# American Institute of Mathematical Sciences

• Previous Article
Balanced growth path solutions of a Boltzmann mean field game model for knowledge growth
• KRM Home
• This Issue
• Next Article
Weighted fast diffusion equations (Part Ⅱ): Sharp asymptotic rates of convergence in relative error by entropy methods
March  2017, 10(1): 93-116. doi: 10.3934/krm.2017004

## A kinetic games framework for insurance plans

 School of Mathematical and Statistical Sciences, Arizona State University, P.O. Box 871804, Tempe, AZ 85287-1804, USA

*Corresponding author: Daniel Brinkman

Received  January 2016 Revised  May 2016 Published  November 2016

Fund Project: The authors are supported by NSF RNMS grant No.1107291.

The dynamics of insurance plans have been under the microscope in recent years due to the controversy surrounding the implementation of the Affordable Care Act (Obamacare) in the United States. In this paper, we introduce a game between an insurance company and an ensemble of customers choosing between several insurance plans. We then derive a kinetic model for the strategies of the insurer and the decisions of the customers and establish the conditions for which a Nash equilibrium exists for some specific customer distributions. Finally, we give some agent-based numerical results for how the plan enrollment evolves over time which show qualitative agreement to "experimental" results in the literature from two plans in the state of Massachusetts.

