Simple Decision Rule Research Articles

Just-in-Time Adaptive Interventions for Behavior Change in Physiological Health Outcomes and the Use Case for Knee Osteoarthritis: Systematic Review.

The prevalence of knee osteoarthritis (KOA) in the adult population is high and patients profit from individualized therapy approaches. Just-in-time adaptive interventions (JITAIs) are upcoming digital interventions for behavior change. This systematic review summarizes the features and effectiveness of existing JITAIs regarding important physiological health outcomes and derives the most promising features for the use case of KOA. The electronic databases PubMed, Web of Science, Scopus, and EBSCO were searched using keywords related to JITAIs, physical activity (PA), sedentary behavior (SB), physical function, quality of life, pain, and stiffness. JITAIs for adults that focused on the effectiveness of at least 1 of the selected outcomes were included and synthesized qualitatively. Study quality was assessed with the Quality Assessment Tool Effective Public Health Practice Project. A total of 45 studies with mainly weak overall quality were included in this review. The studies were mostly focused on PA and SB and no study examined stiffness. The design of JITAIs varied, with a frequency of decision points from a minute to a day, device-based measured and self-reported tailoring variables, intervention options including audible or vibration prompts and tailored feedback, and decision rules from simple if-then conditions based on 1 variable to more complex algorithms including contextual variables. The use of frequent decision points, device-based measured tailoring variables accompanied by user input, intervention options tailored to user preferences, and simple decision rules showed the most promising results in previous studies. This can be transferred to a JITAI for the use case of KOA by using target variables that include breaks in SB and an optimum of PA considering individual knee load for the health benefits of patients.

Dispersal of optimal foragers in a patchy environment: Simulations with a mathematical model and tests of predictions in field experiments

To predict the effect of density on the dispersal of foraging parasitoids, we developed a spatially explicit individual-based model in which parasitoids move among host patches at random but use an optimal decision-rule about when to leave these patches. We used a simple decision rule where an individual forager exploits a patch as long as the instantaneous attack rate is higher than the average attack rate it has experienced in the environment. Such a rule implies that foragers compute and remember attack rates as they forage. Simulations with different combinations of patch distribution, host density, mutual interference, and parasitoid density predicted that dispersal distance should increase with parasitoid density. To test this prediction, we used data from two field experiments in which we released either few or many adults of the aphid parasitoid Aphelinus asychis in replicated sites, and subsequently assessed dispersal from the spatial distribution of parasitoid offspring. In the first experiment, we did not to detect a relation between the number released and dispersal distance, but in the second experiment, dispersal distance increased with initial density, as predicted by our model. We propose two hypotheses to explain the discrepancy between the experiments. Different levels of environmental variability among experiments, resulting from differences in experimental designs, could cause differences in statistical conclusions. However, there could be a threshold density below which dispersal is not density-dependent, and this threshold may have been exceeded in the second experiment where large releases involved many more individuals than in the first experiment. In any case, our approach linked individual behavior and spatial distribution, and our results show that the insect distributions in the field can be predicted, qualitatively, from theory about individual behavior.

Supervised learning for integrated forecasting and inventory control

We explore the use of supervised learning with custom loss functions for multi-period inventory control with feature-driven demand. This method directly considers feature information such as promotions and trends to make periodic order decisions, does not require distributional assumptions on demand, and is sample efficient. The application of supervised learning in inventory control has thus far been limited to problems for which the optimal policy structure is known and takes the form of a simple decision rule, such as the newsvendor problem. We present an approximation approach to expand its use to inventory problems where the optimal policy structure is unknown. We test our approach on lost sales, perishable goods, and dual-sourcing inventory problems. It performs on par with state-of-the-art heuristics under stationary demand. It outperforms them for non-stationary perishable goods settings where demand is driven by features, and for non-stationary lost sales and dual-sourcing settings where demand is smooth and feature-driven.

