Probability Of Future Events Research Articles

Student'S activity management system using qr code and C4.5 algorithm

Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. With this process, it helps to find patterns within large data sets to predict outcomes. The process of data mining uses big data, therefore, it is very suitable to be incorporated to the system. The development of this website system is by using the Laravel framework with PHP. C4.5 Algorithm is being integrated in the system to predict the students ’achievements on the soft skills which has been outlined in the Graduate Employability Skills (GEmS) based on the activities that the students have joined during their studies in Management and Science University (MSU). By using this prediction, the students are analysed on their soft skills achievements. This system is also being developed to track the activities for each student in each semester according to the GEmS planner where students are able to manage their activities for each semester accordingly. It can also identify the students who are more prioritized to join the activities that have been created by the lecturer. Hence, the lecturers can easily control the number of students that are needed to join the activities that the lecturers created. As a validation for students ’activity registration, the QR Code is also going to be incorporated in the system.

Learning What is Similar: Precedents and Equilibrium Selection

We argue that a precedent is important not only because it changes the relative frequency of a certain event, making it positive rather than zero, but also because it changes the way that relative frequencies are weighed. Specifically, agents assess probabilities of future events based on past occurrences, where not all of these occurrences are deemed equally relevant. More similar cases are weighed more heavily than less similar ones. Importantly, the similarity function is also learnt from experience by second-order induction. The model can explain why a single precedent affects beliefs above and beyond its effect on relative frequencies, as well as why it is easier to establish reputation at the outset than to re-establish it after having lost it. We also apply the model to equilibrium selection in a class of games dubbed Statis- tical Games, suggesting the notion of Similarity-Nash equilibria, and illustrate the impact of precedents on the play of coordination games.

Natural Time, Nowcasting and the Physics of Earthquakes: Estimation of Seismic Risk to Global Megacities

This paper describes the use of the idea of natural time to propose a new method for characterizing the seismic risk to the world's major cities at risk of earthquakes. Rather than focus on forecasting, which is the computation of probabilities of future events, we define the term seismic nowcasting, which is the computation of the current state of seismic hazard in a defined geographic region.

O7-2 - Simple Screening Test for Sarcopenia Predicts Future Cardiovascular Events in Patients with Chronic Kidney Disease

Introduction: Recently, a simple screening test that can identify sarcopenia using three variables (age, grip strength and calf circumference) has been developed. However, clinical utility of sarcopenia score for predicting future cardiovascular events in patients with kidney disease remains unclear. Methods and Results: We calculated sarcopenia score of 265 chronic kidney disease (CKD) patients, and followed the patients for cardiovascular events. The endpoint of this study was a composite of total mortality and cardiovascular hospitalization, including heart failure decompensation. The patients in high sarcopenia score (greater than or equal to 105 in men and 120 in women) group showed significantly higher plasma BNP levels (median 103.1, interquartile range [46.3–310.0] vs. 46.7, [18.0–91.8]pg/mL, P < .0001) than those in low sarcopenia score group. Kaplan-Meier curve revealed that the risk of adverse cardiovascular events was significantly increased in high sarcopenia score group (log-rank test: P < .0001), even after potential confounding factors were corrected using propensity score matching. Multivariate Cox hazard analysis identified high sarcopenia score (hazard ratio: 3.00; 95% confidence interval: 1.44–6.27; P = .0003) as independent predictor of primary endpoints. Furthermore, the combination of high sarcopenia score and high BNP identified patients with significantly higher probability of future events (P < .0001). Conclusions: Simple screening score for sarcopenia could be a useful tool for estimating future adverse event risk in patients with CKD.

Precedents, Reputation, and Higher-Order Induction

We argue that a precedent is important not only because it changes the relative frequency of a certain event, making it positive rather than zero, but also because it changes the way that relative frequencies are weighed. Specifically, agents assess probabilities of future events based on past occurrences, where not all of these occurrences are deemed equally relevant. More similar cases are weighed more heavily than less similar ones. Importantly, the similarity function is also learnt from experience by induction. The model can explain why a single precedent affects beliefs above and beyond its effect on relative frequencies, as well as why it is easier to establish reputation at the outset than to re-establish it after having lost it. Finally, we discuss more sophisticated forms of learning, by which similarity is defined not only on cases but also on attributes, and the importance of some attributes, learnt from the data by second-order induction, can also affect the perceived importance of other attributes.

