Stochastic World Research Articles

A stochastic world model on gravity for stability inference.

The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects' stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants' sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity's vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants' subjective sense of objects' stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.

A stochastic world model on gravity for stability inference

The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects’ stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants’ sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity’s vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants’ subjective sense of objects’ stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.

The stochastic world of emerging viruses.

The acquisition of new hosts is a fundamental mechanism by which parasitic organisms expand their host range and perpetuate themselves on an evolutionary scale. Among pathogens, viruses, due to their speed of evolution, are particularly efficient in producing new emergence events. However, even though these phenomena are particularly important to the human species and therefore specifically studied, the processes of virus emergence in a new host species are very complex and difficult to comprehend in their entirety. In order to provide a structured framework for understanding emergence in a species (including humans), a comprehensive qualitative model is an indispensable cornerstone. This model explicitly describes all the stages necessary for a virus circulating in the wild to come to the crossing of the epidemic threshold. We have therefore developed a complete descriptive model explaining all the steps necessary for a virus circulating in host populations to emerge in a new species. This description of the parameters presiding over the emergence of a new virus allows us to understand their nature and importance in the emergence process.

Postclassical Rationality of Law in the Era of Postmodernity

The paper provides for debatable issues of rationality as a general philosophical category and as a legal concept. The author argues for the transition in philosophy and science from classical rationality to postclassical. Classical rationality is based on the presumption of the knowability of the world and the possibility of its transformation in accordance with the reasonable goals of the progressive development of mankind. However, the 20th century demonstrated the limitations of the human mind, unpredictability of the consequences of applying scientific rationality. Hence, the paradox of classical rationality follows: its growth, associated (or manifested) with the development of technology and technology, turns into an increased risk in all spheres of life. Post-classical rationality (post-non-classical, according to V. S. Stepin’s terminology) not only postulates the limitations of our knowledge about an opaque and stochastic world where uncertainty reigns, but also it assumes the involvement of a person socialized in the corresponding culture in the process of cognition (more broadly, perception, nomination, classification and categorization of the world and its development), its historical, valuebased and socio-cultural contextuality. The most developed and elaborated variant of postclassical rationality is the communicative contextuality which, however, is not devoid of problematic issues. According to the author, postclassical rationality of law is dialogical and it includes a transcendent aspect — ensuring the normal reproduction of humanity -- and an immanent one — the legitimacy of the accepted norms of law also in the process of their reproduction. Postclassical rationality of the law norms construction is expressed in the struggle of the main social groups for the right to determine the significance (including legal) of various social problems. The rationality of the implementation of the norms of law is problematized by the paradox of «following the rule» elaboarted by by L. Wittgenstein that never contains the entirety of the prescriptions of its — rule — implementation. The author believes that in order to resolve the problems of rationality of law it is necessary to reconsider classical rationality of law as a postclassical rationality, rather than to reject this category and principle of law.

Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds

We consider the problem of learning action models for planning in unknown stochastic environments that can be defined using the Probabilistic Planning Domain Description Language (PPDDL). As input, we are given a set of previously executed trajectories, and the main challenge is to learn an action model that has a similar goal achievement probability to the policies used to create these trajectories. To this end, we introduce a variant of PPDDL in which there is uncertainty about the transition probabilities, specified by an interval for each factor that contains the respective true transition probabilities. Then, we present SAM+, an algorithm that learns such an imprecise-PPDDL environment model. SAM+ has a polynomial time and sample complexity, and guarantees that with high probability, the true environment is indeed captured by the defined intervals. We prove that the action model SAM+ outputs has a goal achievement probability that is almost as good or better than that of the policies used to produced the training trajectories. Then, we show how to produce a PPDDL model based on this imprecise-PPDDL model that has similar properties.

Biotic resistance in a stochastic world: Do rodents act as a filter to alien tree invasion in pampean old fields?

