Random Choice Research Articles

A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.

Almost pairwise independence and resilience to deep learning attacks

Almost pairwise independence (API) is a quantitative property of a class of functions that is desirable in many cryptographic applications. This property is satisfied by Learning with errors (LWE)-mappings and by special Substitution-Permutation Networks (SPN). API block ciphers are known to be resilient to differential and linear cryptanalysis attacks. Recently, security of protocols against neural network-based attacks became a major trend in cryptographic studies. Therefore, it is relevant to study the hardness of learning a target function from an API class of functions by gradient-based methods. We propose a theoretical analysis based on the study of the variance of the gradient of a general machine learning objective with respect to a random choice of target function from a class. We prove an upper bound and verify that, indeed, such a variance is extremely small for API classes of functions. This implies the resilience of actual LWE-based primitives against deep learning attacks, and to some extent, the security of SPNs. The hardness of learning reveals itself in the form of the barren plateau phenomenon during the training process, or in other words, in a low information content of the gradient about the target function. Yet, we emphasize that our bounds hold for the case of a regular parameterization of a neural network and the gradient may become informative if a class is mildly pairwise independent and a parameterization is non-regular. We demonstrate our theory in experiments on the learnability of LWE mappings.

Evacuation from Cramped Interiors with Aisle Seats: Uncertainty Induced by the Random Choice of Initial Positions

Considering evacuation from cramped interiors with aisle seats, the initial seating positions of pedestrians represent a non-negligible source of variance in total evacuation time. By means of Pathfinder simulations, this uncertainty is quantitatively investigated for a train and lecture hall geometry with homogeneous and heterogeneous groups of agents. Furthermore, an energy-inspired description of the occupation configuration is introduced that enables a proper numerical representation of the configuration space of the initial seating positions.

BALANCE OF ECONOMY AND REDUNDANCY IN INFORMATION TRANSMISSION

The article examines the role of the principle of economy in the research of the systemic functioning of language, the history of the formation of a scientific theoretical description of this principle, theoretical ideas about the role of the principle of economy in the evolution of language, attempts to simplify the content in order to facilitate language activity and, accordingly, contradictory theories of the formation of the law of economy of speech. It is proved that the categorical ordering of reality does not arise as a result of some random or free choice, but under the influence of certain types of psychological principles.

The Role of Nutrition in Maintaining the Health and Physical Condition of Sports Volunteers.

Nutrition plays a key role in maintaining health and physical condition, particularly for active individuals, including athletes. It can therefore be assumed that individuals performing physically demanding tasks during the organization of sporting events, such as volunteers, should also pay attention to their nutrition. While the importance of diet for athletes has been widely studied, the impact of nutrition on sports volunteers remains under-researched. Volunteers often have to cope with varying degrees of physical and mental exertion, which may affect their nutritional needs. A qualitative study was conducted using in-depth individual interviews (IDIs) with 17 sports volunteers who had experience in organizing various sporting events. Participants were purposefully selected based on specific inclusion criteria, which included active involvement in sports volunteering (with a minimum of two years of experience in volunteer activities) as well as volunteering experience at sports events of various scales. The interviews aimed to understand the eating habits, dietary awareness, and impact of nutrition on health and physical fitness. The data were transcribed and subjected to thematic analysis, focusing on coding responses and identifying recurring themes. Most participants did not place much importance on their diet, making random food choices due to a busy lifestyle and lack of time. Only a few volunteers consciously adjusted their diet when they had knowledge of the physically demanding tasks they were expected to perform during their volunteer work. The majority of volunteers relied on less reliable sources of nutritional information, such as blogs or social media, rather than credible sources of knowledge. This study revealed that many individuals involved in sports volunteering are unaware of the impact of diet on their fitness and health. There is a need for nutritional education for this group to improve their awareness of the importance of a balanced diet in the context of increased physical activity. It is also advisable to provide better nutritional conditions during sporting events and to promote the use of professional sources of information about healthy eating.

Harmonizing Campylobacter risk assessments across European countries – can the pooled process hygiene criteria data be used in the Danish risk assessment model?

