Digital Organisms Shed Light on Mystery of Altruism
NSF-supported researchers use digital evolution techniques to examine theories about the evolution of altruism
Researchers study the evolution of altruism with evolutionary processes inside a computer. Here, digital organisms in Avida self-replicate and fill empty spaces (black). Mutations can occur during reproduction, which often creates organisms with different fitness levels (represented by different colors). Over many generations, those mutations that happen to create more fit organisms tend to be selected for, as in naturally evolving populations.
Credit: Kaben Nanlohy, Michigan State University
November 15, 2010
One of the major questions in evolutionary biology is how altruism, or the act of helping another individual at your own expense, evolved. At first glance, "survival of the fittest" may seem to be best achieved by selfish individuals. However, altruistic behavior occurs in many species, and if it were not adaptive, we would expect it to disappear through the process of natural selection.
Although, strictly speaking, specific genes usually do not cause specific behaviors, behavior does have a genetic component, and, thus, can be inherited. One classic explanation for the evolution of altruism is that individuals may have genes that cause them to behave altruistically towards their relatives, who also have these "altruism genes," and thus the genes are successfully passed to the next generation.
However, relatives only share a portion of their genes. For example, a mother and daughter typically only share about 50 percent of their rare genes, since the daughter's other 50 percent came from her father. Half siblings only share 25 percent of their rare genes on average. Therefore, if altruism is directed [SIC ULTRA PLUS 1] only towards relatives, organisms run the risk of helping individuals that don't share the altruism gene.
What if animals had another way to decide whom to help, such as only helping others who were physically very similar to themselves (which could indicate overall genetic similarity) or helping organisms with some sort of physical marker that indicated that they, too, carried the altruism gene?
A recent study that appeared in the journal Proceedings of the Royal Society B by researchers at the BEACON Center for the Study of Evolution in Action at Michigan State University uses digital evolution, in which digital organisms evolve inside a computer, to understand which recognition mechanism best contributes to the spread of altruistic behavior.
Why study digital evolution? As the famous biologist John Maynard Smith once said, "We badly need a comparative biology. So far, we have been able to study only one evolving system and we cannot wait for interstellar flight to provide us with a second. If we want to discover generalizations about evolving systems, we will have to look at artificial ones." [SIC ULTRA PLUS 2]
The software used by Jeff Clune--and his colleagues Heather Goldsby, Charles Ofria and Robert Pennock-- creates [SIC ULTRA PLUS 3] just such an artificial system: the digital organisms live, reproduce and die, and scientists can observe this virtual evolution in action to learn about the dynamics of evolving traits in a population. The digital evolution research platform has been around for a number of years, but using the software to examine the evolution of altruism was a novel application.
The researchers looked at different ways that individual organisms could direct their altruism to see which method would evolve most successfully. First, they allowed organisms to either help relatives or to help genetically similar individuals. The researchers found that, if given the choice, organisms were more successful when they helped genetically similar organisms than if they were altruistic towards their kin. [SIC ULTRA PLUS 4]
The BEACON team then went one step further: what if the organisms were able to tell who was altruistic, and then only help those individuals? Humans apparently prefer to help others who are also willing to help, according to the following article. Could organisms without complex cognitive abilities do the same?
It turns out that some can. Richard Dawkins, evolutionary biologist at Oxford University and author of "The Selfish Gene," suggested that traits indicating the presence of an altruism gene, such as green beards, could assist organisms in choosing where to direct altruistic behavior. These so-called "greenbeard genes" have been found to exist in nature: for example, in one species of fire ant, ants with a particular gene will kill other ants that are lacking it, sparing the ants who share the gene.
The scientists gave the digital organisms the equivalent of greenbeard genes to see if they would use them to direct altruistic behavior.
"Initially the greenbeard mechanism did not evolve, which had us scratching our heads because theory predicts it should," Clune said. "However, with additional experiments, we determined that the greenbeard mechanism will only work with many beard colors instead of just one, where each color indicates a different level of altruism."
Otherwise, the organisms would only do the minimum amount necessary to reap the benefits of being in the altruistic greenbeard club, and no more--which keeps the altruism levels low.
