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Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low accuracy of the simplified analytical models and insufficient interpretability and stability of the adaptive data-driven algorithms. I make the case that boosting (a novel, ensemble learning technique) can serve as a simple and robust framework for combining the best features of the analytical and data-driven models. Boosting-based frameworks for typical financial and econometric applications are outlined. The implementation of a standard boosting procedure is illustrated in the context of the problem of symbolic volatility forecasting for IBM stock time series. It is shown that the boosted collection of the generalized autoregressive conditional heteroskedastic (GARCH)-type models is systematically more accurate than both the best single model in the collection and the widely used GARCH(1,1) model.

The concept of modularity has gained considerable traction in technology studies as a way to conceive, describe, and innovate complex systems, such as product design or organizational structures. In the recent literature, technological modularity has often been intertwined with business model innovation, and scholarship has started investigating how modularity in technology affects changes in business models, both at the cognitive and activity system levels. Yet we still lack a theoretical definition of what modularity is in the business model domain. Business model innovation also encompasses different possibilities of modelling businesses, which are not clearly understood nor classified. We ask when, how, and if modularity theory can be extended to business models in order to enable effective and efficient modelling. We distinguish theoretically between modularity for technology and for business models, and investigate the key processes of modularization and manipulation. We introduce the basic operations of business modelling via modular operators adapted from the technological modularity domain, using iconic examples to develop an analogical reasoning between modularity in technology and in business models. Finally, we discuss opportunities for using modularity theory to foster the understanding of business models and modelling, and develop a challenging research agenda for future investigations.

The purpose of this contribution is to investigate whether popular reports can strengthen public governance by fostering greater transparency and public participation.

The analysis is based on the case of the “Bilancio in Arancio” of the Municipality of Milan. Data are collected through a triangulation of sources, including the authors’ direct observation, conversational interviews, the press, and a questionnaire distributed to the citizens participating in the experience.

The analysis discusses how popular reports can improve Public Governance, and identify related critical issues. More specifically, four key aspects of Popular reporting appear to play a central role in strengthening governance, that is, their capacity to ensure greater transparency, neutrality, enhanced participation and impacts on decision making. We suggest that every aspect represents an important “step” to be taken in an ideal “ladder of participation.”

Governments that want to enhance public governance may have an interest in developing popular reports, paying attention at ensuring transparency, neutrality, stakeholders’ participation, and their contribution to decision-making processes.

Popular reports can provide to citizens the education on public budgeting issues required to consciously participate in public decision-making processes and give them greater voice and power to express their instances. Popular reports can also promote a two-way communication and dialogue between citizens and governments.

Drawing on the experience of the Municipality of Milan, more general lessons are learnt on the role of popular reports in strengthening public governance, and on the related strengths and weaknesses.