Optimal Intervention Policies Research Articles

Optimal Interventions for Robust Optimization under Time-Dependent Uncertainty with Application to Radiotherapy

Uncertainties can be time-dependent, particularly in areas such as machine repair, maintenance scheduling, and cancer radiotherapy, where the condition of the system (or patient) can change during the course of the operation (or treatment). When solving such problems, it is crucial to intervene and observe the condition of the system prior to adapting the decisions. However, observations can be costly, and the timing of observations are directly impacted by time-dependent uncertainties. We address these challenges by developing an optimally-intervened robust optimization model that employs a time-dependent uncertainty set. We formulate counterparts of robust linear programs and propose an algorithm to compute adaptive robust solutions. Then, optimal intervention policies are derived that recommend the optimal number and timing of observations as functions of the observation cost and parameters of the uncertainty set. The proposed methodology is demonstrated using an application in cancer radiotherapy, where the optimally-intervened robust model improves tumor dose by up to 3% subject to nominal, best-case, and worst-case conditions, compared to a naive robust model that employs a single intervention. Furthermore, the optimally-intervened model improves tumor dose by up to 20% compared to three deterministic approaches used in the clinic; this method also reduces dose to healthy organs by up to 18%. Improvements in tumor and organ dose criteria can lead to a better quality of life for the patient. The proposed methodology is generic and can be applied to other time-dependent processes, such as machine repair or maintenance scheduling.

Disease dispersion as a spatial interaction: The case of Flavescence Dorée

AbstractFlavescence dorée is a serious and incurable vine disease transmitted by an insect vector. Focusing on its spatial diffusion and on its control with pesticides, this paper investigates the private strategies of wine producers and their socially optimal counterparts. The socially optimal regulation has to address two externalities regarding private treatment decisions: (a) the insufficient consideration of collective benefits from controlling the vector populations; (b) the failure to take into account environmental damage related to pesticide application. The probability of infection is estimated on French data from a spatial econometric specification. Three alternative assumptions are examined regarding producers' anticipation of the impact of their own treatment: naive, myopic, or farseeing, in increasing order of sophistication. Because of the two dimensions of externalities, no type of anticipation leads to a systematically preferable situation and optimal policy intervention requires a tax for environmental externalities and a subvention for protection externalities.Recommendations for Resource Managers: Current policy of compulsory treatment is justified by the positive protection externalities. This policy is particularly appropriated if producers' anticipations are naive or myopic. Taking into account negative externalities decreases the case for compulsory treatment. With two externalities, sophisticated anticipations are not necessary closer to the optimum.

Safety-Critical Control of Active Interventions for COVID-19 Mitigation.

The world has recently undergone the most ambitious mitigation effort in a century, consisting of wide-spread quarantines aimed at preventing the spread of COVID-19. The use of influential epidemiological models of COVID-19 helped to encourage decision makers to take drastic non-pharmaceutical interventions. Yet, inherent in these models are often assumptions that the active interventions are static, e.g., that social distancing is enforced until infections are minimized, which can lead to inaccurate predictions that are ever evolving as new data is assimilated. We present a methodology to dynamically guide the active intervention by shifting the focus from viewing epidemiological models as systems that evolve in autonomous fashion to control systems with an “input” that can be varied in time in order to change the evolution of the system. We show that a safety-critical control approach to COVID-19 mitigation gives active intervention policies that formally guarantee the safe evolution of compartmental epidemiological models. This perspective is applied to current US data on cases while taking into account reduction of mobility, and we find that it accurately describes the current trends when time delays associated with incubation and testing are incorporated. Optimal active intervention policies are synthesized to determine future mitigations necessary to bound infections, hospitalizations, and death, both at national and state levels. We therefore provide means in which to model and modulate active interventions with a view toward the phased reopenings that are currently beginning across the US and the world in a decentralized fashion. This framework can be converted into public policies, accounting for the fractured landscape of COVID-19 mitigation in a safety-critical fashion.

