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The Policy and Regulatory Engagement with Corruption: Insights from Complexity Theory

Published online by Cambridge University Press:  19 May 2021

Andrea MINTO Open the ORCID record for Andrea MINTO [Opens in a new window]  and
Edoardo TRINCANATO
Andrea MINTO
Affiliation:
Jean Monnet Professor in Law and Economics at Ca’ Foscari University of Venice, Visiting Professor at Cyprus International Institute of Management (CIIM), Visiting Professor at the University of Southern Denmark – Juridisk Institute; email: andrea.minto@unive.it.
Edoardo TRINCANATO
Affiliation:
VERA Fellow at Ca’ Foscari University, Venice Centre in Economic and Risk Analytics for Public Policies, Department of Economics.
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Abstract

One of the few certainties we have in dealing with corruption lies in its adaptive nature. Over time, corruption has in fact proved to be able to change, evolve and adapt within all political systems. Such an adaptive nature calls for close scrutiny of the setting or space where corruption spreads out. Therefore, this raises questions about the unappreciated risks and immeasurable opportunities for corruption in the ever-changing and interconnected world of techno-social systems we live in. This article aims to advance the policy and regulatory debate surrounding corruption by focusing on its complex and adaptive nature. In applying the main tenets of complexity theory, the analysis builds on the well-known Cynefin framework. This decision-aiding framework proves to be an insightful tool for shedding light on some critical features of corruption (eg its perception and the affected confidence).

Type
Articles
Information
European Journal of Risk Regulation , Volume 13 , Issue 1 , March 2022 , pp. 21 - 44
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

This research has been carried out thanks to the generous support and funding of the Prince Mohammad Bin Fahd Center for Futuristic Studies at Prince Mohammad Bin Fahd University (first “Futures Research Grant”).

References

1 See, eg, JC Andvig et al, “Corruption. A review of contemporary research” (2001) <http://hdl.handle.net/11250/247368> (last accessed 3 April 2021); M Riccardi and F Sarno, “Corruption” in G Bruinsma and D Weisburd (eds), Encyclopedia of Criminology and Criminal Justice (Berlin, Springer 2014) pp 630–41; E Dávid-Barrett et al, “Controlling corruption in development aid: new evidence from contract-level data” (2020) 55 Studies in Comparative International Development 481–515.

2 A Vespigiani, “Predicting the behavior of techno-social systems” (2009) 325 Science 425–28.

3 D Byrne and G Callaghan, Complexity Theory and the Social Sciences. The State of the Art (London, Routledge 2014) p 8.

4 D Snowden, “Cynefin, a sense of time and place: an ecological approach to sense making and learning in formal and informal communities” (2000) <https://www.researchgate.net/publication/264884267_Cynefin_A_Sense_of_Time_and_Place_an_Ecological_Approach_to_Sense_Making_and_Learning_in_Formal_and_Informal_Communities> (last accessed 3 April 2021).

5 Such contexts are not clear-cut categories as they rather present blurred boundaries, as explained in Section IV.

6 Such a debate has been extremely relevant not only for the conceptualisation of corruption as such, but also for the study of it. In this respect, see, eg, MJ Farrales, “What is corruption? A history of corruption studies and the great definitions debate” (2005) <https://ssrn.com/abstract=1739962> (last accessed 3 April 2021). The author stresses that, since the beginning of such study, it has not aimed to develop a universally acceptable definition of “corruption” because this would be an undertaking both “Herculean” and “Sisyphean” in nature – “Herculean” in terms of the immense amount of knowledge historically developed in the field and “Sisyphean” because our perception of corruption has evolved (and continues to evolve) over time. See also G Brooks et al, “Defining corruption” in Preventing Corruption, Crime Prevention and Security Management (London, Palgrave Macmillan 2013).

7 The term “corruption”, per se, has been analysed in depth by scholars over recent decades. See, eg, AJ Heidenheimer and M Johnston, Political Corruption. Concepts & Contexts (3rd edn, London, Routledge 2001). This work has incorporated economic, cultural and linguistic perspectives, with a systemic focus on the relationship between the terminology and the concepts involved in it. Due to the importance of this distinction, more specifically, the authors have always stressed the importance of conducting scientific research regarding it. As highlighted by Génaux, the term “corruption” appears in many languages, “… but behind it lie several contrasting strands of thought and language”. See M Génaux, “Social sciences and the evolving concept of corruption” (2004) 42 Crime, Law and Social Change 13. Along these lines, Génaux analyses the evolution of the term within the context of Anglo-Saxon legal thought from its Roman roots, also considering the French lexicographic and the Biblical origins of the same. In such a way, the author shows how the richness of the term is often lost in the “more technical usages that dominate contemporary debate and analysis”.

