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The Boltzmann distribution in the problem of rational choice by population of a patch under an imperfect information about its resources

https://doi.org/10.18255/1818-1015-2023-3-234-245

Abstract

The problem of rational choice by the population of a patch containing energy (nutritive) resources is considered. This problem belongs to the theory of optimal foraging, which, in turn of, studies issues related to the behavior of the population when it leaves the patch or chooses the most suitable one. In order to define the optimal patch choice for population, a variational approach, based on the idea of the Boltzmann distribution is proposed. To construct the probability distribution the utility functions are used, that take into account factors that can influence the patch choice of a population: available information about the quality of patches, the energy utility of patches, the cost of moving to the patch, the cost of information about the quality of patches. The main goal of the paper is to investigate the influence of available information about the amount of resources, contained in patches, on a decision-making process generated by the foragers while a suitable patch choosing. The optimal rationality is determined in the cases taking into account the information cost, the average energy utility of all patches, the rationality depending on the patch. The conditions under which the population, with the lack of information, select the “poor” patch, in sense of its resources, are obtained. The latter provides a theoretical justification of experimental observations, according to which a population can choose a patch with worse quality. The obtained results have a general character and may be used not only in behavioral ecology but when constructing any decision making processes.

About the Authors

Alexander N. Kirillov
Karelian Research Centre of the Russian Academy of Sciences
Russian Federation


Inna V. Danilova
Petrozavodsk State University
Russian Federation


References

1. R. B. Aumann, “Rationality and Bounded Rationality,” Games and econimic behavior, vol. 21, no. 1, pp. 2–14, 1997.

2. P. A. Ortega, D. A. Braun, J. Dyer, K.-E. Kim, and N. Tishby, “Information-Theoretic Bounded Rationality.” 2015, [Online]. Available: https://arxiv.org/abs/1512.06789.

3. D. A. Braun and P. A. Ortego, “Information-Theoretic Bounded Rationality and ε-Optimality,” Entropy, vol. 16, pp. 4662–4676, 2014.

4. M. D. Breed and J. Moore, Encyclopedia of animal behavior. Elsevier Ltd., 2019.

5. E. Kagan and I. Ben-Gal, Search and foraging individual motion and swarm dynamics. Taylor and Francis Group, LLC, 2015.

6. B. Y. Hayden and M. E. Walton, “Neuroscience of foraging,” Frontiers in Neuroscience, vol. 8, p. 81, 2014.

7. D. L. Barack, C. S. W., and P. M. L., “Posterior cingulate neurons dynamically signal decisions to disengage during foraging,” Neuron, vol. 96, no. 2, pp. 339–347, 2017.

8. J. S. Greene et al., “Balancing selection shapes density-dependent foraging behaviour,” Nature, vol. 539, pp. 254–258, 2016.

9. R. Cressman and V. Krivan, “The ideal free distribution as an evolutionarily stable state in density-dependent population games,” Oikos, vol. 119, no. 8, pp. 1231–1242, 2010.

10. R. Cressman and V. Krivan, “Two-patch population models with adaptive dispersal: the effects of varying dispersal speeds,” Mathematical Biology, vol. 67, pp. 329–358, 2013.

11. M. Shuichi, R. Arlinghaus, and U. Dieckmann, “Foraging on spatially distributed resources with suboptimal movement, imperfect information, and travelling

12. costs: departures from the ideal free distribution,” Oikos, vol. 119, no. 9, pp. 1469–1483, 2010.

13. L. D. Landau and E. M. Lifshitz, Statistical physics. Nauka, 1976.

14. I. P. Kornfeld, Y. G. Sinai, and S. V. Fomin, Ergodic theory. Nauka, 1980.

15. R. Bowen, Methods of symbolic dynamics. Mir, 1979.

16. C. J. C. H. Watkins and P. Dayan, “Technical note Q-Learning,” Machine Learning, vol. 8, no. 3, pp. 279–292, 1992.

17. A. Kianercy and A. Galstyan, “Dynamics of Boltzmann Q learning in two-player two-action games,” Physical review, vol. 85, no. 4, p. 041145, 2012.

18. P. A. Ortega and D. A. Braun, “Thermodynamics as a theory of decision-making with information-processing costs,” Proceedings of the Royal Society, vol. 469, no. 2153, p. 20120683, 2013.

19. S. K. Mitter and N. J. Newton, “Information and entropy flow in the Kalman-Bucy filter,” Journal of Statistical Physics, vol. 118, pp. 145–176, 2005.

20. P. Pirolli, Information foraging theory. Oxford university press, 2007.

21. K. Lerman and A. Galstyan, “Mathematical model of foraging in a group of robots: effect of interference,” Autonomous robots, vol. 13, pp. 127–141, 2002.

22. A. N. Kirillov and I. V. Danilova, “Dynamics of population patch distribution,” Modeling and Analysis of Information Systems, vol. 25, no. 3, pp. 268–275, 2018.

23. A. N. Kirillov and I. V. Danilova, “Utility function in the foraging problem with imperfect information,” Information and Control Systems, vol. 105, no. 2, pp. 71–77, 2020.

24. I. V. Danilova, A. N. Kirillov, and A. A. Krizhanovsky, “Boltzmann distribution in relation to the problem of population migration,” Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, no. 2, pp. 92–102, 2020.


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For citations:


Kirillov A.N., Danilova I.V. The Boltzmann distribution in the problem of rational choice by population of a patch under an imperfect information about its resources. Modeling and Analysis of Information Systems. 2023;30(3):234-245. (In Russ.) https://doi.org/10.18255/1818-1015-2023-3-234-245

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ISSN 1818-1015 (Print)
ISSN 2313-5417 (Online)