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Police administrators are looking for concrete pathways to fight the phenomenon of (ethnic) prejudice among members of the police force. Surprisingly, few studies have assessed the effectiveness of existing police ethics training programs on prejudice and social dominance orientation. Therefore, we assessed the impact of a 1-day training called the Holocaust, Police, and Human Rights (HPH) program on the attitudes related to ethnic prejudice and social dominance orientation of 223 members of the Belgian police. Analyses of three-wave panel data indicate that HPH training reduced ethnic prejudice and social dominance orientation. For ethnic prejudice, the reduced effects were maintained after 1 month in the follow-up study. However, the effect of training on prejudice was weaker for police officers who were more often exposed to victims and perpetrators of crime. No lasting effect of HPH training was found for social dominance orientation.

Individual treatment effect models allow optimizing decision-making by predicting the effect of a treat- ment on an outcome of interest for individual instances. These predictions allow selecting instances to treat in order to optimize the overall efficiency and net treatment effect. In this article, we extend upon the expected value framework and introduce a cost-sensitive causal classification boundary for select- ing instances to treat based on predictions of individual treatment effects and for the case of a binary outcome. The boundary is a linear function of the estimated individual treatment effect, the positive outcome probability and the cost and benefit parameters of the problem setting. It allows causally clas- sifying instances in the positive and negative treatment class in order to maximize the expected causal profit, which is introduced as the objective at hand in cost-sensitive causal classification. We present the expected causal profit ranker which ranks instances for maximizing the expectedcausal profit at each possible threshold that results from a constraint on the number of positive treatments and which differs from the conventional ranking approach based on the individual treatment effect. The proposed ranking approach is experimentally evaluated on synthetic and marketing campaign data sets. The results indicate that the presented ranking method outperforms the cost-insensitive ranking approach.

In this article, we engage with the emerging literature that studies the increased enrolment of first-generation college students (FGCS), that is, students from households where neither parent has obtained a bachelor’s /master’s degree. Our article answers two research questions. First, data from 2,338 first-year students are used to investigate the extent to which FGCS differ from continuing-generation college students (CGCS) concerning the reason why one enrols in university education. Second, to what degree do these motives explain differences in study choice? Our results show that FCGS, compared to CGCS, more strongly endorsed the economic investment motive and what we call the social investment motive, that is, the motivation to become a role model for one’s community. In addition, our findings reveal that the choice for more economically rewarding fields of study is related to these motives to start a university education. In the conclusion, we discuss the implications of our findings.

The question of truth is as old as humankind itself. Deliberating on what is true and untrue is a uniquely human activity. However, it is precisely for that reason that truth is not an evident or static fact. It is brought about through human questioning and, as such, is the object of endless reflection and debate. This volume in the Poincaré book series invited the VUB community to ponder its approach to truth. Therefore, the diverse contributions to this book represent a mosaic of voices and reflections on this ever-evolving and never-ending question. This colourful and varied set of reflections from radically different disciplines and with widely differing interpretations of what truth means perhaps makes a good vantage point from which to tackle the theme of ‘truth.’

Multi-actor multi-criteria analysis is a group decision-making framework that allows multiple stakeholder groups to be involved in the decision-making process, facilitating the understanding of the points of consensus and conflict among the stakeholder groups. Carefully selecting suitable criteria is important as they illustrate the possibly divergent priorities of the respective stakeholder group, and overlooking important criteria can lead to erroneous outcomes. Furthermore, the number of criteria needs specific consideration, as a too large number poses problems for human cognition, but a too small number inaccurately represents the stakeholder's interest. In stakeholder groups with many members, such as those representing citizens, defining a criteria set is likely to be even more complicated. Currently, there is no formal guideline to assist facilitators in defining these criteria sets. In this paper, we propose a novel framework for criteria preprocessing with stakeholder involvement that includes a guideline for firstly selecting criteria into a tentative list and secondly selecting the final criteria set. It provides a procedure on how to determine criteria considering the priorities of stakeholder groups with regard to the context. As a final step, we propose a mathematical model for selecting a number of criteria that are both cognitively manageable and representative for the participants' priorities. Based on the principles of the Pareto analysis, as well as the cognitive judgment theory “magic number seven plus or minus two”, a recommendation list of the criteria is generated. It prevents key criteria from being omitted while at the same time limiting the overall number of criteria. This framework is applied to a social decision-making case for construction logistics, and the results are compared with the conventional approach of criteria definition.

Multitask learning (MTL) can improve performance on one task by sharing representations with one or more related auxiliary tasks. Usually, MTL networks are trained on a composite loss function formed by a fixed weighted combination of separate task losses. In practice, however, static loss weights lead to poor results for two reasons. First, the relevance of the auxiliary tasks gradually drifts throughout the learning process. Second, for minibatch-based optimization, the optimal task weights vary significantly from one update to the next depending on the minibatch sample composition. Here, we introduce HydaLearn, an intelligent weighting algorithm that connects the main-task gain to the individual task gradients, to inform dynamic loss weighting at the minibatch level, addressing the two above shortcomings. We demonstrate significant performance increases on synthetic data and two real-world data sets.

Background: Compassionate Communities have been put forward as a new model for community-based palliative care to positively impact the health and wellbeing of those experiencing challenges of serious illness, death, dying, and loss. Despite the growing international movement to develop these public health initiatives to end-of-life care, only a handful of initiatives have undergone some form of evaluation. Aim: To provide guidance on designing evaluation research by identifying theoretical frameworks to understand the development, implementation, and underlying mechanisms of Compassionate Communities. Methods: To identify suitable theoretical frameworks for the study of Compassionate Communities, we applied two steps. The first step examined the characteristics of Compassionate Communities and translated them into assessment criteria for the selection of theoretical frameworks. The second step consisted of applying the identified assessment criteria to a list of widely used and highly cited theoretical frameworks. Results: Three well-established theoretical frameworks were identified as being most suitable to study the development, implementation, and underlying mechanisms of Compassionate Communities: The Consolidated Framework for Implementation Research (CFIR), the integrated-Promoting Action on Research Implementation in Health Services framework (i-PARIHS), and the Extended Normalization Process Theory (ENPT). Conclusions: The article supports and encourages the use of theoretical frameworks to evaluate the complex processes behind public health palliative care initiatives. The complementary use of two determinant frameworks and an implementation theory provides theoretical grounding to gain rich insights into the emergent and shifting interplays between agency, social processes, and contextual factors that shape the development and implementation of Compassionate Communities.

Police integrity is a crucial steppingstone in police administrators’ long-term goals of obtaining legitimacy and yielding citizens’ evaluations of the police as a legitimate institution. This paper investigates the relationship between work-related experiences and police integrity, as well as the degree to which this relationship is mediated by well-known socio-political attitudes such as authoritarianism, ethnic prejudice, and social dominance orientation. Using structural equation modelling of the responses provided by 1,255 members of the Belgium police, the work-related characteristics, socio-political attitudes, and organisational dimensions of police integrity are related to three outcomes of police integrity. Results show that police officers in the field and those who had more frequent contacts with victims and perpetrators of crimes evaluate misconduct to be less serious, are less willing to say that they would report the behaviour, and advocate less severe disciplinary sanctions. Generalised socio-political attitudes related to prejudice mediate work-related experiences and perceptions of peer police integrity and are in turn negatively related to police integrity outcomes. These findings illustrate that police officers do not operate in a social vacuum as these general socio-political attitudes are non-occupation specific. This suggests that police integrity training programmes not only need to target work-related characteristics, such as an open culture to discuss problems, but also need to target general socio-political attitudes to foster police integrity.

Compassionate Communities are places and environments in which people, networks, and institutions actively work together and are empowered to improve the circumstances, health, and well-being of those facing serious illness, death, dying, and loss. The study of their development, implementation, and evaluation requires an interdisciplinary research approach that has hitherto been lacking. In 2020, 8 research groups from 4 faculties at Vrije Universiteit Brussel united in the interdisciplinary Compassionate Communities Center of Expertise (COCO) to investigate Compassionate Communities. This article describes the first results of COCO: (a) an interdisciplinary mode of collaboration, (b) a shared conceptual understanding and definition of Compassionate Communities, and (c) a shared research agenda on Compassionate Communities.

In a Markov model the transition probabilities between states do not depend on the time spent in the current state. The present paper explores two ways of selecting the states of a discrete-time Markov model for a system partitioned into categories where the duration of stay in a category affects the probability of transition to another category. For a set of panel data, we compare the likelihood fits of the Markov models with states based on duration intervals and with states defined by duration values. For hierarchical systems, we show that the model with states based on duration values has a better maximum likelihood fit than the baseline Markov model where the states are the categories. We also prove that this is not the case for the duration-interval model, under conditions on the data that seem realistic in practice. Furthermore, we use the Akaike and Bayesian information criteria to compare these alternative Markov models. The theoretical findings are illustrated by an analysis of a real-world personnel data set.

The ability to solve differential equations represents a key step in the modeling and understanding of complex systems. There exist several analytical and numerical methods for solving differential equations, each with their own advantages and limitations. Physics-informed neural networks (PINNs) offer an alternative perspective. Although PINNs deliver promising results, many stones remain unturned about this method. In this paper, we introduce a method that improves the efficiency of PINNs in solving differential equations. Our method is related to the formulation of the problem: Instead of training a network to solve an nth order differential equation, we propose transforming the problem into the equivalent system of n first-order equations in phase space. The target of the network is to solve all equations of the system simultaneously, effectively introducing a multitask optimization problem. We compare both approaches empirically on various problems, ranging from second-order differential equations with constant coefficients to higher-order and nonlinear problems. We also show that our approach is suited for solving partial differential equations. Our results show that the system approach performs equal or better in most experiments performed. We analyze the learning process for the few runs that did not perform well and show that the problem stems from conflicting gradients during training, effectively obstructing multitask learning. The result of this paper is a straightforward heuristic that can be incorporated into any subsequent research that builds on PINNs solving differential equations. Moreover, it also shows how to make PINNs even more efficient by implementing techniques from multitask learning literature.

An important part of healthcare decision making is to understand how certain actions relate to desired and undesired outcomes. One key challenge is to deal with confounding variables, i.e., variables that influence the relation between actions and outcomes. Existing techniques aim to uncover the underlying statistical relations between actions and outcomes, but either do not account for confounding variables or only consider the process or case level instead of the event level. Therefore, this paper proposes a novel relation mining approach for healthcare processes that 1) explicitly accounts for confounding variables at the event level, and 2) transparently communicates the effect of the confounding variables to the user. We demonstrate the applicability and importance of our approach using two evaluation experiments. We use a real-world healthcare dataset to show that the identified relations indeed provide important input for decision making in healthcare processes. We use a synthetic dataset to illustratethe importance of our approach in the general setting of causal model estimation.