8 See, eg, VJ Klaveren, “Corruption as a historical phenomenon” in AJ Heidenheimer and M Johnston (eds), Political Corruption. Concepts & Contexts (3rd edn, London, Routledge 2001); A Barr. and D Serra, “Corruption and culture: an experimental analysis” (2010) 94(11–12) Journal of Public Economics 862–69; E Fein and J Weibler, “Review and shortcomings of literature on corruption in organizations in offering a multi-faceted and integrative understanding of the phenomenon” (2014) 19(3) Behavioral Development Bulletin 67–77; D Torsello and B Venard, “The anthropology of corruption” (2016) 25(1) Journal of Managment Inquiry 34–54.

9 N Mocan, “What determines corruption? International evidence from microdata” (2008) 46(4) Economic Inquiry 493–510.

10 See, eg, S Pillay and N Dorasamy, “Linking cultural dimensions with the nature of corruption: an institutional theory perspective” (2010) 10(3) International Journal of Cross Cultural Management 363–78; VG Fitzsimons, “Economic models of corruption” in S Bracking (ed.), Corruption and Development: The Anti-Corruption Campaigns (London, Palgrave Macmillan 2007).

11 Transparency International, instead, recently defined corruption as “the abuse of public office for private gain” and, as is known, this definition represents one of the most adopted notions to refer to it, being also shared by other organisations such as the World Bank. It is important to note that Transparency International used another, more general version of the same definition (ie “the abuse of entrusted power for private gain”) that, in 2012, was selected as the best option for the purposes of the Corruption Perception Index (CPI) of the same non-governmental organisation.

12 The layering of governmental institutions and the relationship between individuals and economic actors have reached levels of interconnectedness and complexity that are very hard to describe. As has been stated, a key role in this ongoing condition is certainly played by the impact of technological change. An example of such interaction comes from environmental studies. See, eg, AB Jaffe et al, “Environmental policy and technological change” (2002) 22 Environmental and Resource Economics 41–69. In addition, technological progress had changed how transactions take place in the entire global financial system. For a functional overview of “how technologies are making us rethink leading, regulation and compliance, risk management, insurance, stock trading, payments, and money in the fourth industrial age”, see T Lynn et al, Disrupting Finance: Fintech and Strategy in the 21st Century (Berlin, Springer Nature 2019).

13 UNODC, “The United Nations and action against corruption: a global response to a global challenge” <https://www.unodc.org/pdf/9dec04/Action_E.pdf≥ (last accessed 3 April 2021).

14 See, eg, BE Ashforth et al, “Re-viewing organizational corruption” (2008) 33(3) Academy of Management Review 670–84; A Persson et al, “Why anticorruption reforms fail – systemic corruption as a collective action problem” (2013) 26(3) Governance 449–71; A Minto, “Characterising corruption by adopting a systemic risk perspective: importing macro prudential financial regulation into the policy debate” (2020) 11(1) European Journal of Risk Regulation 1–17.

15 A Lambert-Mogiliansky, “Why firms pay occasional bribes: the connection economy” (2002) 18(1) European Journal of Political Economy 47–60. More specifically, the author, regarding the legal and administrative system, provides arguments in support of the claim that “instability and complexity are factors that favour corruption”.

16 Brooks et al, supra, note 6.

17 Y Luo, “An organizational perspective of corruption” (2004) 1(1) Management and Organization Review 119–54. This study proposes that “institutional transparency, institutional fairness and institutional complexity” are important components in relation to corruption environment.

18 See, eg, Ashforth et al, supra, note 14.

19 See, eg, S Saha et al, “Is there a ‘consensus’ towards Transparency: international’s corruption perceptions index?” (2012) 20(1) International Journal of Business Studies 1–9.

20 R Calderón and JL Álvarez-Arce, “Corruption, complexity and governance: the role of transparency in highly complex systems” (2011) 8(3) Corporate Ownership and Control 245–57.

21 F Habtemichael and F Cloete, “Complexity thinking in the fight against corruption: some perspectives from South Africa” (2010) 37(1) Politikon 93. For a more “conceptual” view on the complexity of corruption, see also J Hazy et al, “Notes on the complexity of corruption” (Academy of Management Review Annual Meeting Proceedings 2017) <https://doi.org/10.5465/AMBPP.2017.13073abstract> (last accessed 3 April 2021).

