrom [16], who identified an institution as a struc-
tured rule-set intended to regulate and/or constrain the behaviour of people,
especially with regard to solving collective action problems like the long-term
sustainability of a common-pool resource.
It has been a major challenge for computational social intelligence to under-
stand, explain and engineer the processes underlying the formation, selection and
modification of conventional rules for use in electronic institutions. The challenge
ahead is leveraging this research in the convergence of computational intelligence
with human intelligence in the representation of, and reasoning with, such rules
in socio-technical systems. These systems would be especially beneficial in the
resolution of collective action problems – for example, using local knowledge and
behaviour to avoid undesirable macro-level outcomes and achieve desirable ones.
This paper is concerned with exploring how algorithmic self-governance,
which interleaves dynamic social psychology, holonic systems and self-organising
electronic institutions, as a basis for developing such socio-technical systems
which empower local solutions to collective action problems. Section 2 consid-
ers the background and motivation to this work, including a critical analysis
of Ostrom’s work and its suitability for designing socio-technical systems. Sec-
tion 3 surveys the three research areas contributing to the idea of algorithmic
self-governance. Section 4 describes a case study in shared living spaces from
which we derive our innovative proposal for developing smart(er) cities founded
on, but going beyond, Ostrom’s principles, which we call Ostromopolis.
2 Background and Motivation
Ostrom’s pioneering work [16] showed how self-governing institutions could over-
come the ‘tragedy of the commons’, which claimed to show that a group of
appropriators with common, unrestricted access to a shared resource would in-
evitably act so as to deplete the resource in the short term, even if it was in
no-one’s interest in the long term. Based on extensive fieldwork, she showed how
institutions (identified as structured rulesets which prescribe who could perform
what actions in a specific ‘decision arena’ or ‘action situation’, what actions
were permitted, proscribed or obliged, membership conditions, sanctions for not
complying with the rules, etc.) could promote sustainability of a common-pool
resource, without resorting to privatisation or centralisation.
Observing that the presence of a ruleset was not in itself a sufficient condi-
tion for enduring resource management, Ostrom identified common features of
institutions which differentiated success stories from failures (for example, [16,
p. 180], no clear membership boundaries, no support for self-determination, in-
adequate monitoring, or no support for ‘efficient’ conflict resolution). She then
turned her attention to the problem of ‘supply’: faced with a common-pool re-
source management problem, there was no need to ‘hope’ that an institution
with the requisite features for sustainable management would evolve. Instead,
supported by an appropriate framework and accompanying tools and methods,
institutions could be designed with these features specified as requirements.
Algorithmic Self-Governance 3
Although an institution was supposed to identify who could perform what
action in a specific ‘action situation’, Ostrom’s work did not explicitly distinguish
between physical capability, institutionalised power and permission (commonly
made in the study of social, legal and organisational systems). However, by
invoking the concept of institutionalised power [4], the design principles could
be formalised in computational logic and used as an executable specification
for electronic institutions for managing resource allocation in open computer
systems and networks [19].
Moreover, the concept of fairness was more or less implicit in the opera-
tional choice rules for resource allocation – the relevant design principle only
prescribed that those affected by these rules should participate in their selec-
tion, and assumed that those participating would presumably select rules that
were, somehow, fair. For electronic institutions, the formalisation of Ostrom’s
principles was complemented by the formalisation of a theory of distributive
justice [21] to ensure fairness in the distribution of resources [17].
In general, though, it could be argued that Ostrom’s commitment to speci-
fying institutions in concrete form, e.g. through principles, design methods and
grammars, was rooted in political and economic science, but less so in compu-
tational, psychological and complexity sciences. As a result, her definition and
analysis of ‘action situations’ overlooked not just fundamental organisational
concepts such as institutionalised power, but also overlooked both the dynamic
socio-psychological processes involved in the (bottom-up) emergence, (top-down)
supply and (middle-out) self-adaptation of institutions [10, 9, 11], and the role of
social networks in influencing decision-making in such situations [18].
