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Experiments investigating cooperative types in humans: a complement to evolutionary theory and simulations.

ABSTRACT: Unlike other species, humans cooperate in large, distantly related groups, a fact that has long presented a puzzle to biologists. The pathway by which adaptations for large-scale cooperation among nonkin evolved in humans remains a subject of vigorous debate. Results from theoretical analyses and agent-based simulations suggest that evolutionary dynamics need not yield homogeneous populations, but can instead generate a polymorphic population that consists of individuals who vary in their degree of cooperativeness. These results resonate with the recent increasing emphasis on the importance of individual differences in understanding and modeling behavior and dynamics in experimental games and decision problems. Here, we report the results of laboratory experiments that complement both theory and simulation results. We find that our subjects fall into three types, an individual's type is stable, and a group's cooperative outcomes can be remarkably well predicted if one knows its type composition. Reciprocal types, who contribute to the public good as a positive function of their beliefs about others' contributions, constitute the majority (63%) of players; cooperators and free-riders are also present in our subject population. Despite substantial behavioral differences, earnings among types are statistically identical. Our results support the view that our human subject population is in a stable, polymorphic equilibrium of types.

SUBMITTER: Kurzban R

PROVIDER: S-EPMC547861 | biostudies-other | 2005 Feb

REPOSITORIES: biostudies-other

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Project description:There are both pharmacodynamic and evolutionary reasons to use multiple rather than single antibiotics to treat bacterial infections; in combination antibiotics can be more effective in killing target bacteria as well as in preventing the emergence of resistