Book Review: R Michael Alvarez (ed.), Computational Social Science: Discovery and Prediction
| Published date | 01 November 2017 |
| Date | 01 November 2017 |
| DOI | 10.1177/1478929917712143 |
| Subject Matter | Book ReviewsGeneral Politics |
Book Reviews 649
the perspectives of those who experience
poverty. The difference in this qualitative
data and hard quantitative data is striking
(pp. 50, 52).
The second section focuses on the link
between the misleading measurement of
poverty and the consequent faulty formula-
tion of development goals. The contributions
thus focus on implementation of the stated
goals.
The last section seeks a redefinition of
development goals with some thought-provok-
ing alternatives. For instance, Deacon’s essay
seeks to replace the global poverty discourse
with that of ‘social solidarity’ (p. 208). Boltvinik
and Damian’s essay scathingly critiques capi-
talism and the MDGs on the basis of empirical
evidence and seeks to shift the focus onto ‘uni-
versal citizen income’ with an emphasis on dig-
nity (p. 198). In the final essay, Koehler argues
for a ‘radical overhaul’ of the development
agenda post-2015, focussing on the role of
states and civil society in attaining sustainable
development goals (p. 249).
The chapters are the result of a workshop
on the post-MDG agenda. The essays offer a
rigorous review of the literature and present
empirical support for the arguments. The first
two sections serve scholars and students of
politics by presenting a comprehensive analy-
sis of global poverty and the MDGs. The last
section presents the reader with refreshing
societal and policy alternatives, catering to
policymakers as well.
Because it is an edited volume, the chapters
tend to repeat the critique of the MDGs at
times. The approach and solutions, however,
remain distinctively sound and thought-pro-
voking, covering different theoretical perspec-
tives on global poverty and the MDGs. The
book lacks a general conclusion but the essays
do converge on one aspect: the MDGs have not
been able to deliver on their promise, thus
requiring an urgent revamp of the approach to
development goals. The book succeeds well in
pushing this argument forward.
Abhishek Choudhary
(Jawaharlal Nehru University, New Delhi)
© The Author(s) 2017
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DOI: 10.1177/1478929917717445
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Computational Social Science:
Discovery and Prediction by R Michael
Alvarez (ed.). Cambridge: Cambridge University
Press, 2016. 327pp., £18.99 (p/b), ISBN
9781107518414
In Computational Social Science: Discovery
and Prediction, R Michael Alvarez editor of
Political Analysis, puts together a volume
that captures the latest major shift in social
science research methodology. Picking up
where Gary King finishes at the end of the
preface, Alvarez readily informs the reader at
the outset that ‘this book is not about big
data’ (p. 5). Alvarez is quick to point out that
the statement holds true independent of the
‘size’ of the data. Rather, the book is about
the innovative ways that we social scientists
have come up with for dealing with such
data.
The book consists of two parts. Part I is
about tools, and it introduces the new meth-
odological advances at the frontiers of social
science. Topics such as big data and topical-
ity in surveys, generating (near) real-time
event data, network analysis, high dimen-
sionality and ‘fuzzy’ random forests make up
this segment. In contrast, Part II is about their
application to important problems in the
social sciences. Here, the subjects include
protests and social media, election fraud,
representation styles of legislators, social
marketing and finally, a cautionary tale about
a sound statistical analysis that should have
led to altered policy (but, ultimately, failed to
do so). In both cases, the selected domain is
politics and policy, and this is by design.
With that said, the book is clearly aimed at
readers possessing a non-elementary under-
standing of machine learning and ensemble
methods because such knowledge is required
to fully appreciate the contents of the vol-
ume.
In sum, the book is quite successful in
reaching its aims. As is often the case with
edited volumes, it is difficult to balance breadth
and depth without losing overall coherence.
However, Alvarez does a commendable job of
identifying the vanguard of computational
social science and presenting the best it has to
offer in a systematic, articulate fashion. The
combination of striking topics and rigorous
empirics make this book a valuable asset for
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