22 I Luna-Pla and JR Nicolás-Carlock, “Corruption and complexity: a scientific framework for the analysis of corruption networks” (2020) 5(13) Applied Network Science 2–18. Wachs et al have used methods from network science “to analyze corruption risk in a large administrative dataset of over 4 million public procurement contracts from European Union members”. Covering the years 2008–2016, indeed, their work aims to visualise and describe the distribution of corruption risk. See J Wachs, M Fazekas and J Kertész, ‘“Corruption risk in contracting markets: a network science perspective” (2020) International Journal of Data Science Analysis <https://doi.org/10.1007/s41060-019-00204-1> (last accessed 3 April 2021).

23 See, eg, A Mungiu-Pippidi, “The time has come for evidence-based anticorruption” (2017) 1(1) Nature Human Behaviour 1–3.

24 M Walton, “Applying complexity theory: a review to inform evaluation design” (2014) 45 Evaluation and Program Planning 119–26.

25 Byrne and Callaghan, supra, note 3, 8.

26 Complex systems are generally made up of several and different interacting “elements”, “components” or “systems” that are more often than not different between themselves. For an introductory overview see, eg, M Mitchell, Complexity: A Guided Tour (Oxford, Oxford University Press 2009); F Capra and PF Luisi, The Systems View of Life: A Unifying Vision (Cambridge, Cambridge University Press 2016). In the same vein, see also M De Domenico et al, “Complexity explained” (2019) <https://complexityexplained.github.io/ComplexityExplained.pdf> (last accessed 3 April 2021). In this article it is also possible to find a synthetic and well-organised review of some of the most fundamental contributions from the fields of complexity, divided into the following seven sections: “Interactions”, “Emergence”, “Dynamics”, “Self-Organization”, “Adaptation”, Interdisciplinarity” and “Methods”.

27 Nonetheless, it is possible to state, as observed by Byrne and Callaghan (supra, note 3), that “complexity” refers to systems, as it is a property of some of them. On this point, see R Rosen, “On complex systems” (1987) 30(2) European Journal of Operational Research 129–34.

28 NF Johnson, “Two’s company, three is complexity” in Simply Complexity: A Clear Guide to Complexity Theory (London, Oneworld Publications 2009).

29 ibid. In this well-known contribution, the author finds in a crowd a “perfect example” of such an emergent phenomenon, “since it is a phenomenon which emerges from a collection of interacting people”. Furthermore, the author adds that everyday examples of crowds include collections of “commuters, financial markets, traders, human cells, or insurgents – and the associated crowd-like phenomena which emerge are traffic jams, market crashes, cancer tumors, and guerrilla wars”.

30 Luna-Pla and Nicolás-Carlock, supra, note 22, 6.

31 Calderón and Álvarez-Arce, supra, note 20, 245.

32 It has been argued, for example, that “complexity theory does not refer to one theory or set of ideas but rather is an umbrella term for an array of concepts that share similar assumptions about the nature of reality and how researchers come to know this reality”. LM Kallemeyn, JN Hall and E Gates, “Exploring the relevance of complexity theory for mixed methods research” (2020) 14(3) Journal of Mixed Methods Research 288–304.

33 As discussed by Castellani, scholars generally refer to this as “the advance of the complexity sciences or, alternatively, complexity theory or complex systems theory”. B Castellani, “Complexity and the failure of quantitative social science” (2014) 12(12) Focus.

34 See, eg, Kallemeyn et al, supra, note 32.

35 Byrne and Callaghan, supra, note 3. See also DS Byrne, Complexity Theory and the Social Sciences: An Introduction (Hove, Psychology Press 1998); JR Turner and RM Baker, “Complexity theory: an overview with potential applications for the social sciences” (2019) 7(1) Systems 4.

36 Byrne and Callaghan, supra, note 3.

37 Models are of central relevance in many scientific contexts across all fields of study. However, despite the abundance of the recognised types of models, what is important to underline with regards to the main discussion lies in the fact that the mentioned models raise questions “in semantic (how, if at all, do models represent?), ontology (what kind of things are models?), epistemology (how do we learn and explain with models?), and, of course, in other domains within philosophy of science”. See R Frigg and S Hartmann, “Models in science” <https://plato.stanford.edu/archives/spr2020/entries/models-science/> (last accessed 3 April 2021). This quote shows how important semantic, ontological and epistemological questions are in relation to complexity theory and its application to the corruption problem. On this point, see also A Williams, “Complexity from the sciences to social systems” in Political Hegemony and Social Complexity (London, Palgrave Macmillan 2020).