Control of Online-Appointment Systems When the Booking Status Signals Quality of Service

We revisit a service provider’s problem to match supply and demand via an online appointment system such as a doctor in the health care sector. We identify in a survey that an extensive set of available appointments leads to significantly less demand because customers infer a lower quality of the service, as part of an observational learning process. We capture the quality inference effect in a multinomial logit framework and present a Markov decision process for solving the problem of releasing available slots of the appointment system to optimality aiming at maximizing the expected profits. We further evaluate several simple decision rules and provide management insights on which rule to apply under different generic scenarios. Different from current literature, offering all available appointments may lead to suboptimal results when accounting for the quality inference effect. The profit-maximizing strategy then is to offer a subset of the available appointments.

Identifying Rule-Based Rationality

Abstract The revealed preference methodology allows an observer to infer preferences from choices. This paper extends this fundamental idea by experimentally identifying the preference for basing choices on simple decision rules. Subjects not only make case-by-case portfolio allocations but also design a simple investment rule for selecting portfolios. They then choose between these two decision modes for an additional set of problems. The majority opt for the rule interface and in most cases choose a simple investment rule that cannot be rationalized by any simple utility function or accounted for by reductions in decision time or by cognitive costs.

National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series

Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km2 (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the “Sickle Bend” region of China (the “Sickle Bend” policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.

A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm

The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The novelty of the proposed approach lies in the utilization of the typicality eccentricity detection anomaly (TEDA) algorithm for R-peak detection. The proposed method for R-peak detection consists of three phases. Firstly, the ECG signal is preprocessed by calculating the signal’s slope and applying filtering techniques. Next, the preprocessed signal is inputted into the TEDA algorithm for R-peak estimation. Finally, in the third and last step, the R-peak identification is carried out. To evaluate the effectiveness of the proposed technique, experiments were conducted on the MIT-BIH arrhythmia database (MIT-AD) for R-peak detection and validation. The results of the study demonstrated that the proposed evolutive algorithm achieved a sensitivity (Se in %), positive predictivity (+P in %), and accuracy (ACC in %) of 95.45%, 99.61%, and 95.09%, respectively, with a tolerance (TOL) of 100 milliseconds. One key advantage of the proposed technique is its low computational complexity, as it is based on a statistical framework calculated recursively. It employs the concepts of typicity and eccentricity to determine whether a given sample is normal or abnormal within the dataset. Unlike most traditional methods, it does not require signal buffering or windowing. Furthermore, the proposed technique employs simple decision rules rather than heuristic approaches, further contributing to its computational efficiency.

Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning

Street-level bureaucrats interact directly with people on behalf of government agencies to perform a wide range of functions, including, for example, administering social services and policing. A key feature of street-level bureaucracy is that the civil servants, while tasked with implementing agency policy, are also granted significant discretion in how they choose to apply that policy in individual cases. Using that discretion could be beneficial, as it allows for exceptions to policies based on human interactions and evaluations, but it could also allow biases and inequities to seep into important domains of societal resource allocation. In this paper, we use machine learning techniques to understand street-level bureaucrats' behavior. We leverage a rich dataset that combines demographic and other information on households with information on which homelessness interventions they were assigned during a period when assignments were not formulaic. We find that caseworker decisions in this time are highly predictable overall, and some, but not all of this predictivity can be captured by simple decision rules. We theorize that the decisions not captured by the simple decision rules can be considered applications of caseworker discretion. These discretionary decisions are far from random in both the characteristics of such households and in terms of the outcomes of the decisions. Caseworkers typically only apply discretion to households that would be considered less vulnerable. When they do apply discretion to assign households to more intensive interventions, the marginal benefits to those households are significantly higher than would be expected if the households were chosen at random; there is no similar reduction in marginal benefit to households that are discretionarily allocated less intensive interventions, suggesting that caseworkers are using their knowledge and experience to improve outcomes for households experiencing homelessness.