New Goodness-of-Fit Diagnostics for Conditional Discrete Response Models

This paper proposes new specification tests for conditional models with discrete responses. In particular, we can test the static and dynamic ordered choice model specifications, which is key to apply efficient maximum likelihood methods, to obtain consistent estimates of partial effects and to get appropriate predictions of the probability of future events. The traditional approach is based on probability integral transforms of a jittered discrete data which leads to continuous uniform iid series under the true conditional distribution. We investigate in this paper an alternative transformation based only on original discrete data. We show analytically and in simulations that our approach dominates the traditional approach in terms of power. We apply the new tests to models of the monetary policy conducted by the Federal Reserve.

New goodness-of-fit diagnostics for conditional discrete response models

This paper proposes new specification tests for conditional models with discrete responses, which are key to apply efficient maximum likelihood methods, to obtain consistent estimates of partial effects and to get appropriate predictions of the probability of future events. In particular, we test the static and dynamic ordered choice model specifications and can cover infinite support distributions for e.g. count data. The traditional approach for specification testing of discrete response models is based on probability integral transforms of a jittered discrete data which leads to continuous uniform i.i.d. series under the true conditional distribution. Then, standard specification testing techniques for continuous variables could be applied to the transformed series, but the extra randomness from jitters affects the power properties of these methods. We investigate in this paper an alternative transformation based only on original discrete data that avoids any randomization. We analyze the asymptotic properties of goodness-of-fit tests based on this new transformation and explore the properties in finite samples of a bootstrap algorithm to approximate the critical values of test statistics which are model and parameter dependent. We show analytically and in simulations that our approach dominates the methods based on randomization in terms of power. We apply the new tests to models of the monetary policy conducted by the Federal Reserve.

Relationship between Future Time Orientation and Item Nonresponse on Subjective Probability Questions: A Cross-Cultural Analysis.

Time orientation is an unconscious yet fundamental cognitive process that provides a framework for organizing personal experiences in temporal categories of past, present and future, reflecting the relative emphasis given to these categories. Culture lies central to individuals' time orientation, leading to cultural variations in time orientation. For example, people from future-oriented cultures tend to emphasize the future and store information relevant for the future more than those from present- or past-oriented cultures. For survey questions that ask respondents to report expected probabilities of future events, this may translate into culture-specific question difficulties, manifested through systematically varying "I don't know" item nonresponse rates. This study drew on the time orientation theory and examined culture-specific nonresponse patterns on subjective probability questions using methodologically comparable population-based surveys from multiple countries. The results supported our hypothesis. Item nonresponse rates on these questions varied significantly in the way that future-orientation at the group as well as individual level was associated with lower nonresponse rates. This pattern did not apply to non-probability questions. Our study also suggested potential nonresponse bias. Examining culture-specific constructs, such as time orientation, as a framework for measurement mechanisms may contribute to improving cross-cultural research.

Proper Proxy Scoring Rules

Proper scoring rules can be used to incentivize a forecaster to truthfully report her private beliefs about the probabilities of future events and to evaluate the relative accuracy of forecasters. While standard scoring rules can score forecasts only once the associated events have been resolved, many applications would benefit from instant access to proper scores. In forecast aggregation, for example, it is known that using weighted averages, where more weight is put on more accurate forecasters, outperforms simple averaging of forecasts. We introduce proxy scoring rules, which generalize proper scoring rules and, given access to an appropriate proxy, allow for immediate scoring of probabilistic forecasts. In particular, we suggest a proxy-scoring generalization of the popular quadratic scoring rule, and characterize its incentive and accuracy evaluation properties theoretically. Moreover, we thoroughly evaluate it experimentally using data from a large real world geopolitical forecasting tournament, and show that it is competitive with proper scoring rules when the number of questions is small.

