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AbstractBlack box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods such as random forests typically outperform deep learning models on tabular data sets, their built-in variable importance algorithms are known to be strongly biased toward high-entropy features. It was recently shown that the increasingly popular SHAP (SHapley Additive exPlanations) values suffer from a similar bias. We propose debiased or "shrunk" SHAP scores based on sample splitting which additionally enable the detection of overfitting issues at the feature level.
AbstractThis paper examines the influence of the six World Governance Indicators (WGIs), as defined by the World Bank, on the real GDP growth of five emerging markets, the BRICS (Brazil, Russia, India, China, and South Africa) countries, and three advanced economies, the United States, Germany and Japan. The analysis is based on a panel data set containing the six WGIs along with further macroeconomic variables (government debt, external debt, current account balance, trade balance, budget balance, foreign exchange rate and short-term interest rate), with annual data from 1996 to 2018. We find that regulatory quality has a positive impact on economic growth, an effect that remains stable across all robustness tests. This indicates that a sound regulatory environment stimulates economic growth. We also find a negative impact of rule of law on economic growth, but this effect is not robust. The literature, however, documents a negative effect from income to rule of law, expressing that higher income does not necessarily lead to a demand for better institutions. A principal component analysis on the WGIs shows that governance is diverse across countries, while stable over time. The first two PCs capture more than 95% of the WGIs variance and are able to cluster emerging and developed markets.
In this article, the main events of political life in Portugal are discussed. It includes a report on general elections in 2022, the formation of a new Socialist absolute majority government led by Prime Minister Antonio Costa, and major parliamentary decisions and debates. The year 2022 started with early general elections, strengthening the ruling Socialist Party, which won a majority of seats in the national Parliament. However, it took some time for the new government to learn to govern in the new context.
Shark incidents are rare and graphic events, and their
consequences can influence the behavior of beach users,
including bathers, to a great extent. These incidents can
be thought of as a fearsome risk that may lead decision
makers to overreact or respond with inaction. This paper
examines the reaction of recreational beach users, includ-
ing bathers, to changes in the risk of shark incidents. In
addition to valuing recreational visits to Durban Beach,
South Africa, we study the reaction of beach visitors to a
hypothetical scenario in which protective shark nets,
deployed in coastal waters to protect bathers, are to be
removed. To examine potential heterogeneity of the treat-
ment effect in a travel cost-contingent behavior model, we
develop a semiparametric multivariate Poisson lognormal
(MPLN) model to jointly analyze observed and stated visit
counts. Results show that removing protective shark nets
at Durban beach would decrease recreational visits by
more than 20%. Applying the semiparametric MPLN
model we further find that both the value of a recreational
visit and the predicted change in visitation rates vary as a
function of whether recreationists usually enter the water,
whether they have heard of previous shark incidents, and
their general risk attitude.
This paper examines empirically the importance of equity
preferences for the formation of international environmen-
tal agreements (IEA) for transboundary pollution control.
Although it has been shown theoretically that the existence
of equity preferences among countries considering an IEA
increases the chances for formation and stability of a coali-
tion, empirical assessments of such preferences have been
limited to climate change mitigation and single-country
studies. We consider the case of marine plastic pollution, of
which a large share consists of food and beverage con-
tainers, representing a transboundary pollution control
problem of increasing policy concern, with properties that
lead to distinct considerations for equity and the sharing of
abatement costs. We employ a coordinated choice experi-
ment in the United Kingdom and United States to assess
preferences for abatement-cost allocations in a marine plas-
tics IEA. Pairs of cooperating countries and the relative
allocation of abatement costs are varied experimentally.
Results show systematic aversion to both advantageous and
disadvantageous inequality with respect to abatement costs
but also that the relative strength of advantageous and dis-
advantageous inequality aversion differs across countries.
Across both countries, there is evidence that left-leaning
voters generally favor more equal international sharing of
abatement costs. Differences of these results from the case
of greenhouse gas emission reduction, and implications for
current efforts to establish a legally binding global treaty on
marine plastic pollution, are discussed.
