![Examples of four digital assistance toolkits that aim to demonstrate how a broad range of values can be enabled by novel functionalities that guide the democratic upgrade. (i) Nervousnet [15], (ii) Smart Agora [155, 177], (iii) DIAS [178] and (iv) EPOS [172].](https://www.researchgate.net/profile/Dirk-Helbing/publication/364949613/figure/fig3/AS:11431281093621559@1667251567725/Examples-of-four-digital-assistance-toolkits-that-aim-to-demonstrate-how-a-broad-range-of_Q320.jpg)
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Democracy by Design: Perspectives for Digitally Assisted, Participatory
Upgrades of Society
Dirk Helbinga,b, Sachit Mahajana,∗
, Regula H¨angglid, Andrea Mussoa, Carina Ines Hausladena,
Cesare Carissimoa, Dino Carpentrasa, Elisabeth Stockingera, Javier Argota Sanchez-Vaquerizoa,
Joshua Yanga, Mark C. Ballandiesa, Marcin Koreckia, Rohit Kumar Dubeya, Evangelos
Pournarasc
aETH Z¨urich, Computational Social Science, Stampfenbachstrasse 48, 8092 Z¨urich, Switzerland
bComplexity Science Hub Vienna, Josefstaedter Strasse 39, 1080 Vienna, Austria
cSchool of Computing, University of Leeds, Leeds LS2 9JT, UK
dDepartment of Communication and Media Research, University of Fribourg, Boulevard de P´erolles 90, 1700
Fribourg, Switzerland
Abstract
The technological revolution, particularly the availability of more data and more powerful compu-
tational tools, has led to the emergence of a new scientific area called Computational Diplomacy.
Our work focuses on a popular subarea of it. In recent years, there has been a surge of inter-
est in using digital technologies to promote more participatory forms of democracy. While there
are numerous potential benefits to using digital tools to enhance democracy, significant challenges
must be addressed. It is essential to ensure that digital technologies are used in an accessible,
equitable, and fair manner rather than reinforcing existing power imbalances. This paper investi-
gates how digital tools can be used to help design more democratic societies by investigating three
key research areas: (1) the role of digital technologies in facilitating civic engagement in collective
decision-making; (2) the use of digital tools to improve transparency and accountability in gover-
nance; and (3) the potential for digital technologies to enable the formation of more inclusive and
representative democracies. We argue that more research on how digital technologies can be used
to support democracy upgrade is needed, and we make some recommendations for future research
in this direction.
Keywords: Digital Democracy, Participation, Value-Based Engineering, Adaptive Infrastructure,
Computational Diplomacy
Preprint submitted to Computational Social Science Journal October 31, 2022
1. Introduction
Digital democracy refers to the use of digital technologies in the political sphere [1]. It can refer
to a wide range of activities aided by the Internet and other digital technologies that may be used
to empower democratic processes. This can include online voting and petitioning [2] as well as
digital campaigning and issue deliberation. Because the use of digital technologies in the political
sphere is still in its early stages and constantly evolving, there is no one-size-fits-all definition of
digital democracy at the moment. Some of them are pretty different [3].
However, when discussing digital democracy, a few common themes stand out. These include
using digital technologies to increase citizen participation in politics, make governance more ac-
cessible and transparent, and improve the efficiency of democratic institutions [4]. Digital means
allow a higher level of participation [5, 6]. However, current digital democracies also have some
drawbacks. For example, it is easier to spread misinformation and hate speech online, and it can be
challenging to ensure that everyone has equal opportunities to participate (e.g. due to the “digital
divide”). Additionally, there are often concerns about transparency and accountability, trust and
security. In view of pandemics, environmental destruction, financial instability, inflation, and bro-
ken supply chains, the world is currently in crisis. This applies to democratic and non-democratic
countries alike.
To motivate our further discussion, we will start with a quote of Winston S. Churchill, who
said on November 11, 1947:1
“Many forms of Government have been tried, and will be tried in this world of sin and
woe. No one pretends that democracy is perfect or all-wise. Indeed it has been said that
democracy is the worst form of Government except for all those other forms that have
been tried...”
Many people have believed the digital revolution would change this and would overcome the
weaknesses of previous governance forms, by taking an evidence-based, perhaps even technocratic
approach. In times of Big Data and Artificial Intelligence (AI), it is often suggested that new forms
∗Corresponding author, sachit.mahajan@gess.ethz.ch
1https://winstonchurchill.org/resources/quotes/the-worst-form-of-government/
2
of governance would be feasible, which would deliver better results. Here are a number of quotes
exemplifying this:
In a TV contribution of “Titel Thesen Temperamente” on April 13, 2014,2Randolph Hencken
famously said [translation into English]:
“Democracy is an outdated technology. (...) It has brought wealth, health and happiness
for billions of people all over the world. But now we want to try something new.”
Larry Page of Google apparently saw things quite similarly, when he stated:3
“I think as technologists we should have some safe places where we can try out some
new things and figure out what is the effect on society, what’s the effect on people,
without having to deploy [them] into the normal world. And people [who] like those
kind of things can go there and experience that, [but] we don’t have mechanisms for
that.”
Peter Thiel as well seems to have a similar point of view:4
“We are in a deadly race between politics and technology... The fate of our world may
depend on the effort of a single person who ... makes the world safe for capitalism.”
However, not only politics has been questioned: science has been challenged, too, namely, when
Chris Anderson famously claimed5
“The data deluge makes the scientific method obsolete.”
All the recent quotes should, of course, be seen in the light of Big Data. Here, the state-of-the-art
was famously summarized by CIA director Gus Hunt back in 2013:6
“You’re already a walking sensor platform,” he said, and: “It is really very nearly within
our grasp to be able to compute on all human generated information.”
2entitled “Mikrogesellschaften. Hat die Demokratie ausgedient?” [“Micro-Societies: Is Democracy Outdated?”],
3https://www.businessinsider.com/google-ceo-larry-page-wants-a-place-for-experiments-2013-5
4https://www.businessinsider.com/peter-thiel-is-trying- to-save-the- world-2016-12
5https://www.wired.com/2008/06/pb-theory/
6http://www.huffingtonpost.com/2013/03/20/cia-gus-hunt-big- data n 2917842.html
3
This data would be used to run societies in a cybernetic, data-driven way. Some ideas go so far as
to create a post-choice, post-voting society [7]. Accordingly, in an increasingly automated society,
everything would eventually be “decided” by algorithms [8]. Some experts, however, doubt that
algorithms make decisions at all when compared to the way humans take decisions7.
1.1. Previous Literature
The literature on digital democracy and participation has recently grown a lot and covers a wide
range of topics, from big data to social media and communication technologies to value-sensitive
design. Nowadays, there is an increasing emphasis on the potential of digital technologies [9, 10] to
help people participate and contribute to society in more effective and efficient ways. There is also
greater recognition of the need for digital democracy and participation initiatives to be tailored to
the specific needs and context of each country, region, or neighborhood.
To better understand the main themes within the literature on digital democracy and partici-
pation, we first performed a thematic analysis [11]. For this, we used a keyword search8in the Web
of Science database to find relevant journal articles published in the last two years which discuss
digital democracy and participation. The search resulted in 140 papers. The search results were
then used to identify the thematic evolution over two time periods, 2003 to 2015 and 2016 to 2022.
As shown in Figure 1, during the first period, research focused primarily on citizen participation,
social media, and digital democracy. During the second period, the emphasis shifted towards using
digital technologies to enable public participation in advancing digital democracy.
As the field continues to grow, digital democracy scholars are increasingly focusing on issues of
inclusion and equity, examining how new technologies can be used to empower marginalized groups
[12], promote more inclusive forms of participation and upgrade democracies. Digital upgrades of
democracies appear indeed to be appealing. As various references show, the subject of “digital
democracies” has received increasing attention recently [1, 3, 4, 13, 14, 15, 16, 17, 18]. By now,
it appears to be a trend in many countries to establish new forms of citizen participation, e.g.
in participatory budgeting processes, and via the creation of citizen councils that discuss difficult
7https://www.philomag.de/artikel/algorithmen-entscheiden-nichts
8for “Digital Democracy” AND “Participat*”
4
Figure 1: Thematic map showing the evolution of literature related to the subject areas of “digital democracy” and
“participation”.
political issues [19]. In the following, we will discuss a number of points that matter in this context.