This study investigated the possibility of harmonizing quantitative microbiological risk assessment (QMRA) for Campylobacter spp. across European (EU) countries. French Campylobacter data (2020–2021) from neck skin (NS) pools, sampled at slaughterhouses under the European surveillance component for Food Business Operators (FBOs), were adapted to inform a QMRA model that, among others, has been used within the Danish Action Plan against Campylobacter, on the basis of single leg skins (LS) data. Datasets included culture results (in colony forming unit per gram, CFU/g) from 1,284 broiler flocks slaughtered at 13 slaughterhouses representing broiler production in western France. Five pools (of 2–4 NS samples each) per flock were tested. One pool per tested flock was randomly chosen for the analysis. After conducting descriptive statistics (on flock prevalence and meat contaminations across months and years), three contamination transformation factors (CTFs) were estimated to translate NS pools contaminations into single LS contamination, based on data from French and Danish studies. A reference simulation scenario (ScenRef) was set with CTF = 3.2 (i.e. NS pool concentration divided by 3.2); while other 13 scenarios represented an alternative scenario analysis to investigate the impact of: the CTF value (ScenMin with CTF = 2 and ScenMax with CTF = 10), censored test results (ScenUncens) and random choice of pool per flock (ScenSampling-1 to 10), on the risk estimates. The average monthly/annual risk of human disease per poultry meal and the monthly/annual relative risk (RR) of 2021 compared to 2020, were estimated. In ScenRef, the annual RR was 1.22, suggesting an increase of risk of ≈ 22 % in 2021 compared to 2020. The impact of CTFs, censored data and randomized pool sampling per flock, on the annual and (most) monthly RRs, appeared limited. This study gives an overview of the strengths and limitations to be considered for adapting the French FBO data into the Danish model and to harmonize risk assessments across EU countries, accordingly. To reduce uncertainty in risk estimates, it could be considered increasing representativeness of NS tested flock populations and/or using LS rather than NS samples; because LS samples are more representative of actually retailed meat contaminations. If NS pools are maintained, the relationships between concentrations on NS pools and those on consumed meat requires further investigation.

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

Null models confirm nest site fidelity by male smallmouth bass, Micropterus dolomieu

BackgroundMany animals appear to preferentially renest in proximity to a site they previously occupied. Evidence of nest fidelity is often inferred from a right skewed distribution of distances between the nests of individuals that breed in two consecutive reproduction episodes, where many individuals nest some arbitrarily close distance to their prior nest and others, in the extended right tail of the distribution, nest far from the nest they previously occupied. Because right skewed distributions of inter-nest distances can arise even when individuals choose nest locations randomly, however, such inferences are prone to error. The importance of null models—used to generate patterns of individual inter-nest distances by processes that do not involve site attachment—for inferences about site fidelity has been known for decades but is still often unappreciated or ignored.MethodsThe right skewed distributions of inter-nest distances observed in two earlier studies of male smallmouth bass (Micropterus dolomieu) suggest prima facie that males exhibit nest site fidelity between annual reproduction episodes, but patterns of inter-nest distances have yet to be compared to an adequate null model. Here, we evaluate the nest site fidelity of marked male M. dolomieu in a decade-long dataset, where we apply a randomization procedure based on the rencontre probability problem to generate null models. Eight observed distributions of individual, annual inter-nest distances are compared to a year-specific null model to determine whether random processes are sufficient to explain the observed distributions of inter-nest distances.ResultsThrough contrasts between observed annual inter-nest distances and results derived from null models that imposed realistic constraints on behavior, we show that some males were undoubtedly nest-site faithful. To reinforce the utility of null models and to make these kinds of models more accessible, we also provide a supplemental tutorial. The tutorial illustrates how random site choices, subject to common ecological and behavioral constraints, and even how distance is measured, can produce patterns of inter-nest distances that falsely imply nest site fidelity, or a lack of fidelity. The R code needed to reproduce these null models is included. The inference errors evident in our examples generalize to other forms of site fidelity, such as the apparent patch fidelity of certain sea bird foragers.ConclusionsThe comparisons of observed distributions of inter-nest distances with those generated by null models imply that, as suggested in prior studies, male M. dolomieu indeed exhibit annual nest site fidelity. Procedures like those we apply are necessary first steps in analyses when distributions of distances between the nests of individuals in consecutive reproduction episodes are used to infer nest-site fidelity. Why male M. dolomieu are site faithful is a question yet to be answered.

Machine learning parameter systems, Noether normalisations and quasi-stable positions

We discuss the use of machine learning models for finding “good coordinates” for polynomial ideals. Our main goal is to put ideals into quasi-stable position, as this generic position shares most properties of the generic initial ideal, but can be deterministically reached and verified. Furthermore, it entails a Noether normalisation and provides us with a system of parameters. Traditional approaches use either random choices which typically destroy all sparsity or rather simple human heuristics which are only moderately successful. Our experiments show that machine learning models provide us here with interesting alternatives that most of the time make nearly optimal choices.

Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Minimal dispersion on the cube and the torus

We improve some upper bounds for minimal dispersion on the cube and torus. Our new ingredient is an improvement of a probabilistic lemma used to obtain upper bounds for dispersion in several previous works. Our new lemma combines a random and non-random choice of points in the cube. This leads to better upper bounds for the minimal dispersion.

