Volume 37 • Number 10 • October 2018 Banking & Financial Servi

Volume 37 • Number 10 • October 2018 Banking & Financial
Services Policy Report • 5
“Smart Contracts” Are Neither Smart Nor
Contracts. Discuss.
Judah A. Druck
Hiram Walker has a problem. As a breeder of Angus cattle, he
has agreed to purchase a barren
cow named Rose 2d of Aberlone for the discounted
price of five and a half cents—an absolute steal. But
the seller, a farmer named Theodore Sherwood, has
reneged on the deal, having discovered that sweet
Rose is far from barren, and actually pregnant. Luckily,
Hiram is a big proponent of blockchain technology,
and need not worry about a failure to deliver. The par-
ties’ “smart contract,” listed on a public ledger for all to
see, requires that Sherwood tender delivery of the cow
within 30 days of agreement, at which time he will
automatically receive his five and a half cents worth of
bitcoin, or pay a cancelation fee to Hiram adequately
compensating him for the breach. Thus, Hiram sits
back and relaxes, knowing that he will walk away fully
compensated even if that charlatan Sherwood breaches
their contract.
Unfortunately for law students, the blockchain did
not exist at the time of Sherwood v. Walker,1 a staple of
every first-year Contracts course. Instead, the court
allowed the recession of the parties’ contract under
the doctrine of mutual mistake, thereby leaving Hiram
without the great deal he had expected (and argu-

ably deserved). The practical lesson, we are taught, is
that effective contract formation requires careful due
diligence, precise language, and ample contingency
planning.
All of these things, of course, require lawyers. But
the involvement of lawyers invariably raises costs,
increases transactional complexity, and frequently delays
ostensibly straightforward agreements. Wouldn’t smart
contracts—written on distributed ledgers, with clear,
irreversible terms, and automatic execution—remove
these needless inefficiencies?
Blockchain proponents seem to think so. A Google
search for “smart contract” and “lawyers,” for example,
brings up such headlines as “Smart Contracts Are Taking
Over Functions of Lawyers,”2 “Smart Contracts: The
Blockchain Technology That Will Replace Lawyers,”3
and—with a not-so-subtle hint of optimism—“Hello
Blockchain . . . Goodbye Lawyers?”4 State law has
similarly begun to recognize the potential impact of
the blockchain revolution, with both Arizona5 and
Tennessee6 passing legislation recognizing the enforce-
ability of smart contracts. Investors have taken notice as
well: even after the epic rise and fall of cryptocurrency
prices in 2017—led by bitcoin, whose value which
peaked at $19,000 and now hovers around $7,000—the
prospect of disrupting global industries through smart
contracts and other blockchain technology remains the
darling of financiers, venture capitalists, and the larger
crypto community. A recent special report from The
Economist, for example, noted that more than $1.3 bil-
lion was invested in blockchain startups during the first
half of 2018 alone.7
But can this technology, and its promises of greater

economical approaches to contracting, actually deliver?
This article assesses the current state of smart contracts,
including their usefulness in performing the tasks of
lawyers, and limitations in being able to fully disrupt the
legal industry. Ultimately, as when discussing any new
technology, predicting long-term effects would be fool-
hardy, though this article concludes that smart contracts
(in their current form) provide few practical benefits,
and will do little to remove lawyers from anything but
the simplest of transactions.
The Blockchain
Smart contracts are predicated on the blockchain,
which in turn was, at least for a time, predicated on cryp-
tocurrencies. Cryptocurrencies are digital assets that act
like regular currencies—they can be purchased, traded,
and exchanged. Rather than relying on bank or gov-
ernment control, however, cryptocurrencies are wholly
decentralized, allowing anyone to easily transfer funds
Judah Druck, an Associate at Maslon LLP in Minneapolis, MN,
represents clients in complex commercial disputes, including
tort
and product liability, breach of contract, and noncompete and
trade
secret litigation.
6 • Banking & Financial Services Policy Report Volume 37 •
Number 10 • October 2018
with few restrictions. Oversight exists solely by virtue of
transactions being publicly listed, utilizing a public, uni-
versal ledger—the aforementioned blockchain. Before

a transaction can occur, it must be verified to ensure
that the transfer is valid, which includes confirmation
that the funds (say, bitcoins) being transferred are not
duplicates or counterfeit. Once the transaction is veri-
fied, its details (including source, destination, and date/
time) form a “block,” which is added to the ever-ex-
panding blockchain. This process repeats itself each time
a transaction takes place.
The need to put one’s trust as a distributed ledger
assures its trustworthiness. Any attempt to change trans-
action records or edit the underlying code would be
futile, as millions of users, each with their own copy of
the blockchain, would quickly spot inconsistencies and
discard them. Complete transparency and multi-party
verification remove (at least theoretically) the concerns
typically associated with any financial transaction, and
in doing so eliminate the need to put ones trust in a
neutral central authority to regulate the marketplace.
Unsurprisingly, while blockchain technology was ini-
tially unitized solely for facilitating cryptocurrency
exchanges, its usefulness and continued development
has enabled it to branch out into other industries, with
different variations and mechanisms tailored to assure
trustless authentication.
Smart Contracts
If the blockchain’s decentralization can assure trust-
worthiness in financial transactions, then why wouldn’t
it be able to do the same in all transactions, including
for goods and services? That was the thinking of Nick
Szabo, a computer scientist and cryptographer (and
the rumored inventor of bitcoin), who in 1996 theo-
rized that “many kinds of contractual clauses . . . can be
embedded in the hardware and software we deal with,”

