Leveraging the power of experiential learning to achieve higher-order proficiencies

Although experiential learning approaches, such as service-learning, have been shown to increase student motivation and academic achievement, faculty concerns about the costs of developing and implementing such courses have limited their adoption within economics. One cost that can be eliminated is the opportunity cost typically associated with “required” reflection exercises that are often perceived as taking time away from economic instruction. This article contributes to the existing literature by introducing a reflection mechanism based on programmatically appropriate project-management reports. This approach not only links experience and course content, essential elements of service-learning best-practice, but also facilitates the successful management of a complex project, thereby contributing to the development of higher-order proficiencies.     

Green process innovation and operational performance: The role of proactive environment strategy,technological capabilities,and organizational learning

With increase in the focus on environmental protection, firms are integrating sustainability issues in process and product innovations. Firms can contribute toward sustainability and also improve operational performance through green innovation practices. However, the adoption of green innovation practices depends on the proactivity of top managers toward environment management. In this paper, we study the relationships between proactive environment strategy, green process innovation (GPRI), and operational performance. Further, we examine the mediating effect of dynamic capabilities (DCs) (technological capabilities and organizational learning) between GPRI and operational performance. The proposed framework is tested by data collected from the Indian manufacturing industries and using partial least squares (PLS) technique. The findings demonstrate proactive environment strategy as an important antecedent for GPRI practices. Second, technological capabilities and organizational learning mediate between GPRI and operational performance. The results of the study are useful for managers in the manufacturing firms, who are interested to implement GPRI practices or who are in the early stage of implementing the GPRI practices.     

Gastronomic image in the foodstagrammer’s eyes – A machine learning approach

Given the rich content that foodstagrammers, people who actively share their dining experiences using photographs and texts on social media, post, they considerably shape a destination's gastronomic image. Using big data analytics, this study examined the formation of gastronomic images from foodstagrammers' perspectives and the associated emotions. Moreover, it demonstrated the applicability of the proposed machine learning approach to evaluate both textual and pictorial content on social media. The study findings extend the current understanding of gastronomic images by identifying the underlying attributes based on the interplay of the three dimensions of food, environment, and activities. Furthermore, the results reveal specific image clusters and dimensions that arouse positive sentiments among foodstagrammers and influence users' engagement with the post. For practitioners, this study provides valuable insights into foodstagrammers' behaviors by identifying the aspects of gastronomic images that effectively arouse interest and engagement, thus promoting gastronomic destinations.     

Machine learning methods for GEFCom2017 probabilistic load forecasting

This paper describes the preprocessing and forecasting methods used by team Orbuculum during the qualifying match of the Global Energy Forecasting Competition 2017 (GEFCom2017). Tree-based algorithms (gradient boosting and quantile random forest) and neural networks made up an ensemble. The ensemble prediction quantiles were obtained by a simple averaging of the ensemble members’ prediction quantiles. The result shows a robust performance according to the pinball loss metric, with the ensemble model achieving third place in the qualifying match of the competition.     

Does governance efficiency matter for national innovative capacity? One tale from different countries

Various factors possibly impact on national innovative capacity. Governments are makers and implementers of national innovative policies, then, does its governance efficiency matter for the innovation of a country? Empirical evidence about the relationship between governance efficiency and national innovative capacity is still inadequate. To fill this research gap, the authors, based on the application of machine learning technique, have selected valid variables, conduct analysis with different models using the panel data of 117 countries from 1995 to 2015. The results of different models show that governance efficiency has a significant and positive impact with stability and reliability on innovation. In addition, the authors divide the 117 countries into different regions in accordance with economic development and geographic proximity, found that governance efficiencies of different regions have a heterogeneous but positive impact on national innovative capacity. Finally, potential policy implications are discussed.     

The QLBS Q-Learner goes NuQLear: fitted Q iteration,inverse RL,and option portfolios

The QLBS model is a discrete-time option hedging and pricing model that is based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines the famous Q-Learning method for RL with the Black–Scholes (–Merton) (BSM) model's idea of reducing the problem of option pricing and hedging to the problem of optimal rebalancing of a dynamic replicating portfolio for the option, which is made of a stock and cash. Here we expand on several NuQLear (Numerical Q-Learning) topics with the QLBS model. First, we investigate the performance of Fitted Q Iteration for an RL (data-driven) solution to the model, and benchmark it versus a DP (model-based) solution, as well as versus the BSM model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for the model, where we only observe prices and actions (re-hedges) taken by a trader, but not rewards. Third, we outline how the QLBS model can be used for pricing portfolios of options, rather than a single option in isolation, thus providing its own, data-driven and model-independent solution to the (in)famous volatility smile problem of the Black–Scholes model.     

Fast and slow cancellations and trader behavior

We investigate how short-lived liquidity supply due to order cancellations affects the order-placement behavior of slow traders. When order cancellations increase, slow traders submit fewer and less aggressive orders. Both short- and long-lived liquidity supply have positive effects on the market overall, reducing spreads and increasing depth. We conclude that it is not necessary to require limit orders to have a minimum lifespan. We develop econometric and machine-learning frameworks that allow traders to predict whether a quote is likely to have a short or long life, increasing the ability of slow traders to respond strategically to changing order flow.     

The supply of education quality in a spatial model with asymmetric moving costs

The paper analyses the characteristics of the supply of higher education in different geographical macroareas using a strategic interaction framework. It focuses on universities operating in centralised funding system that autonomously set the quality of education showing that in equilibrium it is inversely related to students’ moving costs across areas. We show that in the presence of asymmetric information about workers’ ability and asymmetric costs of moving, the only PBE consistent with forward induction involves that only high ability workers acquire education and the quality of education is lower in macroareas where the moving costs are higher. Our model predicts that in economies with centralised university funding, educational policies must be regulated according to the specific socioeconomic characteristics of the area. Direct subsidies to universities may be ineffective in improving the quality of education in the less developed areas. When regional disparities are not too big, efficiency gains may be obtained by reducing moving costs.     

国际制造业资本转移、最优产业配套与政策转变

国际制造业资本转移通常会带来较本土更高的产业技术水平，如果本土企业能够与国际制造业资本形成产业配套，将有助于本土企业技术水平的提高，有利于形成自主产业创新能力。本文建立了一个两阶段最优产业配套模型，揭示了在产业配套初期，本土企业为国际制造业资本进行产业配套和本土经济增长之间关系呈U型，在产业配套后期，上述关系又呈倒U型。从长期看，政府应当以在产业配套中积极培养本土企业自主的产业创新能力为政策目标，否则，本土企业在一轮轮的国际制造业资本转移中将陷入产业配套的“陷阱”中。     

Bringing a perspective of transformative learning to globalized consumption

This article examines the development of market‐driven values and the creation of consumer‐driven lifestyles. It critically examines the effect of market globalization and its creation of negative quality of life indicators. Several resistance movements are given as examples as to how market values are challenged. The movement away from market desires involves a deep change in perspective which is identified as transformative learning.