6 research outputs found

    Essays on information and mechanism design

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    textMy dissertation studies the optimal design of institutions and information structures for different objectives of a designer or a social planner. The questions addressed are interesting both from a theoretical point of view, and in terms of their real-life applications. The first chapter of the dissertation focuses on supermodular mechanism design in environments with arbitrary finite type spaces and interdependent valuations. In these environments, the designer may have to use Bayesian equilibrium as a solution concept, because ex post implementation may not be possible. We propose direct Bayesian mechanisms that are robust to certain forms of bounded rationality while controlling for equilibrium multiplicity. In quasi-linear environments with informational and allocative externalities, we show that any Bayesian mechanism that implements a social choice function can be converted into a supermodular mechanism that also implements the original decision rule. The proposed supermodular mechanism can be chosen in a way that minimizes the size of the equilibrium set, and we provide two sets of sufficient conditions to this effect: for general decision rules and for decision rules that satisfy a certain requirement. This is followed by conditions for supermodular implementation in unique equilibrium. The second chapter looks at the incentives of a revenue-maximizing seller (designer) who discloses information to a number of interacting bidders (agents). In particular, the designer chooses the level of precision with which agents can infer the quality of a common-value object from their privately observed signals. We restrict attention to the second-price sealed-bid auction format. If the seller has perfect commitment power and can choose the precision level before observing the quality of the object, in the presence of any small cost to precision it is ex ante optimal for her to choose completely uninformative signals. For the case when the seller chooses the precision level after observing the quality of the object, we characterize pooling, partial pooling, and separating equilibria. We show that in this setting the cost associated with precision can be viewed as a form of commitment device: if costs are too low, the best pooling equilibrium ceases to exist as the high type seller is too tempted to separate. Thus, the seller ends up with a lower ex ante expected payoff than in the case when cost parameters are above a certain threshold. The third chapter of this dissertation studies the optimal choice of information structure from the perspective of a designer maximizing a certain objective function. Generally speaking, there are two ways of creating incentives for interacting agents to behave in a desired way. One is by providing appropriate payoff incentives, which is the subject of mechanism design. The other is by choosing the information that agents observe, which we refer to as information design. We consider a model of symmetric information where a designer chooses and announces the information structure about a payoff relevant state. The interacting agents observe the signal realizations, update their beliefs, and take actions which affect the welfare of both the designer and the agents. We characterize the general finite approach to deriving the optimal information structure --- the one that maximizes the designer's ex ante expected utility subject to agents playing a Bayes Nash equilibrium. We then apply the general approach to a symmetric two state, two agent, and two actions environment in a parameterized underlying game and fully characterize the optimal information structure. It is never strictly optimal for the designer to use conditionally independent private signals. The optimal information structure may be a public signal, or may consist of correlated private signals. Finally, we examine how changes in the underlying game affect the designer's maximum payoff. This exercise provides a joint mechanism/information design perspective.Economic

    Information Markets and Nonmarkets

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    As large amounts of data become available and can be communicated more easily and processed more e¤ectively, information has come to play a central role for economic activity and welfare in our age. This essay overviews contributions to the industrial organization of information markets and nonmarkets, while attempting to maintain a balance between foundational frameworks and more recent developments. We start by reviewing mechanism-design approaches to modeling the trade of information. We then cover ratings, predictions, and recommender systems. We turn to forecasting contests, prediction markets, and other institutions designed for collecting and aggregating information from decentralized participants. Finally, we discuss science as a prototypical information nonmarket with participants who interact in a non-anonymous way to produce and disseminate information. We aim to make the reader familiar with the central notions and insights in this burgeoning literature and also point to some open critical questions that future research will have to address

    Privacy Preserving Medical Data Analytics using Secure Multi Party Computation. An End-To-End Use Case.

