EU Citizens perceptions about autonomous trading systems and smart contracts
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Universidad de Deusto
info
Éditeur: Zenodo
Année de publication: 2021
Type: Dataset
Résumé
Taking into account that our genre of study is related to contemporary smart contracts for automated trading systems, this study follows a user-center approach through speculative design, design fiction or design probes. The rationale behind this approach is to address the design of future energy contracts informed from the insights that emerged from a qualitative analysis of fictional futures that participants provided in a series of online questionnaires. Participants were introduced to automatic trading systems via a use case fictional scenario that illustrated a system similar to the energy trading Peer to peer system. <em>“Imagine that you are someone who harvests water from rain and you have created a system to store the water you are not using. Sometimes, you have surpluses and sometimes you might find yourself with less water than you need. Fortunately, you are not isolated but you belong to a community of other people that also harvest water and all of you have decided to establish a trading system. As the community is constantly doing exchanges (overall in rainy and dry seasons) all of you agree that this is a very time-consuming task. Therefore, the community decides to establish a mechanism to automatically trade water through innovative technologies.</em> <strong>In a nutshell, the speculative trading system is working as follows:</strong> 1. Everyone sets up their preference for trading (e.g to what level of water they consider a surplus or a shortage so the trade can start) 2. The system is monitoring each water-tank within the neighbourhood in real-time. 3. Whenever one of the preferences from the members is met, the system publishes an offer for buying or selling water. 4. The rest of the monitoring systems analyse the offer and take an autonomous decision on the basis of the current level of water, their potential necessities for the rest of the day, the weather forecast, or the price (cost-benefit). 5. If the deal is accepted, the transaction of water starts (ideally all the houses are connected with pipes).” The study presented in this section is based on an online survey that explores the perception of users on automatic trading systems. This is done via the previous speculative scenario related to a community-based water trading system established among citizens and retailers. The scenario was structured around several questions questions:<br> Would you be willing to participate in such a market? Do you feel comfortable relaying the burden of trading to an autonomous system? Would you increase your participation in such a market if it is hosted/regulated by reliable third parties? Who is reliable for you? What are the risks and threats associated with this trading system? <br> Participants were recruited through Prolific, a popular crowdsourcing platform for academic studies. Only two prerequisites were determined for participation: being over 18 years of age, and living in one of the countries represented in the PARITY project or living in northern Europe. Furthermore, we obtained some socio-economic information such as gender, socioeconomic status, employment status, whether they are or not students, type of household where they live, whether this was owned by them or rented and finally two questions about emerging technologies and energy sector literacy. We estimated a study completion time of 6 minutes, and provided a £1 incentive for participation (£7.53/hr). This survey can be seen at: https://forms.gle/JQgwWxScLXX9JwK96 There are two csv files. The former (850_responses.csv) has the raw responses obtained through the form explained above. The latter (user_profiles.csv) has further information about the users that responded the questionnaire. Namely: Country of Birth; Current Country of Residence; Employment Status; Socioeconomic Status;Student Status Both csv files can be linked through the participant_id / prolific_id that appear as a column in both csv