It might also be argued that the design principles are well-suited to local
situations, but not for situations that have multiple, deeply entangled priorities
driven by possibly competing or even contradictory policy objectives, or when
there are external authorities whose policies and policy demands have to be ob-
served. However, Ostrom contended that large-scale collective action problems,
with correspondingly large-scale outcomes, are not necessarily better addressed
by top-down policy-making [14]. It was proposed that policies made at national
and international level required local and regional action and enforcement, and
governance should therefore be polycentric – i.e. composed of multiple centres of
decision-making [13]. However, a comprehensive explanation of how polycentric
governance can be identified, designed and delivered is still missing.
Finally, the interaction between computational intelligence and social intel-
ligence (and technology in general) is also absent from Ostrom’s original work.
Given the criticality of the interface between users and their infrastructure [7],
if that infrastructure is highly instrumented, as is the case in smart cities, then
the human-computer interaction and ergonomics issues must also be considered.
It is (some of) these lacunae that we address in this work. In doing so, we
aim to convert what might otherwise have been ‘failures’ into success stories, i.e.
by designing and developing complex socio-technical systems with diverse com-
putational and social intelligences for empowering successful collective action,
using adaptive institutions that build on, but go beyond, Ostrom’s principles.
4 Pitt, Busquets, Diaconescu, Nowak, Rychwalska and Roszczyńska-Kurasińska
3 Building Blocks for Algorithmic Self-Governance
From an abstract perspective, the objective of many socio-technical applications
in the Digital Society (e.g. for infrastructure management, shared living spaces,
and urban transportation) can be construed as managing a collective action
situation. In general, a collective action situation involves several key features:
– it involves a group of people working together in a common space, but . . .
– . . . individuals may have a self-interest which conflicts with the group inter-
est, which encourages free riding, and . . .
– . . . the costs of an action may fall on an individual, but the benefits accrue
to the group, often requiring other incentives to contribute, for example in
the form of social capital [15].
Starting from Ostrom’s design principles for enduring institutions, we pro-
pose interleaving three building blocks for the design and development of socio-
technical systems empowering successful collective action solutions: dynamic so-
cial psychology, electronic institutions, and holonic system architectures.
3.1 Dynamic Social Psychology
Dynamic Social Psychology is concerned with how dynamical systems, in which
sets of components interact in complex, non-linear fashion but nevertheless pro-
duce coherent patterns, can be applied to social psychology, and has led to a
number of theories concerning social change and social cognition:
– The Dynamic Theory of Social Impact, which specifies the processes by which
a collection of private attitudes and beliefs becomes public opinion, common
knowledge, or a form of culture [10].
– The Bubble Theory of Social Change, which specifies how a sustainable
social change may be achieved, and concentrates on changing fragments of
social networks (clusters or bubbles) rather than separate individuals [9]. In
particular, Bubble Theory can be used to understand better the interaction
between these structures.
– The Dynamic Theory of Societal Transition, defining the processes and con-
ditions under which (meso-level) social structures are changed [11]. In par-
ticular a formal model of this theory will identify and specify how grassroots
activists can control these processes in developing meso-level structures (i.e.
institutions) that regulate or constrain micro-level behaviours to achieve de-
sirable outcomes (and/or avoid undesirable ones).
To illustrate the principles and potential of dynamic social psychology for pro-
viding the theoretical foundations of designing socio-technical systems, Project
ROSE (Regional Centres of E-learning) represents an example of an early at-
tempt of programmed emergence of organisation [12]. The challenge was to pro-
mote the use of ICT, especially the Internet, in education in Poland. However,
the rapid advances of ICT usually render any non-evolving educational program
Algorithmic Self-Governance 5
obsolete in just a few years. The solution was to create a learning community in
the form of an expanding network of teachers that constantly adapted to new
developments in ICT.
