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JASA

JASA is a flexible, open-source auction simulator designed for Agent-Based Computational Economics (ACE). It allows agents to submit utility functions for optimal resource allocation and payments, addressing centralized auction scenarios. With mechanisms to maximize social welfare, minimize convergence time, and accommodate diverse auction designs, JASA serves as a valuable tool for researchers experimenting with various trading strategies and learning algorithms. The platform is community-led, encouraging contributions, bug reports, and collaborative improvements. Join us in advancing auction theory and agent-based simulations.

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JASA

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  1. JASA A high performance open-source auction simulator http://www.csc.liv.ac.uk/~sphelps/jasa Steve Phelps sphelps@csc.liv.ac.uk Agent Research & Technology Group University of Liverpool

  2. Background: auctions • Centralised resource allocation • Agents submit their utility functions to a “system agent” (auctioneer), which computes the optimal allocation and payments. • Typically used when: • Valuations (utility functions) vary rapidly over time • Agents are uncertain about their own valuation • Speed of convergence to the optimal allocation is a high-priority design objective • When we have an impromptu need to “thicken” the market: gather many buyers and sellers together simultaneously

  3. Mechanism design • Design objectives can vary: • Maximise social welfare • Maximise seller revenue • Minimise time to convergence • Minimise computational complexity • Budget balance • No single optimal design- auction design is a MOO problem • Auction theory results fail to hold for many real-world auctions • Exchanges are particular hard • Hence simulations can sometimes shed light on the grey areas.

  4. Requirements • A flexible laboratory framework for Agent-based Computational Economics (ACE) • In ACE we often need to run experiments very many times. • We’re interested in applying evolutionary computing to ACE • We would like to experiment with many different auction mechanisms, trading strategies and learning algorithms • Replication work: we would like a set of reference-implementations for the above

  5. Design • Light-weight & High-performance • Highly extensible • Open-source • Readable code • Integration with ECJ for performing experiments using evolutionary computing http://cs.gmu.edu/~eclab/projects/ecj/

  6. .. Demo ..

  7. Open Source • JASA is a community-led project • Hosted at Sourceforge: http://sourceforge.net/projects/jasa • Current contributors: • Jinzhong Niu (CUNY) • Marek Marcinkiewicz (Columbia) • We welcome further contributions in the form of: • New functionality (eg new trading strategies, learning algorithms, auction types) • Suggestions for improvement • Bug reports • Bug fixes • Anything else! • Contact sphelps@csc.liv.ac.uk to become involved.

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