Feeding the technopolis : an overview of the potential emergence of homemade food markets in neighborhoods and its energy savings : a trans-disciplinary approach

Abstract

Individual decision making (e.g. deciding which type of food to eat) has recently significantly benefited from the widespread use of machine learning in artificial intelligence (AI) applications (e.g. search-friendly metadata) used by companies like Yelp, Google, Amazon, and Facebook. Nevertheless, certain goals are difficult to achieve via the collective use of AI. As a result, the centralized authority framework of control prevails, governing the factors of collective intelligence (e.g. countries, cities, neighborhoods, groups). AI gets its value from large data sets obtained from exploiting collective human interactions, mathematical models, and computing power. So, while AI is usually compared with human intelligence (e.g. Deep Blue beat chess' master in 1997 and AlphaGo beat Go's master in 2017), both complement each other more than is usually admitted. The emergence of new ideas often mimics the way natural systems survive and evolve. Some living organisms such a slime mold, ant colonies, and beehives work collectively especially when food is scarce. For instance, ant colonies follow rules written in the form of pheromones with different intensities, a “chemical alphabet” to signal specific requests and warnings, ultimately creating an autocatalytic processes that functions as a collective brain to find food and survive. By extension, the social and economic value of technological innovations for humans is comparable. Linking previously disconnected agents through electronic platforms increases the number of interactions and the number of choices available, minimizing their transaction cost. Moreover, the blurring roles of actors interacting in the cyberspace and physical space are a distinctive characteristic of the new economy (e.g. Amazon’s acquisition of Whole Foods). People work collectively producing, consuming, and improving solutions aided by Peer-to-Peer (P2P) networks, enabling the rise of self-management systems without central planning (e.g. Wikipedia, DIY bio). Democracy and human rights are notable examples of social innovations that historically challenged the status-quo. The rise of self-organizing political and economic structures without a central authority could be the next one. Moreover, if technologies are configured to enable regular, honest, and cooperative behavior through social norms and programmable trust, it can be a powerful tool for the emergence of new collective actions without central planning in neighborhoods, cities, and countries. One of the affordances of AI applications is to solve problems that scale globally such as energy and food security. AI applications are helpful, for example, by providing more efficient grid operation or an optimal inventory and delivery management system. They also reduce waste and energy consumption by supporting the operation of networked collaborative systems capable of changing the very nature of food consumption habits and improving the overall efficiency in the use of natural resources. As a result of this study, I have found that the potential development of a self-organizing homemade food market in neighborhoods could improve the energy efficiency of the food system, allowing energy savings equal to 1.2 percent of the total energy used by a city’s food system. To support the development of such a market, tools used in AI applications such as sentiment analysis and the Blockchain, can play a significant role in their ability to anticipate market needs, and provide a secure, transparent, and efficient transaction platforms. Therefore, these AI tools can have a significant impact in reducing the need of trustees such as banks and companies that provide access to peer-to-peer service platforms like Uber and Airbnb.