We provide greater theoretical precision to the concept of productive opportunities of Penrose. We show firm emergence as a recursive cycle of changing productive opportunities. We show how those opportunitiesresult from the technological base of the firm and are associated with the particular characteristics of the technology.We also show how productive opportunities require the assembly of different internal and externalresources, and therefore partners. We address explicitly how the firm and its potential partners perceive uncertainty and single out the different mechanisms used by the firm to address uncertainty—envisioning, pooling, and staging—to secure resources from external partners and exploit the identified productive opportunities in a timely manner.
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This is the last of a series of 3 columns on uncertainty. A response to uncertainty can be radicalization, It is argued that confirmation bias plays a role in this initial “radicalization”: the tendency to prefer information that confirms one's belief as “facts” to contradictory information (“fake news”). People who have just switched to something new, vegetarianism for example, or who have just started a new study, can be very fanatical at first and want to lecture everyone. But no one has the energy to compete again and again on the cutting edge of the “game” (infatuation, new beliefs, etc.), so luckily the nuance returns with time. After the radical "infatuation" (outshout/ignore uncertainty), uncertainty regains its place and space is once again created for the human dimension, for solidarity and nuance. The couple in love who give themselves completely to each other eventually regains an eye for the rest of the world. I have been a vegetarian for over 50 years, so I never really talk about that anymore. Bias fades with time.
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This is the last of a series of 3 columns on uncertainty. A response to uncertainty can be radicalization, It is argued that confirmation bias plays a role in this initial “radicalization”: the tendency to prefer information that confirms one's belief as “facts” to contradictory information (“fake news”). People who have just switched to something new, vegetarianism for example, or who have just started a new study, can be very fanatical at first and want to lecture everyone. But no one has the energy to compete again and again on the cutting edge of the “game” (infatuation, new beliefs, etc.), so luckily the nuance returns with time. After the radical "infatuation" (outshout/ignore uncertainty), uncertainty regains its place and space is once again created for the human dimension, for solidarity and nuance. The couple in love who give themselves completely to each other eventually regains an eye for the rest of the world. I have been a vegetarian for over 50 years, so I never really talk about that anymore. Bias fades with time.
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In many learning spaces in higher professional education, students are required to do research. At the same time they, and many of their tutors, struggle with the doubt, the uncertainty and even the anxiety that often accompanies the research process. Research shows that uncertainty and safety (‘safe uncertainty’) play an important role in students’ experiences of the research process. In order to study this and to answer the question ‘how to cope with uncertainty during the research process?’, we have designed a tool called ‘research mapping’. In a workshop setting, research mapping visualizes first the research process and, secondly, the elements of safe uncertainty within. Subsequently, dialogue between the participants produces generalized insights in the research process and in the role of safe uncertainty in that process. Next to the benefits for students and tutors, also the learning space of doing research can be improved.
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With Brexit looming, start-ups in the London ecosystem may ask themselves whether they are still in the right place for their business. Are they considering a move to the continent due to the ambiguous Brexit developments? This research analyzes the probability of international start-ups based in the London region relocating to another European entrepreneurial ecosystem. We use location decision theory and secondary data from the European Digital City Index to rank the most attractive eco-systems for the possible relocation of London-based start-ups. In addition, we interview London start-up founders asking how likely they are to leave and where they envision continuing their entrepreneurial endeavors. This study examines whether London will lose its top rank as the most attractive entrepreneurial ecosystem in Europe. We ask which of the competing ecosystems of Europe stands to gain from London’s possible loss. Our quantitative analyses show that Amsterdam is the most likely hub to benefit from any exodus. The qualitative analyses conveyed a mixture of concern and ambivalence as only three of the startups considered relocating their headquarters to another ecosystem. Six of the startups have either opened an office in another European ecosystem or are in the process of doing so. This allows them to watch and wait as they want to remain. The attractiveness of the London region, the social capital investments by team and partners, and the lack of finances to leave are the main reasons for not considering relocation of their headquarters currently.
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This contribution explores the anatomy of “safe uncertainty” in the research process of students in higher education. Uncertainty and research go hand in hand, as uncertainty is omnipresent in the process of research. Mostly in the background, but sometimes looming large in the foreground. We tend to expel uncertainty from research. We try to make the research process as predictable as possible by creating clear criteria, planning, making agreements and organizing supervision. Also with regard to the content, uncertainty has to be decreased. We try to reduce uncertainty by emphasizing precision, objectivity, logic, accountablility, measurability, validity, reliablility. And in the process, we are actually searching for the right concepts, language, or even shared images, in order to get as much grip as possible on the intangible research process.
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This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
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The challenges we collectively face, such as climate change, are characterized by more complexity, interdependence, and dynamism than is common for educational practice. This presents a challenge for (university) education. These transition challenges are often described as wicked or VUCA (Volatile, Uncertain, Complex, and Ambiguous) problems. In response, educational innovations that are inspired by ecology such as living labs are starting to emerge, but little is known about how learners engage within and with these more ecological forms of education. This work is an exploratory study into how learners navigate VUCA learning environments linked to tackling sustainability transition challenges, with a focus on the positive qualities of these experiences. This is done through interpretative phenomenological analysis (IPA) of seven students (using semi-structured interviews) of the MSC Metropolitan Analysis, Design and Engineering program, a joint degree from Wageningen University and Delft University of Technology in the Netherlands. The main findings, which are both psychological and educational, of this exploration include openness to new experiences (1), flexibility (2), a process appreciation of learning (3), a desire to create a positive impact on one’s direct biophysical environment (4) and society (5). In addition, we discuss the potential limitations of the malleability of these different qualities and propose future avenues for research into ecological learning for universities. This work closes by highlighting recommendations for educators to consider when designing or engaging in ecological forms of higher education that connect students to sustainability transitions.
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Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
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This paper analyses the performativity of the sociotechnical imaginaries that the online communities interested in blockchain applications (e.g., cryptocurrencies) construct through the memes they share, in the context of a crisis of truth and amid pervasive precarity. These memes adopt a subcultural language that is a mix of financial jargon and blockchain slang, neither building on the established codes of the regulated financial sector nor belonging fully to the colloquial nature of internet banter. Through them, the community collectively constructs ways to overcome the fundamental uncertainty that traverses all aspects of contemporary life – housing, precaritisation of labour, political ruptures, etc – by doubling down on them. Financial speculation is no longer reserved to those with disposable income but becomes a tactic for survival in a scene that actively destabilizes information for competitive market advantage. Through the use of repeated memetic subcultural phrases, blockchain memes blur the difference between fact and fiction in an effort to reconcile the extreme volatility of cryptocurrencies with the neoliberal conviction that the market is always right. As a result, no one is trustworthy, individualism takes on a new dimension, and what Aris Komporozos-Athanasiou calls a “speculative community” arises. Ultimately, this case study highlights how the iterative and distributed character of memes supercharges the normative character of performativity.
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