An Obesity Agent Based Model: A New Decision Support System for the Obesity Epidemic

  • Ali K. Bourisly
Part of the Communications in Computer and Information Science book series (CCIS, volume 402)


An agent based model (ABM) has been designed, developed, and implemented for the obesity epidemic. The Obesity ABM has been shown to serve as a decision support system as well as for running in-silico experiments. Eight in-silico experiment were run with different experimental parameter setups. The results suggest that food prices is an effective strategy to reduce obesity compared to exercise, individual encounter, number of food source allocations, and advertisements.


agent-based model multi-agent system obesity modeling health healthcare actors 


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  1. 1.
    Wang, Y., Beydoun, M.A.: The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiologic Reviews 29, 6–28 (2007)CrossRefzbMATHGoogle Scholar
  2. 2.
    Zhang, Q., Wang, Y.: Trends in the association between obesity and socioeconomic status in U.S. adults: 1971 to 2000. Obesity Research 12, 1622–1632 (2004)CrossRefGoogle Scholar
  3. 3.
    Hammond, R.A.: Complex systems modeling for obesity research. Preventing Chronic Disease 6, A97 (2009)Google Scholar
  4. 4.
    Ogden, C.L., Carroll, M.D., Curtin, L.R., McDowell, M.A., Tabak, C.J., Flegal, K.M.: Prevalence of overweight and obesity in the United States, 1999-2004. JAMA: The Journal of the American Medical Association 295, 1549–1555 (2006)CrossRefGoogle Scholar
  5. 5.
    Bray, G., Bouchard, C., James, W.: Handbook of Obesity: Clinical Applications. Marcel Dekker, New York (1998)Google Scholar
  6. 6.
    Olshansky, S., Passaro, D., Hershow, R., Layden, J., Carnes, B., Brody, J., Hayflick, L., Butler, R., Allison, D., Ludwig, D.: A Potential Decline in Life Expectancy in the United States in the 21st Century — NEJM. The New England Journal of Medicine 352, 1138–1145 (2005)CrossRefGoogle Scholar
  7. 7.
    Bianchini, F., Kaaks, R., Vainio, H.: Overweight, obesity, and cancer risk. The Lancet Oncology 3, 565–574 (2002)CrossRefGoogle Scholar
  8. 8.
    Galea, S., Riddle, M., Kaplan, G.A.: Causal thinking and complex system approaches in epidemiology. International Journal of Epidemiology 39, 97–106 (2010)CrossRefGoogle Scholar
  9. 9.
    Glass, T.A., McAtee, M.J.: Behavioral science at the crossroads in public health: extending horizons, envisioning the future. Social Science & Medicine 62, 1650–1671 (1982)CrossRefGoogle Scholar
  10. 10.
    Finkelstein, E.A., Ruhm, C.J., Kosa, K.M.: Economic causes and consequences of obesity. Annual Review of Public Health 26, 239–257 (2005)CrossRefGoogle Scholar
  11. 11.
    Epstein, L.H., Temple, J.L., Neaderhiser, B.J., Salis, R.J., Erbe, R.W., Leddy, J.J.: Food reinforcement, the dopamine D2 receptor genotype, and energy intake in obese and nonobese humans Google Scholar
  12. 12.
    Stunkard, A., Sorensen, T., Hanis, C., Teasdale, T., Chakraborty, R., Schull, W., Schulsinger, F.: An Adoption Study of Human Obesity. The New England Journal of Medicine 314, 193–198 (1986)CrossRefGoogle Scholar
  13. 13.
    DelParigi, A., Chen, K., Salbe, A.D., Hill, J.O., Wing, R.R., Reiman, E.M., Tataranni, P.A.: Successful dieters have increased neural activity in cortical areas involved in the control of behavior. International Journal of Obesity 31, 440–448 (2005)CrossRefGoogle Scholar
  14. 14.
    Nederkoorn, C., Smulders, F.T.Y., Havermans, R.C., Roefs, A., Jansen, A.: Impulsivity in obese women. Appetite 47, 253–256 (2006)CrossRefGoogle Scholar
  15. 15.