1.2. Computational Diplomacy
Note that “Digital democracies” are one of the major fields of interest to the novel research area
of “Computational Diplomacy”, as the related research focuses on questions such as how to sup-
port consensus between people and/or stakeholders, how to enable better techno-socio-economic-
environmental solutions, and how to promote a thriving, inclusive, sustainable, and resilient society.
Nevertheless, Computational Diplomacy will obviously (have to) care about other societal frame-
works than democratic systems as well.9Overall, we expect that the following fields will be crucial
for the area of Computational Diplomacy:
•data science (combining methods of data analytics with domain knowledge),
•social science approaches (incl. lab/online experiments, and political or communication sci-
ence approaches),
•the science of complex systems (“complexity science”, incl. network science),
9See, for example, this talk on Computational Diplomacy: https://www.youtube.com/watch?v=lH7WRBC1em8
5
•computer-based modeling (game theory, Agent-Based Modeling, etc.),
•institutional and mechanism design, and
•ethics.
These fields are also characteristic of the research area of “Computational Social Science”. The
main difference is that Digital Diplomacy would have a stronger focus on the roles of negotiation,
incentives, and coalition formation, to mention just a few examples. Altogether, however, the
methodological core is pretty similar.
1.3. Computational Social Science
Computational Social Science is a quickly expanding research area [20, 21, 22], even though it
is relatively new. It has resulted from the increasing need of interdisciplinary studies and brings
social, engineering, and natural sciences together. To some extent, it may be seen as a fusion of the
social, computer and complexity sciences plus a couple of other fields. Socio-, econo-, and traffic
physics have certainly contributed to this novel research area as well.
In this paper, we will present a preliminary summary of recent progress regarding how to
promote democracy by design, using digital means. The approaches we describe take ethics on board
by means of value-sensitive design or value-based engineering [9]. They are driven by questions from
the social sciences and aim at better understanding social systems by means of scientifically guided
data analyses or experiments. Such questions—or hypotheses about the way a system works—are
often studied by means of computer-based modeling. This allows for the investigation of “what
if scenarios”, particularly the study of alternative interaction mechanisms (“mechanism design”).
From this, new social mechanisms or other innovative institutional settings may result.
1.4. Design for Values, Value-Based Engineering
Our paper will take a “value-based engineering” [23] and “design for values” approach [24],
also sometimes framed as “value-sensitive design” [25]. In other words, it will ask the question,
how certain democratic values can be supported by digital technologies. “Privacy by design” is a
6
well-known example of this approach [26]. However, people have started to considerably extend
this approach beyond the subject of “privacy”.
Recently, it has been demand that digital technologies should be built in ways, which promote
“democracy by design”. In this connection, it is relevant to ask what are the values underlying this
approach. In one of their featured projects, the Amsterdam Institute for Advanced Metropolitan
Solutions (AMS), for instance, has put a focus on equality, inclusivity, and freedom of choice10 ,
calling for decentralization, separation of power (to prevent conflicts of interest), and platform
ownership by the users (besides a number of further points such as equal enforcement of Intellec-
tual Property Rights, the minimization of data collection needed for a particular purpose, and a
kind of Hippocratic Oath for IT professionals). In another paper, the following values have been
highlighted: “[e]nvironmental conditions and health, safety and security, human dignity, well-being
and happiness, privacy and self-determination (autonomy, sovereignty, freedom), fairness, equality,
and justice, consensus, peace, solidarity, sustainability, and resilience” [9]. Despite its length, this
list is certainly not complete, but still a good starting point for systems design.
1.5. Scope and Structure of This Paper
The remainder of this paper is structured as follows: Section 2 explores democracy by design,
specifically how opinion formation can be improved through diversity, and by using digital tools
and services that aid decision making while reducing polarization and echo chambers. Through
this exploration, we try to develop a more nuanced understanding of the role that technology can
play in supporting or constraining democracy. In Section 3, we discuss how democratically designed
systems can be more robust and adaptive because they allow for a wider variety of perspectives to be
brought to the table. We explore the concepts of adaptive services, infrastructure, and participatory
design approaches. By having a people-centric design approach, we can create systems that are
more responsive to the needs of individuals and communities. Furthermore, we elaborate on how
considering citizen cognition and their direct agency on components of the city from a semantic
perspective. Hence, Semantic Urban Elements (SUE) can create and provide spaces and services
for enhanced inclusivity and responsiveness to citizens’ needs
10https://amsterdamsmartcity.com/events/ams-science-for-the-city-5-democracy-by-design
7
Section 4 highlights that the concepts and tools discussed require both a trusted computing
infrastructure and persistent data; distributed ledger technology can realize both. Section 5 delves
into the benefits and drawbacks of digital assistance tools for digital democracy initiatives and
governance systems. We discuss, why it is critical that digital assistance be designed in such a way
that democratic values are preserved while also being resistant to misuse. Section 6 concludes the
paper.
2. Democracy by Design
2.1. Opinion Formation
While the US constitution appears to put a lot of weight on “free speech” (First Amendment
of the United States Constitution), the UN Universal Declaration of Human Rights goes a step
further. Its Article 19 states:
“Everyone has the right to freedom of opinion and expression; this right includes free-
dom to hold opinions without interference and to seek, receive and impart information
and ideas through any media and regardless of frontiers.”
In other words, the right to hold own opinions has at least three pillars:
1. The possibility to get access to relevant information with a reasonable effort (in particular,
to the facts, which should be recognized as such).
2. The chance to form an own opinion without being manipulated in that process.
3. Sufficient and appropriate opportunities to voice own opinions without fear of being punished,
and without censorship.
The freedom from fear is explicitly mentioned in the Preamble (“human beings shall enjoy
freedom of speech and belief and freedom from fear”). The third point also implies that opinions
should reach the public in a more or less proportional way, i.e. they should not be amplified or
suppressed by algorithms. The “freedom of peaceful assembly and association” (Article 20) is
thought to support this. The same principle should also be considered to apply online, particularly
in Social Media.
8
In the digital age, all three of the above points call for improvements. For example, hate speech
contradicts the “without fear” principle. Opinion manipulation, e.g. by means of (big) nudging or
bots, undermines the second point [27]. Last but not least, limited access to relevant data and
fake news undermine the first point. We also recognize the problems of filter bubbles, attention
harvesting, and information asymmetries. This list could certainly further extended. The following
paragraphs will address some of these issues in more detail.
2.2. Dealing with Mis- and Disinformation
Free and unbiased access to information is a prerequisite to (deliberative) democratic systems
[28]. Hence, mis- and disinformation, no matter if spread by people or algorithms, are serious
threats to democracies. They can cause disorientation and undermine a constructive, fact-based
discourse. Furthermore, they increase the information asymmetry between the people and those
who have access to the facts, thereby creating an imbalance of power that is little compatible with
democratic values and tends to promote conflict.
Disinformation means information that was fabricated to be misleading, for example, by “troll
farms”, while misinformation is inaccurate or fake, but not necessarily intentionally so [29, 30,
31]. By manipulating public opinion [32, 33], disinformation campaigns can serve to destabilize
democratic systems. Such campaigns may spread on traditional as well as social media [34], where
rapid dissemination is facilitated and multiplied by the high connectivity of digital environments
[35].
Governments [36] and scientists explore ways to effectively counter wrong beliefs in disinforma-
tion online and its spread [37, 38, 39]. As shown in Table 1, approaches to counter disinformation
may be passive, reactive, pre-active or proactive [34]. Passive approaches refrain from efforts to
correct misinformation, so as not to increase its visibility and to prevent a backfire-effect [40].
Reactive approaches may take the shape of correcting mis- or disinformation with accurate
information (usually called “debunking”). However, these corrections may not reach the original
piece of disinformation, or they may even backfire by increasing trust in the false piece of informa-
tion [41]. Reactive approaches undertaken by institutions may include swamping social media with
more truthful articles to introduce counterviews [42], while social media providers introduce fact-
9
checking and labelling, platforming or filtering. However, a restriction of information by private
parties and private interests must be scrutinized for its impact on democratic procedures.
Prebunking is a pre-active approach grounded in inoculation theory [43], which aims to build
resistance to anticipated misinformation exposure through preemptive contact in an analogy to
medical immunization. Other pre-active approaches may involve targeting the source of disinfor-
mation or spreading truthful narratives in areas at risk.
