Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale

Uncertainty and Narratives of the Future. A Theoretical Framework for Contemporary Fertility

Explanations for fertility decisions based on structural constraints—such as labor, housing condition, or income—do not account for the contemporary fertility downturn faced by many countries in Europe. In this paper, we posit that the rise of uncertainty is central for understanding contemporary fertility dynamics. We propose a theoretical framework (the Narrative Framework) for the study of fertility decisions under uncertain conditions based on expectations, imaginaries and narratives. Relying on the idea of future–oriented action, we argue that uncertainty needs to be conceptualized and operationalized taking into account that people use works of imagination, producing their own narrative of the future. Narratives of the future are potent driving forces helping people to act according to or despite uncertainty. We present the different elements of the Narrative Framework and address its causal validity. We conclude by highlighting the advantages of taking into account the narratives of the future in fertility research

Discovery and Creation: Alternative Theories of Entrepreneurial Action

As oportunidades empreendedoras existem, independentemente, das percepções dos empreendedores, esperando apenas para serem descobertas? Ou, estas oportunidades são criadas pelas ações dos empreendedores? Duas teorias, internamente, consistentes com as oportunidades empreendedoras são: teoria da criação e teoria da descoberta- as quais serão descritas. Enquanto, será sempre possível, descrever a formação de uma oportunidade particular, como exemplo, de um processo da descoberta ou da criação de oportunidade, estas duas teorias têm implicações importantes para a eficácia de uma variedade ampla de ações empreendedoras em contextos diferentes. As implicações destas teorias para sete destas ações serão descritas, acompanhadas de uma discussão sobre algumas das implicações teóricas mais amplas destas duas teorias para os campos do empreendimento e do gerenciamento estratégico

Expectations and Uncertainty: A Common-Source Infection Model for Selected European Countries

We present a common-source infection model for explaining the formation of expectations by households. Starting from the framework of "Macroeconomic expectations of household and professional forecasters" (C.D. Carroll, The Quarterly Journal of Economics, 2003), we augment the original model assuming that also uninformed individuals are able to update expectations according to a naive econometric process. In this novel framework, a key role is played by the parameter measuring the prob- ability of being informed: the dynamics of this factor over time capture the level of uncertainty perceived by households. This new framework is applied to study unemployment expectations for a selected group of European countries (France, Germany, Italy and the UK). Our results show that: (i) the novel framework is supported by data on unemployment expectations; and (ii) the probability of being informed is (negatively) correlated with the level of uncertainty spread by newspapers and conveyed by Internet