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Trust Design: Balancing Smart Contracts Utility and Decentralisation Risk.

JEL Classification G30

According to the World Economic Forum (2016), by 2025, 10% of global gross domestic product (GDP) will be stored on blockchains, a type of decentralized database and distributed shared ledger. Smart contracts are automated computable contracts that are executed in blockchains with the benefits of removing intermediaries and reducing costs. Computable contracts are represented in a machine-understandable way. As a result, computers can process the contracts automatically with a guaranteed degree of accuracy (http://compk.stanford.edu/). The applications in finance include: (i) cross-border payments to capture obligations, minimize operational errors and expedite transfers; (ii) property and casualty claims in insurance to automate claims processing through third-party data sources and codification of business rules; (iii) deposits and lending in syndicated loans to facilitate real-time loan funding and automated servicing activities without intermediaries; (iv) deposits and lending in trade finance, to automate the creation and management of credit facilities ultimately eliminating correspondent banks; (v) contingent convertible bonds in capital raising to alert regulators when loan absorption needs to be activated, minimizing the need for point-in-time stress tests; (vi) compliance in investment management, to execute reporting and facilitate the automated creation of periodic filings; (vii) proxy voting in investment management to automate end-to-end confirmation by the validation of votes, thereby increasing transparency; (viii) asset re-hypothecation in market provisioning to enable the real-time reporting of asset history and the enforcement of regulatory constraints, including facilitating clearing and settlement to eliminate the need for intermediaries and reduce settlement time; and (ix) equity post-trade in market provisioning to simultaneously transfer equity and cash in real time, reducing the likelihood of errors impacting settlement.

The policy implications introduced by decentralization require that economists and lawyers understand this technological shift, and, more importantly, the risks related to tangible (e.g., consensus selection as a security choice) and intangible (e.g., contract incompleteness or coding errors) factors. For instance, on July 25th, 2017, the SEC indicated that U.S. securities laws may apply to blockchain token sales, effectively recognizing digital assets as a new asset class (https://www.sec.gov/news/press-release/2017-131). We demonstrate a decision-making method where utility is measured by "levels of trust" using artifacts from fields finance applied to a portfolio of institutional smart contract companies. Fields finance studies the financial systems using techniques from quantitative behavioral finance and statistical physics, including vector fields (http://fai.econ.muni.cz/2015/3/65). Expected utility is measured by mapping a demand vector field (attention level) and funding by plotting a scalar field (the investment level). The associated risk exposure is implicit in the consensus mechanism tradeoffs according to the progression of firms represented in the system of coordinates. The goal of the research was to provide a device for portfolio analysis and construction. The data come from a panel of 200 million internet users and investment databases (SearchMetrics GmbH, www.searchmetrics.com; SimilarWeb Ltd., www.similarweb.com). The result is a comprehensive and scalable view of decentralized portfolios inspired by behavioral finance. We begin by defining a mapping order for the field plots (Cizinska et al., International Advances in Economic Research, 2016.). No risk ranking is implied in identification assignment (risk is intrinsic to each technology choice according to the business problem and regulatory requirements). Web panel and click stream data were used to plot traffic estimates in the vector field. Investment rounds data were used for the contour plot (CrunchBase Excel Export, v3.22, https://data.crunchbase.com). Crunchbase is a database of corporate data, including funding rounds. The data analysis method is based on vector fields (Weisstein, 2015, http://mathworld.wolfram.com/VectorField. html). The vectors for each entity are seeded in progression from point {1,1} to point {2,3} in two lines from bottom to top and left to right. The vector components are of the form {search, direct}, where search refers to visits received from search engines such as Google and direct-to-direct visits. The selection of these components is deliberate. Over the 12-month period from December 2015 to November 2016, the search contributed ~31.5% of desktop visits to the group and direct contributed ~38%, respectively.

We found that a fields approach for portfolio construction reveals key demand signals in relation to levels of trust. Trust is built when visibility increases. Trust is resolved when funding is realized. However, the nature of some of this attention can be pernicious, such as in the case of negative brand associations. In the segment where the companies analyzed operate (international settlements), where there is a clear winner-take-all, due to network effects and adoption, being a current loser can also be an opportunity for value (e.g., if technology is superior and the team strong, there is scope). Digital businesses operate in the economy of attention. In our case, smart contracts (and the companies that develop them) operate in an economy of attention as well. Attention economics is an approach to the management of information that treats human attention as a scarce commodity and applies economic theory to solve various information management problems (https://en.wikipedia.org/wiki/Attention_economy). Digital market share, the ability to gain adopters or investors quickly and retain users, is crucial to the survival of most platforms. Therefore, enterprise users should gather competitive intelligence and perform comprehensive due diligence before embarking in one or more pilots. To do this, intangible risk and value must be visible. Making these signals tangible is the first step in meta-automation (machines managing machines), the ultimate goal of a smart contract. This work may prove useful to both fund managers and technology developers.

Tomas Krabec (1) * Percy Venegas (2)

[??] Tomas Krabec

krabec@is.savs.cz

(1) SKODA AUTO University, Na Karmeli 1457, 293 01, Mlada Boleslav, Czech Republic

(2) Economy Monitor S.A., San Jose, Costa Rica

Published online: 22 November 2017

[c] International Atlantic Economic Society 2017

https://doi.org/10.1007/s11294-017-9660-x
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Title Annotation:RESEARCH NOTE
Comment:Trust Design: Balancing Smart Contracts Utility and Decentralisation Risk.(RESEARCH NOTE)
Author:Krabec, Tomas; Venegas, Percy
Publication:International Advances in Economic Research
Article Type:Report
Geographic Code:1USA
Date:Nov 1, 2017
Words:983
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