Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.
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Social media firestorms pose a significant challenge for firms in the digital age. Tackling firestorms is difficult because the judgments and responses from social media users are influenced by not only the nature of the transgressions but also by the reactions and opinions of other social media users. Drawing on the heuristic-systematic information processing model, we propose a research model to explain the effects of social impact (the heuristic mode) and argument quality and moral intensity (the systematic mode) on perceptions of firm wrongness (the judgment outcome) as well as the effects of perceptions of firm wrongness on vindictive complaining and patronage reduction. We adopted a mixed methods approach in our investigation, including a survey, an experiment, and a focus group study. Our findings show that the heuristic and systematic modes of information processing exert both direct and interaction effects on individuals’ judgment. Specifically, the heuristic mode of information processing dominates overall and also biases the systematic mode. Our study advances the literature by offering an alternative explanation for the emergence of social media firestorms and identifying a novel context in which the heuristic mode dominates in dual information processing. It also sheds light on the formulation of response strategies to mitigate the adverse impacts resulting from social media firestorms. We conclude our paper with limitations and future research directions.
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Although dozens of empirical studies have been published on effectuation as a whole, much work remains to be done on elaborating each principle in more depth. Based on an exploratory study of seven ventures from the Caribbean island of Curacao, this paper develops an elaborated process model of the affordable loss heuristic in effectuation. The model breaks affordable loss into two components—ability and willingness, and connects these to the concept of loss aversion from prospect theory. Furthermore, these components are encapsulated in a process involving identity, affect, and resourcefulness leading to the entry-stage entrepreneurial investment decision.
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Mediators generally find mediation of hierarchical workplace conflicts difficult, as it often involves structural power imbalances. This dissertation seeks to increase knowledge of how hierarchical conflict affects how parties and mediators perceive mediation across dyads and across time. Three questions are central to this: (a) How effective in the long-term is the mediation of hierarchical workplace conflicts? (b) How does perceived situational power in supervisor-subordinate dyads relate to mediation effectiveness? (c) Do supervisors and subordinates differ in their emotional experiences during mediation, and are mediators able to perceive these emotions accurately? To answer these questions, we rely on the literature on power, emotions, mediation, and conflict management. We introduce our research via a heuristic model (chapter one). We then present our quantitative empirical research in three chapters based on survey data we collected from supervisors, subordinates, and
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In deze animatie bespreken we punten waar je als docent op moet letten als je samenwerkend leren wilt inzetten, waarbij je aan de hand van zeven stappen ondersteund wordt bij te maken keuzes.
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The estimation of the pose of a differential drive mobile robot from noisy odometer, compass and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment. https://doi.org/10.4018/IJAIML.2020010101 https://www.linkedin.com/in/john-bolte-0856134/
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Airports and surrounding airspaces are limited in terms of capacity and represent the major bottleneck in the air traffic management system. This paper proposes a two level model to tackle the integrated optimization problem of arrival, departure, and surface operations. The macroscopic level considers the terminal airspace management for arrivals and departures and airport capacity management, while the microscopic level optimizes surface operations and departure runway scheduling. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on real-world case studies of Paris Charles De-Gaulle airport, show the benefits of this integrated approach.
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Historici denken traditioneel gezien in woorden. Daarbij zijn teksten als ‘primaire bronnen’ favoriet. Sterker nog, na een onderzoek vinden ze alleen het gene wat geschreven is belangrijk. Deze vorm van representatie (boeken en artikelen) is bij historici het meest geliefd. Zelfs bij presentaties zijn historici geneigd hun werk voor te lezen. Pas de afgelopen 20 jaar nemen historici de film als een representatievorm van de geschiedenis serieus. Toch meer in de vorm van studie en kritiek naar films door anderen gemaakt. Films worden niet vaak gekozen door historici om het verleden weer te geven. Het beeld van de historicus is dat van een schrijver. Hier kunnen we een tweedeling in maken, namelijk de literaire schrijver voor het grote publiek en de historicus met een wetenschappelijke en analytische stijl van schrijven. Staley geeft aan dat deze houding effecten heeft op de wijze waarop wij geschiedenis doorgeven aan onze studenten / leerlingen. Bij het toetsen worden vaak geschreven papers en essays gevraagd, daarnaast worden natuurlijk de tentamens afgenomen. De studenten leren te denken in zinnen, paragrafen en narratieve opstellen. De historici zitten in een tekstuele cultuur en dat in een tijd waarin multimedia zijn hoogtij viert. Staley heeft in het kader van zijn onderzoeken al vele geschiedenisdocenten gesproken. Wat hem opviel was dat een groot aantal docenten hun vraagtekens bij de kwaliteit van de multimediaprojecten plaatsten. In de praktijk betekende dit dat veel docenten multimedia liever achterwege hielden, vanwege de vorm en inhoud die in hun belevingswereld niet aansloot bij de academische cultuur.
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![· [Review of: M. Black, Staley, D.J. (2007). A heuristic for visual thinking in history. International Journal of Social Education 22. pp. 24-42]](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20250619195309588467.r_visual_thinking_in_history_-_recensie_Carlos_Garcia_Mu-thumbnail-400x300.png)
