In the 1920s, statistics was developed as a powerful new approach to understanding genetic inheritance, describing how parental genomes combine to become those of offspring. Each offspring is treated as one instance of all possible combinations of parents. These methods continue to play a central role in the analysis of heredity and population biology.

The gene-center view of evolution, popularized in 1976 by Richard Dawkins in The Selfish Gene, is a statistical approach. He argued that natural selection exerts its force on single genes. As far as the individual genes are concerned, the rest of the genome, organism, and species are merely vehicles for its own reproduction.

The analogy involves a group of rowers running races. The rowers represent the gene pool, and the boats represent organisms. Pairs of rowers run heats, and the winners return to the rower pool (duplicating themselves to keep the population size the same).

To represent alleles (different version of the same gene) each rower either speaks English or German. Pairs of same-language rowers are better able to coordinate and win more raises. If the initial pool happens to start with more speakers of one language (say English), then that language will proliferate, eventually wiping out the other language group entirely.

According to this analogy, the only thing that matters is the statistical distribution of the rowers (genes) in the pool (population).

The mean field approximation places winning rowers back into the pool and pairs them up again at random. This is like assuming random mating and well mixed populations in real organisms. But what if, instead of returning to and from the pool randomly, winners went to the back of a line, with new pairs being selected from the front.

The outcome is remarkably different.

Same-language pairs still have an advantage, but instead of the more populous group wiping out the other, they both forms clumps of their own type. Within its own patches, the minority group is not affected by the larger group. The boundaries between these patches will move and change over time, which is an important behavior not captured by the mean field approximation. Even if one group eventually wins out, it will take much longer.

According to the gene-center view, genes are only looking out for copies of themselves. A gene might allow an organism to behave altruistically if it would benefit the reproductive success of an immediate relative. After all, an organism’s nieces and nephews have a good chance of carrying copies of the selfish gene.

In this view, natural selection can only act above the scale of single genes if copies of the same genes are found in a closely related group of organisms, but not on a higher scale. Hence the name Kin Selection.

Altruism directed toward individuals without an immediate blood relation is then considered unsustainable. If an altruistic allele and a selfish allele are both found in one population, the selfish allele will eventually win out. The selfish gene will get the benefit of the altruistic individuals’ reproductive sacrifices, while offering nothing in return.

Group selection is the idea that natural selection can act on scales larger than a gene, i.e. at the organism or social group level. Let’s look at a case of a selfish allele and an altruistic allele in a population that breaks the assumptions of the mean field approximation.

If altruistic organisms form isolated communities, their self-sacrificing tendencies can benefit the members of their community without having to worry about free-loading selfish organisms. They can work together to increase each other’s reproductive success. The key to how this applies in the world is to understand the dynamics of the boundaries between the groups. These boundary dynamics were not treated in kin selection models. For a spatial model, where mating is local, the neighboring association of altruistic individuals is enough for altruistic behavior to evolve naturally.

The formation of groups for selection only requires that individuals have a preference for mating with organisms in the same geographical location where they were born. As with German and English speakers, this is evident from real world examples.

In Kin Selection, altruistic organisms help genetically related individuals to reproduce. In Group Selection, altruistic organisms help members of a group they associate with. It turns out that Kin Selection requires related organisms to associate with each other; that’s what it means to help genetically related individuals. At the same time, Group Selection also requires those who associate with each other to be genetically related. Shared genetics and association mean that kin and group selection are flip sides of the same coin. When statistical averages are used, they can be proven to be equivalent mathematically.

One of the hardest things to explain is why complex systems (like evolutionary populations) are actually different from simple systems. The problems is an interlocking set of assumptions: Every system has a set of properties that can be understood with experiments and modeling. All the collected data and the finished model will tell you everything you need to know.

The flaw in these assumptions is that we may be starting with the wrong set of properties. Key properties might be missing or their importance might change over time. Why don’t we just keep studying properties until the system is understood? The amount of data will be overwhelming and the process will never end. The key is to identify which properties are important, which itself is a dynamic property of the system.

Complexity science allows us to understand complex, dynamic processes like evolution by identifying the right variables. A key component of complexity is scale. As we saw above, focusing at the smallest scale of individual genes fails to capture important features of population biology. These multiscale considerations are why complexity science supports the theory of Group Selection. Kin Selection is not sufficient.

A complex systems understanding can provide insight into other aspects of evolutionary science, besides altruism, which are also missing from the mean field approximation.