Proactive approaches prepare public members to critically analyse and identify new information.
Education, digital literacy, and numeracy effectively are counter-indicators to belief in misinforma-
tion or conspiracy theories [44]. Trust in reliable media sources prevents the rejection of information
by expert authorities [45].
Another point to consider when discussing misinformation is social cohesion. Indeed, while most
of the previously mentioned methods may counter the spreading of mis- or disinformation, they
may also damage social cohesion. For example, it has been shown that conspiracy-like communities
engage online with different types of content, while mostly avoiding interacting with the other
groups [46]. A similar observation is made for partisan political content or even scientific content.
Therefore, promoting scientific content may sometimes even increase the divide between two groups,
thereby undermining social cohesion [47]. Because of that, it is better to rely on methods such as
digital literacy and digital enlightenment [48], allowing people to better understand news contents
and their reliability, while persuasion-oriented methods may increase the divide in the population.
Besides the above discussed issues, one also needs to be aware of propaganda, mis- and disinfor-
mation using bot networks. Unfortunately, it is not always easy to reveal the related bot accounts
and their contents, as they are becoming more sophisticated. To some extent, there is an arms
race going on between detection algorithms and algorithms to produce and spread mis- or disin-
formation. Filtering out suspected fake news by Artificial Intelligence systems is tempting, but has
issues, as it introduces censorship, i.e. undermines free speech. In particular, this approach is not
transparent enough with regard to the kind of information that is lost. According to the familiar
“false positive” classification problem, there could be a significant fraction of truth in the deleted
information. Therefore, an alternative approach to automated AI-based filtering of contents that is
10
increasingly being used and a lot more democratic is to refer users to crowd-sourced content such
as those at Wikipedia [49]. Involving competent, elected community moderators would also be
an option.
Method Example Possible issues
Passive Ignoring the spreaders Misinformation can still spread
Reactive Debunking Backfiring and weakened cohesion
Pre-active Spreading relatable news Backfiring and weakened cohesion
Proactive Teaching digital literacy Is slow and requires commitment
Table 1: The four main paradigms to combat misinformation.
2.3. Sustaining Diversity
In pluralistic (democratic) societies, the existence of diverse opinions is considered to be valuable
and important. It benefits societies in various ways promoting, among others, innovation, societal
resilience, and collective intelligence [50, 51, 52, 53]. Hence, diversity should not be seen as a
concession to individuals, but as a systemic benefit.
While socio-diversity should be protected similarly to bio-diversity, current circumstances are
not always well suited for this. Social Media often affect opinions in ways that reduce diversity.
This may be counterproductive and can be changed.
It has been shown that a population’s interaction network can profoundly affect the long-
term behavioral diversity[54]. Some interaction networks, such as degree-heterogeneous networks,
obstruct behavioral diversity. Then, the population’s diversity level is typically lower than if in-
teractions were unstructured. Other interaction networks, such as highly clustered networks, favor
behavioral diversity. Then, diversity levels are usually higher than in unstructured populations.
Generally, a network’s propensity to sustain diversity depends on its topology in a way that can
be captured by the structural diversity index [54]. This index also suggests approaches to change
interaction networks such that they sustain more diversity. For example, unfollowing extremely
popular people, represented in networks by high-degree nodes, can promote diversity (see Figure
2).
11
Structural diversity index = 0.32
A B
Structural diversity index = 0.78
Figure 2: Behavioral diversity is promoted by removing links to highly connected individuals. If each individual in the
network (A) removes the connection to his/hers most connected neighbor one obtains network (B). The transition
from network (A) to network (B) entails a substantial improvement in the network’s capacity to sustain diversity,
which is quantified by an increase in the structural diversity index.
2.4. Finding Consensus
Political polarization is a major concern for modern democracies as it erodes social cohesion in
favour of partisan interests [55]. This phenomenon can be so strong as to play a major role in the
transformation of democracies into autocratic governance forms [56]. Indeed, in a polarized society
in crisis, even people in favor of democracy are often willing to elect politicians not supporting
democratic values, if they promise to support their interests.
While some may think that polarization can increase diversity and thereby benefit societies, it
is actually the other way round. The term “polarization” is used to refer to cases in which people
are divided over a subject or issue [56], whereas diversity means a distribution over many different
dimensions (subject areas).
Increased polarization has been linked to more extreme opinions [57]. It also has some funda-
mental effects on peoples’ feelings. Indeed, “affective polarization” refers to the dislike and angst
between groups with opposite views [57].
12
An example often discussed in connection with polarization is the United States. Indeed, in
recent years, it has been observed that polarization has constantly increased over there [58]. This
resulted in the fact that democrats and republicans are becoming more and more divided while
also liking each other less and less [59, 57]. This has reached a point where only 4% of couples are
between democrats and republicans [60].
Polarization is not restricted to classical political topics, but can affect many other aspects of
everyday life as well (e.g. the adoption of new technologies and new habits). For instance, in the
early days of Covid-19 it has been found that the two opposing political communities increased
their polarization on topics such as trust in scientists and trust in charity workers [61].
It seems that one obvious solution to this problem would be to apply methods fostering con-
sensus. Going back to Edward Bernays, the author of the book “Propaganda”, it is indeed possible
to engineer public consent [62]. Despite its controversial uses in the past, the application of such
methods is still common in the area of “Public Relations”. In the meantime, they are also used
in advertising and on Social Media platforms [63]. With the availability of personal data, it has
even become possible to individualize these methods, as it is being done by “Big Nudging” [64].
This makes the engineering of consent a lot more effective, but it also creates opportunities to
manipulate elections (see the Cambridge Analytica scandal) [65]). This has raised broad concerns.
Overall, given the potential for misuse, it is questionable whether one should strive to engineer
consent in the future. Instead, we recommend thinking about deliberative elements that should be
strengthened.
There are now digital tools and technologies that can support human decisions and collective
behavior in a meaningful way by enabling large-scale collaboration and exchange. For instance, in
order to combat the lack of legislative transparency in Taiwan, starting in 2014, its civil society
has gained experience in a number of initiatives and platforms that support coordination and
cooperation. One of the more well-known examples is the vTaiwan platform11 and its underlying
system Pol.is [5]. The consensus-building platform allows citizens to set their own agenda for the
conversation. Using upvotes and downvotes to each statement, it visualises real-time opinions using
11https://info.vtaiwan.tw
13
Figure 3: vTaiwan’s use of Pol.is for the discussion of Uber regulation. Source: Screenshot from
https://pol.is/3phdex2kjf.
14
PCA (Principal Component Analysis) and clusters people who voted similarly, using the k-means
algorithm in a transparent manner.
As shown in Figure 3, like-minded groups emerge quickly on the opinion map, showing where
the main consensus and disagreements actually lie. People then naturally try to come up with
comments that will win votes from different groups, gradually overcoming the gaps. The platform
gathers and analyses opinions, and then produces high-level, actionable, and statistically significant
insights. Instead of initiating debates and prompting further polarisation, the process emphasises
constructive co-creation across diverse opinions. The conclusion and insights of 80% of the discussed
topics, such as the regulation of Uber or the FinTech Sandbox, led to decisive and successful
government action.
2.5. (Digital) Participatory Budgeting
In order to engage citizens directly in political decision making, Participatory Budgeting (PB),
a process that involves citizens allocating resources and monitoring public spending, has emerged
as a democratic innovation [66] and a successful participatory instrument [67].
Participatory Budgeting has been used in many cities around the world. Since the emergence of
Participatory Budgeting (PB) in the 1990s, it has helped to confront problems of political clienteles
and social exclusion in Brazil, and has increased political legitimacy by having the budgetary
process transparent, open, and public [68].
The standard process of most Participatory Budgeting programs follows the steps in collective
intelligence (see Section 2.6), namely exploration, information exchange, integration of ideas, and
finally, voting. This approach helps to address the fact that societies in the digital era are becoming
more and more complex. Collective action is increasingly individualised and issue-driven, creating
a new kind of “chaotic pluralism”, which is too dynamic and too complex to be addressed by
traditional democratic processes or politics [69]. In order to deal with this, the idea of using
collective intelligence via digital participation tools is rapidly gaining ground in cities around the
world.
