Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multi-attribute Utility Theoretic Negotiation for Electronic Commerce
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Applying evolutionary game theory to auction mechanism design
Proceedings of the 4th ACM conference on Electronic commerce
Analyzing Market-Based Resource Allocation Strategies for the Computational Grid
International Journal of High Performance Computing Applications
Evolutionary Market Agents for Resource Allocation in Decentralised Systems
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Modeling Implicit Collusion Using Coevolution
Operations Research
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Resource allocation in decentralised computational systems: an evolutionary market-based approach
Autonomous Agents and Multi-Agent Systems
On Evolutionary Exploration and Exploitation
Fundamenta Informaticae
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Markets are useful mechanisms for performing resource allocation in fully decentralised computational and other systems, since they can possess a range of desirable properties, such as efficiency, decentralisation, robustness and scalability. In this paper we investigate the behaviour of co-evolving evolutionary market agents as adaptive offer generators for sellers in a multi-attribute posted-offer market. We demonstrate that the evolutionary approach enables sellers to automatically position themselves in market niches, created by heterogeneous buyers. We find that a trade-off exists for the evolutionary sellers between maintaining high population diversity to facilitate movement between niches and low diversity to exploit the current niche and maximise cumulative payoff. We characterise the trade-off from the perspective of the system as a whole, and subsequently from that of an individual seller. Our results highlight a decision on risk aversion for resource providers, but crucially we show that rational self-interested sellers would not adopt the behaviour likely to lead to the ideal result from the system point of view.