Stochastic Factors Research Articles

Optimal Investment and Risk Control Strategies for an Insurer Subject to a Stochastic Economic Factor in a Lévy Market

Optimal Investment and Risk Control Strategies for an Insurer Subject to a Stochastic Economic Factor in a Lévy Market

Egg microbiota is the starting point of hatchling gut microbiota in the endangered yellow-spotted Amazon river turtle.

Establishment and development of gut microbiota during vertebrates' early life are likely to be important predictors of health and fitness. Host-parental and host-environment interactions are essential to these processes. In oviparous reptiles whose nests represent a source of the parent's microbial inocula, the relative role of host-selection and stochastic environmental factors during gut microbial assemblage remains unknown. We sampled eggs incubated in artificial nests as well as hatchlings and juveniles (up to 30 days old) of the yellow-spotted Amazon river turtle (Podocnemis unifilis) developing in tubs filled with river water. We examined the relative role of the internal egg microbiota and the abiotic environment on hatchling and juvenile turtle's cloacal microbiota assemblages during the first 30 days of development. A mean of 71% of ASVs in hatched eggs could be traced to the nest environmental microbiota and in turn a mean of 77% of hatchlings' cloacal ASVs were traced to hatched eggs. Between day 5 and 20 of juvenile turtle's development, the river water environment plays a key role in the establishment of the gut microbiota (accounting for a mean of 13%-34.6% of cloacal ASVs) and strongly influences shifts in microbial diversity and abundance. After day 20, shifts in gut microbiota composition were mainly driven by host-selection processes. Therefore, colonization by environmental microbiota is key in the initial stages of establishing the host's gut microbiota which is subsequently shaped by host-selection processes. Our study provides a novel quantitative understanding of the host-environment interactions during gut microbial assemblage of oviparous reptiles.

Stochasticity dominates assembly processes of soil nematode metacommunities on three Asian mountains

Nematodes play an important role in ecosystems, yet very little is known about their assembly processes and the factors influencing them. We studied nematode communities in bulk soil from three Asian mountain ecosystems to study the assembly processes of free-living nematode metacommunities and their driving factors. On each mountain, elevations span a range of climatic conditions with the potential to reveal the assembly processes predominating across multiple biomes. A phylogenetic null modeling framework was used to analyze 18S rRNA gene amplicons to quantify various assembly processes. We found that phylogenetic turnover between nematode communities on all mountains was dominated by stochastic processes, with “undominated processes” as the most predominant stochastic factor. Elevation has a significant impact on the relative importance of deterministic and stochastic processes. A variety of climatic and edaphic variables significantly influenced the variation of community assembly processes with elevation, even though their impacts were not consistent between the mountains. Overall, our results indicated that free-living nematode metacommunities in a wide range of environments are largely structured by stochastic processes, rather than by niche-based deterministic processes, which suggests that metacommunities of soil free-living nematodes may respond to climate change in a largely unpredictable way.

Predict, then schedule: Prescriptive analytics approach for machine learning-enabled sequential clinical scheduling

This research considers the design of an appointment system (AS) for sequentially scheduling patients in the presence of stochastic factors (no-show and service time uncertainty) and two patient classes. Although studies on AS design are prevalent in literature, most prior works do not consider heterogeneous patient characteristics (e.g., patient-specific no-show risk and service time duration) and sequential patient call-ins together. This research integrates patient-specific uncertainty estimates into the AS design and assess its impact on AS efficiency (measured as the weighted sum of patient waiting time, doctor idle time, and overtime). Specifically, the patients are scheduled as they sequentially call the clinic for appointments based on their risk of no-show and estimated consultation duration. For this setting, a predict-then-schedule framework is proposed. In the predict step, patient-specific no-show risk and service duration are estimated using a machine learning model. The schedule step determines each patient’s appointment time and interval by integrating the predictions with three scheduling decisions (allocation, sequencing, and overbooking). As a result, new sequencing rules are employed. Results indicate that adopting the predict-then-schedule approach in a sequential framework always dominates conventional approaches and could improve the efficiency by 60%.