AbstractBiotic resistance from post‐dispersal seed predators has been shown to influence invasion and spread of woody plants in grasslands and savannas. Yet, the role of rodent granivores as a barrier to woody plant invasion remains poorly studied. We examined the impact of rodent exclusion and canopy disturbance on two exotic tree seedling establishment (Gleditsia triacanthos and Robinia pseudoacacia) in an old‐field grassland of the Inland Pampa, Argentina. The experiment was conducted over 4 years with substantial climatic variation, which affected both rodent abundance and seedling performance. Our results showed that, for both tree species, rainfall was the main factor affecting both seedling emergence and survival, while the effect of rodent seed predation and disturbance depended on variability in natural rodent density. The response of G. triacanthos seedling emergence after the onset of favorable water conditions was more rapid than the recovery of rodent populations. This delay provided an opportunity to escape from potential seed predation, reducing its importance as a biotic filter to woody invasion. Robinia pseudoacacia seedlings emerged during the first growing period and died because of hydric stress, while remnant seeds did not remain viable in the seed bank after 2 years of sowing. From our results, we suggest that the dynamic of tree invasion depends highly on climatic variability, which has direct effects on seed germination and survival, as well as effects mediated by rodent abundance and herbaceous competition.

Scaling mount SAR: Commentary on Matthews et al. (2021) The Species–Area Relationship: Theory and Application

Scaling mount SAR: Commentary on Matthews et al. (2021) <i>The Species–Area Relationship: Theory and Application</i>

클러터 환경에서 로봇 파지를 위한 역학 예측 신경망 기반 강화학습 방법

본 연구는 둘 이상의 물체가 서로 겹치거나 인접하여 로봇 파지 포인트를 파악하기 어려운 클러터 환경(cluttered environment)에서 역학 예측 신경망(Dynamics Prediction Network, DPN)을 이용하여 작업환경과 로봇 행동 사이의 역학 관계를 학습하고 파지 작업을 수행하는 강화학습 방법을 제안한다. 클러터 환경에서 파지 포인트를 찾을 수 없는 상황을 해결하기 위해 비파지 동작을 활용하며, 제안하는 역학 예측 신경망을 이용한 강화학습 방법은 파지뿐 아니라 비파지 동작 학습에서도 효과적임을 보여준다. 학습 환경을 추계(stochastic world)가 아닌 결정계(deterministic world)로 가정하여 전이 확률(state transition probability)을 1로 고정함으로써 DPN은 현재 상태(current state), 현재 행동(current action), 다음 상태(next state)의 일대일 대응관계를 학습한다. 학습 단계에서 추가적으로 진행되는 DPN으로 인한 학습 부담을 줄이기 위하여 매개변수 공유(parameter sharing)를 학습 모델에 적용하였다. 제안하는 방법은 매개변수 공유와 DPN으로 학습에서의 부담뿐 아니라 동작모델의 가중치 메모리 크기를 절반으로 줄이면서도 블록 테스트 환경에서 8%의 파지 성공률 향상을 보였다. 실세계 물체를 3D 스캔하여 구성한 새로운 테스트 환경 실험에서 23%의 파지 성공률 향상을 보여 물리적 상호작용 학습으로 일반화(generalization)가 잘 이루어졌음을 보였다.

Short-Term Implications of Climate Shocks on Wheat-Based Nutrient Flows: A Global "Nutrition at Risk" Analysis through a Stochastic CGE Model.

Food security analyses of international trade largely overlook the importance of substantial heterogeneity and complexity of nutrient content in food products. This paper quantifies the extent to which wheat-based nutrient supplies, including energy, protein, iron, zinc, and magnesium, are exposed to the risks of realistic productivity and trade shocks. By employing a static and stochastic world trade computable general equilibrium (CGE) model, we find that productivity shocks may result in losses in households’ nutrient consumption of up to 18% for protein, 33.1% for zinc, and 37.4% for magnesium. Significant losses are observed in countries mostly in the Middle East, North Africa, and Central Asia. Since the main centers of wheat exports have recently been shifting to former Soviet Union countries, we also simulated the nutritional risks of export restrictions imposed by the Russian Federation, Ukraine, and Kazakhstan, which have resorted to this policy instrument in recent years. We find that partial export restrictions increase the probability of nutrient shocks by five times or more in most countries that we studied. Increased nutrient deficiencies have a range of public health implications in the affected countries, which could be mitigated and/or avoided by adjusting production and trade policies and by targeting high nutritional risk groups, such as women and children. Since the potential implications of supply shocks are diffused across countries through international trade, the stricter regulation of export restrictions to enhance the predictably and reliability of global food supplies is also needed.