and that what he called a “smart contract” could be
“far more functional than their inanimate paper-based
ancestors.”8 Since then, and particularly in the wake
of the aforementioned cryptocurrency craze of 2017,
commentators and crypto-enthusiasts have built on
Szabo’s thinking, predicting that these digital contracts
would soon disrupt industries ranging from healthcare
to real estate to governmental voting systems.
So how do they work? Consider the oft-repeated
example provided by Szabo himself: a vending machine,
whereby you deposit your currency into the machine,
you “execute” the arrangement by making the selec-
tion, and the product drops out. There is no concern
about the trustworthiness of either party: if you with-
held your funds, the machine would not respond and,
conversely, your money would be returned to you if
your selection was not delivered. The “contract” with
the machine is executed automatically, without any
intermediaries needed. Smart contracts work the same
way, where two or more parties agreed to certain con-
ditions—pay a dollar, get a soda—that are memorial-
ized on the blockchain, free of tampering and subject
to uninterrupted enforcement. When the trigger-
ing event occurs—depositing the dollar into a digital
escrow account—the blockchain automatically exe-
cutes the corresponding action—delivery of soda—at
which time the dollar is released. Again, trustworthi-
ness is irrelevant, as no third-party observers, arbiters,
or enforcers are necessary to execute the conditions of
the contract, which occur automatically. If the soda is
not delivered on the date specified, then the dollar is
returned, along with whatever other penalties the par-
ties agreed to insert into the smart contract. Lawyers
need not apply.

These hypothetical scenarios illustrate the benefits of
smart contracts: they remove dependence on interme-
diaries, thereby cutting costs, time, and increasing effi-
ciency; they remove the trustworthiness of the opposite
party from the equation; they assure complete trans-
parency and security, as the blockchain’s public listing
removes the fear of theft, misappropriation, or tamper-
ing; and they make the entire contractual process move
quickly (if not instantaneously), without need to wait
for third-parties to chime in, confirm terms, or assure
compliance.
The Benefits of Smart Contracts
The implications of this crypto-arrangement, so
the lore goes, are wide-ranging. Real-estate provides
an obvious example of the potential impact of smart
contracts. If I were to rent an apartment, I would typ-
ically need to negotiate and agree to certain terms,
potentially run them by an attorney, and then exe-
cute, hoping that the owner of the apartment delivered
the key on the specified move-in date. With a smart
contract, I put my monthly rent into the blockchain
escrow account, which is released to the owner on the
first of the month, in exchange for my access to the
apartment. If I fail to deposit my rent, the apartment
remains locked; if I deposit but the apartment is not
released, then the funds are returned to me. All without
any brokers in sight.

Similar examples abound. An insurance policy can
be automatically executed after submitting car accident
information or a medical bill, at which time the block-
chain system triggers a clause in your policy remitting
payment. Complex supply chains can be streamlined
through smart contracts, which would arrange pur-
chases of raw materials, track shipments, and adjust
purchases volumes based on delivery trends.9 Payroll
management, securities transactions, airline travel—all
seem ripe for a smart contract reckoning. In this uto-
pian vision, consumers would be able to pick from an
à la carte buffet of contracts, choose the arrangement
that works for them, and execute with minimal cost
or hassle.
The Reality of Smart Contracts
Or so we are told. An astute reader would recog-
nize that all of the aforementioned examples are simply
elaborate “if-then” transactions, with very little room
for extenuating circumstances. Consider our vend-
ing machine example, where funds are deposited and
a selection is made. The automatic execution kicks in
and out pops a soda. But now suppose that the machine
delivers the incorrect product. Or suppose the soda is
damaged in some way. What is the available recourse?
As far as the smart contract can tell, the transaction (“if
money, then soda”) has been completed—the quality
or accuracy of delivery never enters the picture. There
is no way for the blockchain to “know” anything about
the actual reality in the physical space.
The issue lies in the fact that the benefits of smart
contracts come at the price of debilitating inflexibil-
ity. Smart contracts are effective only insofar as execu-

tion includes perfect performance. When performance
falls short, however, the parties are left with only two
impractical options: (1) formulate a new smart con-
tract reversing the prior transaction—recall that the
contract’s inclusion on the blockchain makes even a
simple modification to its terms nigh impossible—
which raises the prospect that the wrongfully paid
party will decline to do so (thereby implicating the
trustworthiness the initial smart contract was meant to
avoid), or (2) proactively create a smart contract that
requires confirmation from a third party that the cor-
rect product was delivered, free of defect, before funds
are released—which requires a concession that over-
sight may be necessary after all. Either way, the smart
contract ends up raising the very concerns its entire
formulation was meant to allay.
Worse yet, the rigidity inherent in smart contract
prevents parties from including subjective terms and
conditions inherent in smart contracts. The entire point
of hiring a lawyer to draft and negotiate a contract is
to arrange a transaction that comports with the parties’
expectations, which are often amorphous and neces-
sitate a degree of flexibility. Consider the number of
contracts that include terms like “reasonable,” “at the
party’s sole discretion,” “good faith,” or which refer to
future discussions and decision (“to be agreed upon,” “at
a reasonable time,” etc.). None of these subjective deter-
minations are conducive to digital formulations, which
take judgment out of the equation. How would the
smart contract determine when a “reasonable” amount
of time has passed, or when an employer’s action was
taken in “good faith”? Unless the parties included unre-
alistically specific terms that prepared for an impracti-
cally large number of contingencies and outcomes, the

blockchain algorithm would ultimately make the deci-
sion, and remove the parties’ discretionary intent.
Finally, widespread implementation of smart con-
tracts would require a low entry cost, which may not
be compatible with the need to meet parties’ specific
requirements. As discussed earlier, in a perfect world,
a consumer would be able to select a form contract
that meets the specific transaction at issue, and proceed
with the agreement immediately. But while a contract
for a one-off purchase or rental of a property can be
simplified to cover a wide range of consumers, more
complex transactions, particularly those between finan-
cial institutions, require a high level of customization.
A simple “if-then” arrangement could not possibly
cover all aspects of a complex merger agreement, for
example, which may implicate different state and fed-
eral laws, regulatory provisions, and detailed financial
terms. Unique circumstances and variations necessarily
require drafting from experts on the subject—namely,
lawyers. This customization carries high, but often nec-
essary, costs. The assumption that smart contracts can
be employed across all industries without considering
the difficulty in drafting their underlying terms paints
8 • Banking & Financial Services Policy Report Volume 37 •
Number 10 • October 2018
an overly optimistic picture, one that fails to consider
the costs of entering into the contract before it is even
placed onto the blockchain.
Beneath all of these obstacles lies the fundamental
problem with smart contracts: they attempt to digitize a