Monotone matrices are stochastic matrices that satisfy the monotonicity conditions as introduced by Daley in 1968. Monotone Markov chains are useful in modeling phenomena in several areas. Most previous work examines the embedding problem for Markov chains within the entire set of stochastic transition matrices, and only a few studies focus on the embeddability within a specific subset of stochastic matrices. This article examines the embedding in a discrete-time monotone Markov chain, i.e., the existence of monotone matrix roots. Monotone matrix roots of ( 2 × 2 ) \left(2\times 2) monotone matrices are investigated in previous work. For ( 3 × 3 ) \left(3\times 3) monotone matrices, this article proves properties that are useful in studying the existence of monotone roots. Furthermore, we demonstrate that all ( 3 × 3 ) \left(3\times 3) monotone matrices with positive eigenvalues have an m m th root that satisfies the monotonicity conditions (for all values m ∈ N , m ≥ 2 m\in {\mathbb{N}},m\ge 2 ). For monotone matrices of order n > 3 n\gt 3 , diverse scenarios regarding the matrix roots are pointed out, and interesting properties are discussed for block diagonal and diagonalizable monotone matrices.

Causal classification concerns the estimation of the net effect of a treatment on an outcome of interest at the instance level, i.e., of the individual treatment effect (ITE). For binary treatment and outcome variables, causal classification models produce ITE estimates that essentially allow one to rank instances from a large positive effect to a large negative effect. Often, as in uplift modeling (UM), one is merely interested in this ranking, rather than in the ITE estimates themselves. In this regard, we investigate the potential of learning to rank (L2R) techniques to learn a ranking of the instances directly. We propose a unified formalization of different binary causal classification performance measures from the UM literature and explore how these can be integrated into the L2R framework. Additionally, we introduce a new metric for UM with L2R called the promoted cumulative gain (PCG). We employ the L2R technique LambdaMART to optimize the ranking according to PCG and show improved results over the use of standard L2R metrics and equal to improved results when compared with state-of-the-art UM. Finally, we show how L2R techniques can be used to specifically optimize for the top-k fraction of the ranking in a UM context, however, these results do not generalize to the test set. ispartof: IEEE Transactions on Knowledge and Data Engineering vol:34 issue:10 pages:4888-4904 status: published

In Markov chain theory, stochastic matrices are used to describe inter-state transitions. Powers of such transition matrices are computed to determine the behaviour within a Markov system. For this, diagonalizable matrices are preferred because of their useful properties. The non-diagonalizable matrices are therefore undesirable. The aim is to determine a nearby diagonalizable matrix A, starting from a non-diagonalizable matrix (A) over tilde. Previous studies tackled this problem, limited to 3 x 3 stochastic matrices. In this paper, these results are generalized for n x n stochastic matrices. Spectral properties of A are preserved in this process, such that A and (A) over tilde have coinciding semisimple eigenvalues and coinciding corresponding eigenvectors. This problem is examined and solved in this study and an algorithm is presented to find such a diagonalizable matrix (A) over tilde.

BACKGROUND: There is a growing interest in HR-analytics because of its ability to analyze employee behavior based on HR data. Predicting voluntary turnover of employees is an important topic of study, both in academia and industry. OBJECTIVE: The current study analyzes determinants for turnover, distinguishing between blue and white collar workers. The turnover analyses are based on a dataset from a payroll company, in contrary to previous turnover studies that used survey and interview data. METHODS: The studied dataset contains demographic and work specific factors for more than 380000 employees in 15692 Belgian corporations. Logistic regression is used to estimate individual turnover probabilities, the goodness of the model is tested with the AUC method. RESULTS: The study confirms turnover determinants and differences between blue and white collar workers that were described in previous work based on survey and interview data. Additionally, the study exposes so far unstudied turnover determinants and differences between blue and white collar workers. Confirmed determinants are among others age, seniority, pay and work distance. New determinants are company car, meal vouchers, night work and sickness. Different relationships to turnover are revealed for blue and white collar workers based on gender, number of children, nationality and pay. CONCLUSIONS: The presented dataset-based approach has its merit in analyzing turnover: it enables to study actual turnover instead of turnover intentions, and reveals new turnover determinants and differences between blue and white collar workers.

The present paper examines matrix root properties and embedding conditions for discrete-time Markov chains with three states and a transition matrix having complex eigenvalues. Necessary as well as sufficient conditions for the existence of an m-th stochastic root of the transition matrix, are investigated. Matrix roots are expressed in analytical form based on the spectral decomposition of the transition matrix and properties of these matrix roots are proved.

