Abstract
Peer review, our current system for determining which papers to accept for journals and conferences, has limitations that impair the quality of scientific communication. Under the current system, each paper receives an equal amount of attention regardless of how good the paper is. We propose to implement a new system for conference peer review based on ant colony optimization (ACO) algorithms. In our model, each reviewer has a set of ants that goes out and finds articles. The reviewer assesses the paper that the ant brings and the reviewer’s ants deposit pheromone that is proportional to the quality of the review. Subsequent ants select the next article based on pheromone strength. We used an agent-based model to determine that an ACO-based paper selection system will direct reviewers’ attention to the best articles and correctly rank them based on the papers’ quality.
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References
Arora, T., Moses, M.: Using ant colony optimization for routing in VLSI. In: 1st International Conference on Bio-Inspired Computational Methods Used for Difficult Problem Solving: Development of Intelligent and Complex Systems. AIP Conference Proceedings, pp. 145–156 (2009)
Beckers, R., Deneubourg, J.L., Goss, S.: Trails and U-turns in the Selection of a Path by the Ant Lasius niger. J. Theor. Biol. 159, 397–415 (1992)
Bell, R., Koren, Y., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. IEEE Computer Society 42, 30–37 (2009)
Chubin, D.E., Hackett, E.J.: Peer review and the printed word. In: Peerless Science: Peer Review and U.S. Science Policy. SUNY Press, Albany (1990)
Deneubourg, J.L., Lioni, A., Detrain, C.: Dynamics of aggregation and emergence of cooperation. Biol. Bull. 202, 262–267 (2002)
Deneubourg, J.L., Pasteels, J.M., Verhaeghe, J.C.: Probablistic Behaviour in Ants: A Strategy of Errors? J. Theor. Biol. 105, 259–271 (1983)
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 26, 29–41 (1996)
Godlee, F., Gale, C.R., Martyn, C.N.: Effect on the quality of peer review of blinding reviewers and asking them to sign their reports: a randomized controlled trial. JAMA 280, 237–240 (1998)
Goss, S., Aron, S., Deneubourg, J.L., Pasteels, J.M.: Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76, 579–581 (1989)
SVD Recommendation System in Ruby, http://www.igvita.com/2007/01/15/svd-recommendation-system-in-ruby
Grimm, V.B., et al.: Ecological Modelling 198, 115-126 (2006)
Kwang, M.S., Weng, H.S.: Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 33, 560–572 (2003)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, AAAI 2002, pp. 187–192 (2002)
Melville, P., Sindhwani, V.: Recommender Systems. In: Sammut, G., Webb, G. (eds.) Encyclopedia of Machine Learning. Springer, Berlin (2010)
Neff, B.D., Olden, J.D.: Is Peer Review a Game of Chance? Bioscience 56, 333–340 (2006)
Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6, 321–332 (2002)
Rothwell, P.M., Martyn, C.N.: Reproducibility of peer review in clinical neuroscience. Is agreement between reviewers any greater than would be expected by chance alone? Brain 123(pt 9), 1964–1969 (2000)
Dorigo, M., Birattari, M., Stützle, T.: Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine 1, 39 (2006)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)
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Flynn, M., Moses, M. (2012). Improving Peer Review with ACORN: ACO Algorithm for Reviewer’s Network. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2012. Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_26
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DOI: https://doi.org/10.1007/978-3-642-32650-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32649-3
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