On environment difficulty and discriminating power

The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). 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Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). Clasificación ABC Multicriterio: Tipos de Criterios y efectos en la asignación de pesos. ITECKNE, 8(2). doi:10.15332/iteckne.v8i2.35Morash, E. A., & Clinton, S. R. (1998). Supply Chain Integration: Customer Value through Collaborative Closeness versus Operational Excellence. Journal of Marketing Theory and Practice, 6(4), 104-120. doi:10.1080/10696679.1998.11501814Fabbe-Costes, N. (2015). Évaluer la création de valeurdu Supply Chain Management. Logistique & Management, 23(4), 41-50. doi:10.1080/12507970.2015.11758621Flores, B. E., & Clay Whybark, D. (1986). Multiple Criteria ABC Analysis. International Journal of Operations & Production Management, 6(3), 38-46. doi:10.1108/eb054765Partovi, F. Y., & Burton, J. (1993). Using the Analytic Hierarchy Process for ABC Analysis. International Journal of Operations & Production Management, 13(9), 29-44. doi:10.1108/01443579310043619Balaji, K., & Kumar, V. S. S. (2014). Multicriteria Inventory ABC Classification in an Automobile Rubber Components Manufacturing Industry. Procedia CIRP, 17, 463-468. doi:10.1016/j.procir.2014.02.044Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700. doi:10.1016/j.cor.2004.07.014Van Kampen, T. J., Akkerman, R., & Pieter van Donk, D. (2012). SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32(7), 850-876. doi:10.1108/01443571211250112Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer Modelling, 16(12), 71-82. doi:10.1016/0895-7177(92)90021-cGajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process. 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Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776-786. doi:10.1080/00207543.2013.838328Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. doi:10.1080/00207543.2012.657966Yu, M.-C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421. doi:10.1016/j.eswa.2010.08.127Tsai, C.-Y., & Yeh, S.-W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656-666. doi:10.1016/j.ijpe.2008.02.017Aydin Keskin, G., & Ozkan, C. (2013). Multiple Criteria ABC Analysis with FCM Clustering. Journal of Industrial Engineering, 2013, 1-7. doi:10.1155/2013/827274Lolli, F., Ishizaka, A., & Gamberini, R. (2014). 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Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. 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Journal of Cleaner Production, 96, 209-219. doi:10.1016/j.jclepro.2014.05.040Ishizaka, A., & Labib, A. (2011). Selection of new production facilities with the Group Analytic Hierarchy Process Ordering method. Expert Systems with Applications, 38(6), 7317-7325. doi:10.1016/j.eswa.2010.12.004Partovi, F. Y., & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers & Industrial Engineering, 41(4), 389-404. doi:10.1016/s0360-8352(01)00064-xAlonso-Manzanedo, M., De-la -Fuente-Aragon, M. V., & Ros-McDonnell, L. (2013). A Proposed Collaborative Network Enterprise Model in the Fruit-and-Vegetable Sector Using Maturity Models. Annals of Industrial Engineering 2012, 359-366. doi:10.1007/978-1-4471-5349-8_42Augusto, M., Lisboa, J., Yasin, M., & Figueira, J. R. (2008). Benchmarking in a multiple criteria performance context: An application and a conceptual framework. European Journal of Operational Research, 184(1), 244-254. doi:10.1016/j.ejor.2006.10.05

Mediatisation in Twitter: an exploratory analysis of the 2015 Spanish general election

[EN] The mediatisation model in politics assumes that media conveys political messages between parties and citizenship, with the risk of promoting issues that frame the electoral content in terms of competition. These dynamics could distract from the debate of ideas and political policies. However, digital media like Twitter provide direct communication channels between parties, candidates and users. The present research explores Twitter content during an electoral campaign focused on the four issues proposed by Patterson (1980) to assess mediatisation: political, policy, campaign and personal (regarding the candidate). The goal of this research study is to evaluate the degree of mediatisation on Twitter using this typology. The research also evaluates the influence of the issue on retweet volume. The study¿s basis was a 15.8 million-tweet corpus obtained during the 2015 Spanish General Election pre-campaign and campaign. This dataset was analysed using an automatic classification system. The results highlighted a predominance of policy issues during both the pre- campaign and campaign, except for the two televised debates, during which campaign issues were the most prevalent. On the election night, users commented much more on political issues. Finally, the kind of issue most likely to be retweeted was policy issues.This research was supported by the Spanish Ministry of Economy and Competitiveness, with Grants CSO2013-43960-R (Los flujos de comunicación en los procesos de movilización política: medios, blogs y líderes de opinión) and CSO2016-77331-C2-1-R (Estrategias, agendas y discursos en las cibercampañas electorales: medios de comunicación y ciudadanos).Baviera, T.; Calvo, D.; Llorca-Abad, G. (2019). Mediatisation in Twitter: an exploratory analysis of the 2015 Spanish general election. Journal of International Communication. 25(2):275-300. https://doi.org/10.1080/13216597.2019.1634619S275300252Antonakaki, D., Spiliotopoulos, D., V. Samaras, C., Pratikakis, P., Ioannidis, S., & Fragopoulou, P. (2017). Social media analysis during political turbulence. PLOS ONE, 12(10), e0186836. doi:10.1371/journal.pone.0186836Barberá, P. (2015). Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis, 23(1), 76-91. doi:10.1093/pan/mpu011Bartholomé, G., Lecheler, S., & de Vreese, C. (2017). Towards A Typology of Conflict Frames. Journalism Studies, 19(12), 1689-1711. doi:10.1080/1461670x.2017.1299033Batrinca, B., & Treleaven, P. C. (2014). Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY, 30(1), 89-116. doi:10.1007/s00146-014-0549-4Baviera, T., Peris, À., & Cano-Orón, L. (2017). Political candidates in infotainment programmes and their emotional effects on Twitter: an analysis of the 2015 Spanish general elections pre-campaign season. Contemporary Social Science, 14(1), 144-156. doi:10.1080/21582041.2017.1367833BLUMLER, J. G., & KAVANAGH, D. (1999). The Third Age of Political Communication: Influences and Features. Political Communication, 16(3), 209-230. doi:10.1080/105846099198596Bor, S. E. (2013). Using Social Network Sites to Improve Communication Between Political Campaigns and Citizens in the 2012 Election. American Behavioral Scientist, 58(9), 1195-1213. doi:10.1177/0002764213490698Brants, K., & Neijens, P. (1998). The Infotainment of Politics. Political Communication, 15(2), 149-164. doi:10.1080/10584609809342363Burnap, P., Gibson, R., Sloan, L., Southern, R., & Williams, M. (2016). 140 characters to victory?: Using Twitter to predict the UK 2015 General Election. Electoral Studies, 41, 230-233. doi:10.1016/j.electstud.2015.11.017Campos-Domínguez, E. 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Considerations about quality in model-driven engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. 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Information Systems Skills Differences between High-Wage and Low-Wage Regions: Implications for Global Sourcing