ROSE was based on the idea that teacher enhancement is a social change pro-
cess rather than a transfer of knowledge. The Bubble Theory of Social Change
[9] specifies how a sustainable social change may be achieved – by concentrat-
ing on changing fragments of social networks (clusters or bubbles) rather than
separate individuals. ROSE was therefore a mixture of face-to-face workshops
and Internet mediated interactions. The workshops enabled the teachers to learn
to collaborate with each other and to develop trust. From each workshop sev-
eral individuals were selected as natural leaders to seed the ROSE network.
After the initial workshop the training was conducted over the Internet using
an e-learning platform. The communication structure resembled a star with the
university performing the role of the central hub, and each school being a spoke.
The leaders in each school initially worked with teachers from their own
school but in the next stage schools already in ROSE collaborated with each
other in the preparation of programmes for other schools. Meso-level structures
(formal groupings with rules, roles, processes, designated groups responsible for
decisions in specific areas; and informal groupings based on friendship circles,
interest groups, and so on) emerged as clusters of collaborating schools, local
administration and businesses etc. Afterwards, the meso-level structures grew
stronger and bigger as more common initiatives were undertaken. The role of
the university decreased as the network became increasingly decentralized.
In summary, project ROSE has exemplified the necessary conditions for
planned emergence, namely multi-functional micro-level components (i.e. peo-
ple able to fulfil different roles in different contexts); the formation, operation
and dissolution of interacting meso-level structures (i.e. institutions); and the
‘shaping’ of the meso-level structures through which objectives at the macro-
level can be achieved by collective, purposeful action at the micro-level. The
open question is how to deliver planned emergence in socio-technical systems,
which includes both computational and social intelligence as micro-level compo-
nents, electronic institutions amongst the meso-level structures, and meso-level
objectives which are global in nature (e.g. climate change). We begin to address
this question by building on the concept of electronic institutions.
3.2 Electronic Institutions
Electronic institutions are used to represent the structures, functions and pro-
cesses of an institution in mathematical, logical and computational form.
In terms of functional representation, an institution’s rules can be divided
into three levels, from lower to higher [16]: operational-choice rules (OC) are con-
cerned with the provision and appropriation of resources, as well as with member-
ship, monitoring and enforcement; social collective-choice rules (SC) drive policy
making and selection of operational-choice rules; and constitutional-choice rules
(CC) deal with eligibility and formulation of the collective-choice rules.
6 Pitt, Busquets, Diaconescu, Nowak, Rychwalska and Roszczyńska-Kurasińska
Resource management institution
k
ASalloc ASmaint
chair chair
v(·) scr3 SC v(·) scr6 SC
wdM ethod wdM ethod wdM ethod wdM ethod
v(·) scr1 v(·) scr2 v(·) scr4 v(·) scr5
chair chair chair chair
raM ethod monF req f eeM ethod sancM ethod
da ocr1 ra0 ocr2 a ocr3 fa0 ocr4
allocator monitor accountant monitor
ra sa fa sa
Fig. 1: Rules relationships: nodes represent rules, edges their inputs/outputs, and
shaded rectangles the role empowered to execute the function.
For example, Figure 1 illustrates a resource management institution with
two action situations, one for resource allocation (ASalloc ) and one for infras-
tructure maintenance (ASmaint ). In ASalloc , there are two operational-choice
rules: ocr1 allocates the resource to the users, according to their demands and
some allocation method (raM ethod); ocr2 applies monitoring to identify any
users that appropriate more resources than they have been allocated. For the
social collective-choice rules, scr1 selects the allocation method (raM ethod);
scr2 selects the monitoring frequency; and scr3 selects the winner determination
method to be used in the voting procedures of scr1 and scr2 . (The functions are
similar for the infrastructure maintenance action situation ASmaint .)