Incorporating lifecycle and environment in loan-level forecasts and stress tests

The new FASB current expected credit loss (CECL) proposal, IASB’s IFRS 9, and regulatory stress testing all require that the industry move toward forecasting probabilities of future events, rather than simply rank-ordering loans. Even more importantly, effective loan pricing requires this same forward-looking, loan-level forecasting.We created a loan-level version of Age-Period-Cohort (APC) models suitable for forecasting individual loan performance at a point-in-time or for the loan’s lifetime. The APC literature explains that any model of loan performance must make either an explicit or implicit assumption around the embedded model specification error between age of the loan, vintage origination date, and performance date. We have made this assumption explicit and implemented a technique using augmented macroeconomic history to stabilize the analysis.The preceding steps provide robust estimates of lifecycle and environmental impacts. We then use a Generalized Linear Model (GLM) with a population odds offset for each age/time combination derived from the lifecycle and environment functions in order to estimate origination and behavior scores. Analyzing a small US auto loan portfolio, we demonstrate that this model is robust out-of-sample and out-of-time for predicting both rank-ordering and probabilities by inserting the odds offset appropriate for the environment being modeled.In addition to producing loan-level forecasts and stress tests, the scores produced have higher rank-order performance out-of-sample and out-of-time than standard scores. The scores prove to be robust years into the future with no measurable degradation in performance because of the stabilizing effect of the offset factor during model construction.

Interventional Strategies in early Atherosclerosis

Atherosclerosis remains clinically mute for a long time and frequently manifests itself with an acute cardiovascular event; therefore, the possibility to detect the disease in a subclinical phase and to reduce or reverse its progression is an issue of relevance. Non-invasive diagnostic procedures such as B-mode ultrasonography of carotid intima-media thickness (CIMT), electron beam computed tomography (EBCT) and magnetic resonance angiography (MRA) allow to identify atherosclerotic disease in its early phases, to evaluate the disease progression and monitor the effects of interventions. In recent years, several therapeutic strategies have been adopted over time to slow early atherosclerosis in asymptomatic individuals at intermediate/high cardiovascular risk. Prospective trials employing multifactorial non-pharmacological interventions (diet, exercise, smoking cessation) have demonstrated a favorable effect on progression of atherosclerosis. Hence lifestyle modification may be an effective therapeutic strategy to be adopted as a first step and a highly cost-effective intervention in a preclinical setting involving a large number of individuals. Drugs should be considered as a second step or should be associated to further reduce the risk in individuals at high probability of future events, who require more intensive interventions. Reducing low-density lipoprotein levels, blood pressure and platelet aggregation seems to be the most effective intervention in these subjects, whereas the treatment of emerging coronary risk factors, e.g. oxidative stress, inflammatory activation and infections has not produced the expected protective effect.

Event Prediction in an IoT Environment Using Naïve Bayesian Models

In many Internet of Things (IoT) scenarios, there is a need to predict events generated by objects. However, because of the dynamicity of IoT environments, it is difficult to predict with certainty if/when such events will occur. Probabilistic reasoning allows us to infer dependent probabilities of events, from other events that are either easier to detect or to predict. In this paper we propose an architecture that employs a Bayesian event prediction model that uses historical event data generated by the IoT cloud to calculate the probability of future events. We demonstrate the architecture by implementing a prototype system to predict outbound flight delay events, based on inbound flight delays, based on historical data collected from aviation statistics databases.

Nonconvex Equilibrium Prices in Prediction Markets

Prediction markets are increasingly being used to estimate probabilities of future events, and market equilibrium prices depend on the distribution of subjective probabilities of underlying events. When each contract requires the payment of a dollar if the underlying event were to occur, equilibrium prices are usually used to estimate the mean probabilities of the corresponding events. This paper shows that under certain conditions, market equilibrium prices of such contracts can lie outside the convex hull of potential traders’ probability beliefs, and where this occurs, market forecasts can induce stochastically dominated group decisions. We describe examples of where this could occur and generalize these examples to characterize conditions for nonconvex prices. A necessary condition for nonconvex prices is that market risk premia for complementary contracts have opposite signs. Preference functions on the lines of prospect theory have this property.

The impact of stress in decision making in the context of uncertainty.

