Optimal intervention policy of bridges considering earthquake occurrence probability increasing over time

2021 ◽  
pp. 1077-1081
Author(s):  
D. Mizutani
Keyword(s):  
Occurrence Probability ◽  
Earthquake Occurrence ◽  
Optimal Intervention ◽  
Intervention Policy ◽  
Over Time

scholarly journals A Theory of Foreign Exchange Interventions

2021 ◽  
Author(s):  
Sebastián Fanelli ◽  
Ludwig Straub
Keyword(s):  
Exchange Rate ◽  
Central Bank ◽  
Interest Rates ◽  
Open Economy ◽  
Small Open Economy ◽  
Bond Markets ◽  
Pecuniary Externality ◽  
Optimal Intervention ◽  
The Exchange Rate ◽  
Intervention Policy

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.

scholarly journals Prediction and prevention of disproportionally dominant agents in complex networks

2020 ◽  
Vol 117 (44) ◽  
pp. 27090-27095
Author(s):  
Sandro Claudio Lera ◽  
Alex Pentland ◽  
Didier Sornette
Keyword(s):  
Complex Networks ◽  
Critical Mass ◽  
Stable State ◽  
Warning System ◽  
Interacting Agents ◽  
Optimal Intervention ◽  
Prediction And Prevention ◽  
The Common ◽  
Intervention Policy ◽  
Winner Takes All

We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (“winner takes all,” WTA) in growing complex networks. This is modeled as a system of interacting agents, whereby the rate at which an agent establishes connections to others is proportional to its already existing number of connections and its intrinsic fitness. We derive an exact four-dimensional phase diagram that separates the growing system into two regimes: one where the “fit get richer” and one where, eventually, the WTA. By calibrating the system’s parameters with maximum likelihood, its distance from the unfavorable WTA regime can be monitored in real time. This is demonstrated by applying the theory to the eToro social trading platform where users mimic each other’s trades. If the system state is within or close to the WTA regime, we show how to efficiently control the system back into a more stable state along a geodesic path in the space of fitness distributions. It turns out that the common measure of penalizing the most dominant agents does not solve sustainably the problem of drastic inequity. Instead, interventions that first create a critical mass of high-fitness individuals followed by pushing the relatively low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility.

scholarly journals Microsimulation Modeling in Oncology

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Çağlar Çağlayan ◽  
Hiromi Terawaki ◽  
Qiushi Chen ◽  
Ashish Rai ◽  
Turgay Ayer ◽  
...  
Keyword(s):  
Transition Probabilities ◽  
Microsimulation Model ◽  
Alternative Interventions ◽  
Oncology Research ◽  
Optimal Intervention ◽  
Potential Applications ◽  
History Of ◽  
Microsimulation Modeling ◽  
Microsimulation Models ◽  
Over Time

Purpose Microsimulation is a modeling technique that uses a sample size of individual units (microunits), each with a unique set of attributes, and allows for the simulation of downstream events on the basis of predefined states and transition probabilities between those states over time. In this article, we describe the history of the role of microsimulation in medicine and its potential applications in oncology as useful tools for population risk stratification and treatment strategy design for precision medicine. Methods We conducted a comprehensive and methodical search of the literature using electronic databases—Medline, Embase, and Cochrane—for works published between 1985 and 2016. A medical subject heading search strategy was constructed for Medline searches by using a combination of relevant search terms, such as “microsimulation model medicine,” “multistate modeling cancer,” and “oncology.” Results Microsimulation modeling is particularly useful for the study of optimal intervention strategies when randomized control trials may not be feasible, ethical, or practical. Microsimulation models can retain memory of prior behaviors and states. As such, it allows an explicit representation and understanding of how various processes propagate over time and affect the final outcomes for an individual or in a population. Conclusion A well-calibrated microsimulation model can be used to predict the outcome of the event of interest for a new individual or subpopulations, assess the effectiveness and cost effectiveness of alternative interventions, and project the future disease burden of oncologic diseases. In the growing field of oncology research, a microsimulation model can serve as a valuable tool among the various facets of methodology available.