In recent years, the increasing use of digital technologies and platforms has enabled cities to
include more citizens in a direct engagement with the collective decision making process. Especially
15
in Europe and parts of North America, the digitalisation of Participatory Budgeting offers great
opportunities for different stakeholders to partake in large-scale political decision-making processes
in a more effective way [70]. According to the Participatory Budgeting World Atlas 2021 [71],
Europe accounts for over half of the Participatory Budgeting initiatives worldwide, with over 5,000
schemes in 2019 alone. The past decade also has seen the rapid development of open-source citizen
participation platforms such as Decidim 12 and Consul 13, which support large-scale collective
intelligence.
Citizen participatory programs can be a useful tool for cities to identify real-time issues on
the ground, and to channel more resources to disadvantaged groups and territories most in need
[72]. These new digital tools are increasingly being used to reinforce citizen participation in an
open culture, thereby strengthening democracy, and supporting cities and institutions to meet the
demands for accountability and transparency [73, 74].
2.6. Collective Intelligence
Complex dynamical systems such as social systems often show a feature characterized as “the
system is more than the sum of its parts” [75]. This observation is a consequence of non-linear or
network interactions and refers to self-organization effects or emergent properties observed in many
complex dynamical systems. One particularly interesting phenomenon of this kind is “collective
intelligence” [76] (sometimes also called “the wisdom of crowds” [77]), which is a generalization of
“swarm intelligence” [78].
“Collective intelligence” refers to the fact that a combination of various solutions often outper-
forms the best individual solution. That is particularly true for complex problem-solving, where it
is important to combine different perspectives to get a fuller picture of a problem and its possible
solutions. However, “collective intelligence” does not result automatically. It has a number of pre-
conditions, particularly that people (re)present a sufficiently diverse set of solutions. Hence, a lack
of diversity can imply poor solutions.
12https://decidim.org/
13https://consulproject.org
16
The following procedure appears to be favorable for the emergence of “collective intelligence”
[79]:
1. Independent exploration: The first phase consists of the search for information and solutions.
This search should be independent from that of others and not externally manipulated.
2. Information exchange: The second phase serves the exchange of information about the solu-
tions found.
3. Integration: In the third phase, various solutions are combined in an innovative way by means
of a deliberative process.
4. Voting: In the fourth phase, the people affected by the problem vote to determine the best
combined solution.
This procedure is in line with insights into what enables successful deliberative public opinion
formation processes [80, 3].
Digital tools can support all four phases listed above. Additionally, one may consider different
voting methods. The best choice might depend on the problem to be addressed (see also the next
subsection). Furthermore, the search for information and the exploration of the solution space may
be promoted by suitable incentive systems [81].
2.7. Voting
2.7.1. Electronic IDs
Discussions on voting in digital societies have recently revolved around the subject of electronic
IDs (e-IDs) and the possibility to avoid paper ballots. Related to this, however, there are a lot of
concerns that democracies might become “hackable”, i.e. election results could be biased.
Furthermore, there have been fierce debates about how an e-IDs should work [82], what biomet-
ric features they should use, and who should be responsible for managing the related platform(s)
and data.
In our paper, we would like to stress instead that there are other, probably more important
points to consider when it comes to voting. Namely, it is possible to apply different voting rules to
determine the outcome of a vote, and this can make a significant difference.
17
2.7.2. Voting Systems
Not only in participatory budgeting contexts, but in democratic systems in general, the choice
of the respective voting system is highly relevant for the decision-making process, its outcome, and
the satisfaction with the result. It is especially important to avoid a “winner takes all effect, also
know as the threat of “Tyranny of the Majority”[83], where one group basically dictates what is
happening. The more diverse or complex the society becomes, the more important this might be.
For example, Quadratic Voting, proposed by Posner and Weyl [84] as a voting mechanism that
aims to prevent this undesired situation of “Tyranny of the Majority” [83], has gained some traction
for collective decision-making and blockchain governance [85]. Rather than ranking their choices,
Quadratic Voting allows voters to express the intensity of their votes using voting credits. The cost
of a decision is calculated as the square of the number of votes cast. By making the cost of choosing
only one option expensive, the authors argues that Quadratic Voting have the effect of protects
minority interests and discourages polarization. As diversity strengthens collective intelligence 2.6,
voting innovations that ensure diverse outcome deserve some serious consideration.
A well-configured voting system should be able to support both, a participatory process and a
fair outcome, which benefits a great majority of people affected:
1. Input: A proper participatory approach requires that voters can effectively express their
preferences through votes.
2. Output: The applied aggregation method then determines a feasible allocation of resources,
which can comprise diverse investments or solutions.
Both of the above-mentioned elements are theoretically well studied. However, for a successful
implementation in practice, for example, in a participatory budgeting project, the user perspective
needs to be taken into account.
In the following, we review both, input and aggregation methods, highlighting two sometimes
contradictory properties: the theoretical characteristics and the user’s perspective towards these.
When Participatory Budgeting settings are studied in the laboratory, in comparison to four
other input methods participants appear to find k-approval the most straightforward to use. k-
approval also outperforms every other input format in terms of consistency of votes and response
18
time [86]. However, voters feel that k-approval is the worst in reflecting their preferences. From
their perspective, ranking by value is the best.
This highlights that it is important to distinguish between what supports an efficient aggre-
gation and what voters feel is essential when casting a vote [86]. Obviously, k-approval does not
capture everything that matters for humans, such as their values. While efficiency is at the centre
of a business or military approach, from a human-centric point of view, one should put a larger
focus on what is valued by humans. Thinking, for example, of the important principle of division
of power (“checks and balances”), democracies are not totally efficient by design. This is to protect
humans, their interests, and their dignity. In order to enable solutions that are not just efficient,
but also sophisticated, democracies use advanced technologies. For example, rather than imple-
menting just solutions that those in power prefer, democracies benefit from engaging in a set of
diverse solutions that satisfy as many people as possible. The latter allows the people to unfold
their talents. In a society built on specialization and division of labor, this unleashes combinatorial
benefits of diversity, which can contribute to a higher quality of life.
It is important to consider that, besides the input method, aggregation choice significantly
affects a vote’s outcome as well. A comparison of five aggregation methods within the context of a
laboratory experiment [87] suggests the following:
1. Considering a bundle of projects chosen by the participants, maximizing the Nash product
[88] appears to be the most appropriate method.
2. Based on the verbal explanations ranked by the participants, maximizing utilitarian social
welfare is the aggregation method that seems to be most appropriate.
Hence, common majority voting is usually not the best method. Also note that an aggregation
method, which ensures that at least one of the citizen’s preferences is realised, increases the will-
ingness of voters to participate [89].
2.8. Legitimacy, Trust and Transparency
When choosing the input and aggregation method, from a democratic point of view, it is key
to put a particular focus on the perceived fairness of the voting outcomes. Furthermore, decisions
19
about sensitive questions require a particular legitimacy. How can this be achieved? Legitimacy is
a multidimensional concept [90, 91, 92]. Interpersonal trust is part of that concept. In our context,
procedural legitimacy plays an important role. To a considerable extent, it is the fairness of applied
procedures, through which institutions receive the authority they exercise. This shapes procedural
legitimacy and the willingness of people to cooperate with institutions, and to comply with the
rules created by them. However, it is not only procedures that matter, but outcomes as well. Marien
and Kern [93] emphasize that involving citizens (fairly) is not sufficient to increase political support
for government. The outcomes of decision-making processes also are relevant. They speculate that
outcome favorability might be less important if more consensus-based procedures are used. So,
decisions about sensitive questions, in particular, might require more sophisticated voting methods
than majority voting.
The illustration in Figure 4 shows how public support and trust in technologies and government
institutions can be gained through transparency and accountability. In the context of digitally-
assisted decision making, it is also important to care of institutional trust. This depends on:
1. the knowledge of institutional norms shared between truster and trustee (e.g., standards
such as open source, non-proprietarian software, a common language to define a problem, a
possibility to participate in the definition of the problem);
2. the truster’s knowledge of the motivation of the trustee (e.g. transparency about motivations
and incentives);
3. professional role profiles combined with proper sanctions that render those in power account-
able to the norms (regulations, rules, and laws) [94].
