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Modelling multi-player strategic decisions in animal healthcare: A scoping review

Hennessey, Mathew; Fournie, Guillaume; Quaife, Matthew; Alarcon-Lopez, Pablo

Authors

Mathew Hennessey

Guillaume Fournie

Matthew Quaife

Pablo Alarcon-Lopez



Abstract

Strategic decision making in animal healthcare involves an array of complex factors interacting for the allocation of scarce resources. These influence the viability and success of livestock enterprises. However, the outcomes of such decisions may not always optimize social benefits over individual gains, with subsequent negative implications for animal and human health. Consequently, modelling techniques which consider the actions and interactions of multiple decision makers, such as game theory and agency theory, have potential to provide insight into past and future interventions and policy which seek to improve economic inefficiency. This scoping review aimed to identify, describe, and synthesise literature relating to multi-actor strategic decision making in animal healthcare.

Embase, Web of Science, PubMed, CAB direct, EconLit, AnthroSource, and Google Scholar were searched for literature published until November 2020. Studies were included if they were written in English, modelled strategic decisions between multiple actors, and contained information that related to animal healthcare practices. Data were analysed within the context of a conceptual framework based on strategic decision-making literature and modelling techniques.

The identified literature (n=17) had a strong focus on livestock healthcare with a particular focus on cattle (n=10). Most studies (12/17) examined decisions involving interventions in the control or management of infectious diseases. Nine studies described symmetrical relationships between multiple farmers, with the remainder describing the asymmetrical relationships (e.g., between farmers and the state) and the incentive structures needed to align interests. Almost all the studies used the monetary outcome of strategic decisions as a basis for expected utility, either through direct profit maximisation or via the aversion of losses.

Six studies used discursive and conceptual models to describe the strategic decision-making process, providing a wide lens by which to view decisions and opportunity to discuss the role of behavioural contributors to utility. Eleven studies used formal mathematical models to describe strategic decisions and used model solutions to provide recommendations to a specific problem. Three studies combined game theory with susceptible-infected-recovered models to allow inclusion of disease prevalence data. Ten articles made recommendations indicating that an increased level of state involvement could help to achieve socially optimum equilibria; including production subsidies and indemnity payments (n=7), promotion of cooperatives (n=3), and increased state regulation (n=2).

This review describes the limited number of studies which have approached strategic decision making in animal healthcare through multi-player modelling techniques, with several sectors, including pigs, poultry, equine and companion animal, being largely absent from the literature. Furthermore, there appears to be a lack of models grounded in qualitative data and justification for solely focusing on monetary outcomes. Nonetheless, these modelling techniques provide an opportunity to consider the perspectives of multiple stakeholders and to combine economic and epidemiological data which may be beneficial to the development of animal health interventions.

Citation

Hennessey, M., Fournie, G., Quaife, M., & Alarcon-Lopez, P. (2022). Modelling multi-player strategic decisions in animal healthcare: A scoping review. Preventive Veterinary Medicine, https://doi.org/10.1016/j.prevetmed.2022.105684

Journal Article Type Review
Acceptance Date May 26, 2022
Publication Date Jun 2, 2022
Deposit Date Dec 16, 2021
Publicly Available Date Mar 28, 2024
Print ISSN 0167-5877
Publisher Elsevier
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1016/j.prevetmed.2022.105684

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