scholarly journals Mathematical and numerical analyses of a stochastic impulse control model with imperfect interventions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hidekazu Yoshioka ◽  
Yuta Yaegashi
Keyword(s):  
Impulse Control ◽  
Bird Population ◽  
Optimal Intervention ◽  
Stochastic Impulse Control ◽  
Quasi Variational Inequality ◽  
Non Local ◽  
Intervention Policy ◽  
Hamilton Jacobi Bellman ◽  
The Many ◽  
The Value Function

AbstractA stochastic impulse control problem with imperfect controllability of interventions is formulated with an emphasis on applications to ecological and environmental management problems. The imperfectness comes from uncertainties with respect to the magnitude of interventions. Our model is based on a dynamic programming formalism to impulsively control a 1-D diffusion process of a geometric Brownian type. The imperfectness leads to a non-local operator different from the many conventional ones, and evokes a slightly different optimal intervention policy. We give viscosity characterizations of the Hamilton–Jacobi–Bellman Quasi-Variational Inequality (HJBQVI) governing the value function focusing on its numerical computation. Uniqueness and verification results of the HJBQVI are presented and a candidate exact solution is constructed. The HJBQVI is solved with the two different numerical methods, an ordinary differential equation (ODE) based method and a finite difference scheme, demonstrating their consistency. Furthermore, the resulting controlled dynamics are extensively analyzed focusing on a bird population management case from a statistical standpoint.

scholarly journals Long-range dependence in earthquake-moment release and implications for earthquake occurrence probability

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Simone Barani ◽  
Claudia Mascandola ◽  
Eva Riccomagno ◽  
Daniele Spallarossa ◽  
Dario Albarello ◽  
...  
Keyword(s):  
Long Range ◽  
Long Range Dependence ◽  
Occurrence Probability ◽  
Earthquake Occurrence ◽  
Earthquake Moment

scholarly journals Verification Method for Accumulative Event Relation of Message Passing Behavior with Process Tree for IoT Systems

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 232
Author(s):  
Mohd Anuaruddin Bin Ahmadon ◽  
Shingo Yamaguchi
Keyword(s):  
Message Passing ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Occurrence Probability ◽  
Tree Model ◽  
Verification Method ◽  
Event Order ◽  
Over Time ◽  
Process Tree

In this paper, we proposed a verification method for the message passing behavior of IoT systems by checking the accumulative event relation of process models. In an IoT system, it is hard to verify the behavior of message passing by only looking at the sequence of packet transmissions recorded in the system log. We proposed a method to extract event relations from the log and check for any minor deviations that exist in the system. Using process mining, we extracted the variation of a normal process model from the log. We checked for any deviation that is hard to be detected unless the model is accumulated and stacked over time. Message passing behavior can be verified by comparing the similarity of the process tree model, which represents the execution relation between each message passing event. As a result, we can detect minor deviations such as missing events and perturbed event order with occurrence probability as low as 3%.

scholarly journals Non-Poissonian Earthquake Occurrence Probability through Empirical Survival Functions

2020 ◽  
Vol 10 (3) ◽  
pp. 838 ◽  
Author(s):  
Paolo Harabaglia
Keyword(s):  
Earthquake Engineering ◽  
Event Rate ◽  
Previous History ◽  
Earthquake Activity ◽  
Earthquake Catalog ◽  
Occurrence Probability ◽  
Survival Functions ◽  
Earthquake Occurrence ◽  
Area Of Interest ◽  
History Of

Earthquake engineering normally describes earthquake activity as a Poissonian process. This approximation is simple, but not entirely satisfactory. In this paper, a method for evaluating the non-Poissonian occurrence probability of seismic events, through empirical survival probability functions, is proposed. This method takes into account the previous history of the system. It seems robust enough to be applied to scant datasets, such as the Italian historical earthquake catalog, comprising 64 events with M ≥ 6.00 since 1600. The requirements to apply this method are (1) an acceptable knowledge of the event rate, and (2) the timing of the last two events with magnitude above the required threshold. I also show that it is necessary to consider all the events available, which means that de-clustering is not acceptable. Whenever applied, the method yields a time-varying probability of occurrence in the area of interest. Real cases in Italy show that large events obviously tend to occur in periods of higher probability.

Sign in / Sign up

Export Citation Format

Share Document