Design and application of deep reinforcement learning algorithms based on unbiased exploration strategies for value functions

Deep Q-networks, as a representation of several classical techniques, have emerged as one of the primary branches in the field of value function-based reinforcement learning. The paper addresses two issues that come up in the realm of reinforcement learning for value function solving: estimating bias and maximizing projected action value function evaluation. By treating the estimation of the highest expected action value as a random selection estimation problem, the suggested approach addresses the estimation bias issue from the standpoint of random selection. A random choice estimate procedure forms the basis of the technique. Firstly, a proposed random choice estimator is presented and its theoretical fairness is established. Second, the estimator is applied to create a reinforcement learning method in a different application. Two techniques, namely stochastic two-depth Q-networks and double-Q learning, are suggested based on the random choice estimation technique. The main parameters of the suggested algorithms are then investigated, and parameter formulas for both predictable and unpredictable scenarios are created. Lastly, a random choice estimation perspective suggests a stochastic two-depth Q-network. The new approach may effectively remove bias in value function estimate, enhance learning performance, and stabilise the learning process, according to simulation findings on Grid World and Atari games.

Two-echelon multi-commodity multimodal vehicle routing problem considering user heterogeneity in city logistics

The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.

Grammar-based test suite construction using coverage-directed algorithms over LR-graphs

In grammar-based testing, the test suites that drive the system under test are typically constructed from a given context-free grammar through a set of derivations that jointly satisfy some coverage criterion. In this paper, we describe and evaluate a new algorithm that instead constructs test suites from a set of valid paths that cover all edges in a labeled directed graph corresponding to an LR-automaton that accepts the language of the grammar. Vertices in this graph correspond to states in the LR-automaton; two vertices are connected by an edge iff the top of the LR-automaton’s stack can change from one state to the other, either by shifting a terminal or non-terminal symbol (push edges), or by reducing with a grammar rule (pop edges). The algorithm constructs a unique reduction path for each pop edge in the graph. These reduction paths are recursively embedded into each other, and any unresolved non-terminal push edges are substituted by shortest derivations for the non-terminal symbol. The algorithm can work with different types of LR-automata, including LR(0)- and LR(1)-automata, and can successfully generate a test suite from an LR-graph even if the underlying LR-automaton construction leads to shift/reduce or reduce/reduce conflicts. The algorithm only constructs valid paths over the LR-graphs that correspond to sentences in the language and thus generates only positive tests. We therefore also describe mutations to the positive paths that are guaranteed to generate negative tests without needing any further verification by an oracle.Our algorithm is substantially more efficient than an earlier algorithm that explores LR-graphs with two consecutive breadth-first graph traversals and our experimental evaluation shows that it scales to large production-quality grammars. It is robust against random choices made to resolve ambiguity in the construction of the tests, while the code coverage of the different test suite variants is relatively uniform. Finally, our evaluation shows that the negative test suites constructed by path mutation identify more faults in a set of student grammars than those constructed by rule mutation.

Optimal temperatures for measuring oil viscosity to restore its viscosity-temperature dependence according to the Filonov–Reynolds, Walter and Vogel–Fulcher–Tamman formulas

Relevance. Increase in the number of high-viscosity and non-Newtonian oils produced and transported. For such oils, it is important to accurately predict the rheological properties and, in particular, the viscosity-temperature dependence. Viscosity measurement for a wide range of different temperatures is excessively time-consuming and irrational, and in practice, viscosity measurement is used at two or three different temperatures and then conversion formulas are used. It is also important to clarify, which formulas and for which oils should be used to obtain the smallest error. It is important, in particular, to determine the optimal temperatures for measuring the viscosity of oil to restore its viscosity-temperature dependence. Aim. To determine the optimal temperatures for measuring oil viscosity to restore its viscosity-temperature dependence using the Filonov–Reynolds, Walter and Vogel–Fulcher–Tamman formulas, including when calculating for the entire available temperature range. Objects. Viscosity-temperature dependences of oils from the Directory of oils of the USSR. Methods. Processing the viscosity measurement data for 564 oils from the USSR Oil Directory; construction of approximating viscosity-temperature dependencies; determination of error when using the obtained dependencies, including using cross-validation. Results. The authors have calculated the error in application of the Filonov–Reynolds, Walter, Vogel–Fulcher–Tamman formulas, using over 500 oils as an example. It was found that with a random choice of temperatures for measuring viscosity, the minimum average error at another temperature for these formulas is, respectively, 13.8, 10.7 and 6.2%. The use of the Filonov–Reynolds formula is undesirable for calculating viscosity at low temperatures. The Vogel–Fulcher–Tamman formula shows the best results in calculating viscosity when using temperatures of 30–50–70 and 10–30–50°C.

Forecasting relative returns for S&P 500 stocks using machine learning

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.

