Abstract
Two of the most important developments of this new century are the emergence of Cloud computing and Big Data. However, the uncertainties surrounding the failure of Cloud service providers to clearly assert “ownership” rights of data during Cloud computing transactions and Big Data services have been perceived as imposing transaction costs and slowing down the capacity of the Internet market to thrive. “Click-through” agreements drafted on a “take it or leave it” basis govern the current state of the art and they do not allow much room for negotiation. The novel contribution of this chapter proffers a new contractual model advocating the extension of the negotiation capabilities of Cloud customers, enabling thus an automated and machine-readable framework, orchestrated by a “Cloud broker.” Cloud computing and Big Data are constantly evolving and transforming into new paradigms where Cloud brokers are predicted to play a vital role as an intermediary adding extra value to the entire life cycle. This chapter situates the theories of behavioral law and economics (“Nudge Theory”) in the context of Cloud computing and Big Data, and takes “ownership” rights of data as a canonical example to represent the problem of collecting and sharing data at the global scale. It does this by highlighting the legal constraints concerning Japan’s Personal Information Protection Act (Act No. 57 of 2003, hereinafter “PIPA”) and proposes a solution outside the boundaries and limitations of the law. By allowing Cloud brokers to establish themselves in the market as entities coordinating and actively engaging in the negotiation of Service Level Agreements (SLAs), individual customers and Small and Medium-sized Enterprises (SMEs) could efficiently and effortlessly choose a Cloud provider that best suits their needs. This can yield radical new results for the development of the Cloud computing and Big Data market.
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Notes
- 1.
Muller (2015), p. 168.
- 2.
Horten (2016), p. 135.
- 3.
Lightman (2002), Preface.
- 4.
See, e.g., Van Schewick (2010), pp. 1–586.
- 5.
- 6.
See, e.g., Bygrave and Bing (2009), pp. 3–4.
- 7.
See, e.g., Jolls, Sunstein and Thaler (1998), pp. 1471–1550.
- 8.
Personal Information Protection Act (No. 57 of 2003).
- 9.
Murugesan and Ananth (2016), p. 4.
- 10.
Balasubramanyam (2013), p. 102.
- 11.
Srinivasan (2014), p. 5.
- 12.
Millham (2012), p. 2.
- 13.
Catlett et al. (2013), Preface.
- 14.
Kasemsap (2015), p. 31.
- 15.
Biswas (2014), p. 333.
- 16.
Marinescu (2013), Preface.
- 17.
Murherjee and Loganathan (2014), pp. 142–143.
- 18.
Kannan et al. (2016), Preface.
- 19.
Chen et al. (2014), pp. 12–13.
- 20.
Mosco (2014), pp. 1–284.
- 21.
- 22.
Corrales and Jurčys (2016).
- 23.
Parsons and Colegate (2015).
- 24.
Metcalfe (2013).
- 25.
The Japan Times (2013).
- 26.
Béranger (2016), p. 85.
- 27.
Crawford (2015).
- 28.
- 29.
Corrales and Jurčys (2016).
- 30.
- 31.
See Personal Information Protection Commission.
- 32.
Corrales and Jurčys (2016). Furthermore, proposals for standardizing contractual model terms like in the case of the SLALOM project may aid towards this direction, given that standardization is inherently an international effort that requires consensus and can achieve interoperability of regulatory and technical frameworks. See generally, Rinaldi and Stella (2016). The SLALOM Project is co-funded by the European Commission through the H2020 Program under Grant Agreement 644720.
- 33.
- 34.
About fiduciary responsibilities of Big Data see, e.g., Berman (2013), pp. 201–211.
- 35.
See, e.g., Al-Khouri (2012), pp. 1–8.
- 36.
Property rights can be categorized in different ways, most of which fall outside the scope of this chapter. Generally, this term can be broken down in two main areas: (a) corporeal: covering items which relate to an object, a thing. Something tangible that is a physical good e.g., a car, a computer; (b) incorporeal: covering items that are not visible to human eye. Something virtual and intangible by nature, e.g., data, information. See Robson and McCowan (1998), p. 15; Corrales et al. (2010), pp. 293–294; Elkin-Koren and Salzberger (2013), p. 44.
- 37.
Angner and Loewenstein (2016).
- 38.
See generally, Zeiller and Teitelbaum (2015).
- 39.
See generally, Minton and Kahle (2013), pp. 1–149.
- 40.
Jolls et al. (1998), pp. 1471–1550.