Probability of default estimation via machine learning on historical data is widely studied in credit risk modeling, where risk is estimated by the probability of an entire company or person to default on a loan. In this work, we investigated the use of machine learning for a finer-grained risk estimation task, namely spot factoring. Here, the goal is to estimate the likelihood that a single invoice will be paid in an acceptable timeframe. In this case, risk is more related to when the invoice was paid (if ever), compared to the contractual payment date, that is, the overdueness of an invoice. In order to express overdueness in a spot factoring context, we construct three new variables to predict. One such target variable directly shows the overdueness in terms of days overdue. A second, graded, target variable that we use stems from the way decision makers categorize invoices in risk groups that reflect increasingly later payment periods and more thorough collection efforts. We obtain a third and binary target variable by merging the two best and merging the three worst risk groups. This amounts to splitting the invoices between those that are paid within the first 25 days after their due day and those that are not. We investigate the suitability of three prediction tasks when using the new target variables: classical binary classification, regression of the days overdue and learning-to-rank methods that learn to optimize the risk-related ranking of all instances rather than predicting a value. We describe different ways of modeling these tasks and evaluate them on real-life spot factoring data, with measures specific to each of the learning tasks. As the goal is to evaluate the tasks, rather than the learning methods, we compare them for three different families of learning methods: linear models, support vector machines and gradient-boosted trees. To better answer the question of the suitability of the different tasks for spot factoring, we also perform a profit-driven evaluation. In the experiments, regression methods achieve consistently high scores across the evaluation measures and for all method families. The profit-based evaluation identified the regressor with XGBoost as an outspokenly good performer. We recommend evaluating from a profit-based perspective in a spot factoring context, as it is easily interpretable, consolidates the business objective and addresses the different possible costs associated with different invoice outcomes.

Recent research has indicated considerable potential for millimeter-waves in copper access, with data-rate estimations up to 1 Terabit per second for a reach of 100 m. This line of research exploits millimeter-waves and their corresponding higher-order propagation modes inside the twisted pair cable binder. Unlike the conventionally used transmission-line mode (currents through copper wires), the approach relies on the copper and plastics present in these cables to form a low-loss waveguide. Here, we take a closer look at the potential of millimeter-wave propagation in twisted pair cables by refining the idealized assumptions, used by Cioffi et al., and identifying the limiting factors. To this end, we introduce the concept of transformation optics as an efficient method of calculating the propagating modes on a twisted pair. Leveraging this technique allows us to calculate modal propagation using realistic material parameters, exposing an important trade-off between loss and confinement. Our modeling results yield achievable data rates that are orders of magnitude lower than those achieved under idealized assumptions. According to our results, 1 Terabit per second can be achieved up to a distance of about 10 m over a twisted-pair with a plastic sheath.

Applying causal methods to fields such as healthcare, marketing, and economics receives increasing interest. In particular, optimising the individual-treatment-effect – often referred to as uplift modelling – has peaked in areas such as precision medicine and targeted advertising. While existing techniques have proven useful in many settings, they suffer vividly in a dynamic environment. To address this issue, we propose a novel optimisation target that is easily incorporated in bandit algorithms. Incorporating this target creates a causal model which we name an uplifted contextual multi-armed bandit. Experiments on real and simulated data show the proposed method to effectively improve upon the state-of-the-art. All our code is made available online at https://github.com/vub-dl/u-cmab.

Expected values are the metric most often used to judge human decision-making; when humans make decisions that do not optimize expected values, these decisions are considered irrational. However, while convenient, expected values do not necessarily describe the evolution of an individual after making a series of decisions. This dichotomy lies at the core of ergodicity breaking, where the expected value (ensemble average) differs from the temporal average of one individual. In this paper, we explore whether the intuition behind human decision-making optimizes for expected values or instead takes time growth rates into account. We do this using several stated choice experiments, where participants choose between two stochastic bets and try to optimize their capital. To evaluate the intuitive choice, we compare two groups, with and without perceived time pressure. We find a significant difference between the responses of the timed and the control group, depending on the dynamic of the choices. In an additive dynamic, where ergodicity is not broken, we observe no effect of time pressure on the decisions. In the non-ergodic, multiplicative setting, we find a significant difference between the two groups. The group that chooses under time pressure is more likely to make the choice that optimizes the experiment’s growth rate. The results of this experiment contradict the idea that people are irrational decision-makers when they do not optimize their expected value. The intuitive decisions deviate more from the expected value optimum in the non-ergodic part of our experiment and lead to more optimal decisions.

Machine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

Orbital angular momentum (OAM) from lasers holds promise for compact, at-source solutions for applications ranging from imaging to communications. However, conjugate symmetry between circular spin and opposite helicity OAM states (±l) from conventional spin–orbit approaches has meant that complete control of light’s angular momentum from lasers has remained elusive. Here, we report a metasurface-enhanced laser that overcomes this limitation. We demonstrate new high-purity OAM states with quantum numbers reaching l = 100 and non-symmetric vector vortex beams that lase simultaneously on independent OAM states as much as Δl = 90 apart, an extreme violation of previous symmetric spin–orbit lasing devices. Our laser conveniently outputs in the visible, producing new OAM states of light as well as all previously reported OAM modes from lasers, offering a compact and power-scalable source that harnesses intracavity structured matter for the creation of arbitrary chiral states of structured light. A metasurface laser generates orbital angular momentum states with quantum numbers reaching l = 100. Simultaneous output vortex beams, with Δl as great as 90, are demonstrated in the visible regime.

