Heuristic-Based Decision Models for Complex System Classification: From Tourism to Healthcare

Shengyu Gu

doi:10.63313/AERpc.9116

Authors

Shengyu Gu School of Geography and Tourism, Huizhou University, Huizhou, China Author

DOI:

https://doi.org/10.63313/AERpc.9116

Keywords:

Complex Systems, Heuristic Decision-Making, Classification Models, Management Science, Decision Support Systems, Tourism Governance, Healthcare Administration, High-Dimensional Data, Institutional Decision-Making

Abstract

Classification in complex systems is not merely a technical or statistical task, but a managerial decision process shaped by uncertainty, high-dimensional information, and institutional constraints. This study conceptualizes classification as a form of heuristic decision-making embedded within complex systems, drawing on theories of bounded rationality, complex adaptive systems, and decision support systems. By comparing tourism management and healthcare governance, the research demonstrates that destination categorization, tourist segmentation, and disease risk stratification share the same structural decision challenges, including information overload, nonlinearity, time pressure, and stakeholder conflicts. Heuristic-based models such as genetic algorithms, ant colony optimization, particle swarm optimization, and rule-based decision rules are examined not as computational tools, but as managerial mechanisms that simplify complexity, enhance interpretability, and support timely action. Using real and verifiable datasets from global tourism and public health systems, the study evaluates classification performance through decision quality indicators, including reliability, stability, interpretability, and governance usability rather than domain-specific accuracy metrics. The findings confirm that heuristic classification models provide robust, transparent, and actionable support for policy modeling, resource planning, and institutional coordination across domains. Healthcare is therefore positioned as an application context rather than a defining research identity. The core contribution lies in establishing heuristic classification as a transferable management science methodology for complex system decision support.

References

[1] Butler, R. (2025). Tourism destination development: the tourism area life cycle model. Tourism Geographies, 27(3-4), 599-607.

[2] Davidson, A., Angulo, F. J., Davis, J., Halsby, K., Brestrich, G., Moïsi, J. C., & Stark, J. H. (2025). Comparison of the number of Lyme neuroborreliosis cases in European Centre for Disease Prevention and Control (ECDC) data and national public surveillance reports, 2018-2023. Ticks and Tick-borne Diseases, 16(6), 102542.

[3] Dorigo, M., & Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA.

[4] Dort, E., Rud, H., Billion, T., & Tauseef, A. (2025). Trends in pulmonary embolism mortality in cancer patients in the United States from 1999-2022–A CDC Wonder database study. Respiratory research, 26(1), 248.

[5] Galli, E. (2011). Kahneman, D., Thinking, Fast and Slow. Journal of Public Finance and Public Choice, 29(1-3), 214-215.

[6] Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.

[7] Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62(2011), 451-482.

[8] Gigerenzer, G., & Selten, R. (Eds.). (2002). Bounded rationality: The adaptive toolbox. MIT press.

[9] Gigerenzer, G., Todd, P. M., & ABC Research Group, T. (2000). Simple heuristics that make us smart. Oxford University Press.

[10] Grzenkowicz, A. (2025). Tourism and Inequality in the European Union: Exploring the Determinants of Tourism Spending through a Multivariate Approach. Journal of Environmental Management and Tourism (JEMT), 16(3 (79)), 287-300.

[11] Haklay, M., & Weber, P. (2008). Openstreetmap: User-generated street maps. IEEE Pervasive computing, 7(4), 12-18.

[12] Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.

[13] Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.

[14] Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.

[15] Maniezzo, V., Gambardella, L. M., & De Luigi, F. (2004). Ant colony optimization. In New optimization techniques in engineering (pp. 101-121). Berlin, Heidelberg: Springer Berlin Heidelberg.

[16] Mitchell, M. (2009). Complexity: A guided tour. Oxford university press.

[17] Morrison, A. M., & Maxim, C. (2021). World tourism cities: a systematic approach to urban tourism. Routledge.

[18] Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm intelligence, 1(1), 33-57.

[19] Power, D. J. (2002). Decision support systems: concepts and resources for managers. Studies in Informatics and Control, 11(4), 349-350.

[20] Reidy, C., Papoutsi, C., Kc, S., Gudgin, B., Laverty, A. A., Greaves, F., & Powell, J. (2025). Qualitative evaluation of the implementation and national roll-out of the NHS App in England. BMC medicine, 23(1), 20.

[21] Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, 553-572.

[22] Simon, H. (1997). Administrative behavior, (Original publication date 1945).

[23] Sivanandam, S. N., & Deepa, S. N. (2008). Genetic algorithms. In Introduction to genetic algorithms (pp. 15-37). Berlin, Heidelberg: Springer Berlin Heidelberg.

[24] Steyerberg, E. W., Moons, K. G., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., ... & Progress Group. (2013). Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS medicine, 10(2), e1001381.

[25] Turban, E. (2011). Decision support and business intelligence systems. Pearson Education India.

[26] Woodside, A. G., & Martin, D. (2008). Tourism management: analysis, behaviour and strategy (pp. xi+-528).