- 41.
In the UK for instance, the Prime Minister David Cameron established a “Behavioral Insights Team” in the Cabinet Office with the specific objective of including the psychological analysis of human behaviour into policy-making initiatives in various areas such as anti-smoking, energy efficiency, consumer protection, organ donation, etc. See Behavioral Insights Team; see also Wright (2014). In the United States, the Obama administration created a team in 2013 in order to do empirical research of behavioral sciences. Recently, in September 2015, President Obama signed an executive order, which encourages federal government agencies to use behavioral science insights to better understand and serve the American people. See Executive Order—Using Behavioral Science Insights to Better Serve the American People, The White House, Office of the Press Secretary (2015).
- 42.
World Bank Development Report (2015).
- 43.
The Economist (2006).
- 44.
See, e.g., generally, Munro (2009).
- 45.
- 46.
As Herbert Simon pointed out: “Human rational behavior (and the rational behavior of all physical symbol systems) is shaped by a scissors whose two blades are the structure of task environments and the computational capabilities of the actor.” See Simon (1990), p. 7.
- 47.
- 48.
- 49.
Viale (2012), p. 25.
- 50.
Connolly and Coburn (2016), p. 41.
- 51.
Kahneman (2011), p. 98.
- 52.
Kahneman (2011), pp. 97–98. The etymology of the word “heuristics” comes from Greek “heureka,” which literally means, “I have found (it)” from the verb “heuriskein” (to find). This expression became famous as it was supposed to be shouted by Archimedes (c. 287–212 B.C.E.) when he found the solution to a scientific problem. See Online Etymology Dictionary; Utts (2014), p. 348.
- 53.
See, e.g., Sunstein (2000).
- 54.
Heukelom (2014), Introduction, pp. 1–10, 168.
- 55.
See Thaler and Sunstein (2003), pp. 1–3.
- 56.
- 57.
The terms System 1 and System 2 were first coined by Stanovich and West and will be used along this chapter. See Stanovich and West (2000), pp. 645–665.
- 58.
Kahneman (2003), p. 698.
- 59.
Kahneman (2003), p. 698.
- 60.
Kahneman (2003), p. 698; Kahneman (2011), pp. 20–26; see also Stanovich (2010). In this book, Stanovich established the difference between rationality and intelligence and suggests that some individuals are closer to System 1 and some others are closer to System 2. He also established a distinction of two parts of System 2, what he calls “two separate minds.” According to him, System 2 has a dual-process and must be divided into the “reflective mind” and the “algorithmic mind.” According to Stanovich’s concept, superficial or “lazy” thinking is a flaw in the reflective mind, and explains how individuals can behave sometimes irrationally. See also Kahneman (2011), pp. 48–49. In evolutionary terms, System 1 is older than System 2. It relates to our animal instincts and is broken down in a subset of systems that include both our innate abilities and “domain-specific knowledge” learnt from a “domain-general learning” system. System 2 is more recent and belongs only to humans. It allows abstract and hypothetical ways of reasoning and thinking. It is linked to language and intelligence but is limited in memory capacity. See Evans (2003), pp. 454–459.
- 61.
Raisinghani (2015), p. 188.
- 62.
Businessballs.com (Nudge Theory).
- 63.
See English Collins Dictionary (Nudge).
- 64.
See Oxford Dictionary (Nudge).
- 65.
Thaler and Sunstein (2009), p. 6.
- 66.
Willis (2015).
- 67.
- 68.
Nudges.org (2008).
- 69.
Thaler and Sunstein (2009), p. 3.
- 70.
- 71.
Behavioral Economics is expanding to different fields in addition to law. See, e.g., Heshmat (2011), Introduction. In addition, nudging techniques can also be used to negatively influence and biase decision-making. The ways many surveys, polls and referendums are framed are all very good examples of this.
- 72.
Sunstein (2014), pp. 1–221.
- 73.
- 74.
Wallace (2008).
- 75.
Sunstein (2014), p. 25.
- 76.
Felin (2014), p. 3.
- 77.
- 78.
See Bloomberg (2013) p. 12.
- 79.
- 80.
See, e.g., Jansen and Bosch (2005), pp. 109–120.
- 81.
Sunstein (2014), pp. 1–30, 179.
- 82.
- 83.
Sunstein (2014), pp. 28, 73, 74, 77 and 149.
- 84.
Biddle et al. (2015), p. 377.
- 85.
- 86.
Biddle et al. (2015), p. 377.
- 87.