Staffing and rostering are two interdependent optimization problems related to personnel planning. Where staffing concernsan organization’s personnel composition, personnel rostering aims to assign that staff to shifts while respecting a set ofconstraints. Staffing decisions therefore impact on the extent to which personnel rostering objectives can be accomplished.Previous research has suggested that it may prove beneficial to address both problems simultaneously and has introducedthe roster quality staffing (RQS) problem to this effect. The RQS aims to assess the appropriateness of a given personnelstructure based on the quality of its corresponding roster. The RQS problem is, however, limited by the fact that it onlyconsiders one rostering period and thus neglects that staffing and rostering span different time horizons. One staffing periodusually consists of multiple rostering periods, during which the rostering objectives and constraints may vary. This differencebetween rostering periods causes roster quality variation within the same staffing period. This paper remedies this issue byintroducing a generalization of the RQS problem: the Extended RQS problem. This generalization considers the impact of apersonnel structure upon possible roster quality over the entire staffing period. There currently exist no suitable algorithms forsolving the extended roster quality staffing problem, and therefore, two local search-based approaches have been developedand assessed by means of publicly available personnel rostering instances. Experimental results demonstrate both algorithms’optimization potential. In addition, the accuracy of the new model is assessed with respect to both staffing decisions andoverall roster quality. ispartof: Journal of Scheduling vol:23 issue:2 pages:253-264 status: published

In general, the transition matrix of a Markov chain is a stochastic matrix. For a system that is modeled by a Markov chain, the transition matrix must reflect the characteristics of that system. The present paper introduces a particular class of transition matrices in order to model Markov systems for which, as the length of the time interval becomes greater, a transition from one state to another is more likely. We call these transition matrices stepwise increasing. Moreover in some contexts it is less desirable that the stocks fluctuate over time. In those situations, one is interested in monotonically proceeding stock vectors. This paper examines monotonically proceeding stock vectors and stepwise increasing transition matrices. We present conditions on the transition matrix such that all stock vectors are monotonically proceeding. In particular, the set of monotonically proceeding vectors is characterized for the two-state and three-state cases.

Uplift modeling is an instrument used to estimate the change in outcome due to a treatment at the individual entity level. Uplift models assist decision-makers in optimally allocating scarce resources. This allows the selection of the subset of entities for which the effect of a treatment will be largest and, as such, the maximization of the overall returns. The literature on uplift modeling mostly focuses on queries concerning the effect of a single treatment and rarely considers situations where more than one treatment alternative is utilized. This article surveys the current literature on multitreatment uplift modeling and proposes two novel techniques: the naive uplift approach and the multitreatment modified outcome approach. Moreover, a benchmarking experiment is performed to contrast the performances of different multitreatment uplift modeling techniques across eight data sets from various domains. We verify and, if needed, correct the imbalance among the pretreatment characteristics of the treatment groups by means of optimal propensity score matching, which ensures a correct interpretation of the estimated uplift. Conventional and recently proposed evaluation metrics are adapted to the multitreatment scenario to assess performance. None of the evaluated techniques consistently outperforms other techniques. Hence, it is concluded that performance largely depends on the context and problem characteristics. The newly proposed techniques are found to offer similar performances compared to state-of-the-art approaches.

We consider two covering variants of the network design problem. We are given a set of origin/destination pairs, called O/D pairs, and each such O/D pair is covered if there exists a path in the network from the origin to the destination whose length is not larger than a given threshold. In the first problem, called the Maximal Covering Network Design problem, one must determine a network that maximizes the total fulfilled demand of the covered O/D pairs subject to a budget constraint on the design costs of the network. In the second problem, called the Partial Covering Network Design problem, the design cost is minimized while a lower bound is set on the total demand covered. After presenting formulations, we develop a Benders decomposition approach to solve the problems. Further, we consider several stabilization methods to determine Benders cuts as well as the addition of cut-set inequalities to the master problem. We also consider the impact of adding an initial solution to our methods. Computational experiments show the efficiency of these different aspects., SCOPUS: ar.j, info:eu-repo/semantics/published

Uplift modeling is the subfield of causal inference that focuses on the ranking of individuals by their treatment effects. Uplift models are typically evaluated using Qini curves or Qini scores. While intuitive, the theoretical grounding for Qini in the literature is limited, and the mathematical connection to the well-understood Receiver Operating Characteristic ROC is unclear. In this paper, we first introduce the ROCini, an uplift evaluation metric similar in intuition to Qini but derived from the well understood ROC. Using Ordinal Dominance Graph theory, the ROCini is extended to the pROCini, a mathematically better behaved metric that facilitates theoretical analysis. Exploiting the theoretical properties of pROCini, confidence bounds are derived. Finally, the empirical performance of ROCini and pROCini is validated in a simulation study.

Recent developments in causal machine learning open perspectives for new approaches that support decision-making in healthcare processes using causal models. In particular, Heterogeneous Treatment Effect (HTE) inference enables the estimation of causal treatment effects for individual cases, offering great potential in a process mining context. At the same time, HTE literature typically focuses on clinical outcome measures, disregarding process efficiency. This paper shows the potential of jointly considering the clinical and operational effects of treatments in the context of healthcare processes. Moreover, we present a simple pipeline that makes existing HTE machine learning techniques directly applicable to event logs. Besides these conceptual contributions, a proof-of-concept application starting from the publicly available sepsis event log is outlined, forming the basis for a critical reflection regarding HTE estimation in a process mining context.