Citation: Daniel Brinkman, Christian Ringhofer. A kinetic games framework for insurance plans. Kinetic and Related Models, 2017, 10 (1) : 93-116. doi: 10.3934/krm.2017004
##### References:
 [1] M. Bruger, A. Lorz and M.-T. Wolfram, On a boltzmann mean field model for knowledge growth, SIAM J. Appl. Math., 76 (2016), 1799-1818(20 pages).  doi: 10.1137/15M1018599. [2] M. Burger, L. A. Caffarelli, P. A. Markowich and M.-T. Wolfram, On a boltzmann-type price formation model, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 469 (2013), 20130126.  doi: 10.1098/rspa.2013.0126. [3] M. Burger, L. A. Caffarelli, P. A. Markowich and M.-T. Wolfram, On the asymptotic behavior of a boltzmann-type price formation model, Communications in Mathematical Sciences, 12 (2014), 1353-1361.  doi: 10.4310/CMS.2014.v12.n7.a10. [4] L. A. Caffarelli, P. A. Markowich and M.-T. Wolfram, On a price formation free boundary model by lasry and lions: The neumann problem, Comptes Rendus Mathematique, 349 (2011), 841–844, URL http://www.sciencedirect.com/science/article/pii/S1631073X11001932. doi: 10.1016/j.crma.2011.07.006. [5] L. A. Caffarelli, P. A. Markowich and J.-F. Pietschmann, On a price formation free boundary model by lasry and lions, Comptes Rendus Mathematique, 349 (2011), 621–624, URL http://www.sciencedirect.com/science/article/pii/S1631073X11001488. doi: 10.1016/j.crma.2011.05.011. [6] C. Camerer and T. Ho, Experience-weighted attraction learning in normal form games, Econometrica, 67 (1999), 827-874.  doi: 10.1111/1468-0262.00054. [7] C. F. Camerer, Behavioral Game Theory: Experiments in Strategic Interaction, Princeton University Press, 2003. [8] K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz and Y. Singer, Online passive-aggressive algorithms, J. Mach. Learn. Res., 7 (2006), 551–585, URL http://dl.acm.org/citation.cfm?id=1248547.1248566. [9] D. M. Cutler and R. J. Zeckhauser, Adverse Selection in Health Insurance, Working Paper 6107, National Bureau of Economic Research, 1998. doi: 10.2202/1558-9544.1056. [10] P. Degond, J. -G. Liu and C. Ringhofer, A nash equilibrium macroscopic closure for kinetic models coupled with mean-field games, 2012. [11] P. Degond, J.-G. Liu and C. Ringhofer, Evolution of the distribution of wealth in an economic environment driven by local nash equilibria, Journal of Statistical Physics, 154 (2014), 751–780, URL http://dx.doi.org/10.1007/s10955-013-0888-4. doi: 10.1007/s10955-013-0888-4. [12] B. Düring, P. Markowich, J.-F. Pietschmann and M.-T. Wolfram, Boltzmann and fokker-planck equations modelling opinion formation in the presence of strong leaders, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 465 (2009), 3687-3708.  doi: 10.1098/rspa.2009.0239. [13] R. Dusansky and C. Koc, Implications of the interaction between insurance choice and medical care demand, Journal of Risk and Insurance, 77 (2010), 129-144.  doi: 10.1111/j.1539-6975.2009.01335.x. [14] J. Hofbauer and K. Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, 1998. doi: 10.1017/CBO9781139173179. [15] S. Hoi, J. Wang and P. Zhao, Libol: A library for online learning algorithms, Journal of Machine Learning Research, 15 (2014), 495–499, URL http://jmlr.org/papers/v15/hoi14a.html. [16] J.-M. Lasry and P.-L. Lions, Mean field games, Japanese Journal of Mathematics, 2 (2007), 229–260, URL http://dx.doi.org/10.1007/s11537-007-0657-8. doi: 10.1007/s11537-007-0657-8. [17] J. F. Nash, Equilibrium points in n-person games, Proceedings of the National Academy of Sciences of the United States of America, 36 (1950), 48-89.  doi: 10.1073/pnas.36.1.48. [18] L. Pareschi and G. Toscani, Wealth distribution and collective knowledge: A boltzmann approach Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372 (2014), 20130396, 15 pp. doi: 10.1098/rsta.2013.0396. [19] D. Schmeidler, Equilibrium points of nonatomic games, Journal of Statistical Physics, 7 (1973), 295-300.  doi: 10.1007/BF01014905. [20] N. Slonim, E. Yom-Tov and K. Crammer, Active online classification via information maximization, in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence -Volume Volume Two, IJCAI'11, AAAI Press, 2011,1498-1504, URL http://dx.doi.org/10.5591/978-1-57735-516-8/IJCAI11-252 [21] J. M. Smith and G. R. Price, The logic of animal conflict, Nature, 246 (1973), 15-18.  doi: 10.1038/246015a0. [22] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction, IEEE Transactions on Neural Networks, 9 (1998), p1054.  doi: 10.1109/TNN.1998.712192. [23] G. Toscani, Kinetic models of opinion formation, Communications in Mathematical Sciences, 4 (2006), 481-496.  doi: 10.4310/CMS.2006.v4.n3.a1. [24] G. Toscani and C. Brugna, Wealth redistribution in boltzmann-like models of conservative economies, in Econophysics and Economics of Games, Social Choices and Quantitative Techniques (eds. B. Basu, S. R. Chakravarty, B. K. Chakrabarti and K. Gangopadhyay), New Economic Windows, Springer Milan, 2010, 71-82, URL http://dx.doi.org/10.1007/978-88-470-1501-2_9. doi: 10.1007/978-88-470-1501-2_9. [25] C. Wilson, A model of insurance markets with incomplete information, Journal of Economic Theory, 16 (1977), 167–207, URL http://www.sciencedirect.com/science/article/pii/0022053177900047. doi: 10.1016/0022-0531(77)90004-7.