Identifying Anomalous Data Entries in Repeated Surveys

The presence of outliers in a dataset can substantially bias the results of statistical analyses. In general, micro edits are often performed manually on all records to correct for outliers. A set of constraints and decision rules is used to simplify the editing process. However, agricultural data collected through repeated surveys are characterized by complex relationships that make revision and vetting challenging. Therefore, maintaining high data-quality standards is not sustainable in short timeframes. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) has partially automated its editing process to improve the accuracy of final estimates. NASS has investigated several methods to modernize its anomaly detection system because simple decision rules may not detect anomalies that break linear relationships. In this article, a computationally efficient method that identifies format-inconsistent, historical, tail, and relational anomalies at the data-entry level is introduced. Four separate scores (i.e., one for each anomaly type) are computed for all nonmissing values in a dataset. A distribution-free method motivated by the Bienaymé-Chebyshev’s inequality is used for scoring the data entries. Fuzzy logic is then considered for combining four individual scores into one final score to determine the outliers. The performance of the proposed approach is illustrated with an application to NASS survey data.

A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare

Artificial intelligence (AI) applications based on deep learning for diagnosing type-II diabetes are sometimes difficult to understand and communicate even as patients are eager to understand the rationale behind the diagnostic results. Accordingly, recent studies have used multiple simple rules to adequately explain the diagnostic process and results to patients. However, this can cause patient confusion as the rules vary. Hence, this study proposes a deep neural network (DNN) with random forest (RF) and modified random forest incremental interpretation (MRFII) approach for diagnosing diabetes. This method first entails constructing a DNN to predict the probability of a patient having diabetes. To make the prediction result explainable, an RF is built to explain the process and results in terms of multiple simple decision rules. Additionally, to eliminate patient confusion, the MRFII is proposed to sort and aggregate the decision rules for a specific patient. A certainty mechanism is also established to feed back the explanation results from RF to improve the effectiveness of the DNN. The proposed method was applied to a diabetes dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, and the results showed that this approach provided a more concise and accurate explanation than existing explainable artificial intelligence (XAI) techniques for the same purpose.

Poster Session I: How important is it to know your own limitations when making perceptual decisions?

Many models of perceptual performance consist of two components: (1) a representation of the available sensory and memory information, and (2) a Bayes-optimal decision computation. If the model accurately predicts performance, then that finding is often interpreted as evidence for both components of the model. However, it is important and informative to also test heuristic decision rules. Here, we illustrate such analyses for the task of covert visual search, where a target can appear at one of n locations (or be absent) with some prior probability distribution (the "prior map"). The available sensory information can be represented by the signal-to-noise ratio at each retinal location (the "d' map") together with the prior map. It can be shown that the Bayes-optimal decision rule is to multiply the response at each location by the d' at that location, add the log prior for that location, and then pick the location that has the maximum. For a wide range of d' and prior maps we simulated performance for the optimal and various heuristic decision rules. As expected, varying the d' and prior maps has a large effect on performance due to the variation in the available sensory information. However, a wide range of simple decision rules perform almost as well as the Bayes-optimal rule. In other words, in the covert search task, our decision processes can be idiosyncratic and almost completely ignorant of the d' and prior maps and still perform optimally.

The role of framing, agency and uncertainty in a focus-divide dilemma

How to prioritise multiple objectives is a common dilemma of daily life. A simple and effective decision rule is to focus resources when the tasks are difficult, and divide when tasks are easy. Nonetheless, in experimental paradigms of this dilemma, participants make highly variable and suboptimal strategic decisions when asked to allocate resources to two competing goals that vary in difficulty. We developed a new version in which participants had to choose where to park a fire truck between houses of varying distances apart. Unlike in the previous versions of the dilemma, participants approached the optimal strategy in this task. Three key differences between the fire truck version and previous versions of the task were investigated: (1) Framing (whether the objectives are familiar or abstract), by comparing a group who placed cartoon trucks between houses to a group performing the same task with abstract shapes; (2) Agency (how much of the task is under the participants’ direct control), by comparing groups who controlled the movement of the truck to those who did not; (3) Uncertainty, by adding variability to the driving speed of the truck to make success or failure on a given trial more difficult to predict. Framing and agency did not influence strategic decisions. When adding variability to outcomes, however, decisions shifted away from optimal. The results suggest choices become more variable when the outcome is less certain, consistent with exploration of response alternatives triggered by an inability to predict success.

