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Background Governments have an ethical imperative to safeguard children in residential care institutions at all times, including in the aftermath of an emergency. Yet, a lack of accurate data about how the magnitude and characteristics of this population may change due to an emergency impedes leaders' ability to formulate responsive policies and services, mobilize resources and foster accountability. The purpose of this study was therefore to determine the feasibility of evaluating movement of children into residential care following an emergency. Methods The pilot study took place in Les Cayes commune in the Sud Department of Haiti in April 2017. Six months prior to the pilot, the area was severely affected by Hurricane Matthew, with widespread devastation to property, livestock and livelihoods. Using a two-stage process, the team created a comprehensive list of residential care institutions in Les Cayes. At each facility, the data collectors attempted to administer four separate tools: a group count tool, a record review tool, interviews with staff, and interviews with children 10 years of age and older. Results Out of 27 known institutions in Les Cayes, 22 institutions consented to participate in the research. Within these 22 institutions, the prevalence of new arrivals to residential care since Hurricane Matthew varied significantly across the four tools, ranging from 0.69% according to the aggregated child interviews to 20.96% according to the aggregated staff interviews. Record availability and quality was very poor and child participation was difficult to arrange due to travel and scheduling constraints. Interpretation Robust measurement of new arrivals to residential care institutions was not feasible in Haiti following Hurricane Matthew. Moreover, many of the challenges encountered are likely to be encountered in humanitarian emergencies in other settings. Therefore, the research team does not recommend scale-up of these methods in most humanitarian settings. Alternative approaches that incorporate household survey methods to ascertain movement into residential care based on reports from caregivers may be more realistic in places with poor pre-existing governance systems and weak registries and records for residential care institutions.

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. ; Funding: Fonds voor Wetenschappelijk Onderzoek (FWO)FWO [1SA2820N]; Flemish GovernmentEuropean Commission; FWOFWO [iBOF/21/027]; National University of Ireland Galway Hardiman Scholarship; FAPERGSFundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS) [19/2551-0001277-2]; FAPESPFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2020/05165-1]; Swedish Governmental Agency for Innovation SystemsVinnova [NFFP7/2017-04885]; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; LIFT - Dutch Research Council (NWO) [019.011]; 2017 Microsoft Research PhD Scholarship Program; 2020 Microsoft Research EMEA PhD Award