The majority of market theory is only concerned with centralised markets. In this paper, we consider a market that is distributed over a network, allowing us to characterise spatially (or temporally) separated markets. The effect of this modification on the behaviour of a market with a heterogeneous population of traders, under selection through a genetic algorithm, is examined. It is demonstrated that better-connected traders are able to make more profit than less connected traders and that this is due to a difference in the number of possible trading opportunities and not due to informational inequalities. A learning rule that had previously been demonstrated to profitably exploit network structure for a homogeneous population is shown to confer no advantage when selection is applied to a heterogeneous population of traders. It is also shown that better-connected traders adopt more aggressive market strategies in order to extract more surplus from the market.
|Name||Studies in Computational Intelligance|
|Publisher||Springer Berlin Heidelberg|
|Conference||Fifth International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2006)|
|Period||8/05/06 → 12/05/06|