A Theory of Foreign Exchange Interventions

Abstract We study a real small open economy with two key ingredients (1) partial segmentation of home and foreign bond markets and (2) a pecuniary externality that makes the real exchange rate excessively volatile in response to capital flows. Partial segmentation implies that, by intervening in the bond markets, the central bank can affect the exchange rate and the spread between home- and foreign-bond yields. Such interventions allow the central bank to address the pecuniary externality, but they are also costly, as foreigners make carry trade profits. We analytically characterize the optimal intervention policy that solves this trade-off: (1) the optimal policy leans against the wind, stabilizing the exchange rate; (2) it involves smooth spreads but allows exchange rates to jump; (3) it partly relies on “forward guidance,” with non-zero interventions even after the shock has subsided; (4) it requires credibility, in that central banks do not intervene without commitment. Finally, we shed light on the global consequences of widespread interventions, using a multi-country extension of our model. We find that, left to themselves, countries over-accumulate reserves, reducing welfare and leading to inefficiently low world interest rates.

FX Intervention in the New Keynesian Model

AbstractWe develop an open‐economy New Keynesian Model with foreign exchange (FX) intervention in the presence of a financial accelerator and shocks to risk appetite in international capital markets. We obtain closed‐form solutions for optimal monetary and FX intervention policies assuming the central bank cannot commit to future policies, and we compare the solution to that under policy commitment. We show how FX intervention can help reduce the volatility of the exchange rate, of inflation, and of the output gap, thus mitigating welfare losses associated with shocks in the international capital markets. We also show that, when the financial accelerator is strong, there is a risk of indeterminacy (self‐fulfilling currency and inflation movements) although FX intervention can reduce this risk and thus reinforce the credibility of the inflation targeting regime. Model simulations match well the impact of a VIX shock obtained by local projections on a panel of inflation targeting emerging markets.

Wait-and-See or Step in? Dynamics of Interventions

We study the optimal intervention policy to stop projects in a relational contract between a principal and a policymaker. The policymaker is privately informed about his ability and privately chooses how much effort to exert. Before a project is completed, the principal receives a signal about its outcome and can intervene to stop it. Intervention may prevent a bad outcome, but no intervention leads to better learning about the policymaker's ability. In the benchmarks with observable effort or observable ability, optimal intervention follows a threshold rule. With unobservable effort and ability, the optimal policy switches between intervention and no intervention.

Capital Flows and Foreign Exchange Intervention

I consider a small open economy model where international financial markets are imperfect and the exchange rate is determined by capital flows. I use this framework to study the effects of portfolio flow shocks, derive the optimal foreign exchange intervention policy, and characterize its interaction with monetary policy. I derive the optimal intervention rule in closed form as a function of three implicit targets. Finally, using Swiss data, I estimate the model to quantify the inefficiencies generated by capital flow shocks and the optimal size of the intervention. (JEL E44, E52, E63, F31, F32, F33, F41)

Intervention on default contagion under partial information in a financial network.

We study the optimal interventions of a regulator (a central bank or government) on the illiquidity default contagion process in a large, heterogeneous, unsecured interbank lending market. The regulator has only partial information on the interbank connections and aims to minimize the fraction of final defaults with minimal interventions. We derive the analytical results of the asymptotic optimal intervention policy and the asymptotic magnitude of default contagion in terms of the network characteristics. We extend the results of Amini, Cont and Minca’s work to incorporate interventions and adopt the dynamics of Amini, Minca and Sulem’s model to build heterogeneous networks with degree sequences and initial equity levels drawn from arbitrary distributions. Our results generate insights that the optimal intervention policy is “monotonic” in terms of the intervention cost, the closeness to invulnerability and connectivity. The regulator should prioritize interventions on banks that are systematically important or close to invulnerability. Moreover, the regulator should keep intervening on a bank once having intervened on it. Our simulation results show a good agreement with the theoretical results.

Optimal Development Policies With Financial Frictions

Is there a role for governments in emerging countries to accelerate economic development by intervening in product and factor markets? To address this question, we study optimal dynamic Ramsey policies in a standard growth model with financial frictions. The optimal policy intervention involves pro-business policies like suppressed wages in early stages of the transition, resulting in higher entrepreneurial profits and faster wealth accumulation. This, in turn, relaxes borrowing constraints in the future, leading to higher labor productivity and wages. In the long run, optimal policy reverses sign and becomes pro-worker. In a multi-sector extension, optimal policy subsidizes sectors with a latent comparative advantage and, under certain circumstances, involves a depreciated real exchange rate. Our results provide an efficiency rationale, but also identify caveats, for many of the development policies actively pursued by dynamic emerging economies.

