MPIDR – Postdocs/Research Scientists in Digital and Computational Demography

The Max Planck Institute for Demographic Research (MPIDR) is recruiting highly qualified Post-Docs or Research Scientists to join the Department of Digital and Computational Demography, headed by MPIDR Director Emilio Zagheni. The positions will be filled in the Lab of Migration and Mobility and/or in the Lab of Population Dynamics and Sustainable Well-Being.

Digital and computational demography is a growing interdisciplinary field that tackles fundamental questions across all domains of demographic research by combining the methods and perspectives of computational sciences, social and behavioral sciences, and statistics. The field has emerged in parallel with rapid technological improvements in computing, the spread of Internet and mobile technologies, and the increased digitalization of people’s lives. Our group brings together methodologists (from areas like statistics, computer science or formal demography) with experts in various areas of the social sciences in order to foster cross-pollination of ideas, to advance methods and theories of population research, and to address pressing scientific and societal questions.

Candidates who can enrich or complement projects in any Research Area of the Lab of Migration and Mobility or of the Lab of Population Dynamics and Sustainable Well-Being will be considered. Across all profiles, ability and willingness to work in interdisciplinary teams in order to conduct cutting-edge research that advances our understanding of population processes is key.

More at: www.demogr.mpg.de

The Prize in Economic Sciences 2024

When Europeans colonised large parts of the globe, the institutions in those societies changed. This was sometimes dramatic, but did not occur in the same way everywhere. In some places the aim was to exploit the indigenous population and extract resources for the colonisers’ benefit. In others, the colonisers formed inclusive political and economic systems for the long-term benefit of European migrants.

The laureates have shown that one explanation for differences in countries’ prosperity is the societal institutions that were introduced during colonisation. Inclusive institutions were often introduced in countries that were poor when they were colonised, over time resulting in a generally prosperous population. This is an important reason for why former colonies that were once rich are now poor, and vice versa.

Some countries become trapped in a situation with extractive institutions and low economic growth. The introduction of inclusive institutions would create long-term benefits for everyone, but extractive institutions provide short-term gains for the people in power. As long as the political system guarantees they will remain in control, no one will trust their promises of future economic reforms. According to the laureates, this is why no improvement occurs.

However, this inability to make credible promises of positive change can also explain why democratisation sometimes occurs. When there is a threat of revolution, the people in power face a dilemma. They would prefer to remain in power and try to placate the masses by promising economic reforms, but the population are unlikely to believe that they will not return to the old system as soon as the situation settles down. In the end, the only option may be to transfer power and establish democracy.

“Reducing the vast differences in income between countries is one of our time’s greatest challenges. The laureates have demonstrated the importance of societal institutions for achieving this,” says Jakob Svensson, Chair of the Committee for the Prize in Economic Sciences.

Read the full article at: www.nobelprize.org

Reconstructing networks from simple and complex contagions

Nicholas W. Landry, William Thompson, Laurent Hébert-Dufresne, and Jean-Gabriel Young

Phys. Rev. E 110, L042301

Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics. Here, we address this gap by developing a nonparametric method to reconstruct a network and dynamics from a series of node states, using a model that breaks the dichotomy between simple pairwise and complex neighborhood-based contagions. We then show that a network is more easily reconstructed when observed through the lens of complex contagions if it is dense or the dynamic saturates, and that simple contagions are better otherwise.

Read the full article at: link.aps.org

A Mathematical Perspective on Neurophenomenology

Lancelot Da Costa, Lars Sandved-Smith, Karl Friston, Maxwell J. D. Ramstead, Anil K. Seth

In the context of consciousness studies, a key challenge is how to rigorously conceptualise first-person phenomenological descriptions of lived experience and their relation to third-person empirical measurements of the activity or dynamics of the brain and body. Since the 1990s, there has been a coordinated effort to explicitly combine first-person phenomenological methods, generating qualitative data, with neuroscientific techniques used to describe and quantify brain activity under the banner of “neurophenomenology”. Here, we take on this challenge and develop an approach to neurophenomenology from a mathematical perspective. We harness recent advances in theoretical neuroscience and the physics of cognitive systems to mathematically conceptualise first-person experience and its correspondence with neural and behavioural dynamics. Throughout, we make the operating assumption that the content of first-person experience can be formalised as (or related to) a belief (i.e. a probability distribution) that encodes an organism’s best guesses about the state of its external and internal world (e.g. body or brain) as well as its uncertainty. We mathematically characterise phenomenology, bringing to light a tool-set to quantify individual phenomenological differences and develop several hypotheses including on the metabolic cost of phenomenology and on the subjective experience of time. We conceptualise the form of the generative passages between first- and third-person descriptions, and the mathematical apparatus that mutually constrains them, as well as future research directions. In summary, we formalise and characterise first-person subjective experience and its correspondence with third-person empirical measurements of brain and body, offering hypotheses for quantifying various aspects of phenomenology to be tested in future work.

Read the full article at: arxiv.org

Press release: The Nobel Prize in Chemistry 2024

The diversity of life testifies to proteins’ amazing capacity as chemical tools. They control and drive all the chemical reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues.

“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.

Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.

The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.

In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.

Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.

Read the full article at: www.nobelprize.org