Behavior Identification in Two-Stage Games for Incentivizing Citizen Science Exploration

  • Yexiang Xue
  • Ian Davies
  • Daniel Fink
  • Christopher Wood
  • Carla P. Gomes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9892)

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.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Anderson, A., Huttenlocher, D.P., Kleinberg, J.M., Leskovec, J.: Steering user behavior with badges. In: 22nd International World Wide Web Conference, WWW (2013)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Bragg, J., Mausam, Weld, D.S.: Crowdsourcing multi-label classification for taxonomy creation. In: HCOMP (2013)Google Scholar
  5. 5.
    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)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Chen, X., Lin, Q., Zhou, D.: Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing. In: ICML (2013)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Conitzer, V., Garera, N.: Learning algorithms for online principal-agent problems (and selling goods online). In: Proceedings of the 23rd ICML (2006)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Fang, F., Stone, P., Tambe, M.: When security games go green: designing defender strategies to prevent poaching and illegal fishing. In: IJCAI (2015)Google Scholar
  12. 12.
    Gens, R., Domingos, P.M.: Discriminative learning of sum-product networks. In: Advances in Neural Information Processing Systems, pp. 3248–3256 (2012)Google Scholar
  13. 13.
    Gomes, C.P., Selman, B.: Satisfied with physics. Science 297(5582), 784–785 (2002)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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 Google Scholar
  17. 17.
    Kawajiri, R., Shimosaka, M., Kashima, H.: Steered crowdsensing: Incentive design towards quality-oriented place-centric crowdsensing. In: UbiComp (2014)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    Li, H., Tian, F., Chen, W., Qin, T., Ma, Z., Liu, T.: Generalization analysis for game-theoretic machine learning. In: AAAI (2015)Google Scholar
  20. 20.
    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 CrossRefGoogle Scholar
  21. 21.
    McFadden, D.: Modeling the choice of residential location. In: Spatial Interaction Theory and Residential Location, pp. 75–96 (1978)Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    Radanovic, G., Faltings, B.: Incentive schemes for participatory sensing. In: AAMAS (2015)Google Scholar
  24. 24.
    Rust, J.: Optimal replacement of gmc bus engines: an empirical model of harold zurcher. Econometrica 55(5), 999–1033 (1987)CrossRefMATHGoogle Scholar
  25. 25.
    Settles, B.: Active learning literature survey. Univ. Wis. Madison 52(55–66), 11 (2010)Google Scholar
  26. 26.
    Shavell, S.: Risk sharing and incentives in the principal and agent relationship. Bell J. Econ. 10, 55–73 (1979)CrossRefGoogle Scholar
  27. 27.
    Singer, Y., Mittal, M.: Pricing mechanisms for crowdsourcing markets. In: Proceedings of the 22nd International Conference on World Wide Web (WWW) (2013)Google Scholar
  28. 28.
    Singla, A., Santoni, M., Bartók, G., Mukerji, P., Meenen, M., Krause, A.: Incentivizing users for balancing bike sharing systems. In: AAAI (2015)Google Scholar
  29. 29.
    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 CrossRefGoogle Scholar
  30. 30.
    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)Google Scholar
  31. 31.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yexiang Xue
    • 1
  • Ian Davies
    • 2
  • Daniel Fink
    • 2
  • Christopher Wood
    • 2
  • Carla P. Gomes
    • 1
  1. 1.Computer Science DepartmentCornell UniversityIthacaUSA
  2. 2.Cornell Lab of OrnithologyIthacaUSA

Personalised recommendations