Traditional gender beliefs and gender prejudice (sexism) are thought to play a key role in the reproduction of gender inequality. It is known that such traditional gender beliefs are more common among strongly religious people. Europe is facing a growing population of religious, often immigrant youth from Islamic countries such as Turkey and Morocco. Against that background, we investigate how ambivalent sexism is related to adolescents’ religious affiliation (being Christian or Muslim), religiosity (how important religion is in one's life), perceived pressure for religious conformity and ethnic background (native, Turkish, Moroccan) by performing multivariate multilevel regression analyses. We rely on data from two independent samples gathered in 2013 and 2018 by the Youth Research Platform among Dutch-speaking boys (N = 1637) and girls (N = 2058) between 14 and 18 years old. The results show the gendered ways in which religion and ambivalent sexism towards women are related. For girls, ambivalent sexism wasrelated to perceived pressure for religious conformity and ethnic background (more group-level aspects). Boys’ ambivalent sexism was related to more individual-level aspects such as higher religiosity. No differences were found between Muslim or Christian youth., Traditional gender beliefs and gender prejudice (sexism) are thought to play a key role in the reproduction of gender inequality. It is known that such traditional gender beliefs are more common among strongly religious people. Europe is facing a growing population of religious, often immigrant youth from Islamic countries such as Turkey and Morocco. Against that background, we investigate how ambivalent sexism is related to adolescents’ religious affiliation (being Christian or Muslim), religiosity (how important religion is in one's life), perceived pressure for religious conformity and ethnic background (native, Turkish, Moroccan) by performing multivariate multilevel regression analyses. We rely on data from two independent samples gathered in 2013 and 2018 by the Youth Research Platform among Dutch-speaking boys (N = 1637) and girls (N = 2058) between 14 and 18 years old. The results show the gendered ways in which religion and ambivalent sexism towards women are related. For girls, ambivalent sexism wasrelated to perceived pressure for religious conformity and ethnic background (more group-level aspects). Boys’ ambivalent sexism was related to more individual-level aspects such as higher religiosity. No differences were found between Muslim or Christian youth. © 2021 European Sociological Association.

Reciprocal copy number variations (CNVs) of 16p11.2 are associated with a wide spectrum of neuropsychiatric and neurodevelopmental disorders. Here, we use human induced pluripotent stem cells (iPSCs)-derived dopaminergic (DA) neurons carrying CNVs of 16p11.2 duplication (16pdup) and 16p11.2 deletion (16pdel), engineered using CRISPR-Cas9. We show that 16pdel iPSC-derived DA neurons have increased soma size and synaptic marker expression compared to isogenic control lines, while 16pdup iPSC-derived DA neurons show deficits in neuronal differentiation and reduced synaptic marker expression. The 16pdel iPSC-derived DA neurons have impaired neurophysiological properties. The 16pdel iPSC-derived DA neuronal networks are hyperactive and have increased bursting in culture compared to controls. We also show that the expression of RHOA is increased in the 16pdel iPSC-derived DA neurons and that treatment with a specific RHOA-inhibitor, Rhosin, rescues the network activity of the 16pdel iPSC-derived DA neurons. Our data suggest that 16p11.2 deletion-associated iPSC-derived DA neuron hyperactivation can be rescued by RHOA inhibition., 16p11.2 CNVs are associated with neurodevelopmental disorders. Here, the authors show that 16p11.2 deletion is associated with hyperactivation of human iPSC-derived dopaminergic neuron networks and is rescued by RHOA inhibition in vitro.

This paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the maximum number of steps it can be composed of and on the minimum step length. Our algorithm relies on dynamic programming and is built on the basis that said curve-fitting task can be tackled as a shortest-path type of problem. Numerical results on synthetic and realistic data sets reveal that our algorithm is able to provide the globally optimal monotone stepwise curve fit for samples with thousands of data points in less than a few hours. Furthermore, the algorithm gives a certificate on the optimality gap of any incumbent solution it generates. From a practical standpoint, this piece of research is motivated by the roll-out of smart grids and the increasing role played by the small flexible consumption of electricity in the large-scale integration of renewable energy sources into current power systems. Within this context, our algorithm constitutes an useful tool to generate bidding curves for a pool of small flexible consumers to partake in wholesale electricity markets., SCOPUS: ar.j, info:eu-repo/semantics/published

In the spring of 2021, Amazon hosted the Last Mile Routing Research Challenge with support from the MIT Center for Transportation & Logistics. The goal of the challenge was to encourage participants to develop innovative approaches to produce solutions to a real-world route sequencing problem, leveraging a large set of real operational data released by Amazon. All finalists were asked to submit short technical papers to document their methodological approach and its expected performance. This proceedings document provides a consolidated overview of the papers submitted by the participants.

Automated decisions based on trained algorithms influence human life in an increasingly far-reaching way. In recent years, it has become clear that these decisions are often accompanied by bias and unfair treatment of different subpopulations.Meanwhile, several notions of fairness circulate in the scientific literature, with trade-offs between profit and fairness and between fairness metrics among themselves. Based on both analytical calculations and numerical simulations, we show in this study that some profit-fairness trade-offs and fairness-fairness trade-offs depend substantially on the underlying score distributions given to subpopulations and we present two complementary perspectives to visualize this influence. We further show that higher symmetry in scores of subpopulations can significantly reduce the trade-offs between fairness notions within a given acceptable strictness, even when sacrificing expressiveness. Our exploratory study may help to understand how to overcome the strict mathematical statements about the statistical incompatibility of certain fairness notions.