Microgrid Operational Planning using a Hybrid Neural Network with Resource-aware Scenario Selection

Modern power systems continue to increase in complexity due to the intermittent behavior of distributed energy resources (DERs) and stochastic climate factors. Considering these factors simultaneously generates enumerable scenarios for decision-making within a microgrid (MG) dispatch policy. An MG dispatch policy can be formulated as a mixed integer linear program (MILP) and solved using traditional solution methods such as simplex, graphical method, etc. However, given the number of potential scenarios, these traditional solution methods incur a high computational burden to obtain near real-time solutions. Thus, dimension reduction techniques are needed to reduce the MILP's complexity. To address the need for a scalable solution method and reduced model complexity, we propose a 2-stage reconfigurable framework for near real-time MG operational planning. The proposed 2-stage reconfigurable framework is comprised of four modules: i) a resource-aware scenario selection (RSS) algorithm to identify the most probable scenarios, ii) an MG dispatch policy to obtain solutions or dispatch decisions for an MG system, iii) a neural network (NN) to map the most probable scenarios from RSS to their corresponding solutions from the MG dispatch policy and iv) a rule-based policy to monitor depreciating predictions from the NN. The RSS module is a probabilistic variate of a full factorial design (FFD) that assigns weights to test points defining the design space based on stochastic climate factors and the number of quartiles used to partition each factor's likelihood function. The most probable scenarios from RSS and their corresponding solutions from the proposed MG dispatch policy were both synthesized into a training set. NNs were then trained using this dataset to predict dispatch decisions for the IEEE 18-bus and the IEEE 33-bus. The proposed RSS reduced the state space by 77%, where the remaining states reflect 98% of the original state space. Furthermore, the NNs applied for MG operational planning showed robust predictive performance with loss function values of 0.0228 and 0.0563 for the IEEE 18-bus and the IEEE 33-bus, respectively.

Optimal Execution: A Review

This review article is intended to collect and summarize many of the results in the field of optimal execution over the last twenty years. In doing so, we describe the general workings of the limit order book so that the sources of costs and risks which need to be optimized are understood. The initial models considered propose simple dynamics for prices which allow easily computable strategies which maximize risk-adjusted profits. Subsequently, the review is divided into two major parts. The first explores several works which investigate how optimal liquidation strategies are modified to account for more complex dynamics, namely other stochastic or non-linear factors. The second presents optimal trading strategies when the agent utilizes benchmarks in addition to risk-adjusted wealth, or when she has objectives beyond optimal liquidation.

Stochastic establishment of β-lactam-resistant Escherichia coli mutants reveals conditions for collective resistance.

For antibiotic resistance to arise, new resistant mutants must establish in a bacterial population before they can spread via natural selection. Comprehending the stochastic factors that influence mutant establishment is crucial for a quantitative understanding of antibiotic resistance emergence. Here, we quantify the single-cell establishment probability of four Escherichia coli strains expressing β-lactamase alleles with different activity against the antibiotic cefotaxime, as a function of antibiotic concentration in both unstructured (liquid) and structured (agar) environments. We show that concentrations well below the minimum inhibitory concentration (MIC) can substantially hamper establishment, particularly for highly resistant mutants. While the pattern of establishment suppression is comparable in both tested environments, we find greater variability in establishment probability on agar. Using a simple branching model, we investigate possible sources of this stochasticity, including environment-dependent lineage variability, but cannot reject other possible causes. Lastly, we use the single-cell establishment probability to predict each strain's MIC in the absence of social interactions. We observe substantially higher measured than predicted MIC values, particularly for highly resistant strains, which indicates cooperative effects among resistant cells at large cell numbers, such as in standard MIC assays.

Efficiency of Water Pollution Control Based on a Three-Stage SBM-DEA Model

With the growing severity of water pollution issues, the prevention and control of water pollution became highly complicated and challenging, and the investment in water pollution control has been constantly increased. Scientific evaluation of efficiency is critical to recognize whether the investments in water pollution control are effective. However, most studies could not exclude the influences of external environmental and random factors when evaluating the efficiency of water pollution control, resulting in biased results. To overcome this shortcoming, this study employed a three-stage SBM-DEA (slacks-based measure-data envelopment analysis) model to determine the efficiency of water pollution control efforts in a city of China from 2003 to 2017. The results showed that water quality in the study area has been significantly improved due to those pollution control efforts. The influences from external environmental and stochastic factors have led to an underestimation of the efficiency of water pollution control in the first stage. After excluding these effects in the second stage, the adjusted efficiency of water pollution control showed a fluctuating upward trend in the third stage, reflecting the true effectiveness of efforts to prevent and control water pollution in the study cities, with an average efficiency of 0.87. Finally, several suggestions for enhancing the efficiency of water pollution control in Chengde were proposed.