process that is fundamentally predicated on the human
condition. The concept of entering into a contract with
another person is dependent on a system that allows
for interpretation and a certain number of defenses to
deal with the ambiguity and gray areas that will always
present themselves in the real world. Indeed, the com-
mon law that established the pillars of contract law
evolved based on people’s real experiences and com-
plications, and developed into a system that could pro-
vide remedies while taking these realities into account.
Blockchain-based contracts, on the other hand, operate
on a different plane of existence, where agreements can
be distilled into black-or-white scenarios. While that
worldview is certainly attractive—who wouldn’t want
that kind of certainty?—it fails to account for natural
conflicts and complexities. Contracting is simply not
a theoretical problem a computer can “solve,” and any
attempt to do so—as reflected in the aforementioned
difficulties—will remain incompatible with the philos-
ophy underlying contract law.
And the Dangers
Even ignoring the practical drawbacks of smart con-
tracts, the assumption that the blockchain can assure
complete security and trustworthiness is itself a flawed
concept. For one, smart contracts are subject to the
same hacking risks typically associated with crypto-
currency exchanges. This past July, for example, an ini-
tial coin offering project called KICKICO lost nearly
$8 million, after hackers obtained the private key to
the project’s smart contract.10 Similarly, in 2016, an
organization called the Decentralized Autonomous
Organization had its smart contract hacked at a loss of
nearly $50 million.11 Recent studies have highlighted
even more security holes underlying current block-

chain-based smart contracts.12 Given the anonymity
provided by blockchain technology, stolen funds are
typically lost forever.
Of course, even if a transaction is successfully com-
pleted, the exchanged funds are subject to similar risks
of theft. Given the lack of central oversight in the
cryptocurrency market, any issues with fraud, theft, or
scamming can often leave victims without recourse.
The U.S. Consumer Financial Protection Bureau, for
example, has received hundreds of complaints about
bitcoin exchanges, and no FDIC-equivalent exists to
protect investors.13 These risks, combined with the costs
of entering into a fulsome contractual agreement, may
prove too daunting for investors and consumers.
Conclusion
Mr. Wichelhaus has a problem. He has contracted to
purchase bales of cotton from a Mr. Raffles, with deliv-
ery being made by a ship named Peerless. But while Mr.
Wichelhaus understood delivery to occur in October,
Mr. Raffles believed that delivery would be made in
December, aboard a different ship named Peerless. Mr.
Wichelhaus now refuses to accept the December ship-
ment of cotton, having expected delivery to occur ear-
lier in the year. Unfortunately for Mr. Wichelhaus, the
parties entered into a smart contract that simply referred
to “delivery from the Peerless,” without specifying a spe-
cific date or which ship it was referring to. As such, the
blockchain algorithm has, on its own, decided that the
contract referred to the December Peerless, and that Mr.
Wichelhaus must either accept this shipment, which he
does not want, or face automatically executed financial
penalties as a result of his breach.

Fortunately for Mr. Wichelhaus, Nick Szabo was not
writing at the time of Raffles v. Wichelhaus,14 yet another
pillar of Contracts law. In reality, the court ruled that
the parties’ mutual mistake rendered the contract void,
and that Mr. Wichelhaus was therefore not required
to pay for the December shipment. Yet, the scenario
demonstrates the difficulties of trusting a smart contract
to handle ambiguity, which can result in consequences
antithetical to the parties’ original intent and expecta-
tions. Given the finality of smart contracts—ostensibly
one of its strongest characteristics—these results are not
easily remedied.
The increased efficacy and cost-cutting promised
by smart contract proponents are tempting, and one
can easily imagine more and more transactions soon
adopting the blockchain’s vending machine model.
But those of us working for sophisticated clients with
complex purchases, mergers, or sales, will likely find
that the distilling contracts into a computer code is
a herculean task, one that carries its own immediate
and long-term costs. Until blockchain technology can
adapt to recognize and effectively compensate for real-
world complexities and uncertainties, smart contracts
will be unable to cause a meaningful disruption in our
everyday lives.
Notes
1. 66 Mich. 568, 33 N.W. 919 (Mich. 1887)

2. https://cointelegraph.com/news/smart-contracts-are-taking-
over-
functions-of-lawyers-expert-blog.
3. https://blockgeeks.com/guides/smart-contracts/.
4. https://www.cablelabs.com/hello-blockchain-goodbye-
lawyers/.
5. https://legiscan.com/AZ/text/HB2417/id/1588180.
6. https://publications.tnsosfiles.com/acts/110/pub/pc0591.pdf.
7. https://www.economist.com/technology-
quarterly/2018/08/30/
what-to-make-of-cryptocurrencies-and-blockchains.
8.
http://www.fon.hum.uva.nl/rob/Courses/InformationInSpeech/
CDROM/Literature/LOTwinterschool2006/szabo.best.vwh.net/
smart_contracts_2.html.
9. https://uk.reuters.com/article/grains-blockchain/u-s-soy-
cargo-to-chi-
na-traded-using-blockchain-idUKL8N1PG0VJ.
10. https://www.ccn.com/another-ico-hacked-kickico-loses-8-
million-af-
ter-smart-contract-breach/.
11. https://www.bloomberg.com/features/2017-the-ether-thief/.
12. https://www.technologyreview.com/s/610392/ethereums-
smart-
contracts-are-full-of-holes/.
13. https://www.bloomberg.com/news/articles/2017-08-
30/bitcoin-
exchange-sees-complaints-soar-as-users-demand-money.

