Community-Maximising Allocation Strategies in P2P Sharing Economies
Reaching Equilibrium without Self-Maximising Agents
R.J. Geuzinge (TU Delft - Electrical Engineering, Mathematics and Computer Science)
G. Iosifidis – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
G. Iosifidis – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A.R. Bidarra – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
L.E. Chatzieleftheriou – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
L.E. Chatzieleftheriou – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
We investigate an agent-based model of a money-free peer-to-peer resource-sharing economy where agents allocate resources to maximise utility of others.
This is distinct from existing research, which focuses on allocation strategies that maximise the utility an agent receives themself (self-maximising strategies).
We introduce the agent-community concept and formulate a community-maximising allocation strategy.
We show that synchronous markets using this strategy do not reach equilibrium, whilst asynchronous (sequential) allocation markets do converge.
The latter is proven via coordinate ascent.
Numerical analysis demonstrates that under certain conditions, the community-optimising strategy performs well in the very short term, but long term performance remains an open question.
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