38 J Cohen and I Stewart, The Collapse of Chaos: Discovering Simplicity in a Complex World (London, Penguin Books 1994).

39 Reduction, as a scientific procedure, has been applied in a variety of domains. Nevertheless, the science of complexity now “is based on a new way of thinking that stands in sharp contrast to the philosophy underlying Newtonian science, which is based on reductionism, determinism, and objective knowledge”. For an overview of the historical development of the philosophical foundation of the field, see F Heylighen et al, “Complexity and philosophy” (2006) <arXiv:cs/0604072> (last accessed 3 April 2021).

40 Turner and Baker, supra, note 35, 2. See also V Vasiliauskaite and FE Rosas, “Understanding complexity via network theory: a gentle introduction” (2020) <https://arxiv.org/pdf/2004.14845.pdf> (last accessed 3 April 2021).

41 Turner and Baker, supra, note 40, 2 (emphasis added).

42 E Yeboah-Assiamah, “‘Strong personalities’ and ‘strong institutions’ mediated by a ‘strong third force’: thinking ‘systems’ in corruption control” 17(4) Public Organization Review 547.

43 I Goldin and T Vogel, “Global governance and systemic risk in the 21st century: lessons from the financial crisis” (2010) 1(1) Global Policy 4–15.

44 See, eg, Minto, supra, note 14.

45 E Morin, “Complex thinking for a complex world – about reductionism, disjunction and systemism” (2014) 2(1) Systema 17. According to the author, looking at complex systems from this perspective reveals something interesting: “not only is the part inside the whole but the whole is inside the part”.

46 As stated by Calderón and Álvarez-Arce (supra, note 20), corruption “… is a system and, therefore, systemic descriptions represent the only way to a correct understanding”. Moreover, the mentioned statement is then followed by four hypotheses regarding, respectively, the high number of “heterogeneous elements” that form such a systemic structure; the relationships among elements, which are essentially “non-trivial interactions”; the fact that the described system is capable of “surprising behaviours”, “by responding in more than one way to any change in its environment”; and, finally, the fact that it is capable of “novelty”, “by evolving into states that are not apparent from its constituents”.

47 Turner and Baker, supra, note 35.

48 G Caldarelli, S Wolf and Y Moreno, “Physics of humans, physics for society” (2018) 14(9) Nature Physics 870. In this letter to the editors, the authors start with a reflection on today’s massive use of information and communication technologies that has made it possible “to attach a traceable set of data to almost any person”. They argue that, since the beginning, these data provide the opportunity to build a “physics of society: describing a society – composed of many interacting heterogeneous entities (people, businesses, institutions) – as a physical system”.

49 A Bastardas-Boada, “Complexics as a meta-transdisciplinary field” (Congrès Mondial Pour la Penséè Complexe. Les Défis d’Un Monde Globalisè. Paris, UNESCO, December 2026) p 6; LG Rodríguez Zoia and P Roggero, “Sur le Lien Entre Pensée et Systèmes Complexes” 60 <http://hdl.handle.net/2042/45460> (last accessed 3 April 2021) pp 151–56; LG Rodríguez Zoia and G Leonardo, “Le Modèle Épistémologique de la Pensée Complexe. Analyse Critique de la Construction de la Connaissance en Systèmes Complexes” (DPhil thesis, University of Toulouse 2013).

50 B Mueller, “Why public policies fail: policymaking under complexity” (2020) 21(2) Economica 311–23.

51 D Helbing, “Globally networked risks and how to respond” (2013) 497 Nature 51–59.

52 Vespignani, supra, note 2.

53 See D Snowden, “Complex Acts of Knowing: Paradox and Descriptive Self-awareness” (2002) 6(2) Journal of Knowledge Management 100–11; CF Kurtz and D Snowden, “The new dynamics of strategy: sense-making in a complex and complicated world” (2003) 42(3) IBM Systems Journal 462–83; D Snowden “Strategy in the context of uncertainty” (2005) 6(1) Handbook of Business Strategy 47–54; D Snowden and ME Boone, “A leader’s framework for decision making” (2007) 85(11) Harvard Business Review 69–76.

54 According to Snowden and Boone, the complex domain’s crucial characteristic is represented by the fact that it is possible to understand why things happen only “in retrospect”. See Snowden and Boone, supra, note 53.

