Skip to main content

Advertisement

Log in

A review of the use of optimal control in social models

  • Review Paper
  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

Optimal control is a powerful optimization technique derived from the mathematical theory of the Calculus of Variations. It can be employed to maximize the returns from and minimize the costs associated with physical, social, and economic processes. In recent years, optimal control theory has been utilized to develop ideal intervention strategies for a variety of “contagious” social ailments that are spread chiefly by contact with affected peers—like crime, substance abuse and the rampant infiltration of internet worms and viruses in computerized systems. As the dynamics of these processes are akin to that of an epidemic, the compartmental models utilized for studying the spread of infectious diseases can be easily adapted for these types of problems. In this article, we review the use of optimal control theory in the design of cost effective intervention strategies for the successful mitigation of social contagion processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Camacho ET (2013) The development and interaction of terrorist and fanatic groups. Commun Nonlinear Sci Numer Simul 18(11):3086–3097

    Article  MathSciNet  Google Scholar 

  2. Campbell M, Ormerod P (1997) Social interaction and the dynamics of crime. Technical report, Volterra Consulting Ltd

  3. Castillo-Chavez C, Song B (2003) Bioterrorism: mathematical modeling applications in homeland security. In: Banks HT, Castillo-Chavez C (eds) Chapter: Models for the transmission dynamics of fanatic behaviors. SIAM, Philadelphia, pp 55–172

  4. Sooknanan J, Bhatt BS, Comissiong DMG (2013) Catching a gang: a mathematical model of the spread of gangs in a population treated as an infectious disease. Int J Pure Appl Math 83(1):25–43

    Article  Google Scholar 

  5. Sooknanan J, Bhatt BS, Comissiong DMG (2012) Life and death in a gang: a mathematical model of gang membership. J Math Res 4(4):10–27

    MathSciNet  Google Scholar 

  6. Sooknanan J, Comissiong DMG (2017) A mathematical model for the treatment of delinquent behavior. Socio-Econ Plan Sci. https://doi.org/10.1016/j.seps.2017.08.001

    Article  Google Scholar 

  7. Mushayabasa S (2017) Modeling optimal intervention strategies for property crime. Int J Dyn Control 5(3):832–841

    Article  MathSciNet  Google Scholar 

  8. Lee S, Jung E, Castillo-Chavez C (2010) Optimal control intervention strategies in low- and high-risk problem drinking populations. Socio-Econ Plan Sci 44(4):258–265

    Article  Google Scholar 

  9. Mulone G, Straughan B (2012) Modeling binge drinking. Int J Biomath 05(01):1250005

    Article  MathSciNet  Google Scholar 

  10. Mushayabasa S (2015) The role of optimal intervention strategies on controlling excessive alcohol drinking and its adverse health effects. J Appl Math 2015, Article ID 238784

  11. Kubo M, Naruse K, Sato H, Matubara T (2007) The possibility of an epidemic meme analogy for web community population analysis. Springer, Berlin, pp 1073–1080

    Google Scholar 

  12. Woo J, Chen H (2016) Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus 5:66

    Article  Google Scholar 

  13. Kribs-Zaleta CM (2013) Sociological phenomena as multiple nonlinearities: MTBI’s new metaphor for complex human interactions. Math Biosci Eng 10(5–6):1587–1607

    Article  MathSciNet  Google Scholar 

  14. Buonomo B, Lacitgnola D, Vargas-De-Leon C (2014) Qualitative analysis and optimal control of an epidemic model with vaccination and treatment. Math Comput Simul 100:88–102

    Article  MathSciNet  Google Scholar 

  15. Di Liddo A (2016) Optimal control and treatment of infectious diseases. The case of huge treatment costs. Mathematics 4(2):21

    Article  Google Scholar 

  16. Sharomi O, Malik T (2015) Optimal control in epidemiology. Ann Oper Res 1(251):55–71

    MathSciNet  MATH  Google Scholar 

  17. Hansen E, Day T (2011) Optimal control of epidemics with limited resources. J Math Biol 62(3):423–451

    Article  MathSciNet  Google Scholar 

  18. Rowthorn R, Walther S (2017) The optimal treatment of an infectious disease with two strains. J Math Biol 74(7):1753–1791

    Article  MathSciNet  Google Scholar 

  19. Grass D, Caulkins JP, Feichetinger G, Tragler G, Behrens DA (2008) Optimal control of nonlinear processes with applications in drugs, corruption and terror. Springer, Berlin

    Book  Google Scholar 

  20. Sargent RWH (2000) Optimal control. J Comput Appl Math 124:361–371

    Article  MathSciNet  Google Scholar 

  21. Nie T, Shi J, Wu Z (2016) Connection between MP and DPP for stochastic recursive optimal control processes: viscosity solution framework in local case. In: American control conference (ACC). IEEE, Boston, pp 7225–7230