3. Sharing Space, Infrastructures, Goods, and Services
Though democracy is often considered to be primarily a government system, it is tightly entan-
gled with how society and the economy are organized. Therefore, it is also important to consider,
how the non-political aspects of citizen lives are affected by and contribute to democracies and
their values. In this section, we focus on the ways in which the use of spaces, infrastructures, goods
and services can be digitally upgraded such that democratic values are supported. Moreover, we
20
Figure 4: Illustration of how public support of and trust in technologies and government institutions (such as par-
ticipation schemes and voting mechanisms) can be established by transparency and accountability.
will elaborate on how considering citizen cognition and the semantics of urban elements (i.e. the
fabric of an urban city) can create and provide more inclusive spaces and services.
3.1. Access
Democracies live from an open exchange of ideas, and a trustful atmosphere supporting ex-
change, which results from interactions among different kinds of people and interest groups. Shared
space in the sense of a collective or public good is an important prerequisite for this [95]. It in-
cludes everything from public parks and plazas to public schools, universities, libraries, and more.
Decades of research on inter-group bias [96] (which compares the behavioural attitudes of people
towards group members and non-group members) suggest, that an inclusion-centered design of
spaces can support the creation of in-group-sentiment (the feeling of belonging to a group) and
21
thereby promote participation and cooperation between persons.
In view of this, we need to highlight a problematic trend: namely, the increasing tendency to
restrict access to all sorts of spaces. Under such conditions, access becomes a privilege for a certain
set of “authorized” persons. Such access restrictions are not necessarily based on good reasons
or qualifications, but often on exclusive, competitive interests. This undermines the principles
of inclusion and space equity [97], even though inclusion plays a major role in the Sustainable
Development Goals (SDGs).
3.2. Adaptable Services and Infrastructures
Beyond the mere physical configuration, the shaping of our built environment encodes and
encapsulates myriads of interactions, power relations, productive systems, and ideologies. Cities
are simultaneously means of empowerment, but also of production and domination, which reflect
and perpetuate a mode of development within an ideology and culture, which mediates politics
between economic and social factors [98, 99, 100]. Along with the ever-lasting technological progress,
new services are continuously emerging and evolving. Most of these new services are geared towards
providing city-dwellers with easy access to the latest technologies.
Adaptive services, which are becoming more common in cities around the world, address the
limitations of one-size-fits-all solutions. This approach mirrors the main ideological tenets of democ-
racies, which treasure individuality and freedoms. For example, adaptive services in smart cities can
include adaptive traffic signal control [101], adaptive infrastructure use [102], adaptive reversible
lanes [103], etc.
Managing complex systems, whose behaviors are difficult to predict, are among the key chal-
lenges of modern societies. Urban traffic flows, for example, and many other complex dynamical
systems are largely unpredictable—one can mainly make statistical statements. As a consequence,
a top-down management of such systems, on the one hand, often falls short or fails, as deviations
from the predicted system behavior occur. On the other hand, decentralised bottom-up approaches
that are based on a flexible response to local short-term predictions often perform surprisingly well.
Furthermore, by distributing decision making processes, such bottom-up approaches can typically
cope surprisingly well with local disruptions or failures, thereby preventing the entire system to
22
fail. Such systemic resilience is highly desirable.
The example of adaptive traffic signal control showcases how the transition from centrally
planned top-down solutions to adaptive bottom-up approaches based on real-time feedback can
lead to significant improvements in the quality of services [104]. It is expected that these findings
can also be extended to logistic systems, the world’s economy, and democratic organization, as well
as other complex systems contributing to modern societies.
Many adaptive services can either be implemented in a centralized or decentralized way [105].
Centralized services are often slightly more efficient and have clear ownership rules, while decen-
tralized services are often characterized by co-ownership or distributed ownership, which can help
to reduce instances of power abuse [9]. Adaptive mobility-sharing [106] is an innovative approach
to transportation that is being piloted in a number of cities around the world. The concept is based
on the sharing of resources (such as vehicles and infrastructure) between different users in order to
improve efficiency and reduce costs. Such mobility-sharing services can be combined with Internet
of Things technologies (IoT) to create additional benefits for the respective citizens. Examples re-
late to real-time air environmental monitoring [107], noise mapping [108], community-based health
services, and much more. Services like these may adapt to individual users, giving them access
to the data relevant for them rather than just to aggregates that are relevant on average. Such
fine-grained data access enables better, data-based decisions [109, 110], without having to be based
on targeting by another system or someone else.
Similar to adaptable services, adaptable infrastructures solution can address the limitations of
a one-kind-fits-all approach. Typically, urban infrastructures do not reflect the diversity of needs
of people relying on them. Indeed, many cityscapes and the rules that set urban space affordances
[111], were established in the beginning of the 20th century.
In the past, for example, urban planners implemented a functional segregation in road networks
based on speed. This was in favor of motorized vehicles [112], resulting in two adverse effects: First,
their static design could not cope with rapidly changing needs of space in the city [113]. Second,
space allocation for motor vehicles was based on peak hours, but the restriction of alternative uses
of this space mostly extended over the entire day. In the future, autonomous vehicle traffic could
23
help to overcome this shortcoming.
The rise of autonomous driving technologies allows one to imagine digitally upgraded urban
infrastructures that can integrate high levels of traffic demand with a higher diversity of uses,
while flexibly responding to changing mobility needs [114, 115]. Moreover, such layouts can be
tested in advance using Virtual Reality technologies. Layouts of possible adaptable infrastructures
of interest, such as reversible lanes, laneless roads, or curbless flat streets, would automatically
react to city’s needs [116, 117, 118]. The integration of such autonomous elements, as well as their
interaction with humans and each other, is increasingly important [119, 120, 121], particularly as
it may support coordination in complex situations [122, 123, 124].
We believe a city should be able to respond to human activity and participation by adaptable
services, infrastructure, and streetscapes. This should enable a globally networked city to be locally
adaptive, coordinated, and cooperative. To get there, one needs to develop
(1) effective technological means that can translate information and knowledge flows into the
transformation and adaptation of physical space (e.g., adaptable services, flexible street uses),
and
(2) informational frameworks and methods, which can handle complexity and diversity, such
that it can constructively deal with the sometimes contradicting agencies and interactions in
a pluralistic urban environment. For this, one needs a better understanding of how diverse
flexible streetscapes may be perceived, designed, operated, and accepted by people [125].
Altogether, the approach of adaptive infrastructures and services seeks for inclusiveness in built
environments and spatial planning, articulated by relational interactive data flows and software
applications, which sense and react to changes in uses, needs, and expectations [126]. The reap-
propiation of the process of city making by people and their direct intervention can be enabled
by Open Source Urbanism [127, 128]. Among others, this aims at co-creating infrastructures for
democracy (new kinds of “commons”), which requires a further development of interoperability
across different data and process phases, including analysis, scenario planning, participation, mon-
itoring and post-evaluation [129].
24
3.3. Semantic Urban Elements
The section above emphasizes why future cities need adaptable services and infrastructure
for sustainable and resilient urban planning. In Figure 5, we illustrate a conceptual framework
highlighting how citizen participation and democracy by design can upgrade the built environment
for citizen well-being, using semantics based on urban elements.
Semantic Urban Elements (SUEs) represent semantic information between urban elements that
is causally necessary to understand their relationship with each other and the resulting urban
fabric. This virtue enables SUEs to represent urban elements as entities and relations, allowing
for a mathematical representation and logical inference from complex urban data, as well as for
computer-based applications.
Current research in urban design planning has focused mainly on extracting syntactic informa-
tion from the “urban elements/resources” perspective. The critical problem of this is the lack of
considering human cognition and perception of urban space. Typically, the role of citizens in city
planning has been restricted to consuming services from various cyber-physical systems. Recently,
however, it has been stressed that the role of citizens should be extended from consumers to “pro-
sumers” and contributors [130, 131]. For example, they may provide feedback in terms of service
ratings, be interaction partners of the system, or even take the role of an actuator implementing
change. This brings us to the subject of co-creation and co-evolution discussed in Sec. 3.4.
Human-centric design of cities is critical to improve the quality of living. There are multiple
solutions to this problem, but this paper focuses on the participatory design approach employing
SUEs. The involvement of citizens in co-designing is essential. However, it is equally important
to understand the implicit relationship between urban elements and the diverse sets of people
interacting with them. For instance, the authors in Ref. [132] examine why the UK’s public space
failed to provide easy access to the city center for the elderly and differently-abled populations.