Discrete time Markov models are used in a wide variety of social sciences. However, these models possess the memoryless property, which makes them less suitable for certain applications. Semi-Markov models allow for more flexible sojourn time distributions, which can accommodate for duration of stay effects. An overview of differences and possible obstacles regarding the use of Markov and semi-Markov models in manpower planning was first given by Valliant and Milkovich (1977). We further elaborate on their insights and introduce hybrid semi-Markov models for open systems with transition-dependent sojourn time distributions. Hybrid semi-Markov models aim to reduce model complexity in terms of the number of parameters to be estimated by only taking into account duration of stay effects for those transitions for which it is useful. Prediction equations for the stock vector are derived and discussed. Furthermore, the insights are illustrated and discussed based on a real world personnel dataset. The hybrid semi-Markov model is compared with the Markov and the semi-Markov models by diverse model selection criteria.

For designing transport policy measures, it is crucial to base decisions on evidence-based insights regarding transport flows and behavior. This paper introduces practical indicators for urban transport, which can be derived from large collections of GPS trajectories of heavy-goods vehicles. The indicators framework enables cities, municipalities, and regions to gain insights into the urban transport activities in their region. We motivate our indicators based on the objectives and action plans described in the strategic plan for goods traffic of the Brussels-Capital Region in Belgium. We provide a case study on data collected from the on-board units of heavy-goods vehicles, which became mandatory in Belgium as part of its dynamic road-pricing scheme in 2016. This paper contributes to the exciting new capabilities that are obtained through GPS trajectories data and the possibilities this offers for smart cities at the tactical, operational, and strategic level.

Student dropout is a major concern in higher education, as it leads to direct economic losses and substantial social costs. Public and private institutions spend considerable resources to prevent student dropout. The efficiency and effectiveness of these investments, however, may be improved by adopting a profit-driven perspective. In this paper, we propose a novel approach for implementing student dropout prediction using data-driven methods. Extending upon profit metrics as used in business analytics, we design a novel performance measure for evaluating predictive models that is tailored to the student dropout problem and that quantifies the net savings of a retention campaign. This metric supports the identification and selection of students to optimally allocate the limited resources for preventing student dropout and to maximize the resulting savings. Experiments were performed using data from three bachelor's programs of a higher education institution containing information on dropouts and participation in a retention program, i.e., tutorials. The proposed metric allows for a better choice of prediction model and classification threshold than conventional approaches and, as a result, yields tangible savings for the institution. Finally, the presented approach and experimental results highlight pathways to design tailored student retention programs.

Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.

People do not change easily. They seem irrationally fond of the status quo and need a great deal of convincing before changing habits, even when the change appears beneficial. For instance, to get people to change modes of transportation, policymakers and researchers have thought up a great deal of incentives. Unfortunately, these incentives do not always have the desired effect or sometimes even have the adverse impact. Withing the novel framework of Ergodicity Economics, it is possible to explains some of these failures by highlighting our overdependence on ensemble averages when designing incentives and judging the benefits of a modal shift. Indeed, when modelling human decision-making, ergodicity is often implicitly assumed. Consequently, ensemble averages judge the (ir)rationality of human decision-making. However, when we step away from this assumption, i.e., ensemble averages and time averages are no longer assumed to be equal, we can rationalize much of this seemingly irrational behaviour by looking at the time average of an individual in a dynamic and stochastic environment, instead of relying on the expected value of the process. Loss aversion, e.g., rather than being a compulsory drive to cling to what you already have, becomes a way of mitigating downside risk and eventual ruin. The status quo bias or the irrational hesitance towards change also makes sense in a non-ergodic world as uncertainty looms greater in a non-ergodic world than in an ergodic one. In this contriution, we will use transportation data to demonstrate that, we can shift the dialogue regarding strategies for inducing behavioural change from one that focuses on idiosyncratic preferences and irrationalities to one that enhances the objective drivers of change. Instead of looking into personal reasons for not opting for sustainable mobility, we look at the determining factors objectively keeping us from change in a non-ergodic world.

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making. ispartof: Decision Support Systems vol:150 status: published

Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class have been successfully addressed via end-to-end learning approaches, which rely on solving one optimization problem for each training instance at every epoch. In this context, we provide two distinct contributions. First, we use a Noise Contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. Second, we address a major bottleneck of all predict-and-optimize approaches, i.e. the need to frequently recompute optimal solutions at training time. This is done via a solver-agnostic solution caching scheme, and by replacing optimization calls with a lookup in the solution cache. The method is formally based on an inner approximation of the feasible space and, combined with a cache lookup strategy, provides a controllable trade-off between training time and accuracy of the loss approximation. We empirically show that even a very slow growth rate is enough to match the quality of state-of-the-art methods, at a fraction of the computational cost., Accepted at IJCAI2021

Recently, a novel method for explaining solutions of constraint satisfaction problems was proposed. An explanation here is a sequence of simple in ference steps, where the simplicity of an infer ence step depends on the number and types of con straints used, eventually explaining all logical con sequences of the problem. The current paper builds on these formal foundations and tackles two emerg ing questions: namely how to generate explana tions that are provably optimal (with respect to the given cost metric) and how to generate them ef ficiently. To answer these questions, we develop 1) an implicit hitting set algorithm for finding op timal unsatisfiable subsets; 2) a method to reduce multiple calls for (optimal) unsatisfiable subsets to a single call that takes constraints on the subset into account, and 3) a method for re-using rele vant information over multiple calls to these algo rithms. We show that this approach can be used to effectively find sequences of optimal explanations for constrained satisfaction problems like logic grid puzzles. ispartof: pages:1381-1388 ispartof: IJCAI : proceedings of the conference / sponsored by the International Joint Conferences on Artificial Intelligence pages:1381-1388 ispartof: International Joint Conference on Artificial Intelligence location:Montreal, Canada date:21 Aug - 26 Aug 2021 status: published

