Contacto: negocios@utdt.edu

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La posibilidad de acceder a y procesar grandes volúmenes de datos habilitó la aplicación de metodologías de las ciencias de la computación, aprendizaje automático e inteligencia artificial a preguntas de las ciencias sociales. De esta confluencia surgen nuevas preguntas, nuevos problemas, y el nacimiento de una nueva subdisciplina conocida como Ciencias Sociales Computacionales. En esta charla vamos a explorar algunas de las metodologías usadas, y presentar varios ejemplos.

Carlos "Greg" Diuk estudió Ciencias de la Computación en la Universidad de Buenos Aires. Realizó un doctorado en Aprendizaje Automático en Rutgers University en Nueva Jersey (EE. UU.). Tras trabajar algunos años como investigador posdoctoral en neurociencias cognitivas en Princeton University, en 2013 se unió al grupo de Core Data Science de Facebook en California, donde se dedica a las ciencias sociales computacionales.

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Abstract

We investigate what we can learn from the financial crisis about the link between accounting and financial stability. The picture that emerges ten years after the crisis is substantially different from the picture that dominated the accounting debate during and shortly after the crisis. Widespread claims about the role of fair-value (or mark-to-market) accounting in the crisis have been debunked. However, we identify several other core issues for the link between accounting and financial stability. Our analysis suggests that banks’ financial reporting going into the financial crisis was characterized by relatively sparse and late disclosures about relevant risk exposures as well as delayed recognition of losses. In addition, bank regulation through its interlinkage with financial accounting may have dampened banks’ incentives for corrective actions. Our analysis illustrates that a number of serious challenges remain if accounting and financial reporting is to contribute to financial stability.

Professor of Finance at the Department of Finance, Accounting and Statistics at WU (Vienna University of Economics and Business) and VGSF (Vienna Graduate School of Finance). He is also a Research Associate of the European Corporate Governance Institute (ECGI) and was a member of the Task Force of the Steering Committee of the European Systemic Risk Board (ESRB) on financial stability implications of the introduction of IFRS 9. Christian has broad research interests in financial regulation, corporate governance, and contract theory, applied to the design of organizations and insurance contracts. In his recent work, he investigates the role of financial reporting, in particular fair value accounting, for financial stability. His work has been published in, for example, the Review of Finance, Journal of Accounting Research, The Accounting Review, Journal of Economic Perspectives, Management Science, and RAND Journal of Economics. Christian held visiting positions at University of Chicago Booth School of Business, The Wharton School of the University of Pennsylvania, London School of Economics, and Harvard University. He earned his doctoral degree in Finance at the Goethe University Frankfurt and Habilitation at the Department of Economics at the University of Mannheim.

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Abstract

Reasoning about an entity’s preferences (be it a user of an application, an individual targeted for marketing, or a group of people whose choices are of interest) has a long history in different areas of study. In this talk, we adopt the point of view that grows out of the intersection of databases and knowledge representation, where preferences are usually represented as strict partial orders over the set of tuples in a database or the consequences of a knowledge base. We describe how probability theory can be used to model uncertainty in preferences in these domains in two complementary ways. First, we introduce order-based Markov models (OMMs), which flexibly combine preferences with probabilistic uncertainty; unfortunately, the complexity of basic reasoning tasks over these models is intractable, involving exponential factors in the number of statements. To ameliorate this problem, we show how we can exploit the structure of the models to do exact inference much more efficiently when the model is comprised of atomic formulas and the query/evidence are conjunctions of atomic preference formulas. Since the potential application of this kind of models is clear in domains such as the Social Semantic Web (where users often express preferences in an incomplete manner and through different means, often in contradiction with each other), in the second part of the talk we study an extension of the Datalog+/– family of ontology languages with two models: one representing user preferences and one representing the (probabilistic) uncertainty with which inferences are made. Assuming that more probable answers are in general more preferable, the main problem that needs to be solved is how to rank answers to a user’s queries, since the preference model may be in conflict with the preferences induced by the probabilistic model.