show all references

##### References:
 [1] M. Bruger, A. Lorz and M.-T. Wolfram, On a boltzmann mean field model for knowledge growth, SIAM J. Appl. Math., 76 (2016), 1799-1818(20 pages).  doi: 10.1137/15M1018599. [2] M. Burger, L. A. Caffarelli, P. A. Markowich and M.-T. Wolfram, On a boltzmann-type price formation model, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 469 (2013), 20130126.  doi: 10.1098/rspa.2013.0126. [3] M. Burger, L. A. Caffarelli, P. A. Markowich and M.-T. Wolfram, On the asymptotic behavior of a boltzmann-type price formation model, Communications in Mathematical Sciences, 12 (2014), 1353-1361.  doi: 10.4310/CMS.2014.v12.n7.a10. [4] L. A. Caffarelli, P. A. Markowich and M.-T. Wolfram, On a price formation free boundary model by lasry and lions: The neumann problem, Comptes Rendus Mathematique, 349 (2011), 841–844, URL http://www.sciencedirect.com/science/article/pii/S1631073X11001932. doi: 10.1016/j.crma.2011.07.006. [5] L. A. Caffarelli, P. A. Markowich and J.-F. Pietschmann, On a price formation free boundary model by lasry and lions, Comptes Rendus Mathematique, 349 (2011), 621–624, URL http://www.sciencedirect.com/science/article/pii/S1631073X11001488. doi: 10.1016/j.crma.2011.05.011. [6] C. Camerer and T. Ho, Experience-weighted attraction learning in normal form games, Econometrica, 67 (1999), 827-874.  doi: 10.1111/1468-0262.00054. [7] C. F. Camerer, Behavioral Game Theory: Experiments in Strategic Interaction, Princeton University Press, 2003. [8] K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz and Y. Singer, Online passive-aggressive algorithms, J. Mach. Learn. Res., 7 (2006), 551–585, URL http://dl.acm.org/citation.cfm?id=1248547.1248566. [9] D. M. Cutler and R. J. Zeckhauser, Adverse Selection in Health Insurance, Working Paper 6107, National Bureau of Economic Research, 1998. doi: 10.2202/1558-9544.1056. [10] P. Degond, J. -G. Liu and C. Ringhofer, A nash equilibrium macroscopic closure for kinetic models coupled with mean-field games, 2012. [11] P. Degond, J.-G. Liu and C. Ringhofer, Evolution of the distribution of wealth in an economic environment driven by local nash equilibria, Journal of Statistical Physics, 154 (2014), 751–780, URL http://dx.doi.org/10.1007/s10955-013-0888-4. doi: 10.1007/s10955-013-0888-4. [12] B. Düring, P. Markowich, J.-F. Pietschmann and M.-T. Wolfram, Boltzmann and fokker-planck equations modelling opinion formation in the presence of strong leaders, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 465 (2009), 3687-3708.  doi: 10.1098/rspa.2009.0239. [13] R. Dusansky and C. Koc, Implications of the interaction between insurance choice and medical care demand, Journal of Risk and Insurance, 77 (2010), 129-144.  doi: 10.1111/j.1539-6975.2009.01335.x. [14] J. Hofbauer and K. Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, 1998. doi: 10.1017/CBO9781139173179. [15] S. Hoi, J. Wang and P. Zhao, Libol: A library for online learning algorithms, Journal of Machine Learning Research, 15 (2014), 495–499, URL http://jmlr.org/papers/v15/hoi14a.html. [16] J.-M. Lasry and P.-L. Lions, Mean field games, Japanese Journal of Mathematics, 2 (2007), 229–260, URL http://dx.doi.org/10.1007/s11537-007-0657-8. doi: 10.1007/s11537-007-0657-8. [17] J. F. Nash, Equilibrium points in n-person games, Proceedings of the National Academy of Sciences of the United States of America, 36 (1950), 48-89.  doi: 10.1073/pnas.36.1.48. [18] L. Pareschi and G. Toscani, Wealth distribution and collective knowledge: A boltzmann approach Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372 (2014), 20130396, 15 pp. doi: 10.1098/rsta.2013.0396. [19] D. Schmeidler, Equilibrium points of nonatomic games, Journal of Statistical Physics, 7 (1973), 295-300.  