Commenced in January 2007
Paper Count: 32451
Public Economic Efficiency and Case-Based Reasoning: A Theoretical Framework to Police Performance
Abstract:At present, public efficiency is a concept that intends to maximize return on public investment focus on minimizing the use of resources and maximizing the outputs. The concept takes into account statistical criteria drawn up according to techniques such as DEA (Data Envelopment Analysis). The purpose of the current work is to consider, more precisely, the theoretical application of CBR (Case-Based Reasoning) from economics and computer science, as a preliminary step to improving the efficiency of law enforcement agencies (public sector). With the aim of increasing the efficiency of the public sector, we have entered into a phase whose main objective is the implementation of new technologies. Our main conclusion is that the application of computer techniques, such as CBR, has become key to the efficiency of the public sector, which continues to require economic valuation based on methodologies such as DEA. As a theoretical result and conclusion, the incorporation of CBR systems will reduce the number of inputs and increase, theoretically, the number of outputs generated based on previous computer knowledge. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 486
 Afonso, A., Schuknecht, L., & Tanzi, V., “Public sector efficiency: an international comparison”. Public choice 123(3-4), 321-347 (2005).
 Getie Mihret, D., & Wondim Yismaw, A., “Internal audit effectiveness: an Ethiopian public sector case study” 22(5), 470-484 (2007).
 Mandl, U., Dierx, A., & Ilzkovitz, F., “The effectiveness and efficiency of public spending (no. 301)”. Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
 Riddiough, M.A., Sisk, J.E., & Bell, J.C., “Influenza vaccination: cost-effectiveness and public policy”. Jama 249(23), 3189-3195 (1983).
 Jackson, G.A., “Public efficiency and private choice in higher education”. Educational evaluation and policy analysis 4(2), 237-247 (1982).
 García Sánchez, I.M., Rodríguez-Domínguez, L., & Domínguez, J.P., “Evaluation of the efficacy and effectiveness of the Spanish security forces”. European Journal of Law and Economics 36(1), 57-75 (2013).
 García Sánchez, I.M., Rodríguez-Domínguez, L., & Domínguez, J.P., “Yearly evolution of police efficiency in Spain and explanatory factors”. Central European Journal of Operations Research 21(1), 31-62 (2013).
 Thanassoulis, E., “Assessing police forces in England and Wales using data envelopment analysis”. European Journal of Operational Research 87(3), 641-657 (1995).
 Collier, P.M., “In search of purpose and priorities: Police Performance indicators in England and Wales. Public Money and Management 26(3), 165-172 (2006).
 Simar, L. & Wilson, P.W.: A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics 27(6), 779-802 (2000).
 Gottschalk, P., “Stages of knowledge management systems in police investigations”. Knowledge-Based Systems 19(6), 381-387 (2006).
 Charnes, A., Cooper, W.W., & Rhodes, E., “Measuring the efficiency of decision-making units”. European Journal of Operational Research 2(6), 429-444 (1978).
 Banker, R. D., Charnes, A., & Cooper, W. W., “Some models for estimating technical and scale inefficiencies in data envelopment analysis”. Management science, 30(9), 1078-1092 (1984).
 Simar, L. & Wilson, P.W., “Statistical inference in nonparametric frontier models: The state of the art”. Journal of Productivity Analysis 13(1), 49-78 (2000).
 Simar, L. & Wilson, P.W.: Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics 136(1), 31-64 (2007).
 Aamodt, A., “Explanation-driven case-based reasoning”. In: European Workshop on Case-Based Reasoning, pp. 274-288. Springer, Berlin (1993).
 Kolodner, J.L., “An Introduction to Case-Based Reasoning”. Artificial Intelligence Review 6, 3-34 (1992).
 Greene, D., Freyne, J., Smyth, B., & Cunningham, P., “An analysis of current trends in CBR research using multi-view clustering”. AI Magazine31(2),45-61(2010).
 Marling, C., Rissland, E., & Aamodt, A., “Integrations with case-based reasoning”. The Knowledge Engineering Review 20(3), 241–245 (2005).
 desJardins, M., Francis, A., & Wolverton, M., “Hybrid planning: An approach to integration generative and case-based planning”. In: Working Notes of the AAAI-98 Workshop on Case-Based Reasoning Integrations. (1998).
 Muñoz-Avila, H., McFarlane, D.C., Aha. D.W., Breslow, L., Ballas, J.A., & Nau, D.S., “Using guidelines to constrain interactive case-based HTN planning”. In: International Conference on Case-Based Reasoning, pp. 288-302. Springer, Berlin. (1999).
 Craw, S., & Aamodt, A., “Case Based Reasoning as a model for Cognitive Intelligence, In: International Conference on Case-Based Reasoning, pp. 62-77. Springer, Cham. (2018).
 Ribaux, O., & Margot, P., “Case based reasoning in criminal intelligence using forensic case data”. Science & Justice 43(3), 135-143 (2003).
 Horsman, G., Laig, C., & Vickers, P., “A case-based reasoning method for locating evidence during digital forensic device triage”. Decision Support Systems 61, 69-78 (2014).
 Skalle, P., Sveen, J., & Aamodt, A.: Improved efficiency of oil well drilling through case-based reasoning. In: Pacific Rim International Conference on Artificial Intelligence, pp. 712-722. Springer, Berlin. (2000).
 Luen, T.W. & AI-Hawamdeh, S., “Knowledge management in the public sector: principles and practices in police work”. Journal of Information Science 27(5), 311-318 (2001).
 Lin, C., Hu, P.J.H., & Chen, H., “Technology implementation management in law enforcement: COPLINK system usability and user acceptance evaluations”. Social Science Computer Review 22(1), 24-36 (2004).
 Chen, H., Atabakhsh, H., Tseng, C., Marshall, B., Kaza, S., Eggers, S., … & Violette, C., “Visualization in law enforcement”. In: CHI’05 extended abstracts on Human factors in computing systems, pp. 1268-1271. ACM. (2005).
 Richter, M.M., “Knowledge containers. Readings in Case-Based Reasoning”. Morgan Kaufman Publishers (2003).
 López-Sánchez, D., Corchado, J.M., & Arrieta, A.G., “Dynamic detection of radical profiles in social networks using image feature descriptors and a Case-Based reasoning methodology”. In: International Conference on Case-Based Reasoning, pp. 219-232. Springer, Cham. (2018).
 Wijekoon, A., Wiratunga, N., Sani, S., Massie, S., & Cooper, K., “Improving kNN for Human Activity recognition with priviledge learning using translation models”. In: International Conference on Case-Based Reasoning, pp. 448-463. Springer, Cham. (2018).
 Veloso, M.M., & Carbonell, J.G., “Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization”. In: Case-Based Learning, pp. 55-84. Springer, Boston (1993).
 Wang, W.M., Cheung, C.F., Lee, W.B., & Kwok, S.K., “Knowledge-based treatment planning for adolescent early intervention of mental healthcare: a hybrid case-based reasoning approach”. Expert System 24(4), 232-251 (2007).
 Kolodner, J.L., Cox, M.T., & González-Calero, P.A., “Case-based reasoning-inspired approaches to education”. The Knowledge Engineering Review 20(3), 299-303 (2005).