55 Using the words of Snowden and Boone, the difference between the complicated and the complex is like the difference between a Ferrari and the Brazilian rainforest. “The car is static, and the whole is the sum of its parts. The rainforest, on the other hand, is in a constant flux – a species becomes extinct, weather patterns change … – and the whole is far more than the sum of its parts”. See Snowden and Boone, supra, note 53.

56 Westerhoff, for example, proposed a computer simulation to show how interactions amongst investors can lead to bubbles and crashes endogenous to the system. See F Westerhoff, “The use of agent-based financial market models to test the effectiveness of regulatory policies” (2008) 228(2–3) Jahrbücher für Nationalökonomie und Statistik 195–227.

57 As pointed out by Browning and Boudès, Snowden uses the compound term “sense-making” to describe a whole set of processes such as, for example, the Story Circles and “knowledge disclosure points” (KDPs), as well as to indicate the use of narrative theory to understand the complexity of organisational environments. See LD Browning and T Boudès, “The use of narrative to understand and respond to complexity: a comparative analysis of the Cynefin and Weickian models” (2005) 7(3–4) E:CO Issue 32–39.

58 ibid.

59 J McLeod and S Childs, “The Cynefin framework: a tool for analyzing qualitative data in information science?” (2013) 35(4) Library & Information Science Research 307. More specifically, in their contribution, the authors explore the potential of the framework not only to structure findings or discussions and draw conclusions, but also to be used as “… a qualitative data analysis tool and also as a collaborative qualitative data collection tool”.

60 Kurtz and Snowden, supra, note 53. More specifically, the framework is rooted in knowledge management studies of process development under uncertain conditions. Nevertheless, its application has grown over the years through the progressive incorporation of insights and methodologies coming from complexity theory. It has been applied in the fields of strategy, management, training, cultural change and policymaking, thus from governments to organisations, and from natural disaster to terrorist attacks.

61 Snowden and Boone, supra, note 53.

62 Browning and Boudès, supra, note 57. According to Kurtz and Snowden, in fact, possible uses of this model include “contextualization” and “narrative history exercises”. On this point, see Kurtz and Snowden, supra, note 53.

63 Kurtz and Snowden, supra, note 53.

64 Snowden and Boone, supra, note 53.

65 RJ Hammer, JS Edwards and E Tapinos, “Examining the strategy development process through the lens of complex adaptive systems theory” (2012) 63 Journal of the Operational Research Society 909–19.

66 Turner and Baker (supra, note 35) observe that some “complicated” states, for example, “could have a touch of complexity up to a point”. See also S French, “Cynefin: uncertainty, small worlds and scenarios” (2015) 66(10) Journal of the Operational Research Society 1635–45. It is crucial to observe that it would be an error to think of the domains as hard categorisations. On this point, French rightly argued that “… the boundaries are soft and contexts lying near these have characteristics drawn from both sides”. However, taken with a suitably large “pinch of salt”, as is maintained by the same author, Cynefin will consistently help our discussion.

67 Turner and Baker, supra, note 35.

68 ibid.

69 Kurtz and Snowden, supra, note 53, 469. More specifically, patterns “… may indeed repeat for a time in this space, but we cannot be sure that they will continue to repeat”.

70 Snowden and Boone, supra, note 53 (emphasis added).

71 ibid.

72 R Geyer, “Beyond the third way: the science of complexity and the politics of choice” (2003) 5(2) British Journal of Political Science 253.

73 K Mainzer, Thinking in Complexity: The Computational Dynamics of Matter, Mind, and Mankind (4th edn, Berlin, Springer 2004).

74 JK Alter and S Meunier, “The politics of international regime complexity” (2009) 7(1) Perspectives on Politics 13–24. In the financial sector, for example, the overarching goal of policy institutions all over the world is to guarantee financial stability over time and protect against systemic threats in the long term.

75 LG Baxter, “Adaptive regulation in the amoral bazaar” (2011) 128(2) South Africa Law Journal 264–68.

76 MA Chinen, “Governing complexity” in J Murray, T Webb and S Wheatley (eds), Complexity Theory and Law: Mapping an Emergent Jurisprudence (London, Routledge 2018) p 155.

77 JH Miller and SE Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton, NJ, Princeton University Press 2007).

78 See. eg, D Helbing (ed.), Managing Complexity: Insights, Concepts, Application (Berlin, Springer 2008); D Helbing, Thinking Ahead. Essays on Big Data, Digital Revolution, and Participatory Market Society (Berlin, Springer 2015).

