Rational Groupthink

We study how long-lived rational agents learn from repeatedly observing a private signal and each others' actions. With normal signals, a group of any size learns more slowly than just four agents who directly observe each others' private signals in each period. Similar results apply to general signal structures. We identify rational groupthink---in which agents ignore their private signals and choose the same action for long periods of time---as the cause of this failure of information aggregation

Online Algorithms for Multi-Level Aggregation

In the Multi-Level Aggregation Problem (MLAP), requests arrive at the nodes of an edge-weighted tree T, and have to be served eventually. A service is defined as a subtree X of T that contains its root. This subtree X serves all requests that are pending in the nodes of X, and the cost of this service is equal to the total weight of X. Each request also incurs waiting cost between its arrival and service times. The objective is to minimize the total waiting cost of all requests plus the total cost of all service subtrees. MLAP is a generalization of some well-studied optimization problems; for example, for trees of depth 1, MLAP is equivalent to the TCP Acknowledgment Problem, while for trees of depth 2, it is equivalent to the Joint Replenishment Problem. Aggregation problem for trees of arbitrary depth arise in multicasting, sensor networks, communication in organization hierarchies, and in supply-chain management. The instances of MLAP associated with these applications are naturally online, in the sense that aggregation decisions need to be made without information about future requests. Constant-competitive online algorithms are known for MLAP with one or two levels. However, it has been open whether there exist constant competitive online algorithms for trees of depth more than 2. Addressing this open problem, we give the first constant competitive online algorithm for networks of arbitrary (fixed) number of levels. The competitive ratio is O(D^4 2^D), where D is the depth of T. The algorithm works for arbitrary waiting cost functions, including the variant with deadlines. We also show several additional lower and upper bound results for some special cases of MLAP, including the Single-Phase variant and the case when the tree is a path

Doctor of Philosophy

dissertationSupply chain management involves coordination and collaboration among organizations at different echelons of a supply chain. This dissertation explores two challenges to supply chain coordination: trade promotion (sales incentive offered by a manufacturer to its downstream customers, e.g., distributors or retailers) and bullwhip effect (a phenomenon of amplification of demand variability from downstream echelons to upstream echelons in the supply chain). Trade promotion represents one of the most important elements of the marketing mix and accounts for about 20% of manufacturers’ revenue. However, the management of trade promotion remains in a relatively under-researched state, especially for nongrocery products. This dissertation describes and models the effectiveness of trade promotion for healthcare products in a multiechelon pharmaceutical supply chain. Trade promotion is identified in the literature as a cause of the bullwhip effect, which has long been of interest to both researchers in academia and industrial practitioners. This dissertation develops a framework to decompose the conventional inter-echelon bullwhip measure into three intra-echelon bullwhips, namely, the shipment, manufacturing, and order bullwhips, and explores the empirical relationship between the bullwhip and the time duration over which it is measured. This dissertation also analyzes the potential bias in aggregated bullwhip measurement and examines various driving factors of the bullwhip effect. Theoretical and managerial implications of the findings are discussed

An energy-efficient and scalable slot-based privacy homomorphic encryption scheme for WSN-integrated networks

YesWith the advent of Wireless Sensor Networks (WSN) and its immense popularity in a wide range of applications, security has been a major concern for these resource-constraint systems. Alongside security, WSNs are currently being integrated with existing technologies such as the Internet, satellite, Wi-Max, Wi-Fi, etc. in order to transmit data over long distances and hand-over network load to more powerful devices. With the focus currently being on the integration of WSNs with existing technologies, security becomes a major concern. The main security requirement for WSN-integrated networks is providing end-to-end security along with the implementation of in-processing techniques of data aggregation. This can be achieved with the implementation of Homomorphic encryption schemes which prove to be computationally inexpensive since they have considerable overheads. This paper addresses the ID-issue of the commonly used Castelluccia Mykletun Tsudik (CMT) [12] homomorphic scheme by proposing an ID slotting mechanism which carries information pertaining to the security keys responsible for the encryption of individual sensor data. The proposed scheme proves to be 93.5% lighter in terms of induced overheads and 11.86% more energy efficient along with providing efficient WSN scalability compared to the existing scheme. The paper provides analytical results comparing the proposed scheme with the existing scheme thus justifying that the modification to the existing scheme can prove highly efficient for resource-constrained WSNs

Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?

This paper explores the potential of Business Survey data for the estimation and disaggregation of macroeconomic variables at higher frequency. We propose a multivariate approach which is an extension of the Stock and Watson (1991) dynamic factor model, considering more than one common factor and low-frequency cycles. The multivariate model is cast in State Space Form and the temporal aggregation constraint is converted into a problem of missing values. An application in real time for the value added of the Industry sector in the Euro area is presented.Temporal Disaggregation. Multivariate State Space Models. Dynamic factor

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Aggregation of dependent risks with heavy-tail distributions

Straightforward methods to evaluate risks arising from several sources are specially difficult when risk components are dependent and, even more if that dependence is strong in the tails. We give an explicit analytical expression for the probability distribution of the sum of non-negative losses that are tail-dependent. Our model allows dependence in the extremes of the marginal beta distributions. The proposed model is flexible in the choice of the parameters in the marginal distribution. The estimation using the method of moments is possible and the calculation of risk measures is easily done with a Monte Carlo approach. An illustration on data for insurance losses is presented

Geotechnical and hydrological characterization of hillslope deposits for regional landslide prediction modeling

We attempt a characterization of the geotechnical and hydrological properties of hillslope deposits, with the final aim of providing reliable data to distributed catchment-scale numerical models for shallow landslide initiation. The analysis is based on a dataset built up by means of both field tests and laboratory experiments over 100 sites across Tuscany (Italy). The first specific goal is to determine the ranges of variation of the geotechnical and hydrological parameters that control shallow landslide-triggering mechanisms for the main soil classes. The parameters determined in the deposits are: grain size distribution, Atterberg limits, porosity, unit weight, in situ saturated hydraulic conductivity and shear strength parameters. In addition, mineral phases recognition via X-ray powder diffraction has been performed on the different soil types. The deposits mainly consist of well-sorted silty sands with low plastic behavior and extremely variable gravel and clay contents. Statistical analyses carried on these geotechnical and hydrological parameters highlighted that it is not possible to define a typical range of values only with relation to the main mapped lithologies, because soil characteristics are not simply dependent on the bedrock type from which the deposits originated. A second goal is to explore the relationship between soil type (in terms of grain size distribution) and selected morphometric parameters (slope angle, profile curvature, planar curvature and peak distance). The results show that the highest correlation between soil grain size classes and morphometric attributes is with slope curvature, both profile and planar