A formal representation of institutional processes can also be given, which
identifies their procedural, temporal and normative aspects, typically of concern
in the study of social and organisational systems. In [19, 17], computational logic
was used to represent these processes, using the Event Calculus (EC) [6]. This
is fundamental to the representation of self-organisation and self-governance.
However, a key challenge now is to encapsulate formal models of social processes,
as specified by Dynamic Social Psychology [10, 9, 11], within the framework.
3.3 Holonic System Architectures
In terms of engineering algorithmic institutions for ‘real world’ socio-technical
applications, we advocate the use of holonic system architectures. Holonic archi-
tectures and their key role in creating viable complex systems were introduced
Algorithmic Self-Governance 7
by Simon [23], refined by Koestler [5], and progressively adopted in software
system engineering. For instance, holonic principles have been referred to as the
“laws of artificial systems. Ignoring these laws is analogous to ignoring gravity
in civil engineering” [24].
In brief, a holonic system is composed of simpler subsystems, which are com-
posed of sub-subsystems and so on, recursively. Each system resource is both:
an autonomous whole controlling its parts; and a dependent part of a supra-
system. This helps construct large systems with macro-goals from intermediary
components able to achieve partial goals. It also improves reactivity, stability
and robustness by enabling local self-* processes and limiting their global ef-
fects. For example, a smart house ‘system’ at one level (i.e. a house with a smart
meter installed and programmable devices) becomes a sub-system itself at the
next scale up (e.g. a district with smart houses and other forms of renewable
generation), while districts themselves are sub-systems at the next higher scale,
and subject to a different set of policies and policy goals (see Figure 2).
Fig. 2: Holonic System Architecture (e.g. for SmartGrid)
A holonic approach is required to address critical complex system issues, such
as scalability, elasticity, adaptability, robustness, resilience and support for multi-
scale, multi-objective policies, via recursive coordination of micro and macro
processes. Furthermore, The holonic systems perspective provides an appropriate
engineering paradigm not just for realising electronic institutions and planned
emergence, but also in representing polycentric governance [13] and in dealing
with psychological processes with span institutional boundaries. In addition, it
would allow this kind of system and its inherent benefits to scale with the number
and dynamicity of participants, which would make it applicable to smart city
eco-systems and provide a better opportunity for the formation and development
of social intelligence in contemporary socio-technical environments.
4 From Shared Spaces to “Ostromopolis”
This section briefly presents a case study in managing a shared living space as
a common pool resource, where the design and development of a socio-technical
system could benefit from the application of dynamic social psychology, elec-
tronic institutions, and holonic system architectures outlined in the previous
8 Pitt, Busquets, Diaconescu, Nowak, Rychwalska and Roszczyńska-Kurasińska
section. From there, we briefly consider how to scale up from small local situa-
tions to socio-technical (eco)systems for larger contexts such as smart(er) cities.
4.1 Shared Living Spaces
Any shared living space, such as a communal flat, an open-plan office, or even
a public space such as a park, require people to share a common space, where
violation of (implicitly or explicitly stated) conventional rules, or social norms,
can cause instances of incivility [20]. Such incivility, characterised by a low-
intensity form of deviance from accepted norms, can be difficult to detect and
resolve, but is also very harmful for the people who experience it regularly.
Therefore, it is a pressing problem in both ergonomics and urban planning to
reduce the negative side-effects of incivility. The technological solution we have
proposed for addressing the incivility problem, is MACS (M—s Affective Condi-
tioning System): a system that attempts to avoid, reduce and/or resolve incivility
before it escalates into a higher-intensity situation, e.g. conflict or aggression [22].
MACS is intended to emphasise stakeholder engagement and empower collective
choice: firstly by avoiding micro-management, as incivility episodes are resolved
between stakeholders (i.e. the occupants of the shared space themselves), and
only as a last resort by appeal to higher authorities; and secondly by providing
social support, through a network of communication and mutual obligations, via
the collective selection, monitoring and enforcement of the stakeholders’ own so-
cial norms and pro-social processes such as forgiveness [25].