79 Helbing (2015), supra, note 78.

80 Helbing (2008), supra, note 78, 7. The author believes that complex systems possess in themselves immanent capacities to self-organise and to create resilient order.

81 D Snowden, “Perspectives around emergent connectivity, sense-making and asymmetric threat management” (2006) 26(5) Public Money Management 275. More specifically, Snowden maintains that sense-making comes in many forms. However, within that context he defines the same with the following question: “How do we make sense of the world so that we can act in it?”. Such a definition falls back on natural science and pragmatism, as well as the naturalism in epistemology (ie a naturalistic approach to epistemological theorising).

82 ibid, p 277.

83 As well as that of anti-terrorism, take the just mentioned contribution by Snowden as an example. See Snowden, supra, note 81.

84 See, eg, PM Heywood, “Measuring corruption, perspectives, critiques and limits” in PM Heywood (ed.), Routledge Handbook of Political Corruption (London, Routledge 2015) pp 137–53.

85 ibid, p 1.

86 Snowden, supra, note 81, 275–76.

87 P Cilliers, Complexity Theory and Postmodernism: Understanding Complex Systems (London, Routledge 1998) pp 139–40.

88 EK Owusu, AP Chan, OM DeGraft, EE Ameyaw and OK Robert, “Contemporary review of anti-corruption measures in construction project management” (2019) 50(1) Project Management Journal 40–56. This article is taken as an example because it identifies thirty-nine anti-corruption measures from thirty-eight selected publications in engineering management research.

89 UNODC, UNDP and UNODC-INEGI, Manual on Corruption Surveys (Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice 2018) p 20 <https://www.unodc.org/documents/data-and-analysis/Crime-statistics/CorruptionManual_2018_web.pdf> (last accessed 3 April 2021).

90 Well-known examples of such indicators are the Control of Corruption Indicator of the World Bank Governance Indicators, the Transparency International Corruption Perceptions Index and the Global Integrity Index of Global Integrity.

91 S Sequeira, “Advances in measuring corruption in the field” in D Serra and L Wantchekon (eds), New Advances in Experimental Research on Corruption. Research in Experimental Economics (Bingley, Emerald Group Publishing 2011) pp 145–76.

92 There is a plethora of studies that highlights such weaknesses. As examples, see S Andersson and PM Heywood, “The politics of perception: use and abuse of Transparency International’s approach to measuring corruption” (2009) 57(4) Political Studies 746–67; K Ko and A Samajdar, “Evaluation of international corruption indexes: should we believe them or not?” (2010) 47(3) The Social Science Journal 508–40; F Méndez and F Sepúlveda, “What do we talk about when we talk about corruption?” (2010) 26(3) Journal of Law, Economics & Organation 493–514; N Charron, “Do corruption measures have a perception problem? Assessing the relationship between experiences and perceptions of corruption among citizens and experts” (2016) 8(1) European Political Science Review; G Qu et al, “Explaining the standard errors of corruption perception indices” (2019) 47(4) Journal of Comparative Economics 907–20.

93 S Michaels, “Matching knowledge brokering strategies to environmental policy problems and settings” (2009) 12(17) Environmental Science & Policy 994–1011.

94 ibid, p 994.

95 M Meyer, “The rise of the knowledge broker” (2010) 32(1) Science Communications 118.

96 Michaels, supra, note 93, p. 996. See also KT Litfin, Ozone Discourses: Science and Politics in Global Environmental Cooperation (New York, Columbia University Press 1994).

97 Meyer, supra, note 95, p 118.

98 Michaels, supra, note 93, p 997. The author considers explicitly how the six mentioned knowledge brokering strategies fit within the four analysed frameworks in order to determine the most constructive “as a useful complement” for use.

99 ibid. What is required from a broker within the chaotic domain is not mentioned in the earlier listed strategies. This is because in such a case it is impossible to learn from experience. Here, the decision model requires acting immediately in order to respond more appropriately.

100 Kurtz and Snowden, supra, note 53, p 8 (emphasis in original).

101 ibid.

102 ibid (emphasis in original).

103 Over recent decades, researchers from all fields have shown how often problems are non-linear. In parallel, complexity theory emerged with the aim of filling the persistent gap left by traditional and linear research assumptions.

104 Kurtz and Snowden, supra, note 53, p 469. Emergent patterns can be perceived but not predicted; the authors call this phenomenon “retrospective coherence”.

