This paper discusses the problem of double-ending fraud in real estate transactions – a type of transactional fraud wherein agents handling real estate transactions unfairly benefit (e.g., by simultaneously representing both the buy and sell side of a real estate transaction in a manner that unfairly boosts the commission they receive, or colluding to increase their commission in a real estate transaction at the expense of the buyer and/or seller of the real property). The paper proposes a unique blockchain solution design that leverages blockchain's properties of transparency and ability to create tamper-resistant audit trails to reduce opportunities for double-ending fraud and increase real estate market participants' trust in the handling of their transactions. The paper discusses the implementation of a prototype of the solution based on hyperledger fabric and sails; it presents the results of an agent-based modelling simulation validating that the inherent transparency of the proposed design offers optimal allocation for both sellers and buyers.

Simulation models are becoming an increasingly popular tool for the analysis and optimization of complex real systems in different fields. Finding an optimal system design requires performing a large parameter sweep. In this paper, we present the design of SOF (Simulation Optimization and exploration Framework on the cloud), a framework which exploits the computing power of a cloud computational environment in order to realize effective and efficient simulation optimization strategies. SOF offers several attractive features: SOF requires "zero configuration" as it does not require any additional software installed on the remote node, SOF is transparent to the user, since the user is totally unaware that system operates on a distributed environment, SOF is highly customizable and programmable, since it enables the running of different simulation optimization scenarios on different simulation toolkits. The tool has been fully developed and is available on a public repository under the Apache public licence.