Until recently, biologists had only been able to look at the results of the one evolutionary process that produced life on Earth. Now, with technology such as digital evolution, scientists can observe evolution as it occurs and make new discoveries about questions that have long interested us all about why we behave the way we do. [SIC ULTRA PLUS 5]
-- Danielle Whittaker, BEACON Center for the Study of Evolution in Action, Michigan State University, djwhitta@msu.edu
This Behind the Scenes article was provided to LiveScience in partnership with the National Science Foundation.
Investigators
Jeff Clune, Heather Goldsby, Charles Ofria, Robert Pennock
Related Institutions/Organizations
Michigan State University
Locations
Michigan
Related Programs
Computer Systems Research
Computing Research Infrastructure Cluster
Emerging Models and Technologies for Computation
Related Awards
#0939454 BEACON: An NSF Center for the Study of Evolution in Action
#0915855 CSR:Small: TEAMS -- Transplanting Artificial Life Behaviors to Mobile Robots
#0643952 CAREER: Digital Evolution and Biocomplexity - From Biological Theory to Computational Applications
#0751155 CRI: IAD - A Testbed for Evolving Adaptive and Cooperative Behavior Among Autonomous Systems
Related Websites
LiveScience.com: Behind the Scenes: Digital Organisms Shed Light on Mystery of Altruism:
BEACON Center for the Study of Evolution in Action: http://beacon-center.org/
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Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory
Jeff Clune1,2,5,*, Heather J. Goldsby2,3,5, Charles Ofria2,3,5 and Robert T. Pennock1,2,3,4,5
-Author Affiliations
1Department of Philosophy, Michigan State University, East Lansing, MI 48824, USA
2Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
3Ecology, Evolutionary Biology and Behavior Program, Michigan State University, East Lansing, MI 48824, USA
4Lyman Briggs College, Michigan State University, East Lansing, MI 48824, USA
5The BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA
*Author for correspondence (jclune@msu.edu).
Abstract
Inclusive fitness theory predicts that natural selection will favour altruist genes that are more accurate in targeting altruism only to copies of themselves. In this paper, we provide evidence from digital evolution in support of this prediction by competing multiple altruist-targeting mechanisms that vary in their accuracy in determining whether a potential target for altruism carries a copy of the altruist gene. We compete altruism-targeting mechanisms based on (i) kinship (kin targeting), (ii) genetic similarity at a level greater than that expected of kin (similarity targeting), and (iii) perfect knowledge of the presence of an altruist gene (green beard targeting). Natural selection always favoured the most accurate targeting mechanism available. Our investigations also revealed that evolution did not increase the altruism level when all green beard altruists used the same phenotypic marker. The green beard altruism levels stably increased only when mutations that changed the altruism level also changed the marker (e.g. beard colour), such that beard colour reliably indicated the altruism level. For kin- and similarity-targeting mechanisms, we found that evolution was able to stably adjust altruism levels. Our results confirm that natural selection favours altruist genes that are increasingly accurate in targeting altruism to only their copies. Our work also emphasizes that the concept of targeting accuracy must include both the presence of an altruist gene and the level of altruism it produces.
kin selection, inclusive fitness, altruism, green beard, digital evolution, Avida
Received July 21, 2010.
Accepted August 24, 2010.
© 2010 The Royal Society
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NOTA DESTE BLOGGER:
Desde Darwin no Origem das Espécies (1859) que a evolução tem sido explicada através de Design Inteligente: a seleção artificial, que o homem que teve a maior ideia que toda a humanidade já teve, sendo extrapolada para a seleção natural. Ué, mas seleção não é uma ação de inteligência???
E agora esta pesquisa com organismos digitais onde os pesquisadores elaboram um programa de computação para realizar o que os programadores inseriram no programa: um algoritmo para produzir ações de altruísmo nesses organismos.
E agora esta pesquisa com organismos digitais onde os pesquisadores elaboram um programa de computação para realizar o que os programadores inseriram no programa: um algoritmo para produzir ações de altruísmo nesses organismos.
QED: Mais uma vez, desde Darwin, a evolução se dá através do design inteligente!
