This thesis investigates the gap between the theoretical ideals and practical realities of distributed exchange protocols in peer-to-peer sharing economies. While classical economic models and stylised trading mechanisms have been extensively studied, their assumptions often overlook the complexities and behavioural nuances of real-world agent-based markets. Conversely, more empirically grounded approaches tend to be highly case-specific, limiting their generalisability and disconnecting them from established economic concepts.
The central aim of this work is to develop and analyse an algorithmic economic model that more faithfully captures the dynamics of decentralised resource sharing — balancing fairness, efficiency, and resistance to manipulation, while retaining a useful level of abstraction.
In light of this purpose, the thesis performs a comparative theoretical analysis of centralised and distributed exchange mechanisms, and introduces a novel exchange market model that incorporates heterogeneous strategies, bounded rationality, and asynchronous timing. A series of numerical experiments evaluates the performance and robustness of different protocols under these features of sharing economies.
The results show that distributed, mixed-strategy protocols can achieve stable and desirable outcomes, however their success is sensitive to population diversity, limited information, and strategic behaviour. These findings highlight the importance of integrating behavioural aspects into protocol design, and provide insights towards building more robust models for the sharing economy.

Inductive Program Synthesis (IPS) has been implemented by a two-stage search algorithm, Brute, and consequently improved upon with a Large Neighborhood Search (LNS) technique, in an algorithm named Vlute. Unmotivated values and design choices within Vlute caused limitations on the performance of IPS tasks. This research improves upon several of these limitations through experiments. Most significant improvements are found in the robot-planning domain through the implementation of a stochastic accept method and a best improvement neighbor search.