doi: 10.1007/BF01014905. [20] N. Slonim, E. Yom-Tov and K. Crammer, Active online classification via information maximization, in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence -Volume Volume Two, IJCAI'11, AAAI Press, 2011,1498-1504, URL http://dx.doi.org/10.5591/978-1-57735-516-8/IJCAI11-252 [21] J. M. Smith and G. R. Price, The logic of animal conflict, Nature, 246 (1973), 15-18.  doi: 10.1038/246015a0. [22] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction, IEEE Transactions on Neural Networks, 9 (1998), p1054.  doi: 10.1109/TNN.1998.712192. [23] G. Toscani, Kinetic models of opinion formation, Communications in Mathematical Sciences, 4 (2006), 481-496.  doi: 10.4310/CMS.2006.v4.n3.a1. [24] G. Toscani and C. Brugna, Wealth redistribution in boltzmann-like models of conservative economies, in Econophysics and Economics of Games, Social Choices and Quantitative Techniques (eds. B. Basu, S. R. Chakravarty, B. K. Chakrabarti and K. Gangopadhyay), New Economic Windows, Springer Milan, 2010, 71-82, URL http://dx.doi.org/10.1007/978-88-470-1501-2_9. doi: 10.1007/978-88-470-1501-2_9. [25] C. Wilson, A model of insurance markets with incomplete information, Journal of Economic Theory, 16 (1977), 167–207, URL http://www.sciencedirect.com/science/article/pii/0022053177900047. doi: 10.1016/0022-0531(77)90004-7.
Numerical examples for the first case of Theorem 3.2. All initial plan prices approach zero price trivial steady-states
Numerical examples for the second case of Theorem 3.2. Initial plan prices below $r = \alpha M = 5$ are essentially stationary steady-states. Initial plan prices above $r = \alpha M = 5$ decay to a steady-state near $r = 5$
Numerical examples for the third case of Theorem 3.2. All initial plan prices $0 < r < 10$ converge to a steady state with $r \approx 7.14$. The first case had a steady state price of $r=7.165$ and the second steady-state occurred for $r=7.160$
Numerical examples for the fourth case of Theorem 3.2. Every initial plan price increases to the maximum $M$. Note that according to Equation (24), a plan with an enrollment of zero has an undefined price. The simulation responds by setting the price to zero
For $\beta = 1.3$, all plans go extinct and profits go to zero. The insurer occasionally realizes negative profits
For $\beta = 4/3$, the theorem is unclear, but numerically we see relative stability of multiple plans. The insurer profits are bounded away from zero
For $\beta = 5/3$, we expect a unique steady state plan price. Although the random accidents eliminate a true steady state, we quickly reach a probabalistic steady state. The long-term profits are higher than the other cases
For $\beta = 7/3$, all plans go extinct and profits go to zero. Unlike the first case, the profits are always positive, but go to zero as the plan prices increase above the costs of the most expensive individual
Normalized profit as a function of the profit parameter $\beta$. Note that a local maximum occurs for the lowest possible surviving profit, suggesting that greediness on the part of the insurer may actually reduce profits