Модель формирования стохастического мировоззрения старшеклассников в условиях цифровизации математического образования

The article is devoted to the study of the peculiarities of the stochastic worldview of high school students. Stochastic world outlook lays the landmarks of creative and practical activities, helps to create favorable conditions for development of intellectual potential of a pupil. &#x0D; Digitalization in education determines the dominant nature of both the active use of modern methods, tools and technologies of education, and the formation of students' digital skills, training in data processing and analysis, which is closely related to the teaching of stochastics and indicates the relevance of research. In this regard, the untapped potential of stochasticity is greater than any other field of knowledge. Widely used probabilistic-statistical methods contribute to the study of variability and complexity of political, economic, social processes. In this regard, a model of formation of stochastic worldview of high school students in the conditions of digitalization of mathematical education is developed. This model is a set of educational and digital information content, which gives the results of the educational process a new quality of education, allows you to realize the integrity of the worldview, values and motivational constructs, creates the necessary conditions under which it becomes possible to generate knowledge by students themselves based on self-development and self-actualization, their active and productive creativity.

Stochastic challenges to interrupting helminth transmission

Predicting the effect of different programmes designed to control both the morbidity induced by helminth infections and parasite transmission is greatly facilitated by the use of mathematical models of transmission and control impact. In such models, it is essential to account for the many sources of uncertainty – natural, or otherwise – to ensure robustness in prediction and to accurately depict variation around an expected outcome. In this paper, we investigate how well the standard deterministic models match the predictions made using individual-based stochastic simulations. We also explore how well concepts which derive from deterministic models, such as ‘breakpoints’ in transmission, apply in the stochastic world. Employing an individual-based stochastic model framework we also investigate how transmission and control are affected by the migration of infected people into a defined community. To give our study focus we consider the control of soil-transmitted helminths (STH) by mass drug administration (MDA), though our methodology is readily applicable to the other helminth species such as the schistosome parasites and the filarial worms. We show it is possible to theoretically define a ‘stochastic breakpoint’ where much noise surrounds the expected deterministic breakpoint. We also discuss the concept of the ‘interruption of transmission’ independent of the ‘breakpoint’ concept where analyses of model behaviour illustrate the current limitations of deterministic models to account for the ‘fade-out’ or transmission extinction behaviour in simulations. Our analysis of migration confirms a relationship between the critical infected human migration rate scale (i.e., order of magnitude) per unit of time and the death rate of infective stages that are released into the free-living environment. This relationship is shown to determine the likelihood that control activities aim at chemotherapeutic treatment of the human host will eliminate transmission. The development of a new stochastic simulation code for STH in the form of a publicly-available open-source python package which includes features to incorporate many population stratifications, different control interventions including mass drug administration (with defined frequency, coverage levels and compliance patterns) and inter-village human migration is also described.

External Validity in a Stochastic World

We examine the generalizability of internally valid estimates of causal effects in a fixed population over time when that population is subject to aggregate shocks. This temporal external validity is shown to depend upon the distribution of the aggregate shocks and the interaction between these shocks and the casual effects. We show that returns to investment in agriculture, small and medium enterprises and human capital differ significantly from year to year. We also show how returns to investments interact with specific aggregate shocks, and estimate the parameters of the distributions of these shocks. We show how to use these estimates to appropriately widen estimated confidence intervals to account for aggregate shocks.