Realizó su Ph.D. en Ciencias de la Computación en University of Maryland College Park (EE. UU.) y sus estudios posdoctorales en el Dept. of Computer Science, University of Oxford (Reino Unido) en Representación de Conocimiento y Bases de Datos, con foco en manejo de inconsistencia e incertidumbre. Hoy es investigadora adjunta de CONICET en el ICC (UBA – CONICET). Con una antigüedad de 12 años en investigación, cuenta con más de 15 publicaciones en revistas internacionales, 4 volúmenes editados, y más de 60 publicaciones en libros y congresos internacionales. Ha participado en múltiples proyectos de investigación financiados, entre otros por, US Office of Naval Research, US Air Force Office of Scientific Research, Google, EPSRC (Reino Unido). Ha sido revisora de journals internacionales y miembro de comité de programa de eventos de primer nivel. En 2017 fue panelista en el Early Career Spotlight Track de IJCAI, donde jóvenes investigadores de a exponer sus ideas para el futuro de IA. En 2018 fue incluida por la IEEE Intelligent Systems entre los 10 investigadores prometedores en IA. Recientemente participó en el “T20 Argentina summit” como panelista en el evento organizado por la UNESCO: “Artificial Intelligence: Ethical, Social and Human Rights Challenges”.

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Abstract

La existencia de comportamientos cooperativos parece estar en contradicción con la selección natural, que favorecería a los organismos egoístas. Mucho se ha avanzado en esta aparente paradoja y ya se conocen al menos cinco mecanismos que seleccionan comportamientos cooperativos. Sin embargo, los humanos nos resistimos a formar lazos de cooperación a escala global. Ahora, la paradoja se encuentra invertida. ¿Por qué no somos capaces de cooperar? En esta charla, Andrés Babino va a contar experimentos y simulaciones sobre la psicología del autoengaño. Según su hipótesis, este mecanismo nos impide confiar entre nosotros y por lo tanto cooperar.

Lic. en Física (UBA) y está a pocos meses de obtener su título de doctor. Es integrante del Laboratorio de Neurociencia de UTDT y coautor de varias publicaciones que involucran investigaciones en redes complejas cerebrales y experimentos sobre conductas y creencias económicas. Entusiasta del software libre, el aprendizaje automático y la inteligencia artificial. Además, en la fundación CiGob, es asesor en estadística y desarrolla software para optimizar y sistematizar el análisis de datos.

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Abstract

In this research, we study an online platform that sells car insurance policies offered by different companies. After a customer visits the platform’s website and enters vehicle, demographic and contact information, the customer is shown several car insurance quotes. Customers who do not purchase online are then contacted via telephone by the platform’s call center agents. Using data from this platform and estimating econometric models with instrumental variables we find that as the time that it takes the platform to contact a customer increases, the probability that the customer purchases one of the insurance policies decreases. This suggests that the platform might gain from improving the speed at which it contacts its website visitors. One approach to accomplish this goal is to increase the call center capacity by hiring more agents. However, doing so reduces their utilization and may hence reduce the compensation obtained by each agent. We empirically find that a reduction in an agent utilization translates a higher probability of the agent quitting her job, thus reducing the average experience of the call center agents. Since more experienced agents exhibit a better performance at converting quotes into purchases, this creates a tradeoff for the platform where on the one hand a greater call center capacity leads to better speed of contact but also reduces the incentives for experienced agents to keep their jobs at the platform. In this paper, we empirically quantify this tradeoff and provide guidelines in terms of how these capacity decisions should be made.

Assistant Professor at the Industrial Engineering Department of the University of Chile. Prior to joining the University of Chile, he was an assistant professor of marketing at the Fuqua School of Business of Duke University. He received a Ph.D. in Marketing and an A.M. in Statistics from The Wharton School of the University of Pennsylvania and holds a B.Sc. in Industrial Engineering and an M.B.A. from the University of Chile. 