14. [1864] EWHC Exch J19 (April 1864).
https://cointelegraph.com/news/smart-contracts-are-taking-over-
functions-of-lawyers-expert-blog
https://cointelegraph.com/news/smart-contracts-are-taking-over-
functions-of-lawyers-expert-blog
https://blockgeeks.com/guides/smart-contracts/
https://www.cablelabs.com/hello-blockchain-goodbye-lawyers/
https://legiscan.com/AZ/text/HB2417/id/1588180
https://publications.tnsosfiles.com/acts/110/pub/pc0591.pdf
https://www.economist.com/technology-
quarterly/2018/08/30/what-to-make-of-cryptocurrencies-and-
blockchains
https://www.economist.com/technology-
quarterly/2018/08/30/what-to-make-of-cryptocurrencies-and-
blockchains
http://www.fon.hum.uva.nl/rob/Courses/InformationInSpeech/C
DROM/Literature/LOTwinterschool2006/szabo.best.vwh.net/sm
art_contracts_2.html
https://uk.reuters.com/article/grains-blockchain/u-s-soy-cargo-
to-china-traded-using-blockchain-idUKL8N1PG0VJ
https://uk.reuters.com/article/grains-blockchain/u-s-soy-cargo-
to-china-traded-using-blockchain-idUKL8N1PG0VJ
https://www.ccn.com/another-ico-hacked-kickico-loses-8-
million-after-smart-contract-breach/
https://www.ccn.com/another-ico-hacked-kickico-loses-8-
million-after-smart-contract-breach/
https://www.bloomberg.com/features/2017-the-ether-thief/
https://www.technologyreview.com/s/610392/ethereums-smart-
contracts-are-full-of-holes/

https://www.technologyreview.com/s/610392/ethereums-smart-
contracts-are-full-of-holes/
https://www.bloomberg.com/news/articles/2017-08-30/bitcoin-
exchange-sees-complaints-soar-as-users-demand-money
https://www.bloomberg.com/news/articles/2017-08-30/bitcoin-
exchange-sees-complaints-soar-as-users-demand-money
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Public Administration and Information
Technology
Volume 10
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Marijn Janssen • Maria A. Wimmer
Ameneh Deljoo
Editors
Policy Practice and Digital
Science
Integrating Complex Systems, Social
Simulation and Public Administration
in Policy Research
2123
Editors
Marijn Janssen Ameneh Deljoo
Faculty of Technology, Policy, and Faculty of Technology,
Policy, and
Management Management
Delft University of Technology Delft University of Technology
Delft Delft
The Netherlands The Netherlands
Maria A. Wimmer
Institute for Information Systems Research
University of Koblenz-Landau
Koblenz
Germany
ISBN 978-3-319-12783-5 ISBN 978-3-319-12784-2 (eBook)
Public Administration and Information Technology
DOI 10.1007/978-3-319-12784-2
Library of Congress Control Number: 2014956771

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Preface
The last economic and financial crisis has heavily threatened

European and other
economies around the globe. Also, the Eurozone crisis, the
energy and climate
change crises, challenges of demographic change with high
unemployment rates,
and the most recent conflicts in the Ukraine and the near East or
the Ebola virus
disease in Africa threaten the wealth of our societies in
different ways. The inability
to predict or rapidly deal with dramatic changes and negative
trends in our economies
and societies can seriously hamper the wealth and prosperity of
the European Union
and its Member States as well as the global networks. These
societal and economic
challenges demonstrate an urgent need for more effective and
efficient processes of
governance and policymaking, therewith specifically addressing
crisis management
and economic/welfare impact reduction.
Therefore, investing in the exploitation of innovative
information and commu-
nication technology (ICT) in the support of good governance
and policy modeling
has become a major effort of the European Union to position
itself and its Member
States well in the global digital economy. In this realm, the
European Union has
laid out clear strategic policy objectives for 2020 in the Europe
2020 strategy1: In
a changing world, we want the EU to become a smart,
sustainable, and inclusive
economy. These three mutually reinforcing priorities should
help the EU and the
Member States deliver high levels of employment, productivity,

and social cohesion.
Concretely, the Union has set five ambitious objectives—on
employment, innovation,
education, social inclusion, and climate/energy—to be reached
by 2020. Along with
this, Europe 2020 has established four priority areas—smart
growth, sustainable
growth, inclusive growth, and later added: A strong and
effective system of eco-
nomic governance—designed to help Europe emerge from the
crisis stronger and to
coordinate policy actions between the EU and national levels.
To specifically support European research in strengthening
capacities, in overcom-
ing fragmented research in the field of policymaking, and in
advancing solutions for
1 Europe 2020 http://ec.europa.eu/europe2020/index_en.htm
v
vi Preface
ICT supported governance and policy modeling, the European
Commission has co-
funded an international support action called eGovPoliNet2. The
overall objective
of eGovPoliNet was to create an international, cross-
disciplinary community of re-
searchers working on ICT solutions for governance and policy
modeling. In turn,
the aim of this community was to advance and sustain research
and to share the

insights gleaned from experiences in Europe and globally. To
achieve this, eGovPo-
liNet established a dialogue, brought together experts from
distinct disciplines, and
collected and analyzed knowledge assets (i.e., theories,
concepts, solutions, findings,
and lessons on ICT solutions in the field) from different
research disciplines. It built
on case material accumulated by leading actors coming from
distinct disciplinary
backgrounds and brought together the innovative knowledge in
the field. Tools, meth-
ods, and cases were drawn from the academic community, the
ICT sector, specialized
policy consulting firms as well as from policymakers and
governance experts. These
results were assembled in a knowledge base and analyzed in
order to produce com-
parative analyses and descriptions of cases, tools, and scientific
approaches to enrich
a common knowledge base accessible via www.policy-
community.eu.
This book, entitled “Policy Practice and Digital Science—
Integrating Complex
Systems, Social Simulation, and Public Administration in Policy
Research,” is one
of the exciting results of the activities of eGovPoliNet—fusing
community building
activities and activities of knowledge analysis. It documents
findings of comparative
analyses and brings in experiences of experts from academia
and from case descrip-
tions from all over the globe. Specifically, it demonstrates how
the explosive growth
in data, computational power, and social media creates new