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    Η νέα εποχή των μεγάλων δεδομένων απαιτεί μεγάλη υπολογιστική ισχύ, αφού το πλήθος των δεδομένων που δημοσιεύονται στο διαδίκτυο μεγαλώνει εκθετικά. Σαν αποτέλεσμα, προέκυψαν τα Νέφη Υπολογιστικών Συστημάτων, παρέχοντας μεγάλη υπολογιστική ισχύ, τόσο για ιδιώτες όσο και για επιχειρήσεις. Παρόλο που τα υπολογιστικά νέφη είναι η απάντηση σε πολλά επιχειρηματικά μοντέλα, υπάρχουν πολλές περιπτώσεις χρήσης όπου τα υπολογιστικά νέφη αποτυγχάνουν να καλύψουν τις απαιτήσεις ιδιωτικότητας των πληροφοριών. Για παράδειγμα, εκθέτοντας οικονομικές και ιατρικές πληροφορίας στο νέφος μπορεί να παραβιάζει το δικαίωμα των ατόμων στην ιδιωτικότητα. Οι άνθρωποι δεν νιώθουν άνετα με το να μοιράζονται τα ευαίσθητα δεδομένα τους, και πιο σημαντικά, δεν εμπιστεύονται κανέναν πάροχο υπολογιστικού νέφους με τις πληροφορίες αυτές. Τα δεδομένα που μεταφορτώνονται στο νέφος μπορεί να εκτεθούν σε επιθέσεις τόσο από τον πάροχο όσο και από τρίτους. Παρόλα αυτά, υπάρχουν πολλές πραγματικές περιπτώσεις χρήσης που χρησιμοποιούν πληροφορίες από διαφορετικές οντότητες προκειμένου να υπολογίσουν από κοινού ουσιαστικά αποτελέσματα, αλλά λόγω των προαναφερθέντων περιορισμών, κάποιες από αυτές αποφεύγονται και άλλες δεν σέβονται πάντα την ιδιωτικότητα των δεδομένων. Η λύση σε αυτό είναι μία τεχνική που ονομάζεται Ασφαλής Υπολογισμός Πολλαπλών Συμμετεχόντων, η οποία αξιοποιεί θεμελιώδεις κρυπτογραφικές ιδιότητες προκειμένου να εκτελέσει υπολογισμούς πάνω από εμπιστευτικά δεδομένα, υπολογίζοντας μία συνάρτηση και μαθαίνοντας τίποτε παραπάνω σε σχέση με το τι θα μάθαιναν N συμμετέχοντες, εάν μία ξεχωριστή έμπιστη οντότητα είχε συλλέξει τις εισόδους τους, είχε εκτελέσει την ίδια συνάρτηση, και τέλος επέστρεφε το αποτέλεσμα σε όλους τους συμμετέχοντες. Παίρνοντας κίνητρο από αυτό το ευρύ φάσμα εφαρμογών, στην εργασία αυτή επικεντρωθήκαμε στο να παρέχουμε μία ολοκληρωμένη υποδομή για υπολογισμό στατιστικών αναλύσεων με σεβασμό στην ιδιωτικότητα. Πιο συγκεκριμένα, έχουμε υλοποιήσει αλγορίθμους ειδικά σχεδιασμένους για κρυπτογραφημένες αρχιτεκτονικές, χρησιμοποιώντας το σενάριο του Ασφαλή Υπολογισμού Πολλαπλών Συμμετεχόντων, όπως ασφαλείς συγκεντρωτικούς αλγορίθμους και ασφαλείς κατηγοριοποιητές με δέντρα απόφασης. Ακόμα, συγκεντρωθήκαμε στο συντονισμό και την επικοινωνία μεταξύ όλων των συμμετεχόντων. Αυτών που παρέχουν δεδομένα, αυτών που εκτελούν τον ασφαλή υπολογισμό και τέλος αυτών που ξεκινούν νέους υπολογισμούς. Οι αλγόριθμοί μας δεν εξαρτώνται από την εφαρμογή που εξυπηρετεί το σύστημά μας, παρόλα αυτά, για λόγους παρουσίασης, στην εργασία αυτή χρησιμοποιούμε νοσοκομεία σας παρόχους δεδομένων και επικεντρωνόμαστε στην ιατρική έρευνα. Ο Στόχος μας είναι να ιδρύσουμε ένα ολοκληρωμένο σύστημα με σκοπό την ανακάλυψη χρήσιμης πληροφορίας με σεβασμό στην ιδιωτικότητα, και επίσης να προσφέρουμε τα δομικά στοιχεία για τυχόν πιο πολύπλοκους αλγορίθμους με σεβασμό στην ιδιωτικότητα.The new era of big data demands high performance computing, since the amount of data published online is growing exponentially. Cloud computing has emerged as a result, providing strong computational power for both individuals and companies. Though cloud computing is the answer to many business models, there are many use-cases where cloud fails to meet the demands of information privacy. For instance, exposing financial and medical information to the cloud may violate the individuals’ right to privacy. People are not comfortable sharing their sensitive data, and more importantly, they do not trust any cloud provider with this information; data that are uploaded in the cloud can be exposed to attacks from both the cloud provider and third parties. Nevertheless, there are many real world use cases that use information from different parties to jointly compute meaningful results, but due to the aforementioned limitations, some are avoided and others do not always respect data privacy. The solution to this is a technique called Secure Multi-Party Computation (SMPC or MPC), which leverages cryptographic primitives to carry out computations on confidential data, computing a function and learning nothing more than what the N parties would have if a separate trusted party had collected their inputs, computed the same function for them, and then return the result to all parties. Motivated by this wide range of applications, in this thesis we have focused on providing an end-to-end infrastructure for computing privacy-preserving analytics. More specifically, we have developed algorithms specifically tailored to encrypted architectures and in the SMPC scenario, such as secure aggregators and secure decision tree classifiers. Moreover, we have focused on the coordination and communication between all involved parties; those who provide their data, those who perform the secure computation, and finally those that initiate new computations. Our algorithms are not dependent to the application that our systems serves, however, in order to demonstrate it, in this thesis we use hospitals as data providers and we focus on medical research. Our goal is to establish an end-to-end system for discovering useful information with respect to data privacy, and also to provide the building blocks for potentially more elaborate privacy-preserving algorithms

