Abstract
We consider two-stage games in which a leader seeks to direct the activities of independent agents by offering incentives. A good leader’s strategy requires an understanding of the agents’ utilities and the ability to predict agent behavior. Moreover, the optimization of outcomes requires an agent behavior model that can be efficiently incorporated into the leader’s model. Here we address the agent behavior modeling problem and show how it can be used to reduce bias in a challenging citizen science application. Adapting ideas from Discrete Choice Modeling in behavioral economics, we develop a probabilistic behavioral model that takes into account variable patterns of human behavior and suboptimal actions. By modeling deviations from baseline behavior we are able to accurately predict future behavior based on limited, sparse data. We provide a novel scheme to fold the agent model into a bi-level optimization as a single Mixed Integer Program, and scale up our approach by adding redundant constraints, based on novel insights of an easy-hard-easy phase transition phenomenon. We apply our methodology to a game called Avicaching, in collaboration with eBird, a well-established citizen science program that collects bird observations for conservation. Field results show that our behavioral model performs well and that the incentives are remarkably effective at steering citizen scientists’ efforts to reduce bias by exploring under-sampled areas. Moreover, the data collected from Avicaching improves the performance of species distribution models.
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Notes
- 1.
Uncertainty measures, often used in active learning [25], are typically tied to one particular predictive model. We did not use them because of the need to meet multiple scientific goals in our application.
- 2.
One needs solve a Mixed Quadratic Program if he uses objective function \(D_2\).
References
Aggarwal, G., Feder, T., Motwani, R., Zhu, A.: Algorithms for multi-product pricing. In: Díaz, J., Karhumäki, J., Lepistö, A., Sannella, D. (eds.) ICALP 2004. LNCS, vol. 3142, pp. 72–83. Springer, Heidelberg (2004)
Anderson, A., Huttenlocher, D.P., Kleinberg, J.M., Leskovec, J.: Steering user behavior with badges. In: 22nd International World Wide Web Conference, WWW (2013)
Bacon, D.F., Parkes, D.C., Chen, Y., Rao, M., Kash, I., Sridharan, M.: Predicting your own effort. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 695–702 (2012)
Bragg, J., Mausam, Weld, D.S.: Crowdsourcing multi-label classification for taxonomy creation. In: HCOMP (2013)
Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16, 1190–1208 (1995)
Chen, X., Lin, Q., Zhou, D.: Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing. In: ICML (2013)
Chiappone, M.: Coral watch program summary. a report on volunteer and scientific efforts to document the status of reefs in the florida keys national marine sanctuary. The Nature Conservancy, Summerland Key, Florida (1996)
Conitzer, V., Garera, N.: Learning algorithms for online principal-agent problems (and selling goods online). In: Proceedings of the 23rd ICML (2006)
Conitzer, V., Sandholm, T.: Computing the optimal strategy to commit to. In: Proceedings of the 7th ACM Conference on Electronic Commerce (EC), pp. 82–90 (2006)
Endriss, U., Kraus, S., Lang, J., Wooldridge, M.: Incentive engineering for boolean games. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22(3), p. 2602 (2011)
Fang, F., Stone, P., Tambe, M.: When security games go green: designing defender strategies to prevent poaching and illegal fishing. In: IJCAI (2015)
Gens, R., Domingos, P.M.: Discriminative learning of sum-product networks. In: Advances in Neural Information Processing Systems, pp. 3248–3256 (2012)
Gomes, C.P., Selman, B.: Satisfied with physics. Science 297(5582), 784–785 (2002)
Guruswami, V., Hartline, J.D., Karlin, A.R., Kempe, D., Kenyon, C., McSherry, F.: On profit-maximizing envy-free pricing. In: SODA, pp. 1164–1173 (2005)
Hartline, J.D., Koltun, V.: Near-optimal pricing in near-linear time. In: Dehne, F., López-Ortiz, A., Sack, J.-R. (eds.) WADS 2005. LNCS, vol. 3608, pp. 422–431. Springer, Heidelberg (2005)
Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., Mckerrow, A., Vandriel, J.N., Wickham, J.: Completion of the 2001 national land cover database for the conterminous United States. Photogram. Eng. Remote Sens. 73(4), 337–341 (2007). http://www.asprs.org/publications/pers/2007journal/april/highlight.pdf
Kawajiri, R., Shimosaka, M., Kashima, H.: Steered crowdsensing: Incentive design towards quality-oriented place-centric crowdsensing. In: UbiComp (2014)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML (2001)
Li, H., Tian, F., Chen, W., Qin, T., Ma, Z., Liu, T.: Generalization analysis for game-theoretic machine learning. In: AAAI (2015)
Lintott, C.J., Schawinski, K., Slosar, A., et al.: Galaxy zoo: morphologies derived from visual inspection of galaxies from the sloan digital sky survey. Mon. Not. R. Astron. Soc. 389(3), 1179–1189 (2008). http://dx.doi.org/10.1111/j.1365-2966.2008.13689.x
McFadden, D.: Modeling the choice of residential location. In: Spatial Interaction Theory and Residential Location, pp. 75–96 (1978)
Paruchuri, P., Pearce, J.P., Marecki, J., Tambe, M., Ordóñez, F., Kraus, S.: Playing games for security: an efficient exact algorithm for solving bayesian stackelberg games. In: AAMAS, pp. 895–902 (2008)
Radanovic, G., Faltings, B.: Incentive schemes for participatory sensing. In: AAMAS (2015)
Rust, J.: Optimal replacement of gmc bus engines: an empirical model of harold zurcher. Econometrica 55(5), 999–1033 (1987)
Settles, B.: Active learning literature survey. Univ. Wis. Madison 52(55–66), 11 (2010)
Shavell, S.: Risk sharing and incentives in the principal and agent relationship. Bell J. Econ. 10, 55–73 (1979)
Singer, Y., Mittal, M.: Pricing mechanisms for crowdsourcing markets. In: Proceedings of the 22nd International Conference on World Wide Web (WWW) (2013)
Singla, A., Santoni, M., Bartók, G., Mukerji, P., Meenen, M., Krause, A.: Incentivizing users for balancing bike sharing systems. In: AAAI (2015)
Sullivan, B.L., Aycrigg, J.L., Barry, J.H., et al.: The ebird enterprise: an integrated approach to development and application of citizen science. Bio. Conserv. 169, 31–40 (2014). http://www.sciencedirect.com/science/article/pii/S0006320713003820
Tran-Thanh, L., Huynh, T.D., Rosenfeld, A., Ramchurn, S.D., Jennings, N.R.: Crowdsourcing complex workflows under budget constraints. In: Proceedings of the AAAI Conference, AAAI (2015)
Xue, Y., Davies, I., Fink, D., Wood, C., Gomes, C.P.: Avicaching: a two stage game for bias reduction in citizen science. In: Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS (2016)
Acknowledgements
We are thankful to the anonymous reviewers for comments, thousands of eBird participants, and the Cornell Lab of Ornithology for managing the database. This research was supported by National Science Foundation (0832782, 1522054, 1059284, 1356308), ARO grant W911-NF-14-1-0498, the Leon Levy Foundation and the Wolf Creek Foundation.
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Xue, Y., Davies, I., Fink, D., Wood, C., Gomes, C.P. (2016). Behavior Identification in Two-Stage Games for Incentivizing Citizen Science Exploration. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_44
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