Quantifying the Effects of Agricultural Autarky Policy: Resilience to Yield Volatility and Export Restrictions

In the wake of the 2007–2008 global food crisis, various national governmental bodies aimed at increasing their food self-sufficiency to stabilize their domestic markets. Despite the fact that food self-sufficiency is a long-standing policy discussion issue, its effectiveness has not been fully scrutinized with a quantitative modeling approach. Japan’s government rigorously protects domestic agricultural producers on the grounds of national food security and, hence, has grappled with enhancing the country’s food self-sufficiency, even though economists have strongly argue against this, in terms of the inefficiency of resource allocation. This study developed a stochastic world trade computable general equilibrium model to quantify the benefit/loss of wheat autarky policies for Japan against wheat yield shocks and export bans. It was found that the comprehensive economic burden to materialize full self-sufficiency in wheat is approximately $8700 million, regardless of which of the two methods of market intervention––increasing the import tariff or subsidizing production--is used. Greater self-sufficiency causes higher volatility in the domestic wheat price due to the yield variability in exporting nations being more destabilized than that in exporting countries. Also, the autarky strategies almost halve the welfare deterioration induced by export restrictions, although it does not pay for the implementation cost.

External Validity in a Stochastic World: Evidence from Low-Income Countries

Abstract We examine empirically the generalizability of internally valid micro-estimates of causal effects in a fixed population over time when that population is subject to aggregate shocks. Using panel data, we show that the returns to investments in agriculture in India and Ghana, small and medium non-farm enterprises in Sri Lanka, and schooling in Indonesia fluctuate significantly across time periods. We show how the returns to these investments interact with specific, measurable, and economically relevant aggregate shocks, focusing on rainfall and price fluctuations. We also obtain lower-bound estimates of confidence intervals of the returns based on estimates of the parameters of the distributions of rainfall shocks in our two agricultural samples. We find that even these lower-bound confidence intervals are substantially wider than those based solely on sampling error that are commonly provided in studies, most of which are based on single-year samples. We also find that cross-sectional variation in rainfall cannot be confidently used to replicate within-population rainfall variability. Based on our findings, we discuss methods for incorporating information on external shocks into evaluations of the returns to policy.

External Validity in a Stochastic World: Evidence from Low-Income Countries

We examine empirically the generalizability of internally valid micro estimates of causal effects in a fixed population over time when that population is subject to aggregate shocks. Using panel data we show that the returns to investments in agriculture in India and Ghana, small and medium non-farm enterprises in Sri Lanka, and schooling in Indonesia fluctuate significantly across time periods. We show how the returns to these investments interact with specific, measurable and economically-relevant aggregate shocks, focusing on rainfall and price fluctuations. We also obtain lower-bound estimates of confidence intervals of the returns based on estimates of the parameters of the distributions of rainfall shocks in our two agricultural samples. We find that even these lower-bound confidence intervals are substantially wider than those based solely on sampling error that are commonly provided in studies, most of which are based on single-year samples. We also find that cross-sectional variation in rainfall cannot be confidently used to replicate within-population rainfall variability. Based on our findings, we discuss methods for incorporating information on external shocks into evaluations of the returns to policy.

Probabilistic assessment of realizing the 1.5 °C climate target

In this paper we develop a probabilistic assessment of the energy transition and economic consequences of limiting global warming by the end of the century to 1.5 °C. The assessment is made by applying a Monte Carlo analysis in MERGE-ETL, a technology rich integrated assessment model with endogenous learning. We assume a deterministic 1.5 °C target and uncertainty in other factors such as economic growth, resources, and technology costs. The distributions of these variables are obtained by the PROMETHEUS stochastic world energy model. The study assumes early actions and quantifies the market penetration of low carbon technologies, the emission pathways and the economic costs for an efficient reduction of greenhouse gas emissions such that the temperature limit is not exceeded. We find that achieving the 1.5 °C Paris target is technically feasible but it requires immediate and global action. Key pillars in the decarbonization of the energy system are large deployment of renewable energy, early retirement of fossil based power plants, energy efficiency and negative emission technologies. Furthermore, that the availability of biomass resources, the rapid decrease of the costs of renewables and improvements in energy efficiency are the factors with the largest effect on the cost of carbon.

