Tyler O'Neal, Staff Editor ECONOMICS

Game theory, economics show how to steer evolution in a better direction

Human behavior drives the evolution of biological organisms in ways that can profoundly adversely impact human welfare. Understanding people’s incentives when they do so is essential to identify policies and other strategies to improve evolutionary outcomes. In a new study published today in the academic journal, PLOS Biology, researchers led by Troy Day at Queens University and David McAdams at Duke University bring the tools of economics and game theory to evolution management.

From antibiotic-resistant bacteria that endanger our health to control-resistant crop pests that threaten to undermine global food production, we are now facing the harmful consequences of our failure to efficiently manage the evolution of the biological world. As Day explains, “By modeling the joint economic and evolutionary consequences of people’s actions we can determine how best to incentivize behavior that is evolutionarily desirable.” game chess strategy CREDIT jeshoots, Unsplash, CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

The centerpiece of the new analysis is a simple mathematical formula that determines when physicians, farmers, and other “evolution managers” will have sufficient incentive to steward the biological resources that are under their control, trading off the short-term costs of stewardship against the long-term benefits of delaying adverse evolution.

For instance, when a patient arrives in an urgent-care facility, screening them to determine if they are colonized by a dangerous superbug is costly, but protects future patients by allowing superbug carriers to be isolated from others. Whether the facility itself gains from screening patients depends on how it weighs these costs and benefits.

The researchers take the mathematical model further by implementing game theory, which analyzes how individuals’ decisions are interconnected and can impact each other – such as physicians in the same facility whose patients can infect each other or corn farmers with neighboring fields. Their game-theoretic analysis identifies conditions under which outcomes can be improved through policies that change incentives or facilitate coordination.

“In the example of antibiotic-resistant bacteria, hospitals could go above and beyond to control the spread of superbugs through methods like community contact tracing,” McAdams says. “This would entail additional costs and, alone, a hospital would likely not have an incentive to do so. But if every hospital took this additional step, they might all collectively benefit from slowing the spread of these bacteria. Game theory gives you a systematic way to think through those possibilities and maximize overall welfare.”

“Evolutionary change in response to human interventions, such as the evolution of resistance in response to drug treatment or evolutionary change in response to harvesting, can have significant economic repercussions,” Day adds. “We determine the conditions under which it is economically beneficial to employ costly strategies that limit evolution and thereby preserve the value of biological resources for longer.”

Japan’s Subaru Telescope, ATERUI II supercomputer join forces to reveal a clear Universe

Tyler O'Neal, Staff Editor ECONOMICS

Japanese astronomers have developed a new artificial intelligence (AI) technique to remove noise in astronomical data due to random variations in galaxy shapes. After extensive training and testing on large mock data created by supercomputer simulations, they then applied this new tool to actual data from Japan’s Subaru Telescope and found that the mass distribution derived from using this method is consistent with the currently accepted models of the Universe. This is a powerful new tool for analyzing big data from current and planned astronomy surveys. Artist’s visualization of this research. Using AI driven data analysis to peel back the noise and find the actual shape of the Universe. (Credit: The Institute of Statistical Mathematics)

Wide area survey data can be used to study the large-scale structure of the Universe through measurements of gravitational lensing patterns. In gravitational lensing, the gravity of a foreground object, like a cluster of galaxies, can distort the image of a background object, such as a more distant galaxy. Some examples of gravitational lensing are obvious, such as the “Eye of Horus”. The large-scale structure, consisting mostly of mysterious “dark” matter, can distort the shapes of distant galaxies as well, but the expected lensing effect is subtle. Averaging over many galaxies in an area is required to create a map of foreground dark matter distributions.

But this technique of looking at many galaxy images runs into a problem; some galaxies are just innately a little funny-looking. It is difficult to distinguish between a galaxy image distorted by gravitational lensing and a galaxy that is actually distorted. This is referred to as shape noise and is one of the limiting factors in research studying the large-scale structure of the Universe.

