A circular economy involves maintaining manufactured products in circulation, distributing resource and environmental costs over time and with repeated use. In a linear supply chain, manufactured products are used once and discarded. In high-income nations, health care systems increasingly rely on linear supply chains composed of singleuse disposable medical devices. This has resulted in increased health care expenditures and health care-generated waste and pollution, with associated public health damage. It has also caused the supply chain to be vulnerable to disruption and demand fluctuations. Transformation of the medical device industry to a more circular economy would advance the goal of providing increasingly complex care in a low-emissions future. Barriers to circularity include perceptions regarding infection prevention, behaviors of device consumers and manufacturers, and regulatory structures that encourage the proliferation of disposable medical devices. Complementary policy-and market-driven solutions are needed to encourage systemic transformation.

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Feed-in-tariff (FIT) policies aim at driving down the cost of renewable energies by fostering learning and accelerating the diffusion of green technologies. Under FIT mechanisms, governments purchase green energy at tariffs that are set above market price. The success or failure of FIT policies, in turn, critically depend on how these tariffs are determined and adjusted over time. This paper provides insights and guidance into designing effective and cost-efficient FIT programs such as these. To that end, we propose a dynamic optimization modeling framework that captures the key network externalities contributing to the technology evolution path. We show in our framework that the investments' profitability guaranteed by the tariffs should either always increase or always decrease as time progresses. This is in contrast with the current practice of FIT-implementing jurisdictions which typically try to maintain the same level of profitability across the policy horizon. Further, we determine how the structure of the optimal policy (ascending vs. descending profitability) changes with technology and market characteristics as well as with the policy objectives. In particular, when the policy horizon is endogenous and the policy goal is a target on the technology cost reduction, our results reveal that the investors' profitability should increase (resp. decrease) over time for low (reps. high) values of learning rates or penetration speeds. We also demonstrate that the annual capacity installation should not always increase over time as existing FIT implementations sometime suggest.

I n diagnostic services, agents typically need to weigh the benefit of running an additional test and improving the accuracy of diagnosis against the cost of delaying the provision of services to others. Our paper analyzes how to dynamically manage this accuracy/congestion trade-off. To that end, we study an elementary congested system facing an arriving stream of customers. The diagnostic process consists of a search problem in which the service provider conducts a sequence of imperfect tests to determine the customer's type. We find that the agent should continue to perform the diagnosis as long as her current belief that the customer is of a given type falls into an interval that depends on the congestion level as well as the number of performed tests thus far. This search interval should shrink as congestion intensifies and as the number of performed tests increases if additional conditions hold. Our study reveals that, contrary to diagnostic services without congestion, the base rate (i.e., the prior probability of the customer type) has an effect on the agent's search strategy. In particular, the optimal search interval shrinks when customer types are more ambiguous a priori, i.e., as the base rate approaches the value at which the agent is indifferent between types. Finally, because of congestion effects, the agent should sometimes diagnose the customer as being of a given type, even if test results indicate otherwise. All these insights disappear in the absence of congestion.T 0ĉ e − t dt ≥ 0, and hence this term can be dropped from the right-hand side. Next, because J u x k p provides a lower bound for J * x k p , we have

The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.

The World Health Organization seeks effective ways to alert its member states about global pandemics. Motivated by this challenge, we study a public agency’s problem of designing warning policies to mitigate potential disasters that occur with advance notice. The agency privately receives early information about recurring harmful events and issues warnings to induce an uninformed stakeholder to take preemptive actions. The agency’s decision to issue a warning critically depends on its reputation, which we define as the stakeholder’s belief regarding the accuracy of the agency’s information. The agency faces then a trade-off between eliciting a proper response today and maintaining its reputation to elicit responses to future events. We formulate this problem as a dynamic Bayesian persuasion game, which we solve in closed form. We find that the agency sometimes strategically misrepresents its advance information about a current threat to cultivate its future reputation. When its reputation is sufficiently low, the agency downplays the risk and actually downplays more as its reputation improves. By contrast, when its reputation is high, the agency sometimes exaggerates the threat and exaggerates more as its reputation deteriorates. Only when its reputation is moderate does the agency send warning messages that fully disclose its private information. Our study suggests a plausible and novel rationale for some of the false alarms or omissions observed in practice. We further test the robustness of our findings to imperfect advance information, disasters without advance notice, and heterogeneous receivers. This paper was accepted by Manel Baucells, decision analysis.

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