Suboptimal Restraint Use as an Emergent Norm via Social Influence
Suboptimal restraint use is a prevalent problem worldwide. In developed countries injuries and deaths related to vehicle accidents persist despite increases in restraint use. In this study we investigate the emergence of patterns of restraint use in groups of agents and the population at large. Using age as an influential factor we simulate random encounters between group members where dominant individuals repeatedly alter the knowledge of less influential individuals. Belief spaces implemented as part of a cultural algorithm are used to preserve prevalent patterns of restraint use both at the group and population levels. The objective is to demonstrate restraint selection and use patterns emerging within a population and to determine whether a focus on influential members might have a positive effect towards optimal restraint use. We demonstrate that prominent patterns of behavior similar to the influential members of the groups do emerge both in the presence of social and cultural influence.
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- 2.Blair, J., Perdios, A., Babul, S., Young, K., Beckles, J., Pike, I., Cripton, P., Sasgese, D., Mulpuri, K., Desapriya, E.: The appropriate and inappropriate use of child restraint seats in Manitoba. International Journal of Injury Control and Safety Promotion 15(3), 151–156 (2008)CrossRefGoogle Scholar
- 3.Brown, J., McCaskill, M.E., Henderson, M., Bilston, L.E.: Serous injury is associated with suboptimal restraint use in child motor vehicle occupants. Journal of Paediatrics 42, 345–349 (2006)Google Scholar
- 5.Bulger, E.M., Kaufman, R., Mock, C.: Childhood crash injury patterns associated with restraint misuse: implications for field triage. Prehospital and Disaster Medicine 23(1), 9–15 (2008)Google Scholar
- 6.Feldman, D.C.: The development and enforcement of group norms. The Academy of Management Review 9(1), 47–53 (1984)Google Scholar
- 7.Howard, A.: Automobile restraints for children: a review for clinicians. Canadian Medical Association Journal 167, 769–773 (2002)Google Scholar
- 8.Kobti, Z., Snowdon, A., Rahaman, S., Dunlop, T., Kent, R.: A cultural algorithm to guide driver learning in applying child vehicle safety restraint. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1111–1118 (2006)Google Scholar
- 9.Mokom, F., Kobti, Z.: Evolution of artifact capabilities. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 476–483. IEEE Press, New Orleans (2011)Google Scholar
- 10.Neumann, M.: Homo socionicus: a case study of simulation models of norms. Journal of Artificial Societies and Social Simulation 11(4-6) (2008), http://jass.soc.surrey.ac.uk/11/4/6.html
- 12.Peden, M., Oyegbite, K., Ozanne-Smith, J., Hyder, A.A., Branche, C., Rahman, A.F., Rivara, F., Bartolomeos, K. (eds.): World report on child injury prevention. World Health Organization (2008)Google Scholar
- 13.Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A.A., Jarawan, E., Mathers, C. (eds.): World report on road traffic injury prevention. World Health Organization (2004)Google Scholar
- 14.Reynolds, R.G.: An adaptive computer model of the evolution of agriculture for hunter-gatherers in the valley of oaxaca. Ph.D. thesis, Dept. of Computer Science, University of Michigan (1979)Google Scholar