opportunities for policy-
making and research. The book provides a first comprehensive
look on how to take
advantage of the development in the digital world with new
approaches, concepts,
instruments, and methods to deal with societal and
computational complexity. This
requires the knowledge traditionally found in different
disciplines including public
administration, policy analyses, information systems, complex
systems, and com-
puter science to work together in a multidisciplinary fashion
and to share approaches.
This book provides the foundation for strongly multidisciplinary
research, in which
the various developments and disciplines work together from a
comprehensive and
holistic policymaking perspective. A wide range of aspects for
social and professional
networking and multidisciplinary constituency building along
the axes of technol-
ogy, participative processes, governance, policy modeling,
social simulation, and
visualization are tackled in the 19 papers.
With this book, the project makes an effective contribution to
the overall objec-
tives of the Europe 2020 strategy by providing a better
understanding of different
approaches to ICT enabled governance and policy modeling, and
by overcoming the
fragmented research of the past. This book provides impressive
insights into various
theories, concepts, and solutions of ICT supported policy
modeling and how stake-
holders can be more actively engaged in public policymaking. It

draws conclusions
2 eGovPoliNet is cofunded under FP 7, Call identifier FP7-ICT-
2011-7, URL: www.policy-
community.eu
Preface vii
of how joint multidisciplinary research can bring more effective
and resilient find-
ings for better predicting dramatic changes and negative trends
in our economies and
societies.
It is my great pleasure to provide the preface to the book
resulting from the
eGovPoliNet project. This book presents stimulating research by
researchers coming
from all over Europe and beyond. Congratulations to the project
partners and to the
authors!—Enjoy reading!
Thanassis Chrissafis
Project officer of eGovPoliNet
European Commission
DG CNECT, Excellence in Science, Digital Science
Contents
1 Introduction to Policy-Making in the Digital Age . . . . . . . . . .
. . . . . . . 1
Marijn Janssen and Maria A. Wimmer

2 Educating Public Managers and Policy Analysts
in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 15
Christopher Koliba and Asim Zia
3 The Quality of Social Simulation: An Example from Research
Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 35
Petra Ahrweiler and Nigel Gilbert
4 Policy Making and Modelling in a Complex World . . . . . . . .
. . . . . . . . 57
Wander Jager and Bruce Edmonds
5 From Building a Model to Adaptive Robust Decision Making
Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 75
Erik Pruyt
6 Features and Added Value of Simulation Models Using
Different
Modelling Approaches Supporting Policy-Making: A
Comparative
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 95
Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter
Davis
and Petra Ahrweiler
7 A Comparative Analysis of Tools and Technologies
for Policy Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 125
Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris,
Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee
and David Price

8 Value Sensitive Design of Complex Product Systems . . . . . . .
. . . . . . . . 157
Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van
Beers,
Paulier Herder and Jeroen van den Hoven
ix
x Contents
9 Stakeholder Engagement in Policy Development: Observations
and Lessons from International Experience . . . . . . . . . . . . . . . .
. . . . . . 177
Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram
Klievink
and Catherine Gerald Mkude
10 Values in Computational Models Revalued . . . . . . . . . . . . .
. . . . . . . . . . 205
Rebecca Moody and Lasse Gerrits
11 The Psychological Drivers of Bureaucracy: Protecting
the Societal Goals of an Organization . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 221
Tjeerd C. Andringa
12 Active and Passive Crowdsourcing in Government . . . . . . . .
. . . . . . . . 261
Euripidis Loukis and Yannis Charalabidis
13 Management of Complex Systems: Toward Agent-Based
Gaming for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 291

Wander Jager and Gerben van der Vegt
14 The Role of Microsimulation in the Development of Public
Policy . . . 305
Roy Lay-Yee and Gerry Cotterell
15 Visual Decision Support for Policy Making: Advancing
Policy
Analysis with Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 321
Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke,
Marco
Gavanelli, Stefano Bragaglia, Federico Chesani, Michela
Milano
and Jörn Kohlhammer
16 Analysis of Five Policy Cases in the Field of Energy Policy .
. . . . . . . . 355
Dominik Bär, Maria A.Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou and Laurence Brooks
17 Challenges to Policy-Making in Developing Countries
and the Roles of Emerging Tools, Methods and Instruments:
Experiences from Saint Petersburg . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 379
Dmitrii Trutnev, Lyudmila Vidyasova and Andrei Chugunov
18 Sustainable Urban Development, Governance and Policy:
A Comparative Overview of EU Policies and Projects . . . . . . . .
. . . . . 393
Diego Navarra and Simona Milio
19 eParticipation, Simulation Exercise and Leadership Training
in Nigeria: Bridging the Digital Divide . . . . . . . . . . . . . . . . . .
. . . . . . . . . 417
Tanko Ahmed

Contributors
Tanko Ahmed National Institute for Policy and Strategic Studies
(NIPSS), Jos,
Nigeria
Petra Ahrweiler EA European Academy of Technology and
Innovation Assess-
ment GmbH, Bad Neuenahr-Ahrweiler, Germany
Tjeerd C. Andringa University College Groningen, Institute of
Artificial In-
telligence and Cognitive Engineering (ALICE), University of
Groningen, AB,
Groningen, the Netherlands
Tina Balke University of Surrey, Surrey, UK
Dominik Bär University of Koblenz-Landau, Koblenz, Germany
Cees van Beers Faculty of Technology, Policy, and
Management, Delft University
of Technology, Delft, The Netherlands
Stefano Bragaglia University of Bologna, Bologna, Italy
Laurence Brooks Brunel University, Uxbridge, UK
Yannis Charalabidis University of the Aegean, Samos, Greece
Federico Chesani University of Bologna, Bologna, Italy
Andrei Chugunov ITMO University, St. Petersburg, Russia