His research interests are focused on the development of empirical methods for the study of consumer and firm behavior, which special emphasis on Bayesian methods, structural models and problems at the interface between marketing, operations management and economics. Some areas of application of his research include retailing, sales promotion, category management, pricing, service quality, customer relationship management and consumer response to stockouts. Some of these research projects have originated from or led to collaborations and consulting projects with retailing and entertainment companies. 

His research has been published in top marketing and management science journals such as the Journal of Marketing Research, Marketing Science and Management Science. His work has been awarded the MSOM Service Management SIG Best Paper Award and the INFORMS MSOM Student Paper Competition and has also been among the finalists for the MSOM Society Best OM paper published in Management Science.

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Abstract

From 1820 to 1990 the share of world income going to today’s wealthy nations soared from 20% to 70%. That share has recently plummeted. Richard Baldwin shows how the combination of high tech with low wages propelled industrialization in developing nations, deindustrialization in developed nations, and a commodity supercycle that is petering out.

In this presentation, the author will talk about the central theme of his book The Great Convergence: Information Technology and the New Globalization.

PhD, Massachussetts Institute of Technology, is Professor of International Economics at the Graduate Institute, Geneva since 1991 and Director of Centre of Economic Policy Research (CEPR), London since 2014. He is Senior Editor of Economic Policy since 2014 Founder and Editor-in-Chief of VoxEU.org since March 2006. He was Visiting Research Professor at the University of Oxford from 2012 to 2015; Visiting Professor at Massachusetts Institute of Technology (Sep 2002 – Jan 2003 & Sep 1998 – Feb 1999). Previously, he was Associate Professor (1989 – 1991) and Assistant Professor (1986 – 1989) at Columbia University Business School.

The author of numerous books and articles, his research interests include international trade, globalisation, regionalism, and European integration; he has worked as consultant for the numerous governments, the European Commission, OECD, World Bank, EFTA, and USAID.

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Abstract:

This research examines customers’ negative behavioral intentions towards firms that stop giving them unconditional gifts. Building on attribution theory we theorize and empirically demonstrate that a mechanism other than expectation disconfirmation is at play. Specifically, we find that when firms offer valuable unconditional gifts repeatedly and regularly, customers develop a sense of entitlement that overshadows their feelings of gratitude to the firm. Consequently, when business-to-consumer unconditional gift-giving initiatives are terminated, this sense of entitlement drives customers to exhibit negative behavioral intentions towards firms. Moreover, we provide evidence that by boosting customers’ gratitude and by changing the framing of the recipient selection criteria, this effect can be attenuated. We discuss theoretical and practical implications of this work for the design of business-to-consumer unconditional gifting initiatives.

Keywords: Business-to-consumer gift-giving, business gifts, sales promotions, customer entitlement, customer gratitude, customer negative behavioral intentions.

Elena earned her doctorate in Marketing at HEC Paris in June 2018. Prior to entering the Ph.D. program, she received her B.A. from Cattolica University in Milan and her M.Sc. in Marketing Management at Bocconi University.

Elena’s research interests fall into the domains of consumer self-identity and emotion in consumer psychology. At the intersection of these domains, her dissertation research investigates the influence of aversive states (i.e., identity threats, negative emotions, negative outcomes) on consumers’ motivations and behavior. She also investigates how consumers manage multiple social identities in a variety of situations such as when they face self-threatening events (e.g., failure on a test for student-athletes) or when they face resource scarcity contingencies (e.g., time allocation for working parents).

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Abstract

Institutions and practices that are controlled by global value chains are particularly hard to change. The apparel value chain, for instance, has been characterized by lack of wage stability, presence of intermediaries and downward cost pressures and our paper presents the results of a grounded theory study of La Salada, South America’s largest informal garment market. We examine how a group of informal entrepreneurs used three specific mechanisms, (1) unintended bricolage; (2) triggering subversive framings and (3) crafting political opportunity to challenge a key practice of the apparel value chain in Argentina: precarious and delayed wage payments. We interpret our results in terms of institutional entrepreneurship and social movement theory, developing a model for understanding institutional change in the context of high economic inequality. Our findings suggest that institutional entrepreneurs embedded in the informal economy exploit the logic of illegitimate formal institutions and mobilize cultural codes that motivated informal entrepreneurs to enter and persist in a growing market and to shape an emerging identity. 