For a number of decades, different fields of knowledge, including psychology, economics, and neurosciences, have focused their research efforts on a better understanding of the decision-making process. Making decisions based on the probability of future events is routine in everyday life; it occurs whenever individuals select an option from several alternatives, each one associated with a specific value. Sometimes subjects decide knowing the precise outcomes of each option, but commonly they have to decide without knowing the consequences (because either ambiguity or risk is involved). Stress has a broad impact on animal behaviors, affects brain regions involved in decision-making processes, and, when maladaptive, is a trigger for neuropsychiatric disorders. This Mini-Review provides a comprehensive overview on how stress impacts decision-making processes, particularly under uncertain conditions. Understanding this can prove to be useful for intervention related to impairments to decision-making processes that present in several stress-triggered neuropsychiatric disorders.

Model for Evaluation of Co–creation Possibilities in the Enterprise of Knowledge – Intensive Business Services

Increasingly growing globalisation in business creates new challenges for enterprises, for instance, stronger competition, changing structure of the branch of business, new technologies and markets emerging, etc. Changing conditions demand new solutions that could improve the way enterprises perform and create innovation generating miscellaneous value. Co–creation is a unique way for a joint creation of knowledge and innovation between the entities involved that helps take into account specific needs of clients, improves productivity, growth potential of enterprises, etc. Scientific literature emphasizes the usefulness of co-creation, but the risk that comes together with it should be noted too. It is related to uncertainty that makes the estimation of probability of future events and their importance to the enterprise problematic. Therefore, it is necessary to have an effective method to evaluate the opportunities of co–creation that may emerge between an enterprise of knowledge intensive business services (KIBS) and its clients and to see potential threats. DOI: http://dx.doi.org/10.5755/j01.ss.83.1.6863

Real-World and Risk-Neutral Probabilities in the Regulation on the Transparency of Structured Products

La versione italiana di questo documento e disponibile al seguente link: http://ssrn.com/abstract=2319271The price of derivatives (and hence of structured products) can be calculated as the discounted value of expected future payoffs, assuming standard hypotheses on frictionless and complete markets and on the type of stochastic processes for the price of the underlying. However, the probabilities used in the pricing process do not represent “real” probabilities of future events, because they are based on the assumption that market participants are risk-neutral. This paper reviews the relevant mathematical finance literature, and clarifies that the risk-neutrality hypothesis is acceptable for pricing, but not to forecast the future value of an asset. Therefore, we argue that regulatory initiatives that mandate intermediaries to give retail investors information on the probability that, at a future date, the value of a derivative will be higher or lower than a given threshold (so-called “probability scenarios”) should explicitly reference probabilities that take into account the risk premium (so-called “real-world” probabilities). We also argue that, though probability scenarios may look appealing to foster investor protection, their practical implementation, if based on the right economic approach, raises significant regulatory and enforcement problems.

The Human Brain Encodes Event Frequencies While Forming Subjective Beliefs

To make adaptive choices, humans need to estimate the probability of future events. Based on a Bayesian approach, it is assumed that probabilities are inferred by combining a priori, potentially subjective, knowledge with factual observations, but the precise neurobiological mechanism remains unknown. Here, we study whether neural encoding centers on subjective posterior probabilities, and data merely lead to updates of posteriors, or whether objective data are encoded separately alongside subjective knowledge. During fMRI, young adults acquired prior knowledge regarding uncertain events, repeatedly observed evidence in the form of stimuli, and estimated event probabilities. Participants combined prior knowledge with factual evidence using Bayesian principles. Expected reward inferred from prior knowledge was encoded in striatum. BOLD response in specific nodes of the default mode network (angular gyri, posterior cingulate, and medial prefrontal cortex) encoded the actual frequency of stimuli, unaffected by prior knowledge. In this network, activity increased with frequencies and thus reflected the accumulation of evidence. In contrast, Bayesian posterior probabilities, computed from prior knowledge and stimulus frequencies, were encoded in bilateral inferior frontal gyrus. Here activity increased for improbable events and thus signaled the violation of Bayesian predictions. Thus, subjective beliefs and stimulus frequencies were encoded in separate cortical regions. The advantage of such a separation is that objective evidence can be recombined with newly acquired knowledge when a reinterpretation of the evidence is called for. Overall this study reveals the coexistence in the brain of an experience-based system of inference and a knowledge-based system of inference.

