1,057 research outputs found
The Max-Min Principle of Product Differentiation: An Experimental Analysis
Theoretical models of multidimensional product differentiation predict that in duopoly firms differentiate maximally along one dimension and minimally along the other dimensions. We experimentally reproduce a market in which firms can differentiate their products along two horizontal dimensions. The main result is that subjects do not differentiate their products and locate near the center consumers' distribution.-
Generalized Markov stability of network communities
We address the problem of community detection in networks by introducing a
general definition of Markov stability, based on the difference between the
probability fluxes of a Markov chain on the network at different time scales.
The specific implementation of the quality function and the resulting optimal
community structure thus become dependent both on the type of Markov process
and on the specific Markov times considered. For instance, if we use a natural
Markov chain dynamics and discount its stationary distribution -- that is, we
take as reference process the dynamics at infinite time -- we obtain the
standard formulation of the Markov stability. Notably, the possibility to use
finite-time transition probabilities to define the reference process naturally
allows detecting communities at different resolutions, without the need to
consider a continuous-time Markov chain in the small time limit. The main
advantage of our general formulation of Markov stability based on dynamical
flows is that we work with lumped Markov chains on network partitions, having
the same stationary distribution of the original process. In this way the form
of the quality function becomes invariant under partitioning, leading to a
self-consistent definition of community structures at different aggregation
scales
Non-mean-field Critical Exponent in a Mean-field Model : Dynamics versus Statistical Mechanics
The mean-field theory tells that the classical critical exponent of
susceptibility is the twice of that of magnetization. However, the linear
response theory based on the Vlasov equation, which is naturally introduced by
the mean-field nature, makes the former exponent half of the latter for
families of quasistationary states having second order phase transitions in the
Hamiltonian mean-field model and its variances. We clarify that this strange
exponent is due to existence of Casimir invariants which trap the system in a
quasistationary state for a time scale diverging with the system size. The
theoretical prediction is numerically confirmed by -body simulations for the
equilibrium states and a family of quasistationary states.Comment: 6 pages, 3 figure
The Predictive Power of Zero Intelligence in Financial Markets
Standard models in economics stress the role of intelligent agents who
maximize utility. However, there may be situations where, for some purposes,
constraints imposed by market institutions dominate intelligent agent behavior.
We use data from the London Stock Exchange to test a simple model in which zero
intelligence agents place orders to trade at random. The model treats the
statistical mechanics of order placement, price formation, and the accumulation
of revealed supply and demand within the context of the continuous double
auction, and yields simple laws relating order arrival rates to statistical
properties of the market. We test the validity of these laws in explaining the
cross-sectional variation for eleven stocks. The model explains 96% of the
variance of the bid-ask spread, and 76% of the variance of the price diffusion
rate, with only one free parameter. We also study the market impact function,
describing the response of quoted prices to the arrival of new orders. The
non-dimensional coordinates dictated by the model approximately collapse data
from different stocks onto a single curve. This work is important from a
practical point of view because it demonstrates the existence of simple laws
relating prices to order flows, and in a broader context, because it suggests
that there are circumstances where institutions are more important than
strategic considerations
The scientific influence of nations on global scientific and technological development
Determining how scientific achievements influence the subsequent process of
knowledge creation is a fundamental step in order to build a unified ecosystem
for studying the dynamics of innovation and competitiveness. Relying separately
on data about scientific production on one side, through bibliometric
indicators, and about technological advancements on the other side, through
patents statistics, gives only a limited insight on the key interplay between
science and technology which, as a matter of fact, move forward together within
the innovation space. In this paper, using citation data of both research
papers and patents, we quantify the direct influence of the scientific outputs
of nations on further advancements in science and on the introduction of new
technologies. Our analysis highlights the presence of geo-cultural clusters of
nations with similar innovation system features, and unveils the heterogeneous
coupled dynamics of scientific and technological advancements. This study
represents a step forward in the buildup of an inclusive framework for
knowledge creation and innovation
Knowledge-centric autonomic systems
Autonomic computing revolutionised the commonplace understanding of proactiveness in the digital world by introducing self-managing systems. Built on top of IBMâs structural and functional recommendations for implementing intelligent control, autonomic systems are meant to pursue high level goals, while adequately responding to changes in the environment, with a minimum amount of human intervention. One of the lead challenges related to implementing this type of behaviour in practical situations stems from the way autonomic systems manage their inner representation of the world. Specifically, all the components involved in the control loop have shared access to the systemâs knowledge, which, for a seamless cooperation, needs to be kept consistent at all times.A possible solution lies with another popular technology of the 21st century, the Semantic Web,and the knowledge representation media it fosters, ontologies. These formal yet flexible descriptions of the problem domain are equipped with reasoners, inference tools that, among other functions, check knowledge consistency. The immediate application of reasoners in an autonomic context is to ensure that all components share and operate on a logically correct and coherent âviewâ of the world. At the same time, ontology change management is a difficult task to complete with semantic technologies alone, especially if little to no human supervision is available. This invites the idea of delegating change management to an autonomic manager, as the intelligent control loop it implements is engineered specifically for that purpose.Despite the inherent compatibility between autonomic computing and semantic technologies,their integration is non-trivial and insufficiently investigated in the literature. This gap represents the main motivation for this thesis. Moreover, existing attempts at provisioning autonomic architectures with semantic engines represent bespoke solutions for specific problems (load balancing in autonomic networking, deconflicting high level policies, informing the process of correlating diverse enterprise data are just a few examples). The main drawback of these efforts is that they only provide limited scope for reuse and cross-domain analysis (design guidelines, useful architectural models that would scale well across different applications and modular components that could be integrated in other systems seem to be poorly represented). This work proposes KAS (Knowledge-centric Autonomic System), a hybrid architecture combining semantic tools such as: ⢠an ontology to capture domain knowledge,⢠a reasoner to maintain domain knowledge consistent as well as infer new knowledge, ⢠a semantic querying engine,⢠a tool for semantic annotation analysis with a customised autonomic control loop featuring: ⢠a novel algorithm for extracting knowledge authored by the domain expert, ⢠âsoftware sensorsâ to monitor user requests and environment changes, ⢠a new algorithm for analysing the monitored changes, matching them against known patterns and producing plans for taking the necessary actions, ⢠âsoftware effectorsâ to implement the planned changes and modify the ontology accordingly. The purpose of KAS is to act as a blueprint for the implementation of autonomic systems harvesting semantic power to improve self-management. To this end, two KAS instances were built and deployed in two different problem domains, namely self-adaptive document rendering and autonomic decision2support for career management. The former case study is intended as a desktop application, whereas the latter is a large scale, web-based system built to capture and manage knowledge sourced by an entire (relevant) community. The two problems are representative for their own application classes ânamely desktop tools required to respond in real time and, respectively, online decision support platforms expected to process large volumes of data undergoing continuous transformation â therefore, they were selected to demonstrate the cross-domain applicability (that state of the art approaches tend to lack) of the proposed architecture. Moreover, analysing KAS behaviour in these two applications enabled the distillation of design guidelines and of lessons learnt from practical implementation experience while building on and adapting state of the art tools and methodologies from both fields.KAS is described and analysed from design through to implementation. The design is evaluated using ATAM (Architecture Trade off Analysis Method) whereas the performance of the two practical realisations is measured both globally as well as deconstructed in an attempt to isolate the impact of each autonomic and semantic component. This last type of evaluation employs state of the art metrics for each of the two domains. The experimental findings show that both instances of the proposed hybrid architecture successfully meet the prescribed high-level goals and that the semantic components have a positive influence on the systemâs autonomic behaviour
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