Assistant Professor in Strategic Management at the IAE Business School, Universidad Austral. Prior to joining the IAE Business School he received his Ph.D. in Management from the University of Edinburgh Business School. He has been recipient of the Principal’s Career Development Ph.D. Scholarship. He is Associate Fellow of the Higher Education Academy (HEA), UK. His research explores how institutional entrepreneurs embedded in the informal economy respond to economic inequality. The empirical setting of his grounded theory study has been La Salada, South America’s largest black market. The context is characterised by entrepreneurs who sell counterfeit and pirated products openly with intermittent enforcement. His Ph.D. dissertation was organized as three empirical studies and collectively, these three essays reveal novel knowledge about the organisational mechanisms behind informal economic activities, constituting a theoretical bridge between the fields of institutional theory, economic inequality and governance.

His research interests lie in processes of institutional and social change. Particularly, he is interested in organizations operating in fragile institutional environments and how they provide alternative forms of institutional infrastructure. His research agenda includes: the study of grand challenges from an organizational perspective in different fields such as global health and education; social issues in management, like inequality and poverty, and how hybrid organizations contribute to minimize those issues through social inclusion; processes of emergence and legitimization of social movements types of organizations. Theoretically, he draws on institutional theory and social movements and organization theory.

Prior to his Ph.D. Carmelo completed his MSc in International Business and a PG Diploma in Local Economic Development. During and after his studies, he has worked during several years as industry and research analyst in the Asset Management industry in Switzerland and Monaco.

He worked on this paper with Rick Woodward and Antonino Vaccaro.

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Abstract

En los últimos 15 años, un gran número de empresas han desarrollado operaciones de producción de etanol en los Estados Unidos. El etanol, producido a partir del maíz, es mezclado con gasolina pura para fabricar combustible. En este paper estudiamos la valuación y operación óptima de una empresa productora de etanol. Implementamos un modelo analítico basado en opciones reales sobre la dinámica del maíz y la gasolina, tomando en cuenta detalles institucionales e incentivos gubernamentales. Luego, usando datos entre 2000 y 2017, testeamos el modelo en su capacidad de reproducir los precios observados de etanol, el output de la industria y la valuación de firmas productoras.

Doctor en Matemáticas Aplicadas por la Universidad de Columbia y Licenciado en Física por la Universidad de Buenos Aires. Ha desarrollado investigación académica y aplicada sobre los determinantes de la volatilidad de precios de commodities, el impacto de eventos climáticos y procesos de globalización en precios, la optimización de procesos logísticos y la evaluación de instrumentos de cobertura de riesgo. Sus trabajos han aparecido en Finance & Stochastics, The Journal of Futures Markets y Quantitative Finance entre otros. Entre 2002 y 2008 trabajó en el área de derivados de tasa de interés en Lehman Brothers, New York.

También tiene experiencia en el desarrollo de estrategias cuantitativas de trading sobre volatilidad, y ha realizado trabajos de consultoría en temas financieros y económicos.

Es decano de la Escuela de Negocios y profesor de Finanzas y Commodities en la Universidad Torcuato Di Tella.

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Abstract

The impact of congestion in transportation has become one of the main concerns regarding urban planning in large cities. In general, vehicle routing problems assume that travel times (or distances, or costs) are time independent, meaning that they remain fixed along the planning horizon. However, in practice the problem is much more complex and the operations are heavily affected by the congestion. Incorporating this aspect at a tactical and operational level results in distribution strategies which are closer to real-world operations, especially for last-mile logistics problems in large cities. In this talk, we present advances on exact and heuristic algorithms based on integer an dynamic programming for vehicle routing problems that include congestion by considering variable travel times. Our extensive computational experiments show that our approach is capable of solving instances with up to 100 customers and has potential to be incorporated into modern decision support systems.

