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Sethi-Skiba point

Sethi-Skiba points, also known as DNSS points, arise in optimal control problems that exhibit multiple optimal solutions. A Sethi-Skiba point is an indifference point in an optimal control problem such that starting from such a point, the problem has more than one different optimal solutions. A good discussion of such points can be found in Grass et al.

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Sethi-Skiba points,1234 also known as DNSS points, arise in optimal control problems that exhibit multiple optimal solutions. A Sethi-Skiba point is an indifference point in an optimal control problem such that starting from such a point, the problem has more than one different optimal solutions. A good discussion of such points can be found in Grass et al.256

Definition

Of particular interest here are discounted infinite horizon optimal control problems that are autonomous.2 These problems can be formulated as

max u ( t ) Ω 0 e ρ t φ ( x ( t ) , u ( t ) ) d t {\displaystyle \max _{u(t)\in \Omega }\int _{0}^{\infty }e^{-\rho t}\varphi \left(x(t),u(t)\right)dt}

s.t.

x ˙ ( t ) = f ( x ( t ) , u ( t ) ) , x ( 0 ) = x 0 , {\displaystyle {\dot {x}}(t)=f\left(x(t),u(t)\right),x(0)=x_{0},}

where ρ > 0 {\displaystyle \rho >0} is the discount rate, x ( t ) {\displaystyle x(t)} and u ( t ) {\displaystyle u(t)} are the state and control variables, respectively, at time t {\displaystyle t} , functions φ {\displaystyle \varphi } and f {\displaystyle f} are assumed to be continuously differentiable with respect to their arguments and they do not depend explicitly on time t {\displaystyle t} , and Ω {\displaystyle \Omega } is the set of feasible controls and it also is explicitly independent of time t {\displaystyle t} . Furthermore, it is assumed that the integral converges for any admissible solution ( x ( . ) , u ( . ) ) {\displaystyle \left(x(.),u(.)\right)} . In such a problem with one-dimensional state variable x {\displaystyle x} , the initial state x 0 {\displaystyle x_{0}} is called a Sethi-Skiba point if the system starting from it exhibits multiple optimal solutions or equilibria. Thus, at least in the neighborhood of x 0 {\displaystyle x_{0}} , the system moves to one equilibrium for x > x 0 {\displaystyle x>x_{0}} and to another for x < x 0 {\displaystyle x<x_{0}} . In this sense, x 0 {\displaystyle x_{0}} is an indifference point from which the system could move to either of the two equilibria.

For two-dimensional optimal control problems, Grass et al.5 and Zeiler et al.7 present examples that exhibit DNSS curves.

Some references on the applications of Sethi-Skiba points are Caulkins et al.,8 Zeiler et al.,9 and Carboni and Russu10

History

Suresh P. Sethi identified such indifference points for the first time in 1977.11 Further, Skiba,12 Sethi,131415 and Deckert and Nishimura16 explored these indifference points in economic models. The term DNSS (Deckert, Nishimura, Sethi, Skiba) points, introduced by Grass et al.,5 recognizes (alphabetically) the contributions of these authors. These indifference points have been also referred to as Skiba points or DNS points in earlier literature.5

Example

A simple problem exhibiting this behavior is given by φ ( x , u ) = x u , {\displaystyle \varphi \left(x,u\right)=xu,} f ( x , u ) = x + u , {\displaystyle f\left(x,u\right)=-x+u,} and Ω = [ 1 , 1 ] {\displaystyle \Omega =\left[-1,1\right]} . It is shown in Grass et al.5 that x 0 = 0 {\displaystyle x_{0}=0} is a Sethi-Skiba point for this problem because the optimal path x ( t ) {\displaystyle x(t)} can be either ( 1 e t ) {\displaystyle \left(1-e^{-t}\right)} or ( 1 + e t ) {\displaystyle \left(-1+e^{-t}\right)} . Note that for x 0 < 0 {\displaystyle x_{0}<0} , the optimal path is x ( t ) = 1 + e t ( x 0 + 1 ) {\displaystyle x(t)=-1+e^{-t\left(x_{0}+1\right)}} and for x 0 > 0 {\displaystyle x_{0}>0} , the optimal path is x ( t ) = 1 + e t ( x 0 1 ) {\displaystyle x(t)=1+e^{-t\left(x_{0}-1\right)}} .