- 88.
Abbots and Lavis (2016), p. 155.
- 89.
Gigerenzer (2015), pp. 361–362.
- 90.
Corrales and Jurčys (2016), pp. 533–536.
- 91.
Sunstein (2014), p. 28.
- 92.
Mill (1859), pp. 21–22.
- 93.
- 94.
For details about “behavioral market failures” and “default rules” see Sunstein (2015), pp. 206 and 218.
- 95.
- 96.
- 97.
Bishop (2009), p. 296.
- 98.
- 99.
See, e.g., Jackson (2006), pp. 68–69.
- 100.
- 101.
- 102.
Corrales and Jurčys (2016), pp. 533–536.
- 103.
- 104.
Kahneman (2011), p. 4.
- 105.
XML is a markup language standard that aims to define a format that is both human and machine understandable.
- 106.
See generally, Herbst et al. (2016).
- 107.
Sunstein (2015), Preface.
- 108.
See, e.g., Iyengar and Lepper (2000), pp. 995–1006. In this empirical research, the authors challenged the assumption that the more choice, the better. In 3 different experiments, they arrived at the conclusion that people are more likely to choose or buy something when limited choices are available. For example, it is easier to choose and buy jam or chocolate from a limited array of 6 options rather than a more extensive selection of 24 or 30 choices.
- 109.
Sunstein (2015), p. 173.
- 110.
Sunstein (2013c), p. 11.
- 111.
Thaler et al. (2013), p. 430.
- 112.
Sunstein (2015), Preface, p. 6.
- 113.
OPTIMIS is an open source toolkit designed to help Cloud service providers to build and run applications in the Cloud. New features that include the clarification of database rights and “ownership” rights of data have been implemented. The toolkit has been integrated into the OpenNebula Ecosystem and the Infrastructure-as-a-Service Cloud computing project OpenStack. See EU Project Enhances Open Source Toolkit Cloud; see also XML Description Schema Improvement.
- 114.
Sunstein (2015), pp. 8–9.
- 115.
Cloud Legal Guidelines (Final Report), Deliverable 7.2.1.4, p. 14.
- 116.
See, e.g., Lessig (2006).
- 117.
See, e.g., Kousiouris et al. (2013), pp. 63 et seq.
- 118.
Barnitzke et al. (2011), pp. 51–55.
- 119.
Kousiouris et al. (2013), p. 63.
- 120.
The XML schema was made in the Eclipse Integrated Development Environment (IDE), which is a graphical tool for creating code (and other things such as the xml example). By having the schema model file, an interested entity may import it in a programming environment such as the Eclipse IDE. Based on the schema (also known as “xsd file” in reference to the file name extension “.xsd”), they can create an instance and populate the individual values (or select from value lists where this input is limited to a predefined selection). More details on this process can be found in Vafiadis et al. (2012), pp. 27–31.
- 121.
See, e.g., Kousiouris et al. (2013), pp. 63 et seq.
- 122.
Morris (1992), p. 591.
- 123.
- 124.
See, e.g., Cunningham and Hart (1993), p. 24.
- 125.
Newman (2010).
- 126.
Ostroff (2011), pp. 10–12.
- 127.
Bayles (2014), p. 331.
- 128.
Nix (2011).
- 129.
An HTTP Get operation is a simple type of web service from which one can retrieve formatted information, similar to a request in a standard browser for a web page.
- 130.
Kousiouris et al. (2013), pp. 64–65.
- 131.
See, e.g., Ilerian, XSD Form Features Overview.
- 132.
Kousiouris et al. (2013), p. 68.
- 133.
Sunstein (2015), pp. 12–13.
- 134.
Lessig (2006), pp. 1–432.
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Acknowledgements
In developing the concepts described in this chapter, we had the opportunity to learn from many people. We would like to thank Prof. Toshiyuki Kono, Prof. Shinto Teramoto, Prof. Mark Fenwick, Paulius Jurčys and Rodrigo Afara. This chapter was partially funded by the Monbukagakusho-MEXT Japanese government scholarship program and the Optimized Infrastructure Framework (OPTIMIS) EU funded project within the 7th Framework Program under contract ICT-257115.
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Corrales, M., Kousiouris, G. (2017). Nudging Cloud Providers: Improving Cloud Architectures Through Intermediary Services. In: Corrales, M., Fenwick, M., Forgó, N. (eds) New Technology, Big Data and the Law. Perspectives in Law, Business and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-10-5038-1_7
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