    Lindroos, A.-K., Lissner, L., Mathiassen, M.E., Karlsson, J., Sullivan, M., Bengtsson, C., Sjöström, L.: Dietary Intake in Relation to Restrained Eating, Disinhibition, and Hunger in Obese and Nonobese Swedish Women. Obesity Research 5, 175–182 (1997)CrossRefGoogle Scholar
  16. 16.
    Temple, J.L., Legierski, C.M., Giacomelli, A.M., Salvy, S.-J., Epstein, L.H.: Overweight children find food more reinforcing and consume more energy than do nonoverweight children. The American Journal of Clinical Nutrition 87, 1121–1127 (2008)Google Scholar
  17. 17.
    Seckl, J.R., Meaney, M.J.: Glucocorticoid programming. Annals of the New York Academy of Sciences 1032, 63–84 (2004)CrossRefGoogle Scholar
  18. 18.
    Cutting, T.M., Fisher, J.O., Grimm-Thomas, K., Birch, L.L.: Like mother, like daughter: familial patterns of overweight are mediated by mothers’ dietary disinhibition. The American Journal of Clinical Nutrition 69, 608–613 (1999)Google Scholar
  19. 19.
    De Castro, J.M., Brewer, E.M., Elmore, D.K., Orozco, S.: Social facilitation of the spontaneous meal size of humans occurs regardless of time, place, alcohol or snacks. Appetite 15, 89–101 (1990)CrossRefGoogle Scholar
  20. 20.
    Booth, K.M., Pinkston, M.M., Poston, W.S.C.: Obesity and the built environment. Journal of the American Dietetic Association 105, S110–S117 (2005)Google Scholar
  21. 21.
    Papas, M.A., Alberg, A.J., Ewing, R., Helzlsouer, K.J., Gary, T.L., Klassen, A.C.: The built environment and obesity. Epidemiologic Reviews 29, 129–143 (2007)CrossRefGoogle Scholar
  22. 22.
    Cutler, D., Glaeser, E., Shapiro, J.: Why Have Americans Become More Obese? Journal of Economic Perspectives 17, 93–118 (2003)CrossRefGoogle Scholar
  23. 23.
    Smed, S., Jensen, J.D., Denver, S.: Socio-economic characteristics and the effect of taxation as a health policy instrument. Food Policy 32, 624–639 (2007)CrossRefGoogle Scholar
  24. 24.
    Schultz, W.: Getting Formal with Dopamine and Reward. Neuron 36, 241–263 (2002)CrossRefGoogle Scholar
  25. 25.
    Wise, R.A.: Brain Reward Circuitry. Neuron 36, 229–240 (2002)CrossRefGoogle Scholar
  26. 26.
    Tran, A.H., Tamura, R., Uwano, T., Kobayashi, T., Katsuki, M., Matsumoto, G., Ono, T.: Altered accumbens neural response to prediction of reward associated with place in dopamine D2 receptor knockout mice. Proceedings of the National Academy of Sciences of the United States of America 99, 8986–8991 (2002)CrossRefGoogle Scholar
  27. 27.
    Lawson, O.J., Williamson, D.A., Champagne, C.M., DeLany, J.P., Brooks, E.R., Howat, P.M., Wozniak, P.J., Bray, G.A., Ryan, D.H.: The Association of Body Weight, Dietary Intake, and Energy Expenditure with Dietary Restraint and Disinhibition. Obesity Research 3, 153–161 (1995)CrossRefGoogle Scholar
  28. 28.
    Leonard, E., Saelens, B.: Behavioral Economics of Obesity: Food Intake and Energy Expenditure. In: Reframing Health Behavior Change with Behavioral Economics, pp. 293–311. Lawrence Erlbaum Associates, New York (2000)Google Scholar
  29. 29.
    Saelens, B.E., Epstein, L.H.: Reinforcing value of food in obese and non-obese women. Appetite 27, 41–50 (1996)CrossRefGoogle Scholar
  30. 30.
    DiPietro, L.: Physical activity, body weight, and adiposity: an epidemiologic perspective. Exercise and Sport Sciences Reviews 23, 275–303 (1995)CrossRefGoogle Scholar
  31. 31.
    Trost, S.G., Owen, N., Bauman, A.E., Sallis, J.F., Brown, W.: Correlates of adults’ participation in physical activity: review and update. Medicine and Science in Sports and Exercise 34, 1996–2001 (2002)CrossRefGoogle Scholar
  32. 32.
    Feunekes, G.I., de Graaf, C., Meyboom, S., van Staveren, W.A.: Food choice and fat intake of adolescents and adults: associations of intakes within social networks. Preventive Medicine 27, 645–656Google Scholar