 [1] Dieter Armbruster, Christian Ringhofer, Andrea Thatcher. A kinetic model for an agent based market simulation. Networks and Heterogeneous Media, 2015, 10 (3) : 527-542. doi: 10.3934/nhm.2015.10.527 [2] Holly Gaff. Preliminary analysis of an agent-based model for a tick-borne disease. Mathematical Biosciences & Engineering, 2011, 8 (2) : 463-473. doi: 10.3934/mbe.2011.8.463 [3] Gianluca D'Antonio, Paul Macklin, Luigi Preziosi. An agent-based model for elasto-plastic mechanical interactions between cells, basement membrane and extracellular matrix. Mathematical Biosciences & Engineering, 2013, 10 (1) : 75-101. doi: 10.3934/mbe.2013.10.75 [4] Enkhbat Rentsen, Battur Gompil. Generalized Nash equilibrium problem based on malfatti's problem. Numerical Algebra, Control and Optimization, 2021, 11 (2) : 209-220. doi: 10.3934/naco.2020022 [5] Cristina Anton, Jian Deng, Yau Shu Wong, Yile Zhang, Weiping Zhang, Stephan Gabos, Dorothy Yu Huang, Can Jin. Modeling and simulation for toxicity assessment. Mathematical Biosciences & Engineering, 2017, 14 (3) : 581-606. doi: 10.3934/mbe.2017034 [6] Junichi Minagawa. On the uniqueness of Nash equilibrium in strategic-form games. Journal of Dynamics and Games, 2020, 7 (2) : 97-104. doi: 10.3934/jdg.2020006 [7] Jian Hou, Liwei Zhang. A barrier function method for generalized Nash equilibrium problems. Journal of Industrial and Management Optimization, 2014, 10 (4) : 1091-1108. doi: 10.3934/jimo.2014.10.1091 [8] Yanhong Yuan, Hongwei Zhang, Liwei Zhang. A penalty method for generalized Nash equilibrium problems. Journal of Industrial and Management Optimization, 2012, 8 (1) : 51-65. doi: 10.3934/jimo.2012.8.51 [9] Nicolas Besse, Florent Berthelin, Yann Brenier, Pierre Bertrand. The multi-water-bag equations for collisionless kinetic modeling. Kinetic and Related Models, 2009, 2 (1) : 39-80. doi: 10.3934/krm.2009.2.39 [10] Nicolas Vauchelet. Numerical simulation of a kinetic model for chemotaxis. Kinetic and Related Models, 2010, 3 (3) : 501-528. doi: 10.3934/krm.2010.3.501 [11] Elvio Accinelli, Bruno Bazzano, Franco Robledo, Pablo Romero. Nash Equilibrium in evolutionary competitive models of firms and workers under external regulation. Journal of Dynamics and Games, 2015, 2 (1) : 1-32. doi: 10.3934/jdg.2015.2.1 [12] Dean A. Carlson. Finding open-loop Nash equilibrium for variational games. Conference Publications, 2005, 2005 (Special) : 153-163. doi: 10.3934/proc.2005.2005.153 [13] Shunfu Jin, Haixing Wu, Wuyi Yue, Yutaka Takahashi. Performance evaluation and Nash equilibrium of a cloud architecture with a sleeping mechanism and an enrollment service. Journal of Industrial and Management Optimization, 2020, 16 (5) : 2407-2424. doi: 10.3934/jimo.2019060 [14] Xiaona Fan, Li Jiang, Mengsi Li. Homotopy method for solving generalized Nash equilibrium problem with equality and inequality constraints. Journal of Industrial and Management Optimization, 2019, 15 (4) : 1795-1807. doi: 10.3934/jimo.2018123 [15] Jinhae Park, Feng Chen, Jie Shen. Modeling and simulation of switchings in ferroelectric liquid crystals. Discrete and Continuous Dynamical Systems, 2010, 26 (4) : 1419-1440. doi: 10.3934/dcds.2010.26.1419 [16] Yue Qiu, Sara Grundel, Martin Stoll, Peter Benner. Efficient numerical methods for gas network modeling and simulation. Networks and Heterogeneous Media, 2020, 15 (4) : 653-679. doi: 10.3934/nhm.2020018 [17] Xiaoli Yang, Jin Liang, Bei Hu. Minimization of carbon abatement cost: Modeling, analysis and simulation. Discrete and Continuous Dynamical Systems - B, 2017, 22 (7) : 2939-2969. doi: 10.3934/dcdsb.2017158 [18] Michael Herty. Modeling, simulation and optimization of gas networks with compressors. Networks and Heterogeneous Media, 2007, 2 (1) : 81-97. doi: 10.3934/nhm.2007.2.81 [19] Lukas Neumann, Christian Schmeiser. A kinetic reaction model: Decay to equilibrium and macroscopic limit. Kinetic and Related Models, 2016, 9 (3) : 571-585. doi: 10.3934/krm.2016007 [20] Pierre Monmarché. Hypocoercive relaxation to equilibrium for some kinetic models. Kinetic and Related Models, 2014, 7 (2) : 341-360. doi: 10.3934/krm.2014.7.341

2020 Impact Factor: 1.432