Profesor asistente de Operaciones en la Escuela de Negocios desde 2016, e investigador asistente del CONICET desde 2014. Se graduó como Lic. en Ciencias de la Computación por la Universidad de Buenos Aires en el año 2007 y obtuvo su título de Dr. en Ciencias de la Computación en la misma Universidad en 2012.

Su área de investigación se centra en la aplicación de técnicas de investigación operativa y optimización a problemas de transporte y logística. Actualmente sus investigaciones trabajan temas relacionados con problemas de distribución bajo efectos de congestión en grandes ciudades, manejo de interrupciones en tiempo real en sistemas ferroviarios y revenue management. Su trabajo ha sido publicado en destacadas revistas científicas internacionales como Operations Research, Discrete Applied Mathematics y Computers and Operations Research, entre otras.

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Abstract

Classic and recent studies demonstrate how we fall for the ‘tyranny of the majority’ and follow the dominant trend, perhaps wisely, when uncertain. In many social interactions, however, there is no clearly identified majority, and aggregating the various opinions of others is non-trivial. We asked whether in such conditions herding behaviour depends on statistical properties of social information, namely, the variance of opinions in a group. To induce imitative behaviour and diversity of views, we selected a behavioural task where individuals were highly uncertain about their private opinions, while still able to venture one, and convinced that the problem had a correct answer (to avoid people thinking that all opinions are as good as one another): We asked participants to estimate the actual market price of a set of paintings previously unknown to them, and to report their confidence in those estimates. First, participants privately estimated the price of eight anonymous paintings. Then, in groups of five, they discussed and agreed on a shared estimate for four paintings. Finally, they provided revised individual estimates for all paintings. We observed that groups converged to each other and boosted their confidence. However, the more diverse groups herded more and showed less confidence. These findings suggest that, in the absence of a clear majority, the diversity of social information produces opposite effects on herding behaviour and subjective uncertainty.

Full-time Assistant Professor in the School of Business and the Laboratory of Neuroscience at Universidad Torcuato Di Tella, and in CONICET. He graduated in Physics at Universidad de Buenos Aires (2010), and obtained his PhD in Neuroscience at the University of Leicester (2014) in the United Kingdom. Between 2014 and 2017, he was postdoctoral research associate at the Institute of Cognitive Neuroscience at University College London. His research was published in journals such as Nature Human Behaviour, Journal of Neuroscience, Journal of Experimental Psychology, and Current Opinion in Behavioral Sciences. He received the ICT Pioneers award in 2012, and was distinguished by La Nacion as one of the 35 young Argentinian leaders under 35.

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Abstract

We study the effect of debtor protection on firm entry and exit dynamics. We find that more lenient personal bankruptcy increase firm entry only in sectors with low entry barriers. We also find that debtor protection increases small firm exit rates and job destruction, and that this effect is stronger for very young firms. This negative effect takes three years to materialize and is persistent in time. Finally, we find that there is no difference between exit rates of firms born before and after the change in debtor protection. Our results overall indicate that the main mechanism affecting firm dynamics is a reduction in credit supply to the smallest and youngest firms.

Associate Professor of Finance at Universidad Torcuato Di Tella, and an extramural fellow at TILEC (Tilburg Law and Economics Center). Her research interests include small business finance, financial intermediation, and corporate finance. She has published in the Journal of Financial Economics, Management Science, Review of Financial Studies, and the Review of Finance among other journals. Her work has been presented at all major finance conferences, including the AFA, WFA, and EFA. Before moving back to Buenos Aires, she was an Associate Professor of Finance, with tenure, at Tilburg University. She also held positions as Economist at the Central Bank of Argentina and at the Field Office of the World Bank in Buenos Aires. She earned a Bachelor´s degree from the University of Buenos Aires, and a doctorate in Economics from the University of Maryland.

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Contacto: negocios@utdt.edu
Tel.: (+54 11) 5169 7301