A stochastic model for estimating sustainable limits to wildlife mortality in a changing world.

Human‐caused mortality of wildlife is a pervasive threat to biodiversity. Assessing the population‐level impact of fisheries bycatch and other human‐caused mortality of wildlife has typically relied upon deterministic methods. However, population declines are often accelerated by stochastic factors that are not accounted for in such conventional methods. Building on the widely applied potential biological removal (PBR) equation, we devised a new population modeling approach for estimating sustainable limits to human‐caused mortality and applied it in a case study of bottlenose dolphins affected by capture in an Australian demersal otter trawl fishery. Our approach, termed sustainable anthropogenic mortality in stochastic environments (SAMSE), incorporates environmental and demographic stochasticity, including the dependency of offspring on their mothers. The SAMSE limit is the maximum number of individuals that can be removed without causing negative stochastic population growth. We calculated a PBR of 16.2 dolphins per year based on the best abundance estimate available. In contrast, the SAMSE model indicated that only 2.3–8.0 dolphins could be removed annually without causing a population decline in a stochastic environment. These results suggest that reported bycatch rates are unsustainable in the long term, unless reproductive rates are consistently higher than average. The difference between the deterministic PBR calculation and the SAMSE limits showed that deterministic approaches may underestimate the true impact of human‐caused mortality of wildlife. This highlights the importance of integrating stochasticity when evaluating the impact of bycatch or other human‐caused mortality on wildlife, such as hunting, lethal control measures, and wind turbine collisions. Although population viability analysis (PVA) has been used to evaluate the impact of human‐caused mortality, SAMSE represents a novel PVA framework that incorporates stochasticity for estimating acceptable levels of human‐caused mortality. It offers a broadly applicable, stochastic addition to the demographic toolbox to evaluate the impact of human‐caused mortality on wildlife.

Breeding forest birds of northeastern Connecticut show a long-term population increase and high species turnover

North American birds have declined by 29% over 48 years, with declines occurring across species and biomes. To examine population patterns at a local scale, we investigated species and population shifts in a forest bird community in northeastern Connecticut. We did so in relation to its changing environments, focusing on patterns consistent with the effects of climate change and habitat manipulation. In 1985, we established survey routes primarily in the intensively managed Yale-Myers experimental forest, which we repeated in 2018 and 2019. Species richness varied little from the initial survey to the recent ones, although population density increased by 24% after 1985. Turnover in species composition exceeded 50%. The 5 most strongly declining species were northerly distributed, forest interior inhabitants, whereas the 7 most strongly increasing species were variously distributed forest interior and edge/successional-associated species. Some species experienced increases by invading new habitats, whereas at least one appeared to decline due to interspecific competition. Expected effects of climate change on populations were consistent with some findings, but habitat effects appeared related to a greater number of shifts. However, much contrary data indicated that these factors were not alone in driving community change. This bird community may best be thought of as a dynamic assemblage that represents the sum of individualistic responses to environmental and perhaps stochastic factors.

Trajectory to local extinction of an isolated dugong population near Okinawa Island, Japan

A small animal population becomes extinct owing to demographic and environmental stochasticity after declining below the minimum viable population (MVP). However, the actual process of extinction derived by stochastic factors after crossing MVP has not been recorded for long-lived marine mammals. Here, we reconstructed the declining history of a small, isolated population of dugongs in Okinawa over 125 years. The initial population size of approximately 280–420 in the nineteenth century declined to approximately < 100 in 1917 because of overfishing, < 70 in 1979, 11 in 1997, 3 after 2006, and all known individuals disappeared or died by 2019. After 1979, a decline in the natural growth rate has led to extinction. Long-lived animals may persist for a few decades after the population falls below the MVP, at which time active conservation measures, such as captive breeding, should be implemented.

