Handbook Of Computational Social Science – Taha Yasseri

They tend to vary in two important ways relevant to the study of corruption risk. First, the distribution of high-risk (e.g., single-bid) contracts is highly non-uniform in all countries studied. Second, the degree of centralization of the market and the tendency for high-risk contracts to appear in the core or periphery of a market vary from country to country. The first observation suggests that there is significant heterogeneity in the level of cor- ruption risk in different parts of a country, whether defined by sector of the economy, public administration, or geography.

Yet the degree of clustering of corruption risk is also found to be robust to randomization within the sector, suggesting that there are idiosyncratic high-risk parts of the market that can be investigated and studied. We visualize one year of procurement Note: Nodes are buyers and suppliers of contracts, connected by an edge if they contract with one another.

Edges are colored red if the single-bidding rate (a key marker of corruption risk) on the edge exceeds the average rate of single bidding that year. Single bidding is significantly overrep- resented among the edges in the top left cluster. Figure 29.2 The main connected component of the 2014 Hungarian procurement market represented as a network, from Wachs et al. (2021) Computational approaches to the study of corruption data from Hungary as a network in Figure 29.2, highlighting contracting relationships with high amounts of single-bidding in red.

The distribution of high-risk contracts across the core and periphery of the market links this work to an ongoing debate in political science and economics. Researchers have long dis- puted whether a centralized government and public administration improve efficiency (Lotti et al., 2024). One important part of this question is whether centralization hinders or fosters corruption. While a highly centralized government may obscure political responsibility for corrupt outcomes and weaken the link between local outcomes and political feedback Persson and Tabellini (2002), previous work finds no significant correlation between centralization and corruption measured via surveys by Charron et al.

(2014). We found that countries with greater corruption risk on average, tended to have more contracts in the core of the market, i.e. greater centralization (Spearman’s correlation 0.64 ˜ > ). At the same time, the central- ization of contracts in general—a purely network-topological observation about a country’s procurement market—had a significant negative correlation with corruption as measured by Charron et al. (2014).

© Taha Yasseri 2025 Every effort has been made to trace all the copyright holders, but if any have been inadver- tently overlooked please notify the publisher. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher.

Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA Authorised representative in the EU for GPSR queries only: Easy Access System Europe – Mustamäe tee 50, 10621 Tallinn, Estonia, [email protected] A catalogue record for this book is available from the British Library Library of Congress Control Number: 2025946978 This book is available electronically in the Sociology, Social Policy and Education subject collection https://doi.org/10.4337/9781802207309 ISBN 978 1 80220 729 3 (cased) ISBN 978 1 80220 730 9 (eBook) ISBN 978 1 0353 8220 0 (ePub) v Contents List of contributors xi Acknowledgments xix PART I FOUNDATIONS 1 My personal and our collective journey in computational social science 2 Taha Yasseri 2 Computational social science: past, present, and future 9 Duncan J.

Watts and David Lazer 3 How computational social science can help to improve public policymaking 30 Helen Margetts and Cosmina Dorobantu 4 Computational social science, artificial intelligence, and the future of media theory 40 Ralph Schroeder 5 On the intersection of analytical sociology and computational social science 56 Martin Arvidsson, Peter Hedström, Benjamin F.

Jarvis, and Marc Keuschnigg 6 Computational inductive research 72 Laura K.

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.

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