Towards strategic-decision quality in Flemish municipalities: the importance of strategic planning and stakeholder participation

Legislation put forth by the Flemish government mandated Flemish municipalities to adopt strategic planning for their 2014-2019 policy cycle. The government’s assumption is that strategic planning’s approach to decision-making results in strategic-decision quality. Despite this assumption, it remains unclear whether and how strategic planning actually contributes to municipal decision-making. This study elucidates this issue. Drawing on survey data from 271 informants within 89 Flemish municipalities, we find that the systematic dimension of formal strategic planning and the participation of both core and peripheral stakeholders contribute to strategic-decision quality. However, the analytic dimension of formal strategic planning offers no significant contribution

Strategic-decision quality in public organizations : an information processing perspective

This study draws on information processing theory to investigate predictors of strategic-decision quality in public organizations. Information processing theory argues that (a) rational planning practices contribute to strategic-decision quality by injecting information into decision-making and (b) decision-makers contribute to strategic-decision quality by exchanging information during decision-making. These assumptions are tested upon fifty-five Flemish pupil guidance centers. Rational planning practices are operationalized as strategic planning, performance measurement and performance management. Information exchange by decision-makers during decision-making is operationalized as procedural justice of the decision-making process. Results suggest that procedural justice, strategic planning and performance management contribute to strategic-decision quality while performance measurement does not

Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201

Rational planning and politicians' attitudes to spending and reform: replication and extension of a survey experiment

The rational planning cycle of formulating strategic goals and using performance information to assess implementation is assumed to assist decision-making by politicians. Empirical evidence for this assumption is, however, scarce. Our study replicates Nielsen and Baekgaard’s (2015) experiment on the relation between performance information and politicians’ attitudes to spending and reform and extends this experiment by investigating the role of strategic goals. Based on a randomized survey experiment with 1.484 Flemish city councilors and an analysis of 225 municipal strategic plans, we found that information on low and high performance as well as strategic goals directly impact decision-making by politicians

Hints against the cold and collisionless nature of dark matter from the galaxy velocity function

The observed number of dwarf galaxies as a function of rotation velocity is significantly smaller than predicted by the standard model of cosmology. This discrepancy cannot be simply solved by assuming strong baryonic feedback processes, since they would violate the observed relation between maximum circular velocity ($v_{\rm max}$) and baryon mass of galaxies. A speculative but tantalising possibility is that the mismatch between observation and theory points towards the existence of non-cold or non-collisionless dark matter (DM). In this paper, we investigate the effects of warm, mixed (i.e warm plus cold), and self-interacting DM scenarios on the abundance of dwarf galaxies and the relation between observed HI line-width and maximum circular velocity. Both effects have the potential to alleviate the apparent mismatch between the observed and theoretical abundance of galaxies as a function of $v_{\rm max}$. For the case of warm and mixed DM, we show that the discrepancy disappears, even for luke-warm models that evade stringent bounds from the Lyman-$\alpha$ forest. Self-interacting DM scenarios can also provide a solution as long as they lead to extended ($\gtrsim 1.5$ kpc) dark matter cores in the density profiles of dwarf galaxies. Only models with velocity-dependent cross sections can yield such cores without violating other observational constraints at larger scales.Comment: Matches published versio