  22. Fleming WH, Rishel RW (1975) Deterministic and stochastic optimal control. Springer, New York

    Book  Google Scholar 

  23. Pontryagin LS, Boltyanskii VT, Gamkrelidze RV, Mishcheuko EF (1962) The mathematical theory of optimal control processes. Wiley, New York

    Google Scholar 

  24. Babor T (2010) Drug policy and the public good. Oxford University Press, Oxford

    Google Scholar 

  25. Reuter P (2006) What drug policies cost: estimating government drug policy expenditures. Addiction 101(3):315–322

    Article  Google Scholar 

  26. United Nations Office on Drugs and Crime, International Standards on Drug Use Prevention, online. https://www.unodc.org/documents/prevention/UNODC_2013_2015_international_standards_on_drug_use_prevention_E.pdf

  27. Rosenquist JN, Murabito J, Fowler JH, Christakis NA (2010) The spread of alcohol consumption behavior in a large social network. Ann Intern Med 152(7):426–433

    Article  Google Scholar 

  28. Studer J, Baggio S, Deline S, N’Goran AA, Henchoz Y, Mohler-Kuo M, Daeppen J, Gmel G (2014) Peer pressure and alcohol use in young men: a mediation analysis of drinking motives. Int J Drug Policy 25(4):700–708

    Article  Google Scholar 

  29. Strickland JC, Smith MA (2014) The effects of social contact on drug use: behavioral mechanisms controlling drug intake. Exp Clin Psychopharmacol 22(1):23–34

    Article  Google Scholar 

  30. Choi S, Lee J, Jung E (2014) Optimal strategies for prevention of ecstasy use. J Korea Soc Ind Appl Math 18(1):1–15

    Article  MathSciNet  Google Scholar 

  31. Song B, Castillo-Chavez C (2006) Raves, clubs and ecstasy: the impact of peer pressure. Math Biosci Eng 3(1):249–266

    MathSciNet  MATH  Google Scholar 

  32. Mushayabasa S, Tapedzesa G (2015) Modeling illicit drug use dynamics and its optimal control analysis. Comput Math Methods Med 2015, Article ID 383154, 11 pages

  33. Haw C, Hawton K, Houston K, Townsend E (2001) Psychiatric and personality disorders in deliberate self-harm patients. Br J Psychiatry 178(1):48–54

    Article  Google Scholar 

  34. Muehlenkamp JJ, Claes L, Havertape L, Plener PL (2012) International prevalence of adolescent non-suicidal self-injury and deliberate self-harm. Child Adolesc Psychiatry Mental Health 6(1):10

    Article  Google Scholar 

  35. Kim BN, Masud MA, Kim Y (2014) Optimal implementation of intervention to control the self-harm epidemic. Osong Public Health Res Perspect 5(6):315–323

    Article  Google Scholar 

  36. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. New Engl J Med 357(4):370–379

    Article  Google Scholar 

  37. Oh C, Masud MA (2015) Optimal intervention strategies for the spread of obesity. J Appl Math 2015, Article ID 217808, 9 pages

  38. Aldila D, Rarasati N, Nuraini N, Soewono E (2014) Optimal control problem of treatment for obesity in a closed population. Int J Math Math Sci 2014, Article ID 273037, 7 pages

  39. CBS News Report, Online (2017). https://www.cbsnews.com/news/wannacry-ransomware-attacks-wannacry-virus-losses/security-7252335/?ito=cbshare

  40. Yan X, Zou Y (2008) Optimal internet worm treatment strategy based on the two-factor model. ETRI J 30(1):81–88

    Article  Google Scholar 

  41. Chen L, Hattaf K, Sun J (2015) Optimal control of a delayed SLBS computer virus model. Physica A 427:244–250

    Article  MathSciNet  Google Scholar 

  42. Bi J, Yang X, Wu Y, Xiong Q, Wen J, Tang YY (2017) On the optimal dynamic control strategy of disruptive computer virus. Discrete Dyn Nat Soc 2017, Article ID 8390784, 14 pages

  43. Kandhway K, Kuri J (2014) Optimal control of information epidemics modeled as Maki Thompson rumors. Commun Nonlinear Sci Numer Simul 19(12):4135–4147

    Article  Google Scholar 

  44. Huo L, Lin T, Fan C, Liu C, Zhao J (2015) Optimal control of a rumor propagation model with latent period in emergency event. Adv Differ Equ 2015:54

    Article  MathSciNet  Google Scholar 

  45. Sooknanan J, Bhatt BS, Comissiong DMG (2013) Another way of thinking: A review of mathematical models of crime. Math Today 131–133. https://pdfs.semanticscholar.org/8d68/5fa908f1fc4933adf8d4806bf82035bc427e.pdf

  46. Castillo-Chavez C, Lee S (2011) Epidemiology modeling in the life and social sciences, CiteSeerX

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. M. G. Comissiong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Comissiong, D.M.G., Sooknanan, J. A review of the use of optimal control in social models. Int. J. Dynam. Control 6, 1841–1846 (2018). https://doi.org/10.1007/s40435-018-0405-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-018-0405-3

Keywords

Navigation