{"title":"优化应急卫生服务政策的社会模拟","authors":"M. Calado, Luís Antunes, Ana Ramos","doi":"10.1109/ISTAFRICA.2014.6880612","DOIUrl":null,"url":null,"abstract":"Very often, public policies can only be evaluated after they have been implemented and deployed. The prior simulation of these policies can ensure several benefits: the design can be more accurately adjusted to the objectives of the policy makers; the policies can better reflect the motivations of the individuals involved in several roles (in the case of health services: users, doctors, nurses, civil servants, auditors, policy makers); the micro-macro connections and mediations are represented explicitly; the simulation may allow the successive improvement of the policies; the decision makers and stakeholders may get to know the territory of decision in such a way to better respond in contingency situations. We propose multi-agent-based simulation as a form of orienting the specification of policies. The multi-agent systems allow for the representation of heterogeneous rational agents and provide an approach to create complex dynamic models of social phenomena. This paper describes how we can tackle the problem of optimization of the policies of medical emergency services, in the case when there is a clear distinction between the design of these policies and the use that people give them. We present the scenario and a model for the simulation, identifying the actors involved, the connections and relationships between them, the measures needed to evaluate the multi-dimensional results of the simulation and how the policies can be fine-tuned and simulated before they are deployed in the real world.","PeriodicalId":248893,"journal":{"name":"2014 IST-Africa Conference Proceedings","volume":"41 12","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Social simulation for optimization of emergency health services policy\",\"authors\":\"M. Calado, Luís Antunes, Ana Ramos\",\"doi\":\"10.1109/ISTAFRICA.2014.6880612\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Very often, public policies can only be evaluated after they have been implemented and deployed. The prior simulation of these policies can ensure several benefits: the design can be more accurately adjusted to the objectives of the policy makers; the policies can better reflect the motivations of the individuals involved in several roles (in the case of health services: users, doctors, nurses, civil servants, auditors, policy makers); the micro-macro connections and mediations are represented explicitly; the simulation may allow the successive improvement of the policies; the decision makers and stakeholders may get to know the territory of decision in such a way to better respond in contingency situations. We propose multi-agent-based simulation as a form of orienting the specification of policies. The multi-agent systems allow for the representation of heterogeneous rational agents and provide an approach to create complex dynamic models of social phenomena. This paper describes how we can tackle the problem of optimization of the policies of medical emergency services, in the case when there is a clear distinction between the design of these policies and the use that people give them. We present the scenario and a model for the simulation, identifying the actors involved, the connections and relationships between them, the measures needed to evaluate the multi-dimensional results of the simulation and how the policies can be fine-tuned and simulated before they are deployed in the real world.\",\"PeriodicalId\":248893,\"journal\":{\"name\":\"2014 IST-Africa Conference Proceedings\",\"volume\":\"41 12\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IST-Africa Conference Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISTAFRICA.2014.6880612\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IST-Africa Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISTAFRICA.2014.6880612","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Social simulation for optimization of emergency health services policy
Very often, public policies can only be evaluated after they have been implemented and deployed. The prior simulation of these policies can ensure several benefits: the design can be more accurately adjusted to the objectives of the policy makers; the policies can better reflect the motivations of the individuals involved in several roles (in the case of health services: users, doctors, nurses, civil servants, auditors, policy makers); the micro-macro connections and mediations are represented explicitly; the simulation may allow the successive improvement of the policies; the decision makers and stakeholders may get to know the territory of decision in such a way to better respond in contingency situations. We propose multi-agent-based simulation as a form of orienting the specification of policies. The multi-agent systems allow for the representation of heterogeneous rational agents and provide an approach to create complex dynamic models of social phenomena. This paper describes how we can tackle the problem of optimization of the policies of medical emergency services, in the case when there is a clear distinction between the design of these policies and the use that people give them. We present the scenario and a model for the simulation, identifying the actors involved, the connections and relationships between them, the measures needed to evaluate the multi-dimensional results of the simulation and how the policies can be fine-tuned and simulated before they are deployed in the real world.