5-HT 2A and 5-HT 2C receptor antagonism differentially modulate reinforcement learning and cognitive flexibility: behavioural and computational evidence

RationaleCognitive flexibility, the ability to adapt behaviour in response to a changing environment, is disrupted in several neuropsychiatric disorders, including obsessive–compulsive disorder and major depressive disorder. Evidence suggests that flexibility, which can be operationalised using reversal learning tasks, is modulated by serotonergic transmission. However, how exactly flexible behaviour and associated reinforcement learning (RL) processes are modulated by 5-HT action on specific receptors is unknown.ObjectivesWe investigated the effects of 5-HT2A receptor (5-HT2AR) and 5-HT2C receptor (5-HT2CR) antagonism on flexibility and underlying RL mechanisms.MethodsThirty-six male Lister hooded rats were trained on a touchscreen visual discrimination and reversal task. We evaluated the effects of systemic treatments with the 5-HT2AR and 5-HT2CR antagonists M100907 and SB-242084, respectively, on reversal learning and performance on probe trials where correct and incorrect stimuli were presented with a third, probabilistically rewarded, stimulus. Computational models were fitted to task choice data to extract RL parameters, including a novel model designed specifically for this task.Results5-HT2AR antagonism impaired reversal learning only after an initial perseverative phase, during a period of random choice and then new learning. 5-HT2CR antagonism, on the other hand, impaired learning from positive feedback. RL models further differentiated these effects. 5-HT2AR antagonism decreased punishment learning rate (i.e. negative feedback) at high and low doses. The low dose also decreased reinforcement sensitivity (beta) and increased stimulus and side stickiness (i.e., the tendency to repeat a choice regardless of outcome). 5-HT2CR antagonism also decreased beta, but reduced side stickiness.ConclusionsThese data indicate that 5-HT2A and 5-HT2CRs both modulate different aspects of flexibility, with 5-HT2ARs modulating learning from negative feedback as measured using RL parameters and 5-HT2CRs for learning from positive feedback assessed through conventional measures.

Redesigning, Subverting, Rolling Back: How East Asia’s Conservatives Rebuilt Legitimacy

This study examines the legitimation strategies adopted by conservative political parties in Japan, Taiwan, and South Korea from the late 2000s to the late 2010s, a period when these parties experienced political setbacks. By focusing on conservatives’ stances on labour issues, this study identifies three legitimation strategies: redesigning (acting as a superior protector of workers), subverting (destabilising an entire political landscape), and rolling back (undoing reforms implemented by a progressive government). This research aims to uncover the factors driving the adoption of these strategies. By revisiting Polanyi’s The Great Transformation, we establish a comparative framework to analyse these three strategies. This study argues that these differences were not random choices but deliberate responses to specific socio-political configurations that emerged after what Polanyi identified as a “double movement.” While the three countries shared common efforts by society to alleviate the detrimental impacts of market-oriented reforms, variations in the effects of the double movement, in terms of public attitudes towards labour reforms implemented by leftist governments, conservatives’ linkages to civil society organisations, and internal unity within conservative parties, gave rise to distinct legitimation strategies among conservatives.

EFEKTIVITAS TEHNIK ROUND ROBIN UNTUK MENINGKATKAN KECAKAPAN BERPIKIR KRITIS DALAM MENULIS PREFERENCE

Critical thinking skills are part of the 21st century skills that must be mastered by students. Whether writing skill is the main competency that should be mastered in learning English. One of the reasons for the lack of development of students' critical thinking skills in writing preference sentences is that their low enthusiasm for learning is caused by learning activities that are not give any other variation. This study aims to obtain findings related to the application of learning activities with the round robin technique, such as the increase in students' critical thinking skills in their writing preferences, by applying and observing how much effectiveness this type of learning has. This research is classified as quantitative research with the type of quasi-experimental research that conducts experiments through a pretest and posttest control group design with samples used by students of classes VII G and VII I at SMPN 21 Semarang. The research subjects were obtained using a random sampling technique, which was determined based on random choices. Essay tests are used as a tool to test critical thinking skills with preferred research material. The research results obtained based on the results of the significance test obtained an N-Gain percentage of 57.35%. This result is include to the quite effective category. In accordance with this results, it can be concluded that the use of round-robin type can improve critical thinking skills in writing preferences by class VII students of SMPN 21 Semarang effectively.

A Random Reference Model

We provide two nested models of random reference-dependent choice in which the reference point is endogenously determined by random processes. Random choice behavior is due to random reference points, even though, from the decision-maker’s viewpoint, choices are deterministic. Through a revealed preference exercise, we establish when and how one can identify the reference-dependent preferences and the random reference rule from observed choice data. We also present behavioral postulates that characterize the empirical content of our models. Lastly, we investigate an application of our model to Bertrand competition with differentiated products. (JEL D11, D91)