105 ibid. The authors therefore conclude that understanding this space “… requires us to gain multiple perspectives on the nature of the system”, stressing that the methods, tools and techniques of the ordered domains do not work in the complexity realm.

106 Michaels, supra, note 93, table 1, p 997 and table 5, p 1005.

107 Kurtz and Snowden, supra, note 53. Within the chaotic domain, as has been seen, Michaels indeed identifies “opportunistic entrepreneurship” as the primary brokering strategy; ibid.

108 B Clausen, A Kraay and Z Nyiri, “Corruption and confidence in public institutions: evidence from a global survey” (2011) 25(2) World Bank Economic Review 212–49. The authors also show how this finding “… can plausibly be interpreted as reflecting at least in part a causal effect from corruption to confidence”. This result is highly relevant since this contribution substantially differs from previous ones, addressing concerns about the endogeneity of the variables and thus about the direction of the causation between the same. In fact, as is maintained by the authors, “Perhaps respondents’ perceptions of the prevalence of corruption drive their low confidence in institutions, but just as plausibly the opposite could be true: individuals who lack confidence in public institutions might as a result express the view that corruption is widespread” (see p 214).

109 ibid, p 243.

110 ibid, p 241. In table 6, these authors also document the relationship between corruption, confidence and the three other mentioned outcomes: the support for violent forms of protest; the desire to emigrate; and, finally, political participation.

111 Specifically, the GWP asks respondents, “Do you have confidence in each of the following?”: “(a) the military, (b) judicial system and courts, (c) national government, (d) health care or medical systems, (e) financial institutions or banks, (f) religious organizations, (g) quality and integrity of the media, and (h) honesty of elections”; ibid., pp 219–20.

112 The question is the following: “Sometimes people have to give a bribe or present in order to solve their problems. In the last 12 months, were you, personally, faced with this kind of situation, or not (regardless of whether you gave a bribe/present)?”. According to the authors, a crucial advantage of such an experience-based question is that it is less likely to suffer from so-called reverse causality (ie here, the possibility that individuals’ confidence in institutions affects their corruption experiences); ibid, p 217.

113 The question is the following: “Is corruption widespread throughout the government in this country, or not?”; ibid.

114 ibid, p 219. In some countries (eg Italy and Japan), this gap is large, and thus they have a “strong” perception of widespread corruption in government.

115 ibid.

116 On the opportunities offered by artificial intelligence see, eg, YK Dwivedi et al, “Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy” (2019) 57 International Journal of Information Management 2–47; H Mehr et al, “Artificial intelligence for citizen services and government” (2017) Ash Center for Democratic Governance and Innovation, Harvard Kennedy School, pp 1–16 <https://ash.harvard.edu/files/ash/files/artificial_intelligence_for_citizen_services.pdf> (last accessed 3 April 2021).

117 See, eg, S Kim et al, “An institutional analysis of an e-government system for anti-corruption: the case of OPEN” (2009) 26(1) Government Information Quarterly 42–50; J Arayankalam et al, “How to deal with corruption? Examining the roles of e-government maturity, government administrative effectiveness, and virtual social networks diffusion” (2020) 58 International Journal of Information Management 102203.

118 TB Andersen, “E-government as an anti-corruption strategy” (2009) 21(3) Information Economics and Policy 201–10.

119 NG Elbahnasawy, “E-government, Internet adoption, and corruption: an empirical investigation” (2014), 57 World Development 114–26.

120 On the range of contributions delving into the relationship between technology and trust, see, eg, P Cofta, Trust, Complexity and Control: Confidence in a Convergent World (Hoboken, NJ, John Wiley & Sons 2007); P Sumpf, System Trust: Researching the Architecture of Trust in Systems (Berlin, Springer 2019); E Keymolen, Trust on the Line: A Philosophical Exploration of Trust in the Networked Era (Nijmegen, Wolf Legal Publishers 2016); M Coeckelbergh, “Can we trust robots?” (2012) 14(1) Ethics and Information Technology 53–60; M Taddeo, “Trust in technology: a distinctive and a problematic relation” (2010) 23(3–4) Knowledge, Technology & Policy 283–86; H Nissenbaum, “Will security enhance trust online, or supplant it?” in RM Kramer and KS Cook (eds), Trust and Distrust in Organizations: Dilemmas and Approaches (New York, Russell Sage Foundation 2004) pp 155–88.