Extensions

For further details and extensions, the reader is referred to Grass et al.5

References

References

  1. Caulkins, Jonathan P.; Grass, Dieter; Feichtinger, Gustav; Hartl, Richard F.; Kort, Peter M.; Prskawetz, Alexia; Seidl, Andrea; Wrzaczek, Stefan (2021-03-01). "The optimal lockdown intensity for COVID-19". Journal of Mathematical Economics. The economics of epidemics and emerging diseases. 93 102489. doi:10.1016/j.jmateco.2021.102489. hdl:10067/1777560151162165141. ISSN 0304-4068. PMC 7857053. PMID 33558783.
  2. Sethi, Suresh P. (2021). Optimal Control Theory. doi:10.1007/978-3-319-98237-3. ISBN 978-3-319-98236-6.
  3. Caulkins, Jonathan P.; Grass, Dieter; Feichtinger, Gustav; Hartl, Richard F.; Kort, Peter M.; Prskawetz, Alexia; Seidl, Andrea; Wrzaczek, Stefan (2022), Boado-Penas, María del Carmen; Eisenberg, Julia; Şahin, Şule (eds.), "COVID-19 and Optimal LockdownStrategies: The Effect of New and MoreVirulent Strains", Pandemics: Insurance and Social Protection, Springer Actuarial, Cham: Springer International Publishing, pp. 163–190, doi:10.1007/978-3-030-78334-1_9, hdl:10419/229887, ISBN 978-3-030-78334-1{{citation}}: CS1 maint: work parameter with ISBN (link)
  4. Caulkins, Jonathan; Grass, Dieter; Feichtinger, Gustav; Hartl, Richard; Kort, Peter M.; Prskawetz, Alexia; Seidl, Andrea; Wrzaczek, Stefan (2020-12-02). "How long should the COVID-19 lockdown continue?". PLOS ONE. 15 (12) e0243413. Bibcode:2020PLoSO..1543413C. doi:10.1371/journal.pone.0243413. ISSN 1932-6203. PMC 7710360. PMID 33264368.
  5. Grass, D.; Caulkins, J. P.; Feichtinger, G.; Tragler, G.; Behrens, D. A. (2008). Optimal Control of Nonlinear Processes: With Applications in Drugs, Corruption, and Terror. Springer. ISBN 978-3-540-77646-8.
  6. Caulkins, J. P., Grass, D., Feichtinger, G., Hartl, R. F., Kort, P. M., Prskawetz, A., Seidl, A., Wrzaczek, A. (2020). "When should the Covid-19 lockdown end?". OR News, Ausgabe 69: 10-13
  7. Zeiler, Irmgard; Caulkins, Jonathan P.; Grass, Dieter; Tragler, Gernot (2010). "Keeping Options Open: An Optimal Control Model with Trajectories That Reach a DNSS Point in Positive Time". SIAM Journal on Control and Optimization. 48 (6): 3698–3707. doi:10.1137/080719741.
  8. Caulkins, J. P.; Feichtinger, G.; Grass, D.; Tragler, G. (2009). "Optimal control of terrorism and global reputation: A case study with novel threshold behavior". Operations Research Letters. 37 (6): 387–391. doi:10.1016/j.orl.2009.07.003.
  9. I. Zeiler, J. P. Caulkins, and G. Tragler. When Two Become One: Optimal Control of Interacting Drug. Working paper, Vienna University of Technology, Vienna, Austria
  10. Carboni, Oliviero A.; Russu, Paolo (2021-06-01). "Taxation, Corruption and Punishment: Integrating Evolutionary Game into the Optimal Control of Government Policy". International Game Theory Review. 23 (2): 2050019. doi:10.1142/S021919892050019X. ISSN 0219-1989.
  11. Sethi, S.P. (1977). "Nearest Feasible Paths in Optimal Control Problems: Theory, Examples, and Counterexamples". Journal of Optimization Theory and Applications. 23 (4): 563–579. doi:10.1007/BF00933297. S2CID 123705828.
  12. Skiba, A.K. (1978). "Optimal Growth with a Convex-Concave Production Function". Econometrica. 46 (3): 527–539. doi:10.2307/1914229. JSTOR 1914229.
  13. Sethi, S. P. (1977-12-01). "Nearest feasible paths in optimal control problems: Theory, examples, and counterexamples". Journal of Optimization Theory and Applications. 23 (4): 563–579. doi:10.1007/BF00933297. ISSN 1573-2878.
  14. Sethi, S.P. (1979). "Optimal Advertising Policy with the Contagion Model". Journal of Optimization Theory and Applications. 29 (4): 615–627. doi:10.1007/BF00934454. S2CID 121398518.
  15. Sethi, S.P., "Optimal Quarantine Programmes for Controlling an Epidemic Spread," Journal of Operational Research Society, 29(3), 1978, 265-268. JSTOR 3009454 SSRN 3587573
  16. Deckert, D.W.; Nishimura, K. (1983). "A Complete Characterization of Optimal Growth Paths in an Aggregated Model with Nonconcave Production Function". Journal of Economic Theory. 31 (2): 332–354. doi:10.1016/0022-0531(83)90081-9.