Lifeboat Foundation

On top of this, the use of quantification has significantly increased over the last decades with the inflation of metrics, indicators, and scores to rank and benchmark options (Muller, 2018). The case of energy policy making in the European Union is again an effective example. The European Union’s recent energy strategy has been underpinned by the Clean Energy for all Europeans packages, which are in turn supported by a number of individual directives, each one characterized by a series of quantitative goals (European Commission, 2023). The quantification of the impact (impact assessment) is customarily required to successfully promote new political measures (European Commission, 2015a) and is in turn based on quantification, often from mathematical models (Saltelli et al., 2023). The emphasis on producing exact figures to assess the contribution of a new technology, political or economic measure has put many models and their users into contexts of decision-making that at times extends beyond their original intent (Saltelli, Bammer et al., 2020). At the same time, the efforts to retrospectively assess the performance of energy models have been extremely limited, one example being the Energy Modeling Forum in the United States (Huntington et al., 1982). In spite of this, retrospective assessments can be very helpful in understanding the sources of mismatch between a forecast and the actual figures reported a posteriori (Koomey et al., 2003). For example, long-range forecast models are typically based on the assumption of gradual structural changes, which are at stake with the disruptive events and discontinuities occurring in the real world (Craig et al., 2002). This dimension is especially important in terms of the nature and pace of technology change (Bistline et al., 2023 ; Weyant & Olavson, 1999). A further critical element in this approach is the cognitive bias in scenario analysis that naturally leads to overconfidence in the option being explored and results in an underestimate of the ranges of possible outcomes (Morgan & Keith, 2008).

Additionally, in their quest for capturing the features of the energy systems represented, models have increased their complicatedness and/or complexity. In this context, the need to appraise model uncertainty has become of paramount importance, especially considering the uncertainty due to propagation errors caused by model complexification (Puy et al., 2022). In ecology, this is known as the O’Neil conjecture, which posits a principle of decreasing returns for model complexity when uncertainties come to dominate the output (O’Neill, 1989 ; Turner & Gardner, 2015). Capturing and apportioning uncertainty is crucial for a healthy interaction at the science–policy interface, including energy policy making, because it promotes better informed decision-making. Yet Yue et al. (2018) found that only about 5% of the studies covering energy system optimization models have included some form of assessment of stochastic uncertainty, which is the part of uncertainty that can be fully quantified (Walker et al., 2003). When it comes to adequately apportioning this uncertainty onto the input parameters and hypotheses through sensitivity analysis, the situation is even more critical: Only very few papers in the energy field have made the use of state-of-the-art approaches (Lo Piano & Benini, 2022 ; Saltelli et al., 2019). Further to that, the epistemic part of uncertainty, the one that arises due to imperfect knowledge and problem framing, has been largely ignored in the energy modeling literature (Pye et al., 2018). For instance, important sources of uncertainties associated with regulatory lag and public acceptance have typically been overlooked. 1

In this contribution, we discuss three approaches to deal with the challenges of non-neutrality and uncertainty in models: The numerical unit spread assessment pedigree (NUSAP) method, diagnostic diagrams, and sensitivity auditing (SAUD). These challenges are especially critical when only one (set of) model(s) has been selected to contribute to decision-making. One practical case is used to showcase in retrospective the relevance of the issue and the associated problems: the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s.

Members’ SeminarTopic: Towards a mathematical model of the brainSpeaker: Lai-Sang YoungAffiliation: New York University; Distinguished Visiting Professor, Sc…

When learning, patients with schizophrenia or depression have difficulty making optimal use of information that is new to them. In the learning process, both groups of patients give greater weight to less important information and, as a result, make less than ideal decisions.

This was the finding of a several-months-long study conducted by a team led by neuroscientist Professor Dr. med. Markus Ullsperger from the Institute of Psychology at Otto von Guericke University Magdeburg in collaboration with colleagues from the University Clinic for Psychiatry & Psychotherapy and the German Center for Mental Health.

By using electroencephalography (EEG) and complex mathematical computer modeling, the team of researchers discovered that learning deficits in depressive and schizophrenic patients are caused by diminished/reduced flexibility in the use of new information.

3 million custom ChatGPTs

The new store is dubbed the GPT Store, where customers who have subscribed to their ChatGPT Plus service for $20 per month can browse through custom chatbots that offer a range of services such as book recommendations, math tutorials, and scientific paper searches. According to a blog post by the company, the store aims to assist users in discovering popular and practical custom versions of ChatGPT. In an official tweet, the company says users can now choose over 3 million different types of GPTs as per their choice and needs.

A new study shows how quantum computing can be harnessed to discover new properties of polymer systems central to biology and material science.

The advent of quantum computing is opening previously unimaginable perspectives for solving problems deemed beyond the reach of conventional computers, from cryptography and pharmacology to the physical and chemical properties of molecules and materials. However, the computational capabilities of present-day quantum computers are still relatively limited. A newly published study in Science Advances fosters an unexpected alliance between the methods used in quantum and traditional computing.

The research team, formed by Cristian Micheletti and Francesco Slongo of SISSA in Trieste, Philipp Hauke of the University of Trento, and Pietro Faccioli of the University of Milano-Bicocca, used a mathematical approach called QUBO (from “Quadratic Unconstraint Binary Optimization”) that is ideally suited for specific quantum computers, called “quantum annealers.”

Mathpresso, the creator of QANDA — Asia’s most extensive AI-driven learning platform — has announced that their large language model called MathGPT has achieved a new world record in math, beating OpenAI and Microsoft models.

MathGPT reportedly is now ranked no. 1 in benchmarks that evaluate mathematical ability such as ‘MATH’ (12,500 difficult math problems) and ‘GSM8K’ (8,500 elementary school math problems), beating Microsoft’s ‘ToRA 13B’, the model that held the previous record.

In the MATH benchmark, MathGPT surpassed the performance of OpenAI’s GPT-4.

Scientists have grown a tiny brain-like organoid out of human stem cells, hooked it up to a computer, and demonstrated its potential as a kind of organic machine learning chip, showing it can quickly pick up speech recognition and math predictions.

Clusters of brain cells grown in the lab have shown potential as a new type of hybrid bio-computer.

A #mathematics “A Bernoulli trial is a #random experiment with exactly two possible outcomes “success” and “failure” in which #probability of success is the same every time the experiment is conducted.”

In the theory of probability and statistics, a Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, “success” and “failure”, in which the probability of success is the same every time the experiment is conducted.^[1] It is named after Jacob Bernoulli, a 17th-century Swiss mathematician, who analyzed them in his Ars Conjectandi (1713).^[2]

The mathematical formalisation of the Bernoulli trial is known as the Bernoulli process. This article offers an elementary introduction to the concept, whereas the article on the Bernoulli process offers a more advanced treatment.

Continue reading “Bernoulli trial” »

Researchers are discovering the shortest knots and fattest Möbius strips, among other “optimal shapes.”