The Effects of E-commerce on the Structure of Intermediation

The paper questions the notion that the diffusion of electronic commerce will lead to disintermediation. Rather than interpreting intermediation as a single service it is pointed out that intermediaries can provide a number of services. The analysis based on the New Institutional Economics, Market Microstructure Theory, and Information Economics shows that the three intermediation services studied are, generally, not under threat by the diffusion of electronic commerce. The overall effects on intermediation depend on the relevance of these services relative to others (e.g. order processing) which are supposed to become obsolete.B2C eCommerce, intermediation, new institutional economics

Rational ignorance is not bliss: When do lazy voters learn from decentralised policy experiments?

A popular argument about economic policy under uncertainty states that decentralisation offers the possibility to learn from local or regional policy experiments. We argue that such learning processes are not trivial and do not occur frictionlessly: Voters have an inherent tendency to retain a given stock of policy-related knowledge which was costly to accumulate, so that yardstick competition is improbable to function well particularly for complex issues if representatives’ actions are tightly controlled by the electorate. Decentralisation provides improved learning processes compared to unitary systems, but the results we can expect are far from the ideal mechanisms of producing and utilising knowledge often described in the literature.Policy decentralisation; fiscal competition; model uncertainty; collective learning.

Blockchain, Leadership And Management: Business AS Usual Or Radical Disruption?

The Internet provided the world with interconnection. However, it did not provide it with trust. Trust is lacking everywhere in our society and is the reason for the existence of powerful intermediaries aggregating power. Trust is what prevents the digital world to take over. This has consequences for organisations: they are inefficient because time, energy, money and passion are wasted on verifying everything happens as decided. Managers play the role of intermediaries in such case: they connect experts with each others and instruct them of what to do. As a result, in our expert society, people's engagement is low because no one is there to inspire and empower them. In other words, our society faces an unprecedented lack of leadership. Provided all those shortcomings, the study imagines the potential repercussions, especially in the context of management, of implementing a blockchain infrastructure in any type of organisation. Indeed, the blockchain technology seems to be able to remedy to those issues, for this distributed and immutable ledger provides security, decentralisation and transparency. In the context of a blockchain economy, the findings show that value creation will be rearranged, with experts directly collaborating with each others, and hierarchy being eliminated. This could, in turn, render managers obsolete, as a blockchain infrastructure will automate most of the tasks. As a result, only a strong, action-oriented, leadership would maintain the organisation together. This leadership-in-action would consist in igniting people to take action; coach members of the organisations so that their contribution makes sense in the greater context of life

Communication Efficiency in Information Gathering through Dynamic Information Flow

This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations

Gathering experience in trust-based interactions

As advances in mobile and embedded technologies coupled with progress in adhoc networking fuel the shift towards ubiquitous computing systems it is becoming increasingly clear that security is a major concern. While this is true of all computing paradigms, the characteristics of ubiquitous systems amplify this concern by promoting spontaneous interaction between diverse heterogeneous entities across administrative boundaries [5]. Entities cannot therefore rely on a specific control authority and will have no global view of the state of the system. To facilitate collaboration with unfamiliar counterparts therefore requires that an entity takes a proactive approach to self-protection. We conjecture that trust management is the best way to provide support for such self-protection measures

Communication-aware information gathering with dynamic information flow

© The Author(s) 2014. We are interested in the problem of how to improve estimation in multi-robot information gathering systems by actively controlling the rate of communication between robots. Communication is essential in such systems for decentralized data fusion and decision-making, but wireless networks impose capacity constraints that are frequently overlooked. In order to make efficient use of available capacity, it is necessary to consider a fundamental trade-off between communication cost, computation cost and information value. We introduce a new problem, dynamic information flow, that formalizes this trade-off in terms of decentralized constrained optimization. We propose algorithms that dynamically adjust the data rate of each communication link to maximize an information gain metric subject to constraints on communication and computation resources. The metric is balanced against the communication resources required to transmit data and the computation cost of processing sensor data to form observations. The optimization process selectively routes raw sensor data or processed observation data to zero, one or many robots. Our algorithms therefore allow large systems with many different types of sensors and computational resources to maximize information gain performance while satisfying realistic communication constraints. We also present experimental results with multiple ground robots and multiple sensor types that demonstrate the benefit of dynamic information flow in comparison to simpler bandwidth-limiting methods

The working of the eurosystem - monetary policy preparations and decision-making – selected issues

The ECB’s monetary policy has received considerable attention in recent years. This is less the case, however, for its regular monetary policy preparation and decision-making process. This paper reviews how the factors usually considered as critical for the success of a central banking system and the federal nature of the Eurosystem are intertwined with its overall design and the functioning of its committee architecture. In particular, it examines the procedures for preparing monetary policy decisions and the role of the decision-making bodies and the committees therein. We suggest that technical committees, involving all national central banks (NCBs), usefully contribute to the regular processing of a vast amount of economic, financial and monetary data, as well as to the consensus building at the level of the Governing Council. A federal organisational structure, including a two-tier committee structure with the Executive Board taking the lead in preparing the monetary policy decisions and the Governing Council in charge of the decisions with collective responsibility for them, as well as committee work at the various hierarchical levels, contributes to the efficiency of the ECB’s monetary policy decision-making, and thereby facilitates the maintenance of price stability in the euro area. A fully-fledged committee structure has also contributed to the smooth integration of non-euro area Member States into the Eurosystem’s monetary policy decision-making process. JEL Classification: E42, E58, F33, F42.European economic and monetary integration, monetary arrangements, central banks and their policies.

A Hybrid Approach for Data Analytics for Internet of Things

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.Comment: Accepted to be published in the Proceedings of the 7th ACM International Conference on the Internet of Things (IoT 2017

Learning from Decentralised Policy: The Demand Side

A popular argument about economic policy under uncertainty states that decentralisation offers the possibility to learn from local or regional policy experiments. We argue that such learning processes are not trivial and do not occur frictionlessly: Voters have an inherent tendency to retain a given stock of policy-related knowledge which was costly to accumulate, so that yardstick competition is improbable to function well particularly for complex issues if representatives' actions are tightly controlled by the electorate. Decentralisation provides improved learning processes compared to unitary systems, but the results we can expect are far from the ideal mechanisms of producing and utilising knowledge often described in the literature.This paper looks at competition in the telecommunication industry.Policy decentralisation, fiscal competition, model uncertainty, collective learning

Planning-Aware Communication for Decentralised Multi-Robot Coordination

© 2018 IEEE. We present an algorithm for selecting when to communicate during online planning phases of coordinated multi-robot missions. The key idea is that a robot decides to request communication from another robot by reasoning over the predicted information value of communication messages over a sliding time-horizon, where communication messages are probability distributions over action sequences. We formulate this problem in the context of the recently proposed decentralised Monte Carlo tree search (Dec-MCTS) algorithm for online, decentralised multi-robot coordination. We propose a particle filter for predicting the information value, and a polynomial-time belief-space planning algorithm for finding the optimal communication schedules in an online and decentralised manner. We evaluate the benefit of informative communication planning for a multi-robot information gathering scenario with 8 simulated robots. Our results show reductions in channel utilisation of up to four-fifths with surprisingly little impact on coordination performance