Gerry Cotterell Centre of Methods and Policy Application in the
Social Sciences
(COMPASS Research Centre), University of Auckland,
Auckland, New Zealand
Jens Dambruch Fraunhofer Institute for Computer Graphics
Research, Darmstadt,
Germany
Peter Davis Centre of Methods and Policy Application in the
Social Sciences
Sharon Dawes Center for Technology in Government,
University at Albany,
Albany, New York, USA
xi
xii Contributors
Zamira Dzhusupova Department of Public Administration and
Development Man-
agement, United Nations Department of Economic and Social
Affairs (UNDESA),
NewYork, USA
Bruce Edmonds Manchester Metropolitan University,
Manchester, UK
Theo Fens Faculty of Technology, Policy, and Management,
Delft University of

Technology, Delft, The Netherlands
Marco Gavanelli University of Ferrara, Ferrara, Italy
Lasse Gerrits Department of Public Administration, Erasmus
University
Rotterdam, Rotterdam, The Netherlands
Nigel Gilbert University of Surrey, Guildford, UK
Jozef Glova Technical University Kosice, Kosice, Slovakia
Natalie Helbig Center for Technology in Government,
University at Albany,
Albany, New York, USA
Paulier Herder Faculty of Technology, Policy, and Management,
Delft University
Jeroen van den Hoven Faculty of Technology, Policy, and
Management, Delft
University of Technology, Delft, The Netherlands
Wander Jager Groningen Center of Social Complexity Studies,
University of
Groningen, Groningen, The Netherlands
Marijn Janssen Faculty of Technology, Policy, and
Management, Delft University
Geerten van de Kaa Faculty of Technology, Policy, and
Management, Delft
University of Technology, Delft, The Netherlands

Eleni Kamateri Information Technologies Institute, Centre for
Research &
Technology—Hellas, Thessaloniki, Greece
Bram Klievink Faculty of Technology, Policy and Management,
Delft University
Jörn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD,
Darmstadt, Germany
Christopher Koliba University of Vermont, Burlington, VT,
USA
Michel Krämer Fraunhofer Institute for Computer Graphics
Germany
Roy Lay-Yee Centre of Methods and Policy Application in the
Social Sciences
Deirdre Lee INSIGHT Centre for Data Analytics, NUIG,
Galway, Ireland
Contributors xiii
Andreas Ligtvoet Faculty of Technology, Policy, and
Management, Delft Univer-
sity of Technology, Delft, The Netherlands
Euripidis Loukis University of the Aegean, Samos, Greece

Dragana Majstorovic University of Koblenz-Landau, Koblenz,
Germany
Michela Milano University of Bologna, Bologna, Italy
Simona Milio London School of Economics, Houghton Street,
London, UK
Catherine Gerald Mkude Institute for IS Research, University of
Koblenz-Landau,
Koblenz, Germany
Rebecca Moody Department of Public Administration, Erasmus
University
Rotterdam, Rotterdam, The Netherlands
Diego Navarra Studio Navarra, London, UK
Adegboyega Ojo INSIGHT Centre for Data Analytics, NUIG,
Galway, Ireland
Eleni Panopoulou Information Technologies Institute, Centre
for Research &
Technology—Hellas, Thessaloniki, Greece
Anastasia Papazafeiropoulou Brunel University, Uxbridge, UK
David Price Thoughtgraph Ltd, Somerset, UK
Erik Pruyt Faculty of Technology, Policy, and Management,
Delft University of
Technology, Delft, The Netherlands; Netherlands Institute for
Advanced Study,
Wassenaar, The Netherlands
Tobias Ruppert Fraunhofer Institute for Computer Graphics

Germany
Efthimios Tambouris Information Technologies Institute, Centre
for Research &
Technology—Hellas, Thessaloniki, Greece; University of
Macedonia, Thessaloniki,
Greece
Konstantinos Tarabanis Information Technologies Institute,
Centre for Research
& Technology—Hellas, Thessaloniki, Greece; University of
Macedonia, Thessa-
loniki, Greece
Dmitrii Trutnev ITMO University, St. Petersburg, Russia
Gerben van der Vegt Faculty of Economics and Business,
University of Groningen,
Groningen, The Netherlands
Lyudmila Vidyasova ITMO University, St. Petersburg, Russia
Maria A. Wimmer University of Koblenz-Landau, Koblenz,
Germany
Asim Zia University of Vermont, Burlington, VT, USA
Chapter 1
Introduction to Policy-Making in the Digital Age
Marijn Janssen and Maria A. Wimmer
We are running the 21st century using 20th century systems on

top of 19th century political structures. . . .
John Pollock, contributing editor MIT technology review
Abstract The explosive growth in data, computational power,
and social media
creates new opportunities for innovating governance and policy-
making. These in-
formation and communications technology (ICT) developments
affect all parts of
the policy-making cycle and result in drastic changes in the way
policies are devel-
oped. To take advantage of these developments in the digital
world, new approaches,
concepts, instruments, and methods are needed, which are able
to deal with so-
cietal complexity and uncertainty. This field of research is
sometimes depicted
as e-government policy, e-policy, policy informatics, or data
science. Advancing
our knowledge demands that different scientific communities
collaborate to create
practice-driven knowledge. For policy-making in the digital age
disciplines such as
complex systems, social simulation, and public administration
need to be combined.
1.1 Introduction
Policy-making and its subsequent implementation is necessary
to deal with societal
problems. Policy interventions can be costly, have long-term
implications, affect
groups of citizens or even the whole country and cannot be
easily undone or are even
irreversible. New information and communications technology
(ICT) and models

can help to improve the quality of policy-makers. In particular,
the explosive growth
in data, computational power, and social media creates new
opportunities for in-
novating the processes and solutions of ICT-based policy-
making and research. To
M. Janssen (�)
Faculty of Technology, Policy, and Management, Delft
University of Technology,
Delft, The Netherlands
e-mail: [email protected]
M. A. Wimmer
University of Koblenz-Landau, Koblenz, Germany
© Springer International Publishing Switzerland 2015 1
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI
10.1007/978-3-319-12784-2_1
2 M. Janssen and M. A. Wimmer
take advantage of these developments in the digital world, new
approaches, con-
cepts, instruments, and methods are needed, which are able to
deal with societal and
computational complexity. This requires the use of knowledge
which is traditionally
found in different disciplines, including (but not limited to)
public administration,
policy analyses, information systems, complex systems, and
computer science. All
these knowledge areas are needed for policy-making in the
digital age. The aim of

