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.

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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.

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When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through con­nections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward.

John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.

Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.

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The information stored within our chromosomes can be likened to an instruction manual for all cells in our body. Every cell contains the same chromosomes, so every cell contains exactly the same set of genes and exactly the same set of instructions. Yet, different cell types, such as muscle and nerve cells, have very distinct characteristics. How do these differences arise? The answer lies in gene regulation, which allows each cell to select only the relevant instructions. This ensures that only the correct set of genes is active in each cell type.

Victor Ambros and Gary Ruvkun were interested in how different cell types develop. They discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery revealed a completely new principle of gene regulation that turned out to be essential for multicellular organisms, including humans. It is now known that the human genome codes for over one thousand microRNAs. Their surprising discovery revealed an entirely new dimension to gene regulation. MicroRNAs are proving to be fundamentally important for how organisms develop and function.

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The Complex Systems Society (CSS) organizes every year a main conference (CCS) — the most important annual meeting for the complex systems research community.
The Complex Systems Society invites bids to host the 2026 and 2027 editions.
The conference is generally held in September/October of each year.

More at: cssociety.org