Urban design often caters to citizens’ average needs rather than the actual distribution of needs.
Therefore, recent trends towards inclusive co-creation have to be further augmented by smart SUE
technologies incorporating such relationships.
Typically, urban data does not follow a standard format and comes from different agencies such
25
Figure 5: Conceptual framework of Semantic Urban Elements for adaptable services and infrastructures based on
citizen participation. The representation of the knowledge graph and Semantic Urban Element ontologies were in-
spired by [133].
as the government, citizens, and private companies. To promote transparency and economic growth,
SUE-based technologies will facilitate the integration of multiple data sources, thus, opening up
new possibilities for urban representation, citizen participation, and the co-creation of urban design
ideas. Moreover, the formal semantic representation of complex urban data can benefit machine
processing and AI-based analysis.
SUE technology will benefit cities by generating diverse designs via participatory tools (see Sec-
tions 2 and 3). In scenarios of adaptable infrastructures, counterfactual queries can be made, using
data from real-time sensors to adapt the configuration of urban elements to increase well-being,
safety, sustainability, and resilience. Furthermore, SUE-based technology will facilitate conceptual-
izing citizen feedback about above-mentioned urban forms via participatory planning [134]. Citizen
feedback will be analyzed, and semantic information about urban elements identified and presented
26
to co-designers, decision-makers, or AI-based urban design tools. This will serve the goal of better,
dynamic knowledge representation of essential building blocks of urban fabrics.
3.4. Participatory Approaches for Open Innovation
Traditionally, innovation has occurred within the confines of an organization. However, there
has been a shift in recent years towards open innovation, which is the process of seeking ideas
and solutions using more collaborative approaches. This shift has been influenced by a number of
factors, including increased information availability, open-access hardware, software, and data, as
well as the use of participatory approaches [135].
Technological advancements have made it easier for people to connect, collaborate, and work
towards common goals. They have helped to connect people and communities, and have given
rise to new forms of social and political participation. Digital technologies have also enabled the
development of new platforms for expression and exchange, which has facilitated the free flow of
information and ideas, cooperation and collective intelligence [136]. Therefore, organizations are
using new ways of innovation that are based on participatory methods such as citizen science, co-
creation workshops, hackathons, etc. These methods allow organizations to tap into a wider pool
of ideas and open-source data and technologies to generate new solutions through collaboration
[137].
Citizen science is an open, participatory approach to scientific research. It is a type of crowd
sourcing that uses the input of communities to contribute to scientific research. The participatory
approach in citizen science is a way for citizens to be actively involved in the process of problem
formulation, data collection and analysis [107, 138]. This means that they are not simply passive
observers, but instead active participants in the process. The concept of co-creation has been widely
used in Citizen Science activities. Co-creation is a process in which groups and/or individuals work
together to develop ideas, solutions, and even services, rather than separately. The goal is for all
stakeholders to share their ideas and knowledge in order to create something mutually beneficial—
often something better than what any one side could have come up with by themselves [139, 140].
Co-creation practices combined with technology-enabled platforms have profoundly changed
democratic decision-making. Such methodologies can be used to tap into the wisdom of the crowd
27
at various stages of the democratic process. There are various approaches to co-create, but some
of the most common are open innovation challenges, hackathons, design sprints, and co-creation
workshops.
Co-creation, in whatever form it takes, should have the following key elements:
1. A common objective or purpose: To be effective, co-creation must have a common goal or
objective that everyone is working toward. This could be as simple as collaborating to develop
a new product or service, or it could be more complex, such as collaborating to solve a societal
problem or challenge.
2. Diverse perspectives: When it comes to co-creation, different isn’t just ok or good; it’s re-
quired. This is because different perspectives lead to different ideas, which can lead to inno-
vative and effective solutions.
3. A space for collaboration and innovation: This refers to creating a room for people to come
together and share ideas. This could be a physical space, such as an office or a workshop,
or a virtual space, such as an online forum or chatroom. The important thing is that it is a
place, where people can feel at ease to collaborate and share ideas.
4. Structures and processes to support co-creation: This includes dedicating resources (people,
time, money, etc.) to co-creation initiatives, as well as clearly defined roles and responsi-
bilities for those involved. It also necessitates the establishment of mechanisms for ongoing
communication and collaboration among stakeholders.
Digital technologies and participatory techniques have the potential to boost innovation and
resilience by actively involving individuals and communities in the problem-solving processes [141].
People are more likely to engage into the process and be committed to the outcome when they
are actively involved in the design and implementation of solutions. Digital technologies can play
an important role in data-informed decision making [142] and the democratic transformation of
society [9]. They can help to improve the quality of data available to decision makers. For example,
they can help collect data more accurately and efficiently, as well as process and analyse data more
effectively. Furthermore, digital technologies can help to make data more accessible to decision
makers, allowing them to be better informed about the issues they face and the options available
28
to them.
4. Distributed Ledgers: An Enabling Technology for Participatory Digital Democra-
cies
Figure 6: Distributed ledger technology (DLT) infrastructure consisting of durable data (Layer I) and trusted com-
putational protocols (Layer II), enabling the definition of interaction patterns (Layer III), which can assist citizen
behavior (Layer IV) such that a participatory digital democracy emerges (Layer V) (illustration extended from
[143, 144]). Each of these layers can be instantiated by bottom-up self-organization such that socioeconomic systems
emerge even when goals are diverse. Thus, a value-based engineering approach is required to guarantee that the
resulting system aligns with the values of the affected people.
A digitally upgraded democracy may leverage Distributed Ledger Technologies (DLTs) to ensure
values such as transparency, trust and autonomy by design [145, 146, 147, 148]. In particular,
DLT can be an enabling technology for a participatory digital democracy with novel governance
mechanisms [149, 150], for example by facilitating durable data storage and trusted computations
[143, 144, 151]. This is illustrated in Figure 6: DLT allows for the implementation of smart contracts,
29
which, in turn, enable the definition of various interaction patterns discussed in this work, such
as voting mechanisms (Section 2.7), participatory budgeting (Section 2.5), machine learning/AI
(Section 5), or free information access 2.6. These interaction patterns steer local agent behavior,
which can express itself in an increased political participation, sharing of resources, or responsible
sustainable behavior. The product of these behaviors, when designed appropriately, can result in
the global goal of a digital participatory democracy as illustrated in this work.
Nevertheless, the challenge is that each of these layers (Figure 6) enables socioeconomic systems
with various properties. This makes the construction of a viable system difficult, requiring respon-
sible engineering. Given its large configuration space [152], on the one hand, a DLT system can be
configured such that it is “permissionless”, meaning that the public can participate in the writing
(also referred to as consensus) and the securing of data, resulting in a system that is very inclusive
and secure. On the other hand, utilizing the same technology, another DLT could be constructed
that optimizes for efficiency and control by restricting the access of the system to very few entities
in the system, resulting in a closed and centralized system setup [152].
Also the interaction patterns (Layer III in Figure 6) can be instantiated in opposite ways.
For instance, electronic identities could be implemented with a top-down approach, requiring a
centralized entity having signatory power, or they could be implemented in a bottom-up peer-
to-peer manner, resulting in a paradigm referred to as self-sovereign identities, where individuals
can create those identities in a self-determined way [153, 154, 155, 156]. Both, however, does not
address the question how to identify users or citizens, whether and when this is necessary, and what
is appropriate. It also leaves the question unanswered, why one would track people rather than
money and resources, which should be sufficient to achieve sustainability goals with less ethical
issues.
Further complexity is faced when designing socioeconomic systems: Mechanisms that appear to
be decentralized, distributed and fair, may become more centralized over time [157] due to power
concentrations in the underlying infrastructure layers (Layer I and II in Figure 6). This could lead
to computational protocols eventually being altered such that an originally fair interaction pattern
might become unfair. So, the evolution of DLT systems over time is a non-trivial issue, requiring
30
great attention and care in the design process. Nevertheless, the governance of a DLT is currently
often neglected when design starts, as technical considerations are typically more dominant [158].
Applying a value-based engineering methodology could support designers in instantiating gover-
nance mechanisms in DLT systems, which align with the values of the stakeholders, particularly the
people affected, thereby potentially reducing the cost and complexity of mechanism implementation
[158].