The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption. Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting., LIPIcs, Vol. 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021), pages 42:1-42:17

Application scorecards allow to assess the creditworthiness of loan applicants and decide on acceptance. The accuracy of scorecards is of crucial importance for minimizing bad debt loss and maximizing returns. In this paper, we extend upon prior benchmarking studies that experimentally compare the performance of classification techniques to discriminate between good and bad applications. We evaluate a range of cost-sensitive learning methods in terms of their ability to boost the profitability of scorecards. These methods allow to take into account the variable misclassification costs that are involved in rejecting good loan applications and accepting bad loan applications. An approach is proposed to estimate these misclassification costs, and various approaches to handle missing credit bureau scores are evaluated. The results of a case study involving a Romanian nonbanking financial institution (NBFI) indicate that cost-sensitive learning complements the existing state-of-the-art scorecard of the NBFI. The best performing cost-sensitive models are found to increase profitability across the three business channels, with a single-digit improvement for two of the channels and a double-digit increase for the other one. The result is partly explained by the default rate, which is higher for this latter channel and therefore offers greater potential for improving profitability.

This article presents a modeling approach to tackle the multidimensional military manpower planning problem. Requirements relevant to military manpower planning include the need to maintain distributions of manpower characteristics (such as rank and age) with respect to career advancement within acceptable proximity to desired norms. Moreover, they include ensuring that the personnel allocated to the various roles of the defence force will be adequate throughout the planning period. Addressing these tasks simultaneously represents a significant challenge for human resources managers. This paper proposes a model that assigns personnel to career paths (CPs) pre-defined as feasible prior to optimization. Adequate solutions through this model are generated by mixed integer goal programming. This solving approach is applied to a case study of the Belgian Defence’s manpower and results in valuable insights for military human resources managers. For the illustration of our approach, we focus on a military organization, but it could be used for any hierarchical organization, such as a police force or a university.

In an inventory system, the lead time is the time between the moment when a decision is made about the quantity to order (or produce) and the moment when this amount is available to satisfy demand. This lead time has an impact on inventory (holding and shortage) costs as well as on the purchase or production costs. Usually, shorter lead times result in lower inventory costs together with higher purchase or production costs. The lower inventory costs are caused by a reduced exposure to demand uncertainty, while the higher purchase or production costs are due to expediting the production and/or delivery process which typically comes with additional expenses. In this paper, we consider a periodic review dynamic order-up-to inventory system in which shortages are backordered. Such a dynamic system is appropriate when demand is non-stationary and demand forecast distributions are a crucial input to the inventory system. We propose a framework to quantify the effect of reducing (or extending) the lead time on the exposure to forecast uncertainty in terms of a justified increase (or required decrease) in unit purchase or production cost. This difference in unit purchase or production cost quantifies the offset to compensate for the change in exposure to forecast uncertainty, and hence the change in inventory costs. The framework is based on the work of de Treville et al. (2014) which deals with valuing lead time in the single-period newsvendor problem. We apply the framework to a case study and illustrate its use as decision support tool for deciding what lead time to use. In the case study, sales history and open order data are available to make demand forecasts. As more and more orders for a specific period are collected when approaching this period, naturally, the open order data strongly affects the evolution of forecast uncertainty in lead time, making the framework of particular interest.

Many companies, such as credit granting companies, have to decide on granting or denying customer or invoice loans on a daily basis. Increasingly, machine learning is used to learn probability-of-default models from previously granted cases and, thus, whether the outcome was positive or negative for the company, i.e. whether the client paid back or defaulted. However, as the outcome can only be observed for the granted cases, the data inherently has sample selection bias and caution should be taken when applying the probability-of-default model to the full through-the-door population. In reject inference, this problem is studied with respect to whether using the unlabeled rejected instances can help improve a classifier that is only trained on granted instances, e.g. using semi-supervised learning. In contrast, we investigate under what circumstances a model trained on the granted instances, with known outcome, can be used on all possible instances. For this, we believe a model should indicate when it cannot reliably predict the outcome. That is, it should refrain from making predictions on instances unlike those on which it was trained. If not, the credit granting company would expose itself to great risk, and experts could lose their trust in the predictions. We discuss similarities and differences of this problem compared to novelty detection, classification with a reject option and reject inference. We compare a number of methods that combine novelty detection with classification, with decent results even for two-stage methods and especially when using data of existing instances with unknown outcome.

ispartof: CRITICAL CARE vol:24 issue:1 ispartof: location:England status: published

The electromagnetic near field enables subwavelength applications such as near-field microscopy and nanoparticle manipulation. Present methods to structure the near field rely on optical antenna theory, involving nanostructures that locally convert propagating waves into confined near-field patterns. We developed a theory of remote rather than local near-field shaping, based on cascaded mode conversion and interference of counterpropagating guided waves with different propagation constants. We demonstrate how to structure at will the longitudinal and transverse variation of the near field, allowing for distributions beyond the conventional monotonic decay of the evanescent field. We provide an experimental realization that confirms our theory. Our method applies to fields with arbitrary polarization states and mode profiles, providing a path toward three-dimensional control of the near field.