Optimal Information Collection Policies in a Markov Decision Process Framework.

The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change over time may be optimal. We present a stochastic dynamic programming approach to simultaneously identify the optimal intervention and information collection policies. We use our framework to evaluate birth cohort hepatitis C virus (HCV) screening. We focus on how the presence of a time-varying parameter (HCV prevalence) affects the optimal information collection policy for a parameter assumed constant across birth cohorts: liver fibrosis stage distribution for screen-detected diagnosis at age 50. We prove that it may be optimal to delay information collection until a time when the information more immediately affects decision making. For the example of HCV screening, given initial beliefs, the optimal policy (at 2010) was to continue screening and collect information about the distribution of liver fibrosis at screen-detected diagnosis in 12 years, increasing the expected incremental net monetary benefit (INMB) by $169.5 million compared to current guidelines. The option to delay information collection until the information is sufficiently likely to influence decisions can increase efficiency. A dynamic programming framework enables an assessment of the marginal value of information and determines the optimal policy, including when and how much information to collect.

A real options approach for joint overhaul and replacement strategies with mean reverting prices

Due to its significant impact on economic performance, an effective equipment overhaul and replacement strategy is a key aspect of physical asset management in capital-intensive industries, such as the mining industry. Classical approaches suggest periodic interventions based on the physical condition of the equipment, considering factors such as availability and operational costs. These fixed models generally ignore two important aspects: first, the flexibility of the decision to overhaul or replace, which may be re-evaluated within a given period, and second, the uncertainty around economic factors that may affect future maintenance decisions, such as the product price. This work improves on classical models by considering the effect of integrated price uncertainty in the definition of joint overhaul and replacement strategy, using a real options approach and a mean reversion binomial model to represent the uncertainty in price. More specifically, we develop a real options model and use a backwards recursion algorithm to determine an optimal intervention policy that maximizes expected profits. We then present a numerical study of the mining industry to validate the effectiveness of the proposed methodology. Results show that the option-based decision model economically outperforms the classical periodic strategy approach from with net present value increments ranging from 36.8 to 8.6%, according to the number of periods in the maintenance cycle, offering evidence that a new approach to equipment overhaul and replacement strategy is needed.

Managing Capital Outflows with Limited Reserves

We analyze the optimal intervention policy for an emerging market central bank that wishes to stabilize the exchange rate during a capital outflow episode, but possesses limited reserves. We show that adding a non-negativity constraint on reserves onto a simple linear-quadratic framework generates a time consistency problem. A central bank with full commitment achieves a gradual depreciation to the pure-float level by promising sustained future intervention, such that reserves are exhausted after particularly adverse shocks. A central bank without commitment intervenes little, wishing to preserve some reserves forever, so it suffers a larger immediate depreciation and achieves lower welfare. For more persistent shocks, the time consistency problem is greater, and simple intervention rules can achieve welfare gains relative to discretion.

Automatic EEG classification: a path to smart and connected sleep interventions

We develop a random forest classifier to automatically classify brain waves into sleep stages by using the publicly available data from PhysioBank. More specifically, we use the EEG signals from a single pair of electrodes (FPz–Cz) recorded from 20 patients and evaluate the impact of data balancing and incorporating signal history on classification results. The accuracy of the model is objectively evaluated using leave-one-out cross-validation. The developed model achieves the mean accuracy of 0.74, with that of the individual sleep stages ranging from 0.65 to 0.91. Next, we leverage this online sleep scoring scheme to introduce dynamic interventions as sleep process evolves over night. We develop a semi-Markov decision process model to determine optimal intervention policies to minimize the gap between the amount of sleep experienced in different stages and predetermined targets and provide computational results.

Optimal Forbearance of Bank Resolution

We analyze optimal strategic delay of bank resolution ('grq forbearance') and deposit insurance coverage. After bad news on the bank's assets, depositors fear for the uninsured part of their deposit and withdraw while the regulator observes withdrawals and needs to decide when to intervene. Optimal policy maximizes the joint value of the demand deposit contract and the insurance fund to avoid inefficient risk-shifting towards the fund while also preventing inefficient runs. Under low insurance coverage, the optimal intervention policy is never to intervene (laissez-faire). Optimal deposit insurance coverage is always interior. The paper sheds light on the differences between the U.S. and the European Monetary Union concerning their bank resolution policies.

