AUCTION SCOPE, SCALE AND PRICING FORMAT - Agent-based Simulation of the Performance of a Water Quality Tender

Atakelty Hailu, John Rolfe, Jill Windle, Romy Greiner

Abstract

Conservation auctions are tender-based mechanisms for allocating contracts among landholders who are intertested in undertaking conservation activities in return for monetary rewards. These auctions have grown in popularity over the last decade. However, the services offered under these auctions can be complex and auction design and implementation features need to be carefully considered if these auctions are to perform well. Computational experiments are key to bed-testing auction design as the bulk of auction theory (as the rest of economic theory) is focused on simple auctions for tractability reasons. This paper presents results from an agent-based modelling study investigating the impact on performance of four auction features: scope of conservation activities in tendered projects; auction budget levels relative to bidder population size (scale effects); endogeneity of bidder participation; and auction pricing rules (uniform versus discriminatory pricing). The results highlight the importance of a careful consideration of scale and scope issues and that policymakers need to consider alternatives to currently used pay-as-bid or discriminatory pricing fromats. Averaging over scope variations, the uniform auction can deliver at least 25\% more benefits than the discriminatory auction.

References

  1. Arifovic, J. and Ledyard, J. (2002). Computer testbeds: the dynamics of groves-ledyard mechanisms. Technical report, Simon Fraser University and California Institute of Technology.
  2. Camerer, C. (2003). Behavioral Game Theory. Princeton University Press, Russell Sage Foundation, New York.
  3. Epstein, J. and Axtell, R. (1996). Growing artificial societies: Social sciences from the bottom up. Brookings Institution Press, Washington DC.
  4. Erev, I. and Roth, A. (1998). Predicting how people play games with unique, mixed strategy equilibria. American Economic Review, 88:848-881.
  5. Greiner, R., Rolfe, J., Windle, J., and Gregg, D. (2008). Tender results and feedback from ex-post participant survey, Research Report 5. Central Queensland University, Rockhampton.
  6. Hailu, A. and Schilizzi, S. (2004). Are auctions more efficient than fixed price schemes when bidders learn? Australian Journal of Management, 29:147-168.
  7. Hailu, A. and Schilizzi, S. (2005). Learning in a 'basket of crabs': An agent-based computational model of repeated conservation auctions. In Lux, T., Reitz, S., and Samanidou, E., editors, Nonlinear Dynamics and Heterogeneous Interacting Agents, pages 27-39. Springer, Berlin.
  8. Hailu, A. and Thoyer, S. (2006). Multi-unit auction format design. Journal of Economic Interaction and Coordination, 1:129-146.
  9. Hailu, A. and Thoyer, S. (2007). Designing multi-unit multiple bid auctions: An agent-based computational model of uniform, discriminatory and generalized vickrey auctions. Economic Record, 83(S1):S57-S72.
  10. Rolfe, J., Greiner, R., Windle, J., and Hailu, A. (2007). Identifying scale and scope issues in establishing conservation tenders: Using Conservation Tenders for Water Quality Improvements in the Burdekin, Research Report 1. Central Queensland University, Rockhampton.
  11. Roth, A. and Erev, I. (1995). Learning in extensive form games: experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior, 8:164-212.
  12. Selten, R., Abbink, K., and Cox, R. (2001). Learning direciton theory and winners curse. Technical Report Discussion Paper 10, Department of Economics, University of Bonn, Germany.
  13. Selten, R. and Stoecker, R. (1986). End behaviour in sequance finite prisoners' dilemma supergames: A learning theory approach. Journal of Economic Behaviour and Organization, 7:47-70.
  14. Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom up. Artificial Life, 8:55-82.
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Paper Citation


in Harvard Style

Hailu A., Rolfe J., Windle J. and Greiner R. (2010). AUCTION SCOPE, SCALE AND PRICING FORMAT - Agent-based Simulation of the Performance of a Water Quality Tender . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-674-022-1, pages 80-87. DOI: 10.5220/0002730500800087


in Bibtex Style

@conference{icaart10,
author={Atakelty Hailu and John Rolfe and Jill Windle and Romy Greiner},
title={AUCTION SCOPE, SCALE AND PRICING FORMAT - Agent-based Simulation of the Performance of a Water Quality Tender},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2010},
pages={80-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002730500800087},
isbn={978-989-674-022-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - AUCTION SCOPE, SCALE AND PRICING FORMAT - Agent-based Simulation of the Performance of a Water Quality Tender
SN - 978-989-674-022-1
AU - Hailu A.
AU - Rolfe J.
AU - Windle J.
AU - Greiner R.
PY - 2010
SP - 80
EP - 87
DO - 10.5220/0002730500800087