Economic versus psychological forecasting. Evidence from consumer confidence surveys

Permanent and widespread psychological biases affect both the subjective probability of future economic events and their retrospective interpretation. They may give rise to a systematic gap between (over-critical) judgments and (over-optimistic) expectations – the “forecast” error. When things go bad, then, psychology suggests that people tend to become particularly bullish, amplifying the forecast error. Also, psychology argues that personal/future conditions are systematically perceived to be better than the aggregate/past ones. All this sharply contrasts with standard economic assumptions. Evidence from a unique dataset covering 10 European countries over 22 years confirms the presence of structural psychologically driven distortions in people’s judgments and expectations formation.     

大数据安全视角下计算机数据刑法保护之反思

大数据安全问题是信息时代刑法面临的全新挑战,在大数据技术的推动下,计算机数据的价值属性通过“量变”实现了“质变”,催生了新的法益保护需求并弱化了传统法益的刑法保护效果,传统的计算机数据刑法保护体系严重滞后。重新解释非法获取计算机信息系统罪并对《刑法修正案（九）（草案）》规定进行微调,是实现计算机数据刑法保护体系完善必经的司法途径和立法途径。     

大数据与微时代： 虚拟社会公共服务体系的双重建构

大数据与微时代引发了虚拟社会的多重变革,新的服务理念和服务模式呼之欲出,虚拟社会公共服务的走向、模式、内涵正在发生深刻变化。但是受到推送途径、参与机制、数据基础、保障措施等因素的制约,虚拟社会公共服务的质量和效率相对有限,其体系面临着解构与重构。大数据与微时代是当前虚拟社会公共服务体系建构中不可或缺的重要因素,通过培育公共服务精神、推进虚拟社会公共服务均等化发展、强化多元参与主体间的良性互动、完善虚拟社会公共服务体系建构秩序等途径,科学、合理地对其加以运用,将会有效助推虚拟社会公众服务的不断创新和发展。     

Data Science: A Powerful Catalyst for Cross-Sector Collaborations to Transform the Future of Global Health—Developing a New Interactive Relational Mapping Tool (Demo)

ABSTRACT

The increasingly complex and rapidly changing global health and socioeconomic landscape requires fundamentally new ways of thinking, acting, and collaborating to solve growing systems challenges. Cross-sectoral collaborations between governments, businesses, international organizations, private investors, academia, and nonprofits are essential for lasting success in achieving the Sustainable Development Goals (SDGs), and securing a prosperous future for the health and well-being of all people (United Nations, n.d United Nations. (n.d.). SDGs: Sustainable development knowledge platform. Sustainable Development United Nations. Retrieved from https://sustainabledevelopment.un.org/ [Google Scholar].). Our aim is to use data science and innovative technologies to map diverse stakeholders and their initiatives around SDGs and specific health targets—with particular focus on SDG 3 (Good Health & Well Being) and SDG 17 (Partnerships for the Goals)—to accelerate cross-sector and multidisciplinary collaborations. Initially, the mapping tool focuses on Geneva, Switzerland as the world center of global health diplomacy with over 80 key stakeholders and influencers present. As we develop the next level pilot, we aim to build on users’ interests, with a potential focus on non-communicable diseases (NCDs) as one of the emerging and most pressing global health issues that requires new collaborative approaches. Building on this pilot, we can later expand beyond only SDG 3 to other SDGs.     

Exploring the link between government funding and efficiency in nonprofit colleges

Previous literature has suggested that federal funding can hinder the efficiency of nonprofit organizations, but this has yet not been empirically tested. This study used a two‐stage data envelopment analysis (DEA) model to measure the efficiency of a set of private nonprofit teaching‐oriented colleges, then estimate the impact of federal and state funding on organizational efficiency. The findings indicate that, on average, increases in neither state nor federal funding effect efficiency of nonprofit colleges. Increasing state funding negatively impacted the efficiency of public colleges (N = 799). This study suggests that the challenges for nonprofits that accompany government funding may not rise to a quantifiable negative effect on efficiency.     

Measuring the health patterns of the ‘mixed/multiple’ ethnic group in Britain: data quality problems,reporting issues,and implications for policy

The ‘mixed’ group, officially recognised in the 2001 Census, is one of the most rapidly growing ethnic groups in Britain. Although ‘mixed’ categorisation was added to ethnic coding in NHS datasets, our knowledge of health patterns for this population is meagre. Data quality problems remain a key obstacle, including poor reproducibility of the data and constraints on reporting due to sparse data bias. The consequent minimal and indicative evidence base has focused mainly on risky health behaviours, mental health and generic measures of self-rated health, as it has in the U.S.A. and Canada. There is negligible information on the main underlying causes of death, such as neoplasms, heart disease and stroke. Consideration should be given to pooling data across multiple years of health and general purpose surveys to enable reporting for the four ‘mixed’ categories and adjustment for mediating factors and relevant confounders, such as measures of socio-economic status.     

Analysis of social innovation on social networking services

ABSTRACT

Social network services are allowing more social connectivity and making possible sharing information and knowledge of a different nature. This article analyses if social workers related to active social policies from Malaga province (Spain) are using social network services to share information and knowledge and if there is a mirror between online and offline relationships. Since it is an experimental model, through virtual ethnography and through social network analysis methodology, we observed the presence, connectivity and analysed the structure of relationships that keep 235 professionals from 52 organizations in Facebook®. Moreover, the model uses the statistical technique of modularity for detecting online communities, which are compared with distribution of professionals in their organizations. Results show how social network services applied to social intervention are massively and frequently used by professionals. On the other hand, the detected communities reflect existence of analogy between online and offline relationships. The online structure shows a high degree of cohesion and how certain professionals have higher capacity of influence and diffusion depending on position in the online structure. It argues about the opportunity to incorporate social network services towards social intervention as a manner of social innovation to improve cooperation and diffusion of information and knowledge.     

Blood,Sweat, and Tears: Navigating Creepy versus Cool in Wearable Biotech

ABSTRACT

This paper considers how personal data protections figure into the design process of wearable technology. The data question is becoming especially important in the face of recent innovations in biotechnology that capitalize on the new fungibility of biology and electronics, in which new biotech wearables capitalize on the ability to analyze and track changes in blood, sweat, and tears. Interviews and participant observation with wearable tech designers, data scientists, fashion tech entrepreneurs, and select experts in cybersecurity and intellectual property law, reveal a range of approaches to data protection in design within the culture where wearables are beginning to merge with biotech. While a few respondents were extremely vigilant about protecting consumer’s privacy, the majority felt that when consumers ‘opt in’ to data sharing, they should be cognizant of the risks. From their perspective, it is not necessarily the producer’s responsibility to protect user's personal data. These attitudes present a problematic logic, which leaves users vulnerable to data exploitation. The paper concludes by arguing that this laissez-faire culture is the environment in which wearable biotech is being developed and will be deployed. This emerging technology raises issues about bodies, data, and ownership in crucial need of analysis and critique to push its move into the mainstream toward more equitable and inclusive ends.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.