Developing Information Systems (IS) skills for a company’s workforce has always been challenging, but global sourcing growth has caused the determination of needed IS skills to be more complex. The increased use of outsourcing to an IS service provider and from high-wage regions to low-wage regions has affected what IS skills are required globally and how to distribute the workforce to meet these needs. To understand what skills are needed in locations that seek and those that provide outsourcing, we surveyed IS service provider managers in global locations. Results from 126 reporting units provide empirical evidence that provider units in low-wage regions value technical skills more than those in high-wage regions. Despite the emphasis on commodity skills in low-wage areas, high- and low-wage providers value project management skills. Low-wage regions note global and virtual teamwork more than high-wage regions do. The mix of skills and the variation by region have implications for domestic and offshore sourcing. Service providers can vary their staffing models in global regions which has consequences for recruiting, corporate training, and curriculum

Genetic analysis indicate superiority of perfomance of cape goosberry (Physalis peruviana L.) hybrids

The use of hybrids as a new type of cape gooseberry (Physalis peruviana L.) cultivars could improve yield in this crop, but little or no information is available on hybrid perfomance. We studied several vegetative characters, yield, fruit weight and fruit shape, soluble solids content (SSC), titratable acidity (TA) and ascorbic acid content (AAC) in three hybrids of cape gooseberry and their parents grown outdoors and in a glasshouse. The highest yields were obtained with hybrids, specially in a glasshouse. Interaction dominance environment for yield was very important; a higher dominance effect was detected in the glasshouse, than that observed outdoors. Quality characters were highly affected by the environment and showed variable results for the different families. For fruit composition traits, the additive and additive environment interactions were most important. Broad-sense heritability for all characters was high to medium (0.48-0.91), indicating that a high response to selection would be expected. Hybrids can improve cape gooseberry yield without impairing fruit quality.Leiva-Brondo, M.; Prohens Tomás, J.; Nuez Viñals, F. (2001). Genetic analysis indicate superiority of perfomance of cape goosberry (Physalis peruviana L.) hybrids. Journal of New Seeds. 3(3):71-84. doi:10.1300/J153v03n03_04718433Abak, K., Güler, H. Y., Sari, N., & Paksoy, M. (1994). EARLINESS AND YIELD OF PHYSALIS (P. IXOCARPA BROT. AND P. PERUVIANA L.) IN GREENHOUSE, LOW TUNNEL AND OPEN FIELD. Acta Horticulturae, (366), 301-306. doi:10.17660/actahortic.1994.366.37Kang, M. S. (1997). Using Genotype-by-Environment Interaction for Crop Cultivar Development. Advances in Agronomy Volume 62, 199-252. doi:10.1016/s0065-2113(08)60569-6Klinac, D. J. (1986). Cape gooseberry (Physalis peruviana) production systems. New Zealand Journal of Experimental Agriculture, 14(4), 425-430. doi:10.1080/03015521.1986.10423060Mather, K., & Jinks, J. L. (1977). Introduction to Biometrical Genetics. doi:10.1007/978-94-009-5787-9Mazer, S. J., & Schick, C. T. (1991). Constancy of population parameters for life history and floral traits in Raphanus sativus L. I. Norms of reaction and the nature of genotype by environment interactions. Heredity, 67(2), 143-156. doi:10.1038/hdy.1991.74Nyquist, W. E., & Baker, R. J. (1991). Estimation of heritability and prediction of selection response in plant populations. Critical Reviews in Plant Sciences, 10(3), 235-322. doi:10.1080/07352689109382313Pearcy, R. W. (1990). Sunflecks and Photosynthesis in Plant Canopies. Annual Review of Plant Physiology and Plant Molecular Biology, 41(1), 421-453. doi:10.1146/annurev.pp.41.060190.002225Péron, J. Y., Demaure, E., & Hannetel, C. (1989). POSSIBILITIES OF TROPICAL SOLANACEAE AND CUCURBITACEAE INTRODUCTION IN FRANCE. Acta Horticulturae, (242), 179-186. doi:10.17660/actahortic.1989.242.24Proctor, F. J. (1990). THE EUROPEAN COMMUNITY MARKET FOR TROPICAL FRUIT AND FACTORS LIMITING GROWTH. Acta Horticulturae, (269), 29-40. doi:10.17660/actahortic.1990.269.