If set up well, a great benefit of DLTs is certainly that all participants can be treated equally
and can be granted equal voting or economic rights in the system. Moreover, should DLTs be
allowed to add a layer of trust between citizens, the burden of trustworthiness would be shifted
from government and political rule to a digital infrastructure. Government bodies may then direct
their attention towards creating DLTs that are sufficiently decentralized such that the conditions
for the immutability and security of DLTs are ensured.
4.1. A Circular and Fair Sharing Economy through Participatory Sustainability
As we have discussed, Distributed Ledger Technologies can be constructed in multiple ways,
which is one of their great strenghts [152]. This flexibility of DLTs allows them to be tailored for
specific applications, and are very suited to value-centric design [143, 148]. In particular, DLTs can
also contribute to the co-creation and co-evolution of a more circular and fair sharing economy [159].
For example, DLTs could help to achieve sustainability goals by means of a participatory
socio-ecological finance, incentive and coordination system such as Finance 4.0 or FIN4+ [160].
According to [143]:
“Non-sustainability has be found to be one of the greatest challenges humanity is facing
at the beginning of the 21st century [161]. In the past, it was tried to solve sustainability
issues by means of laws and regulation [162]. By now, however, we can say it has
not solved the world’s problems on time [163]. We, therefore, need a new approach
to tackle the challenge. Here, a bio-inspired approach [164] is proposed. Ecosystems
are very impressive in terms of their logistics and recycling [165]. Nature has already
managed to build something like a circular economy, i.e. closed cycles of material flows.
It did not get there by regulation and optimisation though, but by (co-)evolution—a
31
principle, which is based on the self-organisation of complex systems. Optimisation, in
contrast, which is often used in economics, tends to be based on a one-dimensional goal
function and, therefore, to oversimplify the needs of complex systems. In particular,
it often neglects other, non-aligned goals. Of course, there are also methods for multi-
objective optimization [166], but co-evolution as we find it in nature seems to work
differently, based on mutation, selection, and multiple feedback loops [167, 168]. Using
such principles underlying self-organization, complex systems may improve over time
in a variety of aspects. A one-dimensional incentive system such as money cannot
accomplish this task in the same way as multi-dimensional incentive systems can do.”
Such an approach establishes a participatory approach to sustainability. Note that mobilizing
citizens and civil society is expected to unleash a lot more transformational potential than if one
would only rely on businesses and governments [135]. Given that finding sustainable solutions is
an extremely pressing challenge, implementing a participatory sustainability approach, designed in
a way compatible with digital democracy principles, is urgent.
5. Designing Digital Assistance for Democracy by Design
Decision-support systems will play a key role in future digital democracy initiatives and gov-
ernance systems, as digital assistance becomes paramount in the decision-making of citizens and
policy-makers:
•Automation: The acquisition and processing of information for decision-making becomes more
complex due to the scale, heterogeneity and variable quality of information. Automated and
efficient approaches to structure, manage, analyze and learn from large amounts of data is
required to support informed decisions.
•Scaling up participation: There is a political mandate to engage larger and more diverse
groups in decision-making processes. This becomes evident from the low turnout rates in
elections and various grassroot participatory initiatives such as citizen assemblies and partic-
ipation budgeting. Digital assistance can simplify participation allowing distributed or remote
individuals as well as diverse communities to raise a voice.
32
•Decision complexity : In a globally networked world, decisions in the public sphere are highly
multi-faceted and often subject to controversies, misinformation and polarization. Guiding
and supporting a more responsible, inclusive and evidence-based decision-making with digital
assistance is required to deal with this growing complexity of decision spaces.
•Limited cognitive bandwidth: Citizens may not be interested or able to get actively and directly
involved in every single decision of the public sphere. Digital assistance is required to match
a manageable number of interests, preferences and opinions, in order to manage the large
numbers of specific decisions that affected citizens need to be able to trust.
However, introducing digital assistance comes with several risks that can undermine the demo-
cratic endeavor. Centralized management of data and computing operations may require trusted
third parties that could result in information asymmetries and power imbalances. Big Tech is cur-
rently established on the basis of processing a massive amount of sensitive personal data. This opens
Pandora’s box for broad privacy violations, which in turn may lead to censorship, discrimination,
manipulation, and loss of personal freedoms [169]. Therefore, it becomes of paramount importance
how digital assistance and decision-support systems are designed to preserve democratic values and
be resilient to misuses that can undermine the purpose, which they have initially been designed
to serve. Therefore, a socially responsible design of digital assistance is a safeguard and important
aspect of democracy by design.
5.1. Design Based on Human-Machine Hybrid Collective Intelligence
Democracy by design in digital societies is not viable without moving from mainstream AI
to human-machine hybrid collective intelligence. This ambitious step requires adding a complex
system design and novel functionality into decision-support systems in order to make sure that
digital assistance does not erode democratic principles, but rather supports them. Figure 7 presents
an interactions model illustrating human-machine hybrid collective intelligence.
Here are some elements of our value-sensitive design framework:
1. Individuals autonomously self-determine parameters and alternative options to choose from
as a contribution to operational flexibility. All personal data and preferences remain local,
33
Figure 7: Decision-support system design based on the concept of human-machine hybrid collective intelligence
to empower democracy by design. Individuals self-determine the parameters and options of their personal digital
assistants, which help taking better decisions and coordinating activities, while operating based on trustworthy,
privacy-preserving and scalable decentralized computation (e.g. federated AI). They provide coordinated feedback
that empowers citizens to make more democratic decisions.
and sharing happens at an aggregate level, or with techniques such as differential privacy and
homomorphic encryption [170].
2. Digital assistants coordinate among each other in order to support individuals in their
decision-making. They carry out computational work efficiently that could not easily be
carried out directly by individuals. For instance, deciding about a fair allocation of resources
could be carried out at small scale within a citizen assembly. At large scale, digital assistants
could solve multi-objective combinatorial optimization problems in a cooperative way, which
would help citizen groups to discover possible new solutions to resource allocation prob-
lems in the public sphere. For instance, this could support participatory budgeting [89] and
sharing economies [171]. Several decentralized algorithms could be applied in this context,
for instance, collective learning [172], gossip-based learning [173], multi-agent reinforcement
learning [174] and federated learning [175]. Such algorithms are trustworthy and resilient as
34
they do not rely on single points of failure and they can enhance privacy.
3. Coordinated feedback by digital assistants can represent recommendations or rankings (based
on personal values) among a number of discrete options to choose from. Individuals can align
to this feedback by adopting one of the highly recommended choices. They can learn from
this feedback, change their behavior intrinsically and even diffuse it in their social network,
thereby building social capital. An example of this is learning to consume products more
sustainably [176]. When consumers reject suggestions, this provides learning feedback to the
digital assistants such that human-machine hybrid collective intelligence results from a co-
evolutionary principle.
5.2. Digital Assistance Exemplars for Democratic Upgrade
In the following, we review the design features of several software exemplars with the purpose of
demonstrating how value-based engineering can support democratic upgrades. Figure 8 illustrates
four software toolkits designed for seven democratic upgrades and ten value-based engineering
principles.
SECURITY
FAIRNESS
DIVERSITY
EVIDENCE-BASED DECISION-MAKING
WITNESSED PRESENCE
INCLUSION
Nervousnet
COORDINATION
PRIVACY
AUTONOMY
PAR TI CI PAT IO N
DECISION QUALITY
INFORMED DECISION-MAKING
RESILIENCE
Smart Agora
DECENTRALIZED COMPUTATIONS
TRUST
LEGITIMACY
INFORMATIONAL SELF-DETERMINATION
DIAS
EPOS
VALUE-BASED ENGINEERING
DEMOCRATIC UPGRADE
DIGITAL ASSISTANCE
Figure 8: Examples of four digital assistance toolkits that aim to demonstrate how a broad range of values can be
enabled by novel functionalities that guide the democratic upgrade. (i) Nervousnet [15], (ii) Smart Agora [155, 177],
(iii) DIAS [178] and (iv) EPOS [172].
35
Nervousnet14 [15, 179] is a general-purpose and open-source data management platform for
pervasive devices such as smart phones. It is based on a data-driven application programming
framework that collects, stores and composes physical and virtual sensor data on personal devices,
without sharing them with third parties. End-users and developers have fine-grained control of what
data are collected and how frequent sampling is performed. This makes it relevant for ubiquitous
citizen engagement and participation applications addressing informational self-determination via
values such as privacy, autonomy, trust and legitimacy.