In 2021, Portuguese politics was strongly affected by the Covid-19 pandemic. In this constraining context, the Portuguese population went twice to the polls in presidential and local elections. Moreover, the Portuguese government was the holder of the presidency of the Council of the European Union. Throughout the year, the Portuguese government negotiated with the political parties to get the 2022 budget approved. However, most opposition parties rejected the budget for different reasons. Therefore, the President of the Republic dissolved Parliament and announced early elections for 30 January 2022. Although it was a difficult year, Portugal was able to implement an aggressive vaccination programme, which led to a high vaccination coverage of 80 per cent of the population.
This paper assesses the relationship between decision util-ity and experienced utility of recreational nature visits.The former is measured as the travel cost to reach that siteas routinely used by the travel cost method (TCM), andthe latter is operationalized through visit-related subjec-tive well-being (SWB). As such, the analysis is a test ofconvergent validity by examining whetherex anteTCM-based assessment of recreational value reflecting decisionutility corresponds to statedex postSWB, reflecting expe-rienced utility. It explores to what extent utility revealedby counts of nature visits are associated with self-reported,visit-related SWB relating to that same visited site. Theanalysis uses two existing datasets providing informationon (i) 3672 recreational visits to green/blue spaces inEngland over the course of four years and (ii) 5937 recrea-tional visits to bluespace sites across 14 European coun-tries over one year. Results show a positive associationbetween travel cost and visit-related SWB while control-ling for trip frequency and a large set of covariates,suggesting convergent validity of the two utility concepts.A breakdown by travel mode suggests this relationshiponly holds for trips involving motorized transport and isnot present for habitual, chore-like walking visits to therecreational site.
The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.
Increased electricity consumption along with the transformations of the energy systems and interruptions in energy supply can lead to a blackout, i.e., the total loss of power in an area (or a set of areas) of a longer duration. This disruption can be fatal for production, logistics, and retail operations. Depending on the scope of the affected areas and the blackout duration, supply chains (SC) can be impacted to different extent. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. Distinctively, we triangulate the design and evaluation of experiments with consideration of SC performance, resilience, and viability. The results allow for some generalizations. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of several SC processes, disruption propagations (i.e., the ripple effect), and a danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts, mitigation and recovery strategies. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the unpredictable customer behavior might become major factors that determine the blackout impact and influence selection of an appropriate recovery strategy. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and viability and develop mitigation and recovery strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, intertwined supply networks, and cross-structural ripple effects.
Unter den derzeit rund 39.000 Planungsbüros in Deutschland steht bei vielen hundert in den nächsten Jahren eine Nachfolgeregelung an. Dieser Artikel befasst sich daher intensiv mit dem Thema der Unternehmensnachfolge und gibt Einblicke in die unterschiedlichen Vorstellungen, Herausforderungen und gewünschten Hilfestellungen aus Perspektive Abgebender und Übernehmender der Fachrichtungen Architektur, Innenarchitektur, Landschaftsarchitektur und
Stadtplanung. Die Analyse beruht auf einer empirischen Umfrage der Architektenkammer Berlin unter wissenschaftlicher Begleitung durch das EMF Institut. Darüber hinaus wird erstmalig eine Schätzung berechnet, wie hoch die Anzahl der zur Nachfolge anstehenden Planungsbüros in Deutschland für die nächsten fünf Jahre ist. Des Weiteren liefert dieser Artikel eine kompakte Zusammenfassung über die Besonderheiten der unterschiedlichen Perspektiven und zeigt die zentralen Erkenntnisse auf.
Die Masterarbeit untersucht die Einflüsse personalisierter Werbung im Umfeld sozialer Medien auf die Wahrnehmung der Werbeanzeigen und das Online-Impulskaufverhalten deutscher Konsumenten.
Dazu wurde ein theoretisches Modell entwickelt, welches anhand einer Online-Befragung überprüft wurde. Die Auswertung der Ergebnisse erfolgte mittels konfirmatorischer Faktorenanalyse und eines Strukturgleichungsmodells.
The following thesis aims to explore the particular changes that occurred in user behavior on social media (Instagram, YouTube, Facebook) during the pandemic from March 2020 to present time, how these changes relate to user engagement rate and what impact these changes have on the online radio show industry from a perspective of emerging independent radio show series “Widows Radio”. For limitation purposes of the research, purposive sampling method is used to identify particular behaviors. As a result of the research, a list of changes of user behavior are identified, specific impact of these changes on user engagement rate is shown. On the base of the conducted research, several recommendations for the online radio company “Widows Radio” regarding their social media strategy are provided.