RDNA array length is a major determinant of replicative lifespan in budding yeast

The complex processes and interactions that regulate aging and determine lifespan are not fully defined for any organism. Here, taking advantage of recent technological advances in studying aging in budding yeast, we discovered a previously unappreciated relationship between the number of copies of the ribosomal RNA gene present in its chromosomal array and replicative lifespan (RLS). Specifically, the chromosomal ribosomal DNA (rDNA) copy number (rDNA CN) positively correlated with RLS and this interaction explained over 70% of variability in RLS among a series of wild-type strains. In strains with low rDNA CN, SIR2 expression was attenuated and extrachromosomal rDNA circle (ERC) accumulation was increased, leading to shorter lifespan. Suppressing ERC formation by deletion of FOB1 eliminated the relationship between rDNA CN and RLS. These data suggest that previously identified rDNA CN regulatory mechanisms limit lifespan. Importantly, the RLSs of reported lifespan-enhancing mutations were significantly impacted by rDNA CN, suggesting that changes in rDNA CN might explain the magnitude of some of those reported effects. We propose that because rDNA CN is modulated by environmental, genetic, and stochastic factors, considering rDNA CN is a prerequisite for accurate interpretation of lifespan data.

A dynamic stochastic search algorithm for high-dimensional optimization problems and its application to feature selection

As the number of dimensions of an optimization problem increases, the process of deriving the solution becomes more complicated and difficult. Metaheuristic algorithms are effective methods for solving many optimization problems, whereas most of these algorithms perform poorly on high-dimensional problems. In this paper, a novel metaheuristic algorithm called Dynamic Stochastic Search (DSS) is proposed for high-dimensional optimization problems. To effectively execute the exploration (diversification) and exploitation (intensification) processes of DSS, a dynamic stochastic search process is designed by defining a stochastic search control factor, a search process based on the Gaussian distribution, two shrink modes inspired by the Whale Optimization Algorithm, and a balance mechanism derived from the Bat Algorithm. The proposed algorithm has the following advantages: no specific control parameters other than the population size and the maximum number of iterations, a simple structure, and less computational effort in the implementation. Analyzing the computational complexity of DSS demonstrates its simplicity. To evaluate the performance of DSS, twenty 300-dimensional and 3000-dimensional classical benchmark functions as well as thirty 100-dimensional CEC2014 benchmark functions are utilized. The statistical results prove the effectiveness and feasibility of DSS for high-dimensional optimization problems because it shows better convergence performance and higher efficiency than various advanced optimization algorithms. Moreover, the superiority of DSS in solving high-dimensional real-world optimization problems is validated by applying it to feature selection for ten real-world benchmark classification datasets from the UCI machine learning repository.

On Regularized Optimal Execution Problems and Their Singular Limits

We investigate the portfolio execution problem under a framework in which volatility and liquidity are both uncertain. In our model, we assume that a multidimensional Markovian stochastic factor drives both of them. Moreover, we model indirect liquidity costs as temporary price impact, stipulating a power law to relate it to the agent's turnover rate. We first analyse the regularized setting, in which the admissible strategies do not ensure complete execution of the initial inventory. We prove the existence and uniqueness of a continuous and bounded viscosity solution of the Hamilton–Jacobi–Bellman equation, whence we obtain a characterization of the optimal trading rate. As a byproduct of our proof, we obtain a numerical algorithm. Then, we analyse the constrained problem, in which admissible strategies must guarantee complete execution to the trader. We solve it through a monotonicity argument, obtaining the optimal strategy as a singular limit of the regularized counterparts.

A New Cooperative Recourse Strategy for Emergency Material Allocation in Uncertain Environments

Emergency material allocation is an important issue in the urgent handling of public health emergencies. This article models the relief allocation and transportation route planning as an uncertain capacitated arc routing problem, which is a classic combinatorial optimization problem that considers stochastic factors such as uncertain demand and travel time in the service. The stochastic of demand leads to the route failure that the vehicle cannot serve the tasks successfully unexpected. Most existing research uses the independent recourse strategy. That is, each vehicle takes a back-and-forth trip separately when its remaining capacity cannot meet the actual demand of the task. This leads to a considerable recourse cost. However, a few studies have considered vehicular cooperation to deal with route failure, which is beneficial for pooling the capacity of multiple vehicles. In this paper, we propose a new recourse strategy called OneFAll that lets one vehicle take charge of all the failed tasks. In this case, other vehicles can finish the service once they are full. We develop the genetic programming hyper-heuristic with the OneFAll recourse strategy for solving the uncertain capacitated arc routing problem. The experimental studies show that our proposed method outperforms the existing genetic programming hyper-heuristic with the independent recourse strategy to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Moreover, our strategy outperforms the recourse strategy that failed tasks are returned to the unassigned task set for any vehicle to complete. This reflects that there exists resource waste if all vehicles are involved to repair the failed routes.

Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships

The paper is focused on comparing the forecasting performance of two relatively new types of Vector Error Correction - Multiplicative Stochastic Factor (VEC-MSF) specifications: VEC-MSF with constant conditional correlations, and VEC-MSF-SBEKK with time-varying conditional correlations. For the sake of comparison, random walks, vector autoregressions (VAR) with constant conditional covariance matrix, and VAR-SBEKK models are also considered. Based on daily quotations on three exchange rates: PLN/EUR, PLN/USD, and EUR/USD, where the cointegrating vector may be assumed to be known a priori, we show that in econometric models it can be more important to allow for cointegration relationships than for time-varying conditional covariance matrix.

The stochastic evolutionary game analysis of public prevention and control strategies in public health emergencies

PurposeThe COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.Design/methodology/approachBased on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.FindingsThe research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government’s punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.Practical implicationsThe study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.Originality/valueTo the best of the authors’ knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

Target sequence capture of Barnadesioideae (Compositae) demonstrates the utility of low coverage loci in phylogenomic analyses

Target sequence capture has emerged as a powerful method to sequence hundreds or thousands of genomic regions in a cost- and time-efficient approach. In most cases, however, targeted regions lack full sequence information for certain samples, due to taxonomic, laboratory, or stochastic factors. Loci lacking molecular data for a large number of samples are commonly excluded from downstream analyses, even though they may still contain valuable information. On the other hand, including data-poor loci may bias phylogenetic analyses. Here we use a target sequence capture dataset of an ecologically and taxonomically diverse group of spiny sunflowers (Asteraceae, or Compositae: Barnadesioideae) to test how the inclusion or exclusion of such data-poor loci affects phylogenetic inference. We investigate the sensitivity of concatenation and coalescent approaches to missing data with matrices of varying taxonomic completeness by filtering loci with different proportions of missing samples prior to data analysis. We find that missing data affect both the topology and branch support of the resulting phylogenies. The matrix containing all loci yielded the overall highest node support values, independently of the amount of missing nucleotides. These results provide empirical support to earlier suggestions based on single genes and data simulations that taxa with high amounts of missing data should not be readily dismissed as they can provide essential information for phylogenomic reconstruction.

Концептуальная структура системы управления транспортно-логистическим проектом на основе имитационного моделирования

The paper highlights the key factors for the development of the transport and logistics industry in the context of the digital transformation of society. The simulation model is shown as a digital twin of the transport and logistics system. A definition of a transport and logistics project is given, and its main (production / delivery of packages to a warehouse / terminal, loading and unloading operations with packages, storage of packages, transportation of packages) and additional (processing/reconstruction of packages; money movement) processes are separated. The technological process of planning and realizing the transport and logistics operations is considered in detail. There have been singled out the specific features of the transport and logistics process, including many stochastic factors, necessitating the use of simulation modeling methods. In terms of the theory of organizational systems, the structure of the management system for a transport and logistics project is proposed. The managed entities - the processes of the transport and logistics project - are described and structured in detail. The control actions, parameters of the external environment and the state of the transport and logistics process are highlighted. A model of decision-making by controlled subjects has been built. It is noted that the value of management efficiency is determined by the chosen strategy for the development of the transport and logistics process. A simulation model of a transport and logistics project for the delivery of equipment for a wind power plant is considered using the system analysis of processes. The description of the submodels of the simulation model is given in accordance with the developed generalized structure of the transport and logistics process management. The experiments have been carried out, the optimal production rates have been identified for a transport and logistics project of delivery of equipment for a wind power plant

Sub- and Supersolution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Markets

Sub- and Supersolution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Markets