To compensate for shape noise, a team of Japanese astronomers first used ATERUI II, one of the world’s most powerful supercomputers and dedicated to astronomy, to generate 25,000 mock galaxy catalogs based on real data from the Subaru Telescope. They then added realist noise to these perfectly known artificial data sets and trained an AI to statistically recover the lensing dark matter from the mock data.

After training, the AI was able to recover previously unobservable fine details, helping to improve our understanding of the cosmic dark matter. Then using this AI on real data covering 21 square degrees of the sky, the team found a distribution of foreground mass consistent with the standard cosmological model.

“This research shows the benefits of combining different types of research: observations, simulations, and AI data analysis.” comments Masato Shirasaki, the leader of the team, “In this era of big data, we need to step across traditional boundaries between specialties and use all available tools to understand the data. If we can do this, it will open new fields in astronomy and other sciences.”

Spanish-German team's computational model simulates movements of hominids via water routes

Tyler O'Neal, Staff Editor ECONOMICS

Scientists from the interdisciplinary research center “The Role of Culture in Early Expansions of Humans” (ROCEEH), funded by the Heidelberg Academy of Sciences and based at the Senckenberg Research Institute and Natural History Museum Frankfurt, modeled for the first time together with a Spanish-German team, the movements of our early ancestors under the inclusion of waterways. The model, presented in the scientific journal “PLOS ONE,” allows the configuration of behavioral scenarios that illustrate different biological and cultural stages of water crossing by hominids. It was developed in the agent-based modeling laboratory of ROCEEH in Frankfurt, Germany. The newly developed model makes it possible for the first time to simulate the water crossing of hominids on a small scale. In their simulation, the researchers sent one thousand individuals (red dots) on their “journey.” Graphic: Senckenberg

According to the “Out-of-Africa” theory, the genus Homo first appeared in Africa about 2.8 million years ago before spreading from there across the entire world. “However, it is often difficult to understand in detail how these movements took place. As a rule, there are only very large-scale models for the migration routes of our early ancestors,” explains Ericson Hölzchen, lead author of the ROCEEH study at the Senckenberg Research Institute and Natural History Museum in Frankfurt, and he continues, “What is certain is that the hominids had to cross bodies of water of different sizes on their migration – but whether and how they were able to do so, without the use of modern maritime technology, has not yet been conclusively clarified. Yet, this is essential for the discussion of potential migration routes.” 

Hölzchen and a Spanish-German team have now closed this gap: A new model they developed – in the “agent-based modeling” laboratory of ROCEEH under the leadership of Dr. Christine Hertler in Frankfurt – makes it possible for the first time to simulate the water crossing of hominids on a small scale. In their simulation, the researchers sent one thousand individuals on their “journey” and equipped them with different, adaptable abilities as well as 45,000 energy units. “Our model hominids have different means of negotiating water barriers: directed swimming, paddling, drifting, or on a raft. In addition, other parameters – such as the width of the water barrier, the water temperature, or the current – can also be adjusted in the simulation,” adds Hölzchen.

The various factors can then be used to derive a “crossing success rate” (CSR), i.e., the probability that the crossing will succeed or fail. “By applying the CSR, we can use small-scale movement decisions to compare different behavioral scenarios and their effects on crossing success,” adds the bioinformatician from Frankfurt.

The researchers show that in two of the modeled scenarios – by directed swimming or with the aid of a raft – the early representatives of the genus Homo were able with a high probability of success to cross-straits up to 15 kilometers wide, such as the Strait of Gibraltar, or wide rivers, such as the Ganges.

"Accordingly, expansion across water barriers is not unlikely and should also be considered in larger-scale models,” Hölzchen adds in summary, and he provides an outlook, “In the future, our model may serve as a template for expansion scenarios involving other natural barriers such as mountains or deserts. Thus, we will be able to gain an increased understanding of how our ancestors have spread from the ‘cradle of mankind’!"