Optimal Forbearance of Bank Resolution

We analyze optimal strategic delay of bank resolution ('grq forbearance') and deposit insurance coverage. After bad news on the bank's assets, depositors fear for the uninsured part of their deposit and withdraw while the regulator observes withdrawals and needs to decide when to intervene. Optimal policy maximizes the joint value of the demand deposit contract and the insurance fund to avoid inefficient risk-shifting towards the fund while also preventing inefficient runs. Under low insurance coverage, the optimal intervention policy is never to intervene (laissez-faire). Optimal deposit insurance coverage is always interior. The paper sheds light on the differences between the U.S. and the European Monetary Union concerning their bank resolution policies.

An approximation scheme for impulse control with random reaction periods

We propose an approximation scheme for impulse control models with random reaction periods (ICRRP) and show that the optimal solutions can be found by solving a sequence of optimal stopping problems. Our work enhances viability of the existing ICRRP framework for applications as well as the general literature on stochastic control theory. The efficacy of our approximation scheme is validated by applying it to compute a market-reaction-adjusted optimal central bank intervention policy for a country.

On the existence and uniqueness of the optimal central bank intervention policy in a forex market with jumps

We study a central bank intervention (CBI) problem in the foreign exchange market when the exchange rate follows a jump-diffusion process and show that the optimal CBI policy is a control-band policy. Our main contribution is a rigorous proof of the existence and uniqueness of the optimal CBI policy.

Using claims data linked with electronic health records to monitor and improve adherence to medication

ABSTRACTPoor adherence to medication is a serious problem in the United States, leading to complications and preventable hospitalizations, particularly for patients with chronic diseases. Interventions have been proposed as a means to improve adherence to medication, but the optimal time to perform an intervention has not been well studied. We provide a use case for how claims data linked with electronic health records (EHRs) can be used to monitor patient adherence to medication and provide a source of information to help decide when to perform an intervention. We propose a Markov decision process (MDP) model to determine when to perform adherence-improving interventions based on a patient’s EHR. We consider the the societal perspective where we trade off maximization of time to first adverse health event and minimization of cost of interventions, medication, and adverse events. We use our model to evaluate the costs and benefits of implementing an EHR-based active surveillance system for adherence-improving interventions in the context of cardiovascular disease management for patients with type 2 diabetes. We also provide some theoretical insights into the structure of the optimal intervention policy and the influence of health risks and costs on intervention decisions.

The Role Of Regulatory Credibility In Effective Bank Regulation

This paper develops a model of regulated Brownian motion with an endogenous profit term to analyze the role of regulatory credibility on the stability and productivity of the banking system. We show that when regulatory intervention is perfect and costless, the volatility of the system can be substantially reduced with no loss of productivity. In fact, perfect credibility can actually reduce the volatility of intrinsically risky banking systems below the volatility of intrinsically less risky systems as banks anticipate intervention and mitigate their investment behaviour accordingly. However, when the credibility of the regime is weakened because of increased uncertainty stemming from regulation, such as random costs or imperfect timing of regulatory intervention, both the stability and productivity of the financial system are impaired. Importantly, we find that in the presence of regulatory costs and imperfect credibility, there is no universal optimal intervention policy rule. The optimal regulatory system depends on the regulator’s level of absolute risk aversion. (This abstract was borrowed from another version of this item.)

Market-reaction-adjusted optimal central bank intervention policy in a forex market with jumps

Impulse control with random reaction periods (ICRRP) is used to derive a country’s optimal foreign exchange (forex) rate intervention policy when the forex market reacts to the interventions. This paper extends the previous work on ICRRP by incorporating a multi-dimensional jump diffusion process to model the state dynamics, and hence, enhance the viability of the extant model for applications. Furthermore, we employ a novel minimum cost operator that simplifies the computations of the optimal solutions. Finally, we demonstrate the efficacy of our framework by finding a market-reaction-adjusted optimal central bank intervention (CBI) policy for a country. Our numerical results suggests that market reactions and the jumps in the forex market are complements when the reactions increase the forex rate volatility; otherwise, they are substitutes.