92,131 research outputs found
Online Misinformation: Challenges and Future Directions
Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view.We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications
Improving accessibility at airports
Analyse and evaluate the accessibility at airports to improve the role of this infrastructure at regional level
Dynamics of High-Technology Firms in the Silicon Valley
The pace of technological innovation since World War II is dramatically accelerating following the commercial exploitation of the Internet. Since the mid 90âs fiber optics capacity (infrastructure for transmission of information including voice and data) has incremented over one hundred times thanks to a new technology, dense wave division multiplexing, and Internet traffic has increased over 1.000 times. The dramatic advances in information technology provide excellent examples of the critical relevance of the knowledge in the development of competitive advantages. The Silicon Valley (SV) that about fifty years ago was an agricultural region became the center of dramatic technological and organizational transformations. In fact, most of the present high-tech companies did not exist twenty years ago. Venture capital contribution to the local economy is quite important not only due to the magnitude of the financial investment (venture investment in SV during 2000 surpassed 25.000 millions of dollars) but also because the extent and quality of networks (management teams, senior employees, customers, providers, etc.) that bring to emerging companies. How do new technologies develop? What is the role of private and public investment in the financing of R&D? Which are the most dynamical agents and how do they interact? How are new companies created and how do they evolve? The discussion of these questions is the focus of the current work.Technological development, R&D, networks
Structurally dynamic spin market networks
The agent-based model of stock price dynamics on a directed evolving complex
network is suggested and studied by direct simulation. The stationary regime is
maintained as a result of the balance between the extremal dynamics, adaptivity
of strategic variables and reconnection rules. The inherent structure of node
agent "brain" is modeled by a recursive neural network with local and global
inputs and feedback connections. For specific parametric combination the
complex network displays small-world phenomenon combined with scale-free
behavior. The identification of a local leader (network hub, agent whose
strategies are frequently adapted by its neighbors) is carried out by repeated
random walk process through network. The simulations show empirically relevant
dynamics of price returns and volatility clustering. The additional emerging
aspects of stylized market statistics are Zipfian distributions of fitness.Comment: 13 pages, 5 figures, accepted in IJMPC, references added, minor
changes in model, new results and modified figure
The dynamic predictive power of company comparative networks for stock sector performance
As economic integration and business connections increase, companies actively interact with each other in the market in cooperative or competitive relationships. To understand the market network structure with company relationships and to investigate the impacts of market network structure on stock sector performance, we propose the construct of a company comparative network based on public media data and sector interaction metrics based on the company network. All the market network structure metrics are integrated into a vector autoregression model with stock sector return and risk. Several findings demonstrate the dynamic relationships that exist between sector interactions and sector performance. First, sector interaction metrics constructed based on company networks are significant leading indicators of sector performance. Interestingly, the interactions between sectors have greater predictive power than those within sectors. Second, compared with the company closeness network, the company comparative network, which labels the cooperative or competitive relationships between companies, is a better construct to understand and predict sector interactions and performance. Third, competitive company interactions between sectors impact sector performance in a slower manner than cooperative company interactions. The findings enrich financial studies regarding asset pricing by providing additional explanations of company/sector interactions and insights into company management using industry-level strategies
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
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