this book is to provide a foundation for this new
interdisciplinary field in which
various traditional disciplines are blended.
Both policy-makers and those in charge of policy
implementations acknowledge
that ICT is becoming more and more important and is changing
the policy-making
process, resulting in a next generation policy-making based on
ICT support. The field
of policy-making is changing driven by developments such as
open data, computa-
tional methods for processing data, opinion mining, simulation,
and visualization of
rich data sets, all combined with public engagement, social
media, and participatory
tools. In this respect Web 2.0 and even Web 3.0 point to the
specific applications of
social networks and semantically enriched and linked data
which are important for
policy-making. In policy-making vast amount of data are used
for making predictions
and forecasts. This should result in improving the outcomes of
policy-making.
Policy-making is confronted with an increasing complexity and
uncertainty of the
outcomes which results in a need for developing policy models
that are able to deal
with this. To improve the validity of the models policy-makers
are harvesting data to
generate evidence. Furthermore, they are improving their
models to capture complex
phenomena and dealing with uncertainty and limited and
incomplete information.
Despite all these efforts, there remains often uncertainty

concerning the outcomes of
policy interventions. Given the uncertainty, often multiple
scenarios are developed
to show alternative outcomes and impact. A condition for this is
the visualization of
policy alternatives and its impact. Visualization can ensure
involvement of nonexpert
and to communicate alternatives. Furthermore, games can be
used to let people gain
insight in what can happen, given a certain scenario. Games
allow persons to interact
and to experience what happens in the future based on their
interventions.
Policy-makers are often faced with conflicting solutions to
complex problems,
thus making it necessary for them to test out their assumptions,
interventions, and
resolutions. For this reason policy-making organizations
introduce platforms facili-
tating policy-making and citizens engagements and enabling the
processing of large
volumes of data. There are various participative platforms
developed by government
agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010;
Welch 2012). Platforms
can be viewed as a kind of regulated environment that enable
developers, users, and
others to interact with each other, share data, services, and
applications, enable gov-
ernments to more easily monitor what is happening and
facilitate the development
of innovative solutions (Janssen and Estevez 2013). Platforms
should provide not
only support for complex policy deliberations with citizens but
should also bring to-

gether policy-modelers, developers, policy-makers, and other
stakeholders involved
in policy-making. In this way platforms provide an information-
rich, interactive
1 Introduction to Policy-Making in the Digital Age 3
environment that brings together relevant stakeholders and in
which complex phe-
nomena can be modeled, simulated, visualized, discussed, and
even the playing of
games can be facilitated.
1.2 Complexity and Uncertainty in Policy-Making
Policy-making is driven by the need to solve societal problems
and should result in
interventions to solve these societal problems. Examples of
societal problems are
unemployment, pollution, water quality, safety, criminality,
well-being, health, and
immigration. Policy-making is an ongoing process in which
issues are recognized
as a problem, alternative courses of actions are formulated,
policies are affected,
implemented, executed, and evaluated (Stewart et al. 2007).
Figure 1.1 shows the
typical stages of policy formulation, implementation, execution,
enforcement, and
evaluation. This process should not be viewed as linear as many
interactions are
necessary as well as interactions with all kind of stakeholders.
In policy-making
processes a vast amount of stakeholders are always involved,

which makes policy-
making complex.
Once a societal need is identified, a policy has to be formulated.
Politicians,
members of parliament, executive branches, courts, and interest
groups may be
involved in these formulations. Often contradictory proposals
are made, and the
impact of a proposal is difficult to determine as data is missing,
models cannot
citizen
s
Policy formulation
Policy
implementation
Policy
execution
Policy
enforcement and
evaluation
politicians
Policy-
makers
Administrative
organizations

b
u
sin
esses
Inspection and
enforcement agencies
experts
Fig. 1.1 Overview of policy cycle and stakeholders
4 M. Janssen and M. A. Wimmer
capture the complexity, and the results of policy models are
difficult to interpret and
even might be interpreted in an opposing way. This is further
complicated as some
proposals might be good but cannot be implemented or are too
costly to implement.
There is a large uncertainty concerning the outcomes.
Policy implementation is done by organizations other than those
that formulated
the policy. They often have to interpret the policy and have to
make implemen-
tation decisions. Sometimes IT can block quick implementation
as systems have
to be changed. Although policy-making is the domain of the
government, private
organizations can be involved to some extent, in particular in
the execution of policies.

Once all things are ready and decisions are made, policies need
to be executed.
During the execution small changes are typically made to fine
tune the policy formu-
lation, implementation decisions might be more difficult to
realize, policies might
bring other benefits than intended, execution costs might be
higher and so on. Typ-
ically, execution is continually changing. Evaluation is part of
the policy-making
process as it is necessary to ensure that the policy-execution
solved the initial so-
cietal problem. Policies might become obsolete, might not work,
have unintended
affects (like creating bureaucracy) or might lose its support
among elected officials,
or other alternatives might pop up that are better.
Policy-making is a complex process in which many stakeholders
play a role. In
the various phases of policy-making different actors are
dominant and play a role.
Figure 1.1 shows only some actors that might be involved, and
many of them are not
included in this figure. The involvement of so many actors
results in fragmentation
and often actors are even not aware of the decisions made by
other actors. This makes
it difficult to manage a policy-making process as each actor has
other goals and might
be self-interested.
Public values (PVs) are a way to try to manage complexity and
give some guidance.
Most policies are made to adhere to certain values. Public value
management (PVM)