Greed is Good: From Super-Harvest to Recovery in a Stochastic Predator-Prey System

This paper demonstrates a predator-prey system of cod and capelin that confronts a possible scenario of prey extinction under the first-best policy in a stochastic world. We discover a novel ‘super-harvest’ phenomenon that the optimal harvest of the predator is even higher than the myopic policy, or the ‘greedy solution’, on part of the state space. This intrinsic attempt to harvest more predator to protect the prey is a critical evidence supporting the idea behind ‘greed is good’. We ban prey harvest and increase predator harvest in a designated state space area based on the optimal policy. Three heuristic recovery plans are generated following this principle. We employ stochastic simulations to analyse the probability of prey recovery and evaluate corresponding costs in terms of value loss percentage. We find that the alternative policies enhance prey recovery rates mostly around the area of 50% recovery probability under the optimal policy. When we scale up the predator harvest by 1.5, the prey recovery rate escalates for as much as 28% at a cost of 5% value loss. We establish two strategies: modest deviation from the optimal on a large area or intense measure on a small area. It seems more cost-effective to target the stock space with accuracy than to simply boost predator harvest when the aim is to achieve remarkable improvement of prey recovery probability.

A multi-stage stochastic optimization model of a pastoral dairy farm

Pastoral dairy farmers make sequential decisions in the face of long-term environmental uncertainty and price volatility. Decisions made early in the season, such as the number of cows to stock per hectare, can have significant effects later in the season if the farmer is forced to import additional feed to meet the cows’ energy demands during a drought. In this paper, we present POWDer: the milk Production Optimizer incorporating Weather Dynamics. POWDer is a novel multi-stage stochastic program that divides the dairy farming season into weeks and links these weeks by a system of linear dynamics. By applying POWDer to a case farm in New Zealand, we demonstrate POWDer’s promise as a tool that can help participants in the New Zealand dairy industry understand and plan for the challenge of farming in a stochastic world.

Egosystem: A Visualization of Wholeness Amidst Environmental Uncertainty and Fragmentation

Students are embedded in a stochastic world. Postmodern practitioners of fragmentation accept this, however they dispute Jungian and Eriksonian wholeness. The existential representation ego as a two-dimensional thing, the Kantian-, Jungian- I-formation is questioned. Similarly, Gardnerian frames of mind and MI are questioned as functional pedagogical models within the context of a stochastic reality. Thus, the term literacy must be expanded to address this enduring reality of both the classroom, and the shape-shifting, kaleidoscopic, urban landscapes through which students move daily. Egosystem (Author, 2005) is a perfect model for this environmental kaleidoscope. This requires a new literacy, a true 'reading the world' (Freire, 1995). We understand that the classical ego is an extension of a system of influential forces of the embedding world that inform, shape and re-shape it. Egosystem is the new complex ego struggling for survival. Uncertainty is the undercurrent beneath volatile educational environments wherein visual arts achieves some measure of control by offering challenging design problems. Archaic and modern confrontation with challenges presented by this stochastic world is an impetus for intellectual development through increasing visualization, heightened awareness, self-healing and self-renewal. The search for wholeness extends the Jungian archetype of teleiosis to an enlightened version of the whole Self within an entropic field that tends towards fragmentation. It is the same ego-consciousness and environmental awareness the genus Homo used to negotiate survival within the original stochastic classroom of the African Rift Valley. We witness the same successive growth of modern students learning to solve challenging design problems, to adapt and to change within an uncertain world. As ego evolves into egosystem ― with its palpable links to a stochastic environmental milieu ― so students evolve through a consequential series of 'successive emancipations of the human will and intellect' (Malraux, 1956).

Explaining opinion polarisation with opinion copulas.

An empirically founded and widely established driving force in opinion dynamics is homophily i.e. the tendency of “birds of a feather” to “flock together”. The closer our opinions are the more likely it is that we will interact and converge. Models using these assumptions are called bounded confidence models (BCM) as they assume a tolerance threshold after which interaction is unlikely. They are known to produce one or more clusters, depending on the size of the bound, with more than one cluster being possible only in the deterministic case. Introducing noise, as is likely to happen in a stochastic world, causes BCM to produce consensus which leaves us with the open problem of explaining the emergence and sustainance of opinion clusters and polarisation. We investigate the role of heterogeneous priors in opinion formation, introduce the concept of opinion copulas, argue that it is well supported by findings in Social Psychology and use it to show that the stochastic BCM does indeed produce opinion clustering without the need for extra assumptions.