Religious Commitment and Intent to Die by Suicide during the Pandemic

Suicide is the second leading cause of death in 10–34-year-olds in the U.S. It is vital to identify protective factors that promote resilience in a suicide crisis. Background: This study explored the contributions of religious commitment (RC) and religious service attendance to decreased suicide intent in 18–34-year-olds. Possible moderators were investigated, including church-based social support, pandemic-related faith struggles (PRFS), and moral objections to suicide. Methods: Participants completed an online survey reporting on RC, suicide intent, church-based social support, religious service attendance, PRFS, and moral objections to suicide. Results: In the convenience sample of 451 18–34-year-olds (M = 24.97; 47.23% female), religious participants reported significantly less suicide intent than non-religious participants. RC and moral objections to suicide were more strongly negatively correlated with suicide intent than religious service attendance, but religious service attendance was associated with lower suicide intent in a regression model. Almost four times more religious young adult participants reported PRFS than not, and PRFS was found to moderate the benefits of social support received in their faith communities. Conclusions: It is suggested that professional caregivers use religious service attendance as a straightforward way to assess a possible protective factor for suicidal religious young adults. Professional caregivers may also assess for moral objections to suicide, which may provide simple decision rules in a suicide crisis. The large number of religious young adults reporting PRFS in this study suggests the need for professional caregivers to assess for spiritual struggles, which may confer suicide risk. Because of the interplay of spiritual risks and protections, mental health providers who are unsure of how to address these in therapy may need to collaborate with and make referrals to faith leaders to increase protections and reduce risks in suicidal religious young adults.

Propagation of conformity statements in compliance with the GUM and ISO 17025

According to ISO/IEC 17025:2017 statements of conformity must identify the decision rule applied, they must be risk-based and account for uncertainty. In legal metrology and often among testing and calibration laboratories, there is the need to reuse measurement-based conformity statements to disseminate acceptability of measurement results. In particular, decision rules are required that allow the statement of conformity for a linear combination of quantities for which, in turn, conformity statements are available. These decision rules should be simple and use information that is typically available, and they should comply with ISO/IEC 17025:2017, again by accounting for the level of risk and for uncertainty following the suite of documents of the GUM (the Guide to the Expression of Uncertainty in Measurement).Existing guidance requires the input and evaluation of standard uncertainties, or even of distributions, to derive statements of conformity, and as such may be inapplicable, or the required effort may deter practitioners. After reviewing the existing guidance, this research will identify typical settings which lead to particularly simple decision rules for stating conformity for linear combinations of quantities. These new decision rules are based on the specification limits and on information implicitly available in the decision rules of each input quantity. The rules will be proven, they are generalizable, intended to comply with ISO/IEC 17025:2017 and the GUM documents, and suitable to easily state the risk of not conforming to the weighted sum of the input specifications.For practitioners, a quick reference on feasible conformity statements for linear combinations of quantities is provided. The applicability of and need for the new decision rules are illustrated by two examples involving the legally regulated weighing of long vehicles and of net loads.