represents the paradigm of achieving PVs as being the primary
objective (Stoker
2006). PVM refers to the continuous assessment of the actions
performed by public
officials to ensure that these actions result in the creation of PV
(Moore 1995). Public
servants are not only responsible for following the right
procedure, but they also have
to ensure that PVs are realized. For example, civil servants
should ensure that garbage
is collected. The procedure that one a week garbage is collected
is secondary. If it is
necessary to collect garbage more (or less) frequently to ensure
a healthy environment
then this should be done. The role of managers is not only to
ensure that procedures
are followed but they should be custodians of public assets and
maximize a PV.
There exist a wide variety of PVs (Jørgensen and Bozeman
2007). PVs can be
long-lasting or might be driven by contemporary politics. For
example, equal access
is a typical long-lasting value, whereas providing support for
students at universities
is contemporary, as politicians might give more, less, or no
support to students. PVs
differ over times, but also the emphasis on values is different in
the policy-making
cycle as shown in Fig. 1.2. In this figure some of the values
presented by Jørgensen
and Bozeman (2007) are mapped onto the four policy-making
stages. Dependent on
the problem at hand other values might play a role that is not
included in this figure.

1 Introduction to Policy-Making in the Digital Age 5
Policy
formulation
Policy
implementation
Policy
execution
Policy
enforcement
and evaluation
efficiency
efficiency
accountability
transparancy
responsiveness
public interest
will of the people
listening
citizen involvement

evidence-based
protection of
individual rights
accountability
transparancy
evidence-based
equal access
balancing of interests
robust
honesty
fair
timelessness
reliable
flexible
fair
Fig. 1.2 Public values in the policy cycle
Policy is often formulated by politicians in consultation with
experts. In the PVM
paradigm, public administrations aim at creating PVs for society
and citizens. This
suggests a shift from talking about what citizens expect in
creating a PV. In this view

public officials should focus on collaborating and creating a
dialogue with citizens
in order to determine what constitutes a PV.
1.3 Developments
There is an infusion of technology that changes policy processes
at both the individual
and group level. There are a number of developments that
influence the traditional
way of policy-making, including social media as a means to
interact with the public
(Bertot et al. …
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showing your
appreciation
Disco
• Founded:2015
• Based California, US
Formerly known as Growbot, Silicon
Valley-based Disco aims to build a “culture
of appreciation” by allowing teams to
recognise the contributions each member
makes to the organisation - whether that’s
nailing a client presentation or bringing in
cakes for colleagues - by awarding ‘stars’.
Think of it as an electronic pat on the back.
Integrating with Slack, Google Hangouts
and Microsoft Teams (with more platforms
on the way), Disco measures and records
all feedback in customisable dashboards,
letting managers know about good things
going on in their organisation that they
otherwise might not hear about. The data
can also be exported for use in other
formats, and optional team leaderboards
throw in a bit o f healthy competition.
peoplemanagement.co.uk 33

Understand
productivity
in real time
Worklytics
• Founded: 2015
• Based: New York, US
Rather than adding yet another platform
to most companies’ ever-growing raft of
digital tools, Worklytics taps into their
existing apps - including Microsoft Office
365, Google Suite, Dropbox Enterprise
and Slack - to analyse productivity,
collaboration and engagement in real
time and feed back to managers.
Created by three engineers who met
while working at Spanish social network
and mobile operator Tuenti, Worklytics
was borne out of their need to manage
engineering and product teams “without
the appropriate tools”.
Rather than just keeping an eye on how
much time employees spend on Twitter, it
analyses how they spend their day across
collaboration platforms, and identifies any
improvements that could be made, such as
unproductive meetings or someone doing
too much overtime.
Monitoring tools, of course, make plenty
of employees (and organisations) nervous,

given the potential to misuse their findings.
Worklytics stresses that it gives strictly
objective feedback and supplements
others forms of managerial observation
rather than replacingthem.
Stop using
multiple H R
platforms
CharlieHR
• Founded: 2015
• Based: London
It’s hard to sum up CharlieHR’s purpose
when it’s been done so well in the tagline:
‘Building a company is hard. Running
one shouldn’t be.’ Founded by serial
entrepreneurs Ben Gateley, Rob O’Donovan
and Tom Carrington Smith (above), it aims
to solve challenges the three came across
when setting up other businesses - namely,
headaches and wasted time caused by
unnecessary HR admin, they say.
Aimed at fellow start-ups and small
companies, it makes the almost too-good-
to-be-true promise to “get rid of HR admin”
and automates several otherwise time-
consuming processes, including holiday
and sickness absence, employee records
(it’s GDPR-compliant, of course) and
onboarding, even down to the small-but-
important birthday and work anniversary
reminders. It’s also the place employees
go to access company policies and other
documentation.

With a full reporting suite available,
managers can download any report they
need in afew clicks, living up to its promise
to give them “more time to do more of what
matters”. Helpfully, the
system also integrates
with existing enterprise
collaboration platforms
such as Slack,
Google Calendar and
Microsoft Outlook.
CharlieHR was
awarded £1m in funding
in 2016, and now has
more than 3,000
companies using
its platform.
Performetric
• Founded:2015
• Based: Portugal
Looking after employees’ mental wellbeing
has rightly moved higher on employers’
agendas recently, but unless they
encourage open conversation among their
workforce, they’re still often none the wiser
if their people are struggling.
Performetric, on the other hand, claims
to be able to spot signs of mental fatigue
in workers without them needing to say a
word. The software tracks interactions with

their keyboard and mouse ‘non-invasively’,
including typing speed, movement
accuracy and keypress duration, while
running in the background and requiring
no data entry or interaction.
It uses machine learning to build an
individual profile of each user, and looks
out for warning signs on the United
States Air Force School of Aerospace
Medicine Mental Fatigue Scale - when
it spots something worrying, it alerts
the user that they’re at risk of burnout,
and makes recommendations to
improve performance.
Three months after officially launching
in 2015, Performetric secured funding from
investors Hovione Capital and eggNEST.
Spot the
signs of mental
fatigue in the
workforce
34 peoplemanagement.oo.uk
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Volume 37 • Number 10 • October 2018 Banking & Financial Servi