Smart Agora15 [177, 155] is a crowd-sensing and living-lab experimentation platform for in-
door and outdoor environments using smartphones. It collects geolocated sensor, survey and voting
data subject to users proving their witnessed presence and verifying conditions for a more informed
and evidenced-based decision-making. Smart Agora turns every urban spot into a digital voting
center, where citizens prove conditions for more informed decision-making. For instance, a Par-
ticipatory Budgeting voter determines the preference for a project after digitally proving to be
sufficiently informed about the different options. Using the Internet of Things [177] and blockchain
technology [155], these proofs verify conditions such as the location of the voter (close to where
the project will be implemented), or situational awareness (participation in local citizen assem-
blies). These democratic updates benefit both citizens and policy-makers. They support diversity,
inclusion and participation at a local level. They also improve decision quality, security, trust and
legitimacy.
DIAS16 [178, 180, 181] is a decentralized real-time data analytics service for large-scale net-
worked users. Users of DIAS share with each other and in a peer-to-peer fashion privacy-enhanced
summaries of local data. This allows each of them to compute locally almost any aggregation func-
tion such as summing up votes, the mean popularity of proposals in different communities or the
top-kagenda priorities within a community. Using an advanced distributed memory system [178],
estimates of aggregates can accurately adapt to actual values even when input values or the pool
of users change dynamically. With informational self-determination in data sharing, resilience and
14available at https://github.com/nervousnet,https://github.com/ethz-coss/nervousnet-iOS
15available at https://github.com/epournaras/SmartAgoraDashboard,https://github.com/epournaras/
SmartAgoraApp,https://epournaras.github.io/SmartAgoraDocumentation
16available at https://github.com/epournaras/DIAS
36
decentralization in updating computations, DIAS supports inclusion, privacy, autonomy, partici-
pation, decision-quality, trust and legitimacy, as depicted in Figure 8.
EPOS17 [172, 171] is a collective learning algorithm for discrete choice multi-objective combi-
natorial optimization problems in the context of decentralized multi-agent systems. EPOS supports
coordinated decision-making when agent choices among self-determined options are inter-dependent
and their goals are modeled by non-linear cost functions. To solve such complex NP-hard optimiza-
tion problems, agents self-organize for resilience in tree network topologies, over which they can
efficiently perform iterative aggregation and intelligent decision-making. The optimization process
addresses three classes of (opposing) agent goals: efficiency, comfort and fairness [172]. EPOS has
been applied to a large spectrum of scheduling and resource allocation problems with balancing
and matching objectives including: prosumer energy management, charging control of electric ve-
hicles, load-balancing of bike sharing stations, traffic rerouting, edge-to-cloud load-balancing and
other [171]. Via informational self-determination, coordination, informed decisions, resilience and
decentralized computations, EPOS covers a large spectrum of values defined in Figure 8.
These examples demonstrate the incremental growing complexity and inter-disciplinary chal-
lenge of integrating engineering values in digital assistance for democratic updates. Further work
is required to augment promising governance and participation platforms such as Decidim [182]
with value-sensitive digital assistance. Other technologies such distributed ledgers can also play a
key role in improving trust and incentives for participation.
6. Discussion and Conclusions
The world is undergoing a digital revolution. Rapid technological advancements are transform-
ing the way we live, work, and communicate. The Internet, social media, cloud computing, and
mobile technologies are just a few of the innovations that are transforming our world. This digital
revolution also has a significant impact on how we govern our societies. A summary of the possible
advantages and dangers of how these innovations can impact democracy is shown in Figure 9.
Overall it appears we need a paradigm shift from
17available at http://github.com/epournaras/epos
37
Figure 9: Summary of positive and negative impact of digital tools on democracy. Particular attention should be given
to the currently existing problems while also looking at the future for new possibility and potential new dangers.
a surveillance-based, data-driven, AI-controlled approach trying to “optimize” a society
by targeting people
towards
a measurement-enabled, data-oriented, AI-supported co-evolving society that is em-
powering people to contribute better to the society of the future.
In fact, traditional top-down governance models are recently being challenged by new bottom-up,
participatory approaches enabled by digital technologies. Planning and policy-making should be a
continuous conversational process seeking for consensus or at least for the acceptance by the various
involved parties, taking into account different meaning systems as well as bounded communication
and cognition [183]. One of the most promising approaches to improving our societies is to use
digital technologies that enable participatory governance. Such technologies are already empowering
individuals and communities to have a direct say in decisions that affect them, resulting in more
adaptable, trustable, responsive and effective societies [139, 184].
In this paper, we have explained how digital tools can assist in the democratic upgrade of society
38
by providing platforms for people to engage in dialogue and debate, by facilitating the exchange
of information and ideas, by empowering individuals to take action, and by adopting technologies
that can support value-based design. They can also help to improve government transparency and
accountability, as well as increase citizen engagement in the democratic process. However, it is
important to remember that digital tools are not a panacea for all ills. They need to be used
in conjunction with other measures, such as public education, awareness-raising campaigns, and
spatial planning that promotes inclusion and spatial equity to truly improve democracy.
While there are many challenges to be addressed, such as ensuring that all voices are heard
and that everyone has access to digital tools, the potential for digital technologies to democratize
society is great. With continued effort and engagement from all sectors of society, digital tools can
help to create a more inclusive, participatory, and responsive democracy.
Author Contributions
DH has proposed the concept of the paper and assembled the author team. He has also con-
tributed to the writing of most sections and much of the underlying research reported. SM co-
organized, structured, edited, and proofread the paper. He contributed to the writing of the Ab-
stract, Sections 1, 1.1, 1.5, 3.4,1 and 6. MCB wrote section 4 and proof-read other sections. RH
contributed to the writing of parts on legitimacy, transparency, and trust, and provided feedback
to the paper (esp. part 1 and 2), and its structure. AM contributed subsection 2.3 and Figure
2. CIH contributed to the writing of the initial draft and contributed to improving the writing
of advanced versions for the subsection “dealing with mis- and disinformation” and subsection
“voting systems”. She integrated those subsections into the red thread of the paper. Furthermore,
she contributed to spelling and grammar checking. EP has carried out and contributed to a large
body of the reported research. He contributed the section on digital assistance and decision-support
systems. He has also edited and proofread this paper. DC contributed to subsections 2.2 2.4 and
figure 9. RKD contributed to the writing of Section 3. Specifically, subsections 3.2 and 3.3. RKD
also contributed to the initial drafting and conceptualizing of Figure 5. JASV contributed to the
conceptualization and writing of Section 3 and particularly subsection 3.2 as well as to final design
39
of Figure 5. MK contributed to conceptualizing and writing the subsection 3.2. ES contributed to
the subsection 2.2 in content and form. JY contributed to the writing of section on 2.4, 2.7 and
2.5. CC contributed to the writing of section 3 and proofreading of the document.
All authors contributed to the manuscript and approved it.
Acknowledgments
The authors would like to thank all software developers of the Nervousnet, Smart Agora, DIAS,
EPOS, and VoteApp projects for their contributions to implementing and using concepts such as
the ones discussed above.
Funding Information
DH, RH, EP, JY would like to thank for support by the Swiss National Science Foundation
(SNSF). This study is financed by the SNSF as part of the National Research Programme NRP77
Digital Transformation, project no. 187249.
SM, AM, CIH, CC, DC, MCB, MK are grateful for support by the project “CoCi: Co-Evolving
City Life”, which received funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme under grant agreement No. 833168. ES
acknowledges support by the HumanE AI Network project, which is also financed under the same
Horizon 2020 programme under the grant agreement No. 952026.
RKD and JASV acknowledge financial support by the Semantic Urban Elements module fi-
nanced by the Future Cities Lab Global of the Singapore-ETH Centre, which was established
collaboratively between ETH Zurich and the National Research Foundation Singapore.
The work of EP and his team is supported by a UKRI Future Leaders Fellowship (MR-
/W009560/1): Digitally Assisted Collective Governance of Smart City Commons– ARTIO, the
Swiss National Science Foundation NRP77 ‘Digital Transformation’ project (#407740 187249):
Digital Democracy: Innovations in Decision-making Processes, the White Rose Collaboration Fund:
Socially Responsible AI for Distributed Autonomous Systems and a 2021 Alan Turing Fellowship.
40
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