Put it in the bag: Order fulfillment with a pocket sorter system

AbstractDue to high real estate costs in urban areas, shop floor space is scarce in most brick‐and‐mortar stores. Maneuvering newly arrived merchandise through narrow aisles during shelf replenishment is time‐consuming for the sales staff and impedes customers. Therefore, many retail chains nowadays aim for store‐friendly shipments (SFS). By mirroring the layout of a store in the buildup of its dedicated shipments, the need for a zigzag movement through the store when replenishing shelves can be avoided. On the negative side, however, additional effort arises in the distribution centers. A suitable warehousing system to assemble SFS without excessive effort is a pocket (or pouch or bag) sorter, where each item is put into its separate bag. These bags, filled with items, are automatically transported while hanging from an overhead conveyor and can be sorted into any sequence before being delivered to the workstations that build SFS. This article investigates the assembly of SFS with a pocket sorter and presents scheduling procedures to enhance the efficiency of this process for a given set of store orders. We demonstrate that, despite its notorious complexity, the problem can be solved by simple decision rules with good performance. In a case study, we show that this approach can dramatically reduce the completion times of store orders, resulting in savings of more than 60% of the total working hours compared to a simple real‐world policy. Another 30% of reduction can be obtained by standardized store layouts.

Derivation of a clinical decision rule for a bone marrow aspirate concentrate injection in knee osteoarthritis.

Aim: To develop a simple clinical decision rule (CDR) to identify people with knee osteoarthritis who are likely or unlikely to benefit from bone marrow aspirate concentrate (BMAC) injection. Materials & methods: A total of 92 people with clinical and radiographic evidence of refractory knee osteoarthritis received a single intra-articular (IA) BMAC injection. Multiple logistic regression analysis was used to determine which combination of risk factors predicted BMAC responsiveness. A responder was defined as a person whose knee pain improved more than 15% from baseline 6months postprocedure. Results: The CDR demonstrated that those with lower pain levels, or high pain levels with previous surgery, could be predicted to benefit from a single IA BMAC injection. Conclusion: A simple CDR containing three variables predicted responsiveness to a single IA knee BMAC injection with high accuracy. Further validation of the CDR is required prior to routine use in clinical practice.

Joint Inventory and Scheduling Control in a Repair Facility

Operations Management in a Repair Facility Capital goods are machines or products that are used in the production of goods or service deliveries. Maintenance service providers keep spare parts on stock so that, whenever a critical component of a capital good breaks down, the broken part is replaced with a spare part to prevent long and costly downtime. In “Joint Inventory and Scheduling Control in a Repair Facility,” Özkan and van Houtum study inventory and repair scheduling decisions of a maintenance service provider for repairable capital goods. In case of a stock-out, the service provider should decide whether to back-order the demand or execute an emergency repair, which is an urgent but expensive repair operation for a broken part. The authors derive a simple and intuitive decision rule stating if the emergency repairs are necessary to achieve a close-to-optimal system performance. Moreover, they propose a simple, intuitive, and easy-to-implement heuristic control policy that performs well in numerical experiments.

Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices

ObjectiveThe present study aimed to early identify patients with persistent somatic symptoms (PSS) in primary care by exploring routine care data-based approaches.Design/settingA cohort study based on routine primary care data...

Dealing with low‐probability shocks: The role of selected heuristics in farmers’ risk management decisions

Dealing with weather extremes is a major challenge for farmers and often comes at high costs for public budgets. Therefore, we investigate the influence of specific simplified decision rules, so-called heuristics, on farmers’ willingness to pay (WTP) for protecting themselves against low-probability and high-consequence weather shocks. To this end, we conducted a framed field experiment with 237 farmers in Germany, using incentivized lottery-based multiple price lists. We explored the effects of different heuristics within the prospect theory framework. Our results indicate that, on average, farmers exhibit risk-loving behavior towards monetary losses, leading to a low WTP for risk mitigation. The results also suggest that the imitation heuristic, shock experience heuristics, and the threshold of concern heuristic influence farmers’ WTP. Farmers specifically imitate successful farmers when these are risk-loving. The lack of personal experience with low-probability events induces farmers to assign less weight to low-probability shocks, which lowers their WTP. Farmers also systematically assign less weight to low-probability shocks that they consider “too rare to be concerned about.” Accounting for the use of these heuristics can help design improved risk management instruments and policies.

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.