We envision the shared living space as a common pool resource which we
seek to manage according to the institutional design principles of Elinor Ostrom
[16]. In this respect, the metaphor we are pursuing is that the (intangible) ‘office
ambience’ is a pooled resource which the office occupants can deplete by anti-
social behaviour and re-provision by pro-social behaviour. Furthermore, what is
(and is not) anti-social behaviour is determined by the occupants themselves –
a specific instantiation of Ostrom’s third principle (that those affected by col-
lective choice arrangements participate in their selection). Consequently, MACS
implements a voting system for social norms, which allows for those (and only
those) admitted to a shared space to vote positively or negatively for a norm. It
also allows people to suggest new norms, as the dynamic nature of offices might
mean there is a constant need to change norms, so MACS provides support for
this process.
Figure 3(a) depicts the first screen displayed for a user, after a successful
login to MACS. The navigation bar, on top, and the footer bar, at the bottom of
the screen, are constant throughout MACS. The navigation bar provides direct
access to the home screen, the social norms screen and the historical information
about events where the logged-in user has been involved in, as an offender. Below
the navigation bar, is the set of avatars representing all the people the logged
used shares the workplace with. By hovering on each of the avatars the text “Flag
person’s name’s violation of norms” shows up, where person’s name is replaced
by the chosen person’s name. By clicking on an avatar, the user is taken to the
flagging screen, where they can create a new event, by flagging a violation of
Algorithmic Self-Governance 9
norms by the person they chose. At the bottom left area of the screen there are
two different items regarding the logged user: their current reputation (standing
with the community for compliance with norms) and its evolution graph for the
previous 10 days, their avatar and their name.
A core function of MACS is to keep the users informed about the social norms
they must abide by. Besides being able to check the norms at all times, users
must also be able to vote for them, positively or negatively, and to suggest new
norms. Figure 3(b) displays the “Social Norms” screen for an open plan office.
Here all norms are presented, ordered by severity level, from the most to the
least critical. Each norm is printed in the colour code that reflects its severity.
Red means the norm is very critical, orangey-red means critical, orange means
average, and finally yellow means minor. In this case, there aren’t any minor
severity norms to be displayed. In front of each norm, in square brackets, is its
category. Categories are “noise”, “privacy”, “food”, “environment”, “politeness”
and “borrowing items”. Below each norm is its description. And finally by each
norm are an approve (thumbs up) and a disapprove (thumbs down) buttons,
which can be used to vote positively, or negatively, respectively, for the norm.
At the bottom of the list of norms is the suggestion box, where the user may
suggest a new norm for their workplace.
(a) (b)
Fig. 3: (a) MACS user start screen; (b) MACS social norms interface
To ensure that MACS meets its objective of reducing incidents of incivility in
shared spaces, the contribution of formal models of social processes (e.g. conflict
and forgiveness), self-governance by self-selection and modification of rules, and
the requirement for a holonic systems approach (i.e. a user is a holon in a flat;
a flat is a holon in a building; a building is a holon in a district, and so on) are
all evident.
4.2 “Ostromopolis”
The aim of these case studies has been to show how the varied and cross-cutting
‘building blocks’ of algorithmic self-governance provide a foundation for an inno-
vative approach to the design and development of socio-technical (eco)systems.
10 Pitt, Busquets, Diaconescu, Nowak, Rychwalska and Roszczyńska-Kurasińska
The current “big data” approach to making ‘sense’ out of vast amounts of con-
flicting and unstructured data flowing in from ICT devices deployed in various
areas of social life is leading to some advances in predictive, and prescriptive
analytics, with outcomes that range, arguably, from the beneficial and insightful
to the unwarranted, alarming and intrusive.
The foundational and original character of the approach outlined here is
to redefine the problem: instead of thinking of “global” as vast, unstructured
and/or conflicting we define “global” as complex and holonic. The fundamental
solution we offer is to empower the social structure at different levels of or-
ganization so that the self-organizing institutions may collaborate with policy
makers to govern the smart infrastructures and the data they are generating
through tailored ICT platforms implementing the electronic institution engine.