  33. 33.
    Morland, K., Wing, S., Diez Roux, A.: The contextual effect of the local food environment on residents’ diets: the atherosclerosis risk in communities study. American Journal of Public Health 92, 1761–1767 (2002)CrossRefGoogle Scholar
  34. 34.
    Cariani, P.: Emergence and Artificial Life. In: Langton, C., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.) Artificial Life II, pp. 775–797. Addison-Wesley (1992)Google Scholar
  35. 35.
    Gell-Mann, M.: What is Complexity? Complexity 1 (1995)Google Scholar
  36. 36.
    Fichter, L.S., Pyle, E.J., Whitmeyer, S.J.: Strategies and Rubrics for Teaching Chaos and Complex Systems Theories as Elaborating, Self-Organizing, and Fractionating Evolutionary Systems. Journal of Geoscience Education 58, 65–85 (2010)CrossRefGoogle Scholar
  37. 37.
    Fichter, L.S., Pyle, E.J., Whitmeyer, S.J.: Expanding Evolutionary Theory Beyond Darwinism with Elaborating, Self-Organizing, and Fractionating Complex Evolutionary Systems. Journal of Geoscience Education 58, 58–64 (2010)CrossRefGoogle Scholar
  38. 38.
    Kastens, K., Manduca, C., Cervato, C., Frodeman, R., Goodwin, C., Liben, L., Mogk, D., Spangler, T., Stillings, N., Titus, S.: How geoscientists think and learn. Eos Trans. AGU 90, 265–266 (2009)CrossRefGoogle Scholar
  39. 39.
    Holland, J.H.: Exploring the evolution of complexity in signaling networks. Complexity 7, 34–45 (2001)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Holland, J.H., Miller, J.H.: Artificial adaptive agents in economic theory. American Economic Review 81, 365–370 (1991)Google Scholar
  41. 41.
    Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J., Deadman, P.: Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Annals of the Association of American Geographers 93, 314–337 (2003)CrossRefGoogle Scholar
  42. 42.
    Axelrod, R.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton (1997)Google Scholar
  43. 43.
    Wang, J., Gwebu, K., Shanker, M., Troutt, M.D.: An application of agent-based simulation to knowledge sharing. Decision Support Systems 46, 532–541 (2009)CrossRefGoogle Scholar
  44. 44.
    Wilensky, U.: Netlogo (1999)Google Scholar
  45. 45.
    Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. The New England Journal of Medicine 357, 370–379 (2007)CrossRefGoogle Scholar
  46. 46.
    Andreyeva, T., Long, M., Brownell, K.: The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food. Journal Information 100 (2010)Google Scholar
  47. 47.
    Von Tigerstrom, B., Larre, T., Sauder, J.: Using the Tax System to Promote Physical Activity: Critical Analysis of Canadian Initiatives. American Journal of Public Health 101, e10–e16 (2011)Google Scholar
  48. 48.
    Fowler, J.H., Christakis, N.A.: Estimating peer effects on health in social networks: a response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, and Pais. Journal of Health Economics 27, 1400–1405 (2008)CrossRefGoogle Scholar
  49. 49.
    Veenstraa, G., Luginaahb, A., Wakefield, S., Birchd, S., Eyles, J., Elliott, S.: Who you know, where you live: social capital, neighbourhood and health. Social Science & Medicine 60, 2779–2818 (2005)Google Scholar
  50. 50.
    Kernper, K.A., Sargent, R.G., Drane, J.W., Valois, R.E., Hussey, J.R.: Black and White Females’ Perceptions of Ideal Body Size and Social Norms. Obesity Research 2, 117–126 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ali K. Bourisly
    • 1
  1. 1.Biomedical Engineering Unit, Department of Physiology, Faculty of MedicineKuwait UniversityKuwait

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