    Proceedings of the inaugural construction management and economics ‘Past, Present and Future’ conference CME25, 16-18 July 2007, University of Reading, UK

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    This conference was an unusual and interesting event. Celebrating 25 years of Construction Management and Economics provides us with an opportunity to reflect on the research that has been reported over the years, to consider where we are now, and to think about the future of academic research in this area. Hence the sub-title of this conference: “past, present and future”. Looking through these papers, some things are clear. First, the range of topics considered interesting has expanded hugely since the journal was first published. Second, the research methods are also more diverse. Third, the involvement of wider groups of stakeholder is evident. There is a danger that this might lead to dilution of the field. But my instinct has always been to argue against the notion that Construction Management and Economics represents a discipline, as such. Granted, there are plenty of university departments around the world that would justify the idea of a discipline. But the vast majority of academic departments who contribute to the life of this journal carry different names to this. Indeed, the range and breadth of methodological approaches to the research reported in Construction Management and Economics indicates that there are several different academic disciplines being brought to bear on the construction sector. Some papers are based on economics, some on psychology and others on operational research, sociology, law, statistics, information technology, and so on. This is why I maintain that construction management is not an academic discipline, but a field of study to which a range of academic disciplines are applied. This may be why it is so interesting to be involved in this journal. The problems to which the papers are applied develop and grow. But the broad topics of the earliest papers in the journal are still relevant today. What has changed a lot is our interpretation of the problems that confront the construction sector all over the world, and the methodological approaches to resolving them. There is a constant difficulty in dealing with topics as inherently practical as these. While the demands of the academic world are driven by the need for the rigorous application of sound methods, the demands of the practical world are quite different. It can be difficult to meet the needs of both sets of stakeholders at the same time. However, increasing numbers of postgraduate courses in our area result in larger numbers of practitioners with a deeper appreciation of what research is all about, and how to interpret and apply the lessons from research. It also seems that there are contributions coming not just from construction-related university departments, but also from departments with identifiable methodological traditions of their own. I like to think that our authors can publish in journals beyond the construction-related areas, to disseminate their theoretical insights into other disciplines, and to contribute to the strength of this journal by citing our articles in more mono-disciplinary journals. This would contribute to the future of the journal in a very strong and developmental way. The greatest danger we face is in excessive self-citation, i.e. referring only to sources within the CM&E literature or, worse, referring only to other articles in the same journal. The only way to ensure a strong and influential position for journals and university departments like ours is to be sure that our work is informing other academic disciplines. This is what I would see as the future, our logical next step. If, as a community of researchers, we are not producing papers that challenge and inform the fundamentals of research methods and analytical processes, then no matter how practically relevant our output is to the industry, it will remain derivative and secondary, based on the methodological insights of others. The balancing act between methodological rigour and practical relevance is a difficult one, but not, of course, a balance that has to be struck in every single paper

    Applied Metaheuristic Computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    Applied Methuerstic computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
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