This way, the “global” is deconstructed into local aggregates that themselves
analyse, structure, interpret and utilise their data flows. In such a holonic ar-
chitecture of global systems policies turn from single-paths set towards globally
defined goals into constraints defining the boundaries for micro-governance at
each level of the social structure.
In this way we believe it can be possible to develop socio-technical applica-
tions which empower users, and an ecosystem which unites these applications in
managing multiple resources for the common good. We propose to start from, but
go beyond Ostrom’s theories, to overcome the limitations outlined in Section 2,
to provide the foundations for promoting awareness, responsiveness and pro-
social incentives for collective action in a socio-technical ecosystem for smart(er)
cities. This innovative vision we call Ostromopolis.
5 Related Work
In realising the vision of Ostromopolis, there are, in fact, several other pieces of
the ‘jigsaw’ required, beyond the ‘building blocks’ of Section 3. This includes:
– the social computer [8]: in which the designers of socio-technical applications
seek to synthesise the intelligence of human and automated computational
units to tackle so-called ‘wicked’ problems;
– social capital: the role of social capital [15] and the rise of cryptocurrencies
such as Bitcoin and Venn, in the creation of incentives and alternative market
arrangements has yet to be fully explored;
– serious games and gamification: gamification is a natural extension of serious
games from artificial settings with self-contained game-defined rewards and
“win” conditions, to real-life situations where the rewards and win condi-
tions may be rather different. In real-life scenarios concerning common-pool
resources, the “win” condition is very often sustainability, rather than ter-
mination of the game, i.e. the aim is to keep the game going.
– knowledge commons: Ostrom’s design principles reflect a pre-World Wide
Web era of scholarship and content creation, and despite some insightful
work [3], these developments make it difficult to apply the principles to
non-physical shared sources such as data or knowledge commons, and a
Algorithmic Self-Governance 11
further extension of the theory is required to develop applications based on
participatory sensing;
– privacy: new platforms which respect data privacy as a fundamental design
principle are required, such as Open Mustard Seed (OMS) being developed
by ID3 (The Institute for Data Driven Design) [2];
– scale: the scale of the applications requires systems to process many thou-
sands of events per second. This is beyond the capacity of simple versions
of the Event Calculus, and new dialect is required, such as the Run-Time
Event Calculus (RTEC) [1].
6 Summary and Conclusions
In this paper, we started from Ostrom’s pioneering work on self-governing in-
stitutions, but noticed there were, perhaps, some aspects where under-specified
(or even not at all): for example the representation of empowerment, fairness,
psychological processes, and polycentric governance.
The ROSE project has demonstrated that by applying principles of dynamic
social psychology, people can be empowered to develop systems-of-systems, based
on organisations and institutions, from the middle-out, which are user-centric
and ‘fit for purpose’ because they are self-designed by and for the users. Based
on this, we proposed that algorithmic models of such processes could be accom-
modated within the formal specification of self-organising electronic institutions,
and furthermore, that a polycentric governance model for a ‘system of systems’
(of such institutions) could be realised using holonic system architectures.
In conclusion, it has been the aim of this position statement to indicate
the opportunities, challenges and potential benefits of the cross-collaboration
between the three research fields of dynamic social psychology, electronic in-
stitutions, and holonic systems. It is our contention that this original inter-
disciplinary composition can provide the foundations for designing and devel-
oping an (eco)system of socio-technical applications for smart(er) cities. This
innovative proposal, i.e. founding smart cities on Ostrom’s principles for self-
governance and successful collective action, is what we have called Ostromopolis.
Acknowledgements
This work was supported by funds from Polish National Science Centre (project
no. DEC-2011/02/A/HS6/00231). We would also like to thank the anonymous
reviewers for their helpful comments.
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