- freely available
Safety 2019, 5(3), 52; https://doi.org/10.3390/safety5030052
2. Background and Motivation
3. The Existing and Proposed Models
3.1. Models Overview
3.2. Knowles Model for Food Safety Inspections
- CONSUME and HEAL: A healthy consumer consumes food from a store. However, the consumer has to wait to get healthy, if he/she has consumed contaminated food. A consumer randomly selects an uncontaminated store in his/her proximity—within the accessible range, which is neither declared contaminated by the store/regulators nor listed in the list of bad stores based on the previous experience of the consumer. The uncontaminated stores are listed as good stores. The consumer is declared sick when he/she consumes food, whose contamination—a randomly selected plausible health hazard value, exceeds his/her immunity level. Consequently, the store is added to the list of bad stores. A sick consumer performs the healing process when his/her healing counter is less than the recommended threshold. Figure 4a illustrates the whole process of CONSUME AND HEAL operations.
- SPREAD and SIGNAL: A store may randomly become contaminated. When such a situation arises, the store may decide to use the SIGNAL process to indicate the regulators about its state being contaminated. Figure 4b illustrates the SPREAD AND SIGNAL processes.
- TEST: A regulator visits a randomly selected store, among the signaling stores, within his/her proximity. The proximity (the assigned accessible range) of a regulator is usually more than the proximity of a consumer. Otherwise, the regulator visits a randomly selected store in his/her proximity. The store, in response to a regulator’s visit, becomes sterilized. Figure 4c provides the Test procedure.
3.3. The Extended Knowles Model
3.4. The Proposed Model
- total connections depend on the connectivity index—the percentage of people connected to each other.
- many connections are local which depends on local connections index.
- the remaining connections, quite a few in number, are remote.
4. Model Simulation and Results
- Case 1: represents the Knowles’s Model but without using the Signaling option.
- Case 2: represents the Knowles’s Model using the Signaling option.
- Case 3: includes the UPGRADE process, and the concept of progressive improvement or degradation of food quality.
- Case 4: considers the UPGRADE process along with the impact of crowd on progressive quality improvement by the vigilant stores only.
- Case 5: considers the UPGRADE process along with the impact of crowd on progressive quality improvement by all stores.
- Case 5A: is the same as Case 5.
- Case 5B: Case 5 but without the wandering process.
- Case 5C: uses friends network when no store is found in the consumer’s proximity.
4.2. Analysis and Discussion
- Sick Consumers: represents the number of sick consumers at a given time.
- Contaminated Stores: provides the number of contaminated stores at a given time.
- Sterilized Stores: represents the number of sterilized stores at a given time.
- Stores Usage: represented in terms of, average value and standard deviation, based on 100 executions for a given scenario.
Conflicts of Interest
- Marks, A.B. A New Governance Recipe for Food Safety Regulation. Loyola Univ. Chic. Law J. 2015, 47, 907. [Google Scholar]
- Song, C.; Zhuang, J. Regulating food risk management—A government–manufacturer game facing endogenous consumer demand. Int. Trans. Oper. Res. 2018, 25, 1855–1878. [Google Scholar] [CrossRef]
- da Silva Farias, A.; Akutsu, R.D.C.C.D.; Botelho, R.B.A.; Zandonadi, R.P. Good Practices in Home Kitchens: Construction and Validation of an Instrument for Household Food-Borne Disease Assessment and Prevention. Int. J. Environ. Res. Public Health 2019, 16, 1005. [Google Scholar] [CrossRef] [PubMed]
- Wallace, C.A.; Sperber, W.H.; Mortimore, S.E. Food Safety for the 21st Century: Managing HACCP and Food Safety throughout the Global Supply Chain; John Wiley & Sons: Hoboken, NJ, USA, 2018. [Google Scholar]
- McPhee-Knowles, S. Growing Food Safety from the Bottom Up: An Agent-Based Model of Food Safety Inspections. J. Artif. Soc. Soc. Simul. 2015, 18, 9. [Google Scholar] [CrossRef]
- Schlenker, W.; Villas-Boas, S.B. Consumer and market responses to mad cow disease. Am. J. Agric. Econ. 2009, 91, 1140–1152. [Google Scholar] [CrossRef]
- Bleda, M.; Shackley, S. Simulation modelling as a theory building tool: The formation of risk perceptions. J. Artif. Soc. Soc. Simul. 2012, 15, 2. [Google Scholar] [CrossRef]
- Birk-Urovitz, E. The 2008 Canadian listeriosis outbreak: A result of knowledge ignored. Mcmaster Univ. J. Med. 2011, 8, 65–67. [Google Scholar]
