Quantum Probabilistic Description of Dealing with Risk and Ambiguity in Foraging Decisions
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A forager in a patchy environment faces two types of uncertainty: ambiguity regarding the quality of the current patch and risk associated with the background opportunities. We argue that the order in which the forager deals with these uncertainties has an impact on the decision whether to stay at the current patch. The order effect is formalised with a context-dependent quantum probabilistic framework. Using Heisenberg’s uncertainty principle, we demonstrate the two types of uncertainty cannot be simultaneously minimised, hence putting a formal limit on rationality in decision making. We show the applicability of the contextual decision function with agent-based modelling. The simulations reveal order-dependence. Given that foraging is a universal pattern that goes beyond animal behaviour, the findings help understand similar phenomena in other fields.
KeywordsQuantum Probabilistic Description Current Patch FOR DEALING Order Dependence Bayesian Decision Process
This work was partially supported by the European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES. Xavier Rubio-Campillo is supported by the SimulPast Project (CSD2010-00034), funded by the CONSOLIDER-INGENIO2010 program of the Ministry of Science and Innovation – Spain. We also thank the reviewers for their insights, they helped us clarify conceptual issues.
- 7.Knight, F.: Risk, Uncertainty and Profit. Houghton Mifflin, Boston (1921)Google Scholar
- 10.Kacelnik, A., Bateson, M.: Risky theories - the effects of variance on foraging decisions. Am. Zool. 36(4), 402–434 (1996)Google Scholar
- 17.Anand, P.: Foundations of Rational Choice Under Risk. Oxford University Press, Oxford (1995)Google Scholar
- 27.Aerts, D., Czachor, M., Kuna, M., Sinervo, B., Sozzo, S.: Quantum probabilistic structures in competing lizard communities. arXiv preprint arXiv:1212.0109 (2012)Google Scholar
- 28.Aerts, D., Czachor, M., Kuna, M., Sozzo, S.: Systems, environments, and soliton rate equations: a non-Kolmogorovian framework for population dynamics. arXiv preprint arXiv:1303.0281 (2013)Google Scholar
- 31.Cohen-Tannoudji, C., Diu, B., Laloë, F.: Quantum Mechanics. Wiley, New York (1996)Google Scholar
- 32.Kitto, K., Boschetti, F., Bruza, P.: The quantum inspired modelling of changing attitudes and self-organising societies. In: Busemeyer, J.R., Dubois, F., Lambert-Mogiliansky, A., Melucci, M. (eds.) QI 2012. LNCS, vol. 7620, pp. 1–12. Springer, Heidelberg (2012)Google Scholar
- 33.Rubio-Campillo, X.: Pandora: an HPC agent-based modelling framework. https://github.com/xrubio/pandora/ (2013). Accessed 01 April 2013
- 34.Wittek, P., Rubio-Campillo, X.: Scalable agent-based modelling with cloud HPC resources for social simulations. In: Proceedings of CloudCom-12, 4th IEEE International Conference on Cloud Computing Technology and Science, Taipei, Taiwan, pp. 355–362, December 2012Google Scholar
- 35.Bonet, B., Geffner, H.: Action selection for MDPs: anytime AO* vs. UCT. In: Proceedings of AAAI-12, 26th Conference on Artificial Intelligence, Toronto, Canada, July 2012Google Scholar