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U.S. consumers generate more pharmaceutical revenue per person than Europeans do. This has led some U.S. policymakers to call for limits on U.S. pharmaceutical spending and prices. Using a microsimulation approach, we analyze the welfare impacts of lowering U.S. prices toward European levels, and how these impacts vary with key modeling assumptions. Under the assumptions most favorable to them, price controls generate modest benefits (a few thousand dollars per person). However, for the remainder of plausible assumptions, price controls generate costs that are an order of magnitude higher. In contrast, publicly financing reductions in consumer prices, without affecting manufacturer prices, delivers benefits in virtually all plausible cases. [ABSTRACT FROM AUTHOR]

Standardized and/or centralized proactive research governance can lessen tensions.

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty., Competing Interests: P. Nascimento de Lima, R. Lempert, L. Baker, J.S. Ringel, R. Vardavas and C. Rutter are affiliated with the RAND Corporation, a non-profit research organization. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Notwithstanding current heavy dependence on gas-fired electricity generation in the Eastern African Power Pool (EAPP), hydropower is expected to play an essential role in improving electricity access in the region. Expansion planning of electricity infrastructure is critical to support investment and maintaining balanced consumer electricity prices. Variations in water availability due to a changing climate could leave hydro infrastructure stranded or result in underutilization of available resources. In this study, we develop a framework consisting of long-term models for electricity supply and water systems management, to assess the vulnerability of potential expansion plans to the effects of climate change. We find that the most resilient EAPP rollout strategy corresponds to a plan optimised for a slightly wetter climate compared to historical trends. This study demonstrates that failing to climate-proof infrastructure investments can result in significant electricity price fluctuations in selected countries (Uganda & Tanzania) while others, such as Egypt, are less vulnerable.

This article considers methodological issues arising from recent efforts to provide field tests of eyewitness identification procedures. We focus in particular on a field study (Mecklenburg 2006) that examined the "double blind, sequential" technique, and consider the implications of an acknowledged methodological confound in the study. We explain why the confound has severe consequences for assessing the real-world implications of this study.

Agent-based modeling (ABM) is a powerful representational formalism that has wide utility for modeling nonlinear systems. For ABM to achieve its potential as a scientific tool, our ability to build models that embody our knowledge must be complemented by rigorous means for making inferences using such models. Due to nonlinearity, this rigor cannot in general be based solely on demonstrating that a model reliably predicts the outcomes of available physical measurements. In this paper we describe an alternative approach to robust reasoning based on the concept of ensembles of alternative models. Ensembles of models can be defined that plausibly span classes of systems including the system of interest. Research methodologies for searching and sampling from such ensembles can be used to support plausible conclusions about invariant properties of ensembles of ABMs and hence of the classes of systems they represent. Notable among these are approaches that implement a competition between ensembles of problem formulations or challenges and conclusions robust to these challenges. This approach is demonstrated using examples drawn from our research.

Agent-based models are an increasingly powerful tool for simulating social systems because they can represent important phenomenon difficult to capture in other mathematical formalisms. But, agent-based models have provided only limited support for policy-making because their distinctive abilities are often most useful in situations where the future is unpredictable. In such situations, the traditional analytic methods for applying simulation models to support decision-making are least effective. Fortunately, new analytic approaches for decision-making under conditions of deep uncertainty--emphasizing large ensembles of model-created scenarios and adaptive policies evaluated with the criteria of robustness, rather than with optimality or efficiency--can unleash the full potential of agent-based policy simulators.