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Economic Evaluations with Agent-Based Modelling: An Introduction

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  • Jagpreet Chhatwal
  • Tianhua He

Abstract

Agent-based modelling (ABM) is a relatively new technique, which overcomes some of the limitations of other methods commonly used for economic evaluations. These limitations include linearity, homogeneity and stationarity. Agents in ABMs are autonomous entities, who interact with each other and with the environment. ABMs provide an inductive or ‘bottom-up’ approach, i.e. individual-level behaviours define system-level components. ABMs have a unique property to capture emergence phenomena that otherwise cannot be predicted by the combination of individual-level interactions. In this tutorial, we discuss the basic concepts and important features of ABMs. We present a case study of an application of a simple ABM to evaluate the cost effectiveness of screening of an infectious disease. We also provide our model, which was developed using an open-source software program, NetLogo. We discuss software, resources, challenges and future research opportunities of ABMs for economic evaluations. Copyright Springer International Publishing Switzerland 2015

Suggested Citation

  • Jagpreet Chhatwal & Tianhua He, 2015. "Economic Evaluations with Agent-Based Modelling: An Introduction," PharmacoEconomics, Springer, vol. 33(5), pages 423-433, May.
    • Handle: RePEc:spr:pharme:v:33:y:2015:i:5:p:423-433
    DOI: 10.1007/s40273-015-0254-2
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    References listed on IDEAS

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    1. Natasha Stout & Sue Goldie, 2008. "Keeping the noise down: common random numbers for disease simulation modeling," Health Care Management Science, Springer, vol. 11(4), pages 399-406, December.
    2. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023, October.
    3. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    4. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    5. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
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    Cited by:

    1. Enayati, Shakiba & Özaltın, Osman Y., 2020. "Optimal influenza vaccine distribution with equity," European Journal of Operational Research, Elsevier, vol. 283(2), pages 714-725.
    2. Beate Jahn & Sarah Friedrich & Joachim Behnke & Joachim Engel & Ursula Garczarek & Ralf Münnich & Markus Pauly & Adalbert Wilhelm & Olaf Wolkenhauer & Markus Zwick & Uwe Siebert & Tim Friede, 2022. "On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 349-382, September.
    3. Jaap Sok & Egil A J Fischer, 2020. "Farmers' heterogeneous motives, voluntary vaccination and disease spread: an agent-based model," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 1201-1222.
    4. Onur YENİ & Zeynep YENER-GÖK & Özgür TEOMAN, 2020. "Irrigation Systems Transformation in Cotton Production in the Harran District, Turkey: Implications of an Agent-Based Model," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(45).
    5. Michail Kovanis & Raphaël Porcher & Philippe Ravaud & Ludovic Trinquart, 2016. "Complex systems approach to scientific publication and peer-review system: development of an agent-based model calibrated with empirical journal data," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 695-715, February.

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