- Carrillo, C.D.; Koziol, A.; Vary, N.; Blais, B.W. Applications of Genomics in Regulatory Food Safety Testing in Canada. In New Insight into Salmonella, Listeria and E. coli Infections; IntechOpen: London, UK, 2019. [Google Scholar]
- Thanh, T.N.C. Food Safety Behavior, Attitudes and Practices of Street Food Vendors and Consumers in Vietnam. Master’s Thesis, Ghent University, Ghent, Belgium, 2015. [Google Scholar]
- Zhang, H.; Gao, N.; Wang, Y.; Han, Y. Modeling risk governance and risk perception in personal prevention with regard to food safety issues. Br. Food J. 2018, 120, 2804–2817. [Google Scholar] [CrossRef]
- Kher, S.V.; De Jonge, J.; Wentholt, M.T.; Deliza, R.; de Andrade, J.C.; Cnossen, H.J.; Luijckx, N.B.L.; Frewer, L.J. Consumer perceptions of risks of chemical and microbiological contaminants associated with food chains: A cross-national study. Int. J. Consum. Stud. 2013, 37, 73–83. [Google Scholar] [CrossRef]
- Bredahl, L. Determinants of consumer attitudes and purchase intentions with regard to genetically modified food–results of a cross-national survey. J. Consum. Policy 2001, 24, 23–61. [Google Scholar] [CrossRef]
- Lazer, D.; Pentland, A.S.; Adamic, L.; Aral, S.; Barabasi, A.L.; Brewer, D.; Christakis, N.; Contractor, N.; Fowler, J.; Gutmann, M.; et al. Life in the network: The coming age of computational social science. Science 2009, 323, 721. [Google Scholar] [CrossRef] [PubMed]
- Polhill, J.G.; Ge, J.; Hare, M.P.; Matthews, K.B.; Gimona, A.; Salt, D.; Yeluripati, J. Crossing the chasm: A ‘tube-map’for agent-based social simulation of policy scenarios in spatially-distributed systems. GeoInformatica 2019, 23, 169–199. [Google Scholar] [CrossRef]
- Utomo, D.S.; Onggo, B.S.; Eldridge, S. Applications of agent-based modelling and simulation in the agri-food supply chains. Eur. J. Oper. Res. 2017, 269, 794–805. [Google Scholar] [CrossRef]
- Surowiecki, J. The Wisdom of Crowds; Anchor: San Diego, CA, USA, 2005. [Google Scholar]
- Bozkurt, I.; Padilla, J.J. On the Epistemological, Ontological, Teleological and Methodological Currents in Modeling and Simulation: An Overview. Int. J. Agent Technol. Syst. (IJATS) 2013, 5, 1–18. [Google Scholar] [CrossRef]
- Zia, K.; Saini, D.K.; Farooq, U.; Ferscha, A. Web of Social Things: Socially-Influenced Interaction Modeling. In Proceedings of the 15th International Conference on Advances in Mobile Computing & Multimedia, Salzburg, Austria, 4–6 December 2017; pp. 123–130. [Google Scholar]
- Zafar, M.; Zia, K.; Saini, D.K.; Muhammad, A.; Ferscha, A. Modeling human factors influencing herding during evacuation. Int. J. Pervasive Comput. Commun. 2017, 13, 211–234. [Google Scholar] [CrossRef]
- Zia, K.; Ferscha, A.; Din, A.; Shahzad, K.; Majeed, A. Impact of ICT-mediated collective awareness on urban mobility. Complex Adapt. Syst. Model. 2016, 4, 10. [Google Scholar] [CrossRef]
- Zia, K.; Shaheen, M.; Farooq, U.; Nazir, S. Conditions of Depleting Offender Behavior in Volunteering Dilemma: An Agent-Based Simulation Study. In Proceedings of the International Conference on Simulation of Adaptive Behavior, Aberystwyth, UK, 23–26 August 2016; pp. 352–363. [Google Scholar]
- Ge, H.; Nolan, J.; Gray, R.; Goetz, S.; Han, Y. Supply chain complexity and risk mitigation–A hybrid optimization–simulation model. Int. J. Prod. Econ. 2016, 179, 228–238. [Google Scholar] [CrossRef]
- Verwaart, T.; Valeeva, N.I. An agent-based model of food safety practices adoption. In Emergent Results of Artificial Economics; Springer: Berlin/Heidelberg, Germany, 2011; pp. 103–114. [Google Scholar]
- Talley, J.B. Modeling Individual Consumer Food Contamination Progression with Interventions. Ph.D. Thesis, North Carolina Agricultural and Technical State University, Greensboro, NC, USA, 2016. [Google Scholar]
- Eck, J.E. Examining routine activity theory: A review of two books. Justice Q. 1995, 12, 783–797. [Google Scholar] [CrossRef]
- Caskey, T.R.; Wasek, J.S.; Franz, A.Y. Deter and protect: Crime modeling with multi-agent learning. Complex Intell. Syst. 2018, 4, 155–169. [Google Scholar] [CrossRef]
- Zia, K.; AlBadi, K.; Saini, D.K.; Muhammad, A. Conditions leading towards a more robust food safety system: The results of an agent-based social simulation. In Proceedings of the 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kiev, Ukraine, 24–27 May 2018. [Google Scholar]
- Railsback, S.F.; Grimm, V. Agent-Based and Individual-Based Modeling: A Practical Introduction; Princeton University Press: Princeton, NJ, USA, 2019. [Google Scholar]
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).