121 See, eg, M Warkentin and C Orgeron, “Using the security triad to assess blockchain technology in public sector applications” (2020) 52 International Journal of Information Management 102090.

122 See, eg, P De Filippi et al, “Blockchain as a confidence machine: the problem of trust & challenges of governance” (2020) 62 Technology in Society 101284. The authors observe that blockchain’s premise, indeed, is based on the fact that “… users subject themselves to the authority of a technological system that they are confident is immutable, rather than to the authority of centralized institutions which are deemed untrustworthy”. However, they also maintain that the academic discussion only considers this central property from a negative perspective (ie that this technology does not need trust to operate). By contrast, drawing on complexity, they argue that blockchain technology should be regarded as a “confidence machine”, in the sense that “… it increases the confidence in the operation of a particular system, and only indirectly (i.e. as a corollary to that) reduces the need for trust in that system” (see p 11).

123 N Bautista-Beauchense, “Corruption and anti-corruption: a folklore problem?” (2020) 73 Crime, Law and Social Change 162.

124 See, as indicated by Bautista-Beauchense, ibid, G Myrdal, Asian Drama. An Inquiry into the Poverty of Nations (New York, Pantheon 1968); G Myrdal, “Corruption as a hindrance to modernization in South Asia” in AJ Heidenheimer and M Johnston (eds) Political Corruption, Concepts and Contexts (London, Routledge 2011). See also N Melgar et al, “The perception of corruption” (2010) 22(1) International Journal of Public Opinion Research 120. Melgar et al indeed maintain that both corruption and corruption perception can be considered as cultural phenomena “… because they depend on how a society understands the rules and what constitutes a deviation”. According to the authors, therefore, this implies its influence not only on societies, but also on personal values and moral views.

125 Bautista-Beauchense, supra, note 123.

126 ibid.

127 As has been seen, such measures play a pivotal role in focusing attention on the phenomenon, exercising great influence at individual, academic and government levels of the analysis.

128 M Leach, “Complexity regulatory space and banking” in Murray et al (eds), supra, note 76, p 170. The author in fact stresses that complexity theory “… studies large-scale, multi-bodied composite structures that have interacting, networked components”.

129 Turner and Baker, supra, note 35, p 6.

130 Kurtz and Snowden, supra, note 53, p 469.

131 This is a report from the European Commission to the Council and the European Parliament. See EC Report on Eu-Anti-Corruption, released on 3 February 2014 <https://www.tagesschau.de/wirtschaft/eukorruptionsbericht100.pdf> (last accessed 3 April 2021).

132 S Shalvi, “Corruption corrupts” (2016) 531(7595) Nature 456–57.

133 Melgar et al, supra, note 124. The authors indeed observe that “Even when corruption perception may strongly differ from the current level of corruption, the latter influences the former. Hence, high levels of corruption perception are enough to cause negative effects in the economy (the growth of institutional instability and the deterioration of the relationships among individuals, institutions and states)”.

134 Among others, see Campos et al, “The impact of corruption on investment: predictability matters” (1999) 27(6) World Development 1059–67; M Habib and L Zurawicki, “Country-level investments and the effect of corruption – some empirical evidence” (2001) 10(6) International Business Review 687–700; M Habib and L Zurawicki, “Corruption and foreign direct investment” (2002) 33(2) Journal of International Business Studies 291–307; E Asiedu and J Freeman, “The effect of corruption on investment growth: evidence from firms in Latin America, Sub-Saharan Africa, and transition countries” (2009) 13(2) Review of Development Economics 200–14; S Richey, “The impact of corruption on social trust” (2010) 38(4) American Politics Research 676–90; A Pellegata and V Memoli, “Can corruption erode confidence in political institutions among European countries? Comparing the effects of different measures of perceived corruption” (2016) 128(1) Social Indicators Research 391–412; A Cieślik and Ł Goczek, “Control of corruption, international investment, and economic growth–evidence from panel data” (2018) 103 World Development 323–35; A Solé-Ollé and P Sorribas-Navarro, “Trust no more? On the lasting effects of corruption scandals” (2018) 55 European Journal of Political Economy 185–203; L Olmos et al, “The effects of mega-events on perceived corruption” (2020) 61 European Journal of Political Economy 101826.

135 RT Golembiewski, “Trust and power: two works by Niklas Luhmann” (1981) 75(2) American Political Science Review 480–81. See also N Luhmann, Trust and Power (New York, John Wiley & Sons 2018).

136 De Filippi et al, supra, note 122, p 6.

137 ibid.