Blockchain and smart contracts enable the network of hybrid autonomous human and machine agents. In this paper, we propose the concept of engineered ownership, a blockchain-based socio-technical governance system. A system of coded rules that defines the boundaries, shapes incentives and distributes rights among such autonomous agents. To lay a foundation for engineered ownership, we first study the nature of ownership by examining the concept of property. Shaped by history and ideologies, property rights are the most formalized and studied ownership system. We then untangle the layered structures and system design impacts of property by investigating three property rights theories. Finally, we derive from these learnings the system features of engineered ownership, identify related challenges, and present a roadmap towards a holistic theory of engineered ownership.

No abstract

Graph Neural Networks (GNNs) have become a popular toolkit for generative floor plan design. Although design variation has improved greatly, few studies consider nongeometrical characteristics, such as building performance, in the generative design process. This paper presents a GNN-based approach to predict the energy performance for floor plan customization (energy-aware design). The approach lays the foundation for a performance-aware generative design using GNN. The results show that the GNN can achieve high accuracy in energy performance prediction.

No abstract