Cardiovascular risk in the young type 1 diabetes population with a low 10‐year, but high lifetime risk of cardiovascular disease

Diabetes mellitus is associated with excess cardiovascular mortality that is evident in all age groups, but is most pronounced in young people with type 1 diabetes. Cardiovascular risk estimation models generally estimate the probability of future events over a 10-year time horizon. Due to the dependency on age, children and adolescents with type 1 diabetes would be considered at low short-term risk but high life-time risk of developing a cardiovascular event. Guidelines recommend screening particularly for microvascular complications including nephropathy and retinopathy beginning around puberty. Identification of early microvascular abnormalities in children and adolescents not only predict later development of long-term microvascular complications and further end-organ damage but are associated with an increased risk for future macrovascular events. This may be because of the fact that the same glycaemic mechanisms responsible for the occurrence of microvascular disease may also apply to the development of atherosclerosis. Alternatively, interventions that reduce the development of microvascular end-organ damage may also delay the development of associated macrovascular disease. Screening for subclinical atherosclerosis, especially in the coronary and carotid vessels, has been advocated as a means of detecting early atherosclerotic disease in asymptomatic individuals with the aim of potentially reclassifying cardiovascular risk and guiding therapeutic interventions. Currently there is no randomized clinical trial evidence that additional screening using non-invasive imaging techniques alters cardiovascular disease outcomes. We do not know the best approach or combination of approaches to assess risk and reduce cardiovascular disease burden in type 1 diabetes mellitus. All screening interventions carry harms as well as benefits and until further evidence becomes available additional screening using non-invasive imaging tests for the detection of subclinical atherosclerosis cannot be currently recommended for patients with type 1 diabetes.

P.3.f.003 Life stress is associated with excess calorie intake and sugar consumption in subjects with early psychosis

How do infants predict the next future event, when such a prediction requires estimating the event's probability? The literature suggests that adult humans often fail this task because their probability estimates are affected by heuristics and biases or because they can reason about the frequency of classes of events but not about the probability of single events. Recent evidence suggests instead that already at 12 months infants have an intuitive notion of probability that applies to single, never experienced events and that they may use it to predict what will happen next. We present a theory according to which infants' intuitive grasp of the probability of future events derives from their representation of logically consistent future possibilities. We compare it and other theories against the currently available data. Although the evidence does not speak uniquely in favor of one theory, the results presented and the theories currently being developed to account for them suggest that infants have surprisingly sophisticated reasoning abilities. These conclusions are incompatible with most current theories of adult logical and probabilistic reasoning.

Small enough to fail: a systems approach to financial systems reform

Introduction The “Credit Scrunch” of 2007 was a systemic failure. Interactions between elements of the system (banks, rating agencies, regulators, governments, financial instruments, etc.) mattered more than the specific behaviour of a particular actor. If you believe the crisis was an apocalypse or foreshadows an apocalypse, then you should be considering fundamental reform. You want to redesign, and design principles would be handy. What might they be? Systems theory, touching on if not encompassing chaos theory and complexity theory, gives us a rich background to some simple design parameters, namely input process output, governance, monitoring, feed back and feed forward. Systems theory goes some way to explaining why financial systems, due to their large amount of feed forward (positive feed back), tend to exhibit “fat tail” outcome distributions and more instability than physical systems. Bob Giffords, the technology analyst, groups together feed back, monitoring, feed forward and governance components as “feed through”, highlighting the effect of people’s perceptions on the probability of future events (Mainelli and Giffords, 2009). If people change their perception of a risk, e.g. terrorism, that perception feeds through to alter future behaviour, such as passenger levels on public transport. Systems with feed through – and human systems are marked by this – typically have non-normal event distributions. Systems theory suggests that, for the sake of resilience and robustness, larger systems should be broken into smaller, more discrete and redundant systems where possible. Feed-through suggests a need to slow processes down and introduce variety, leading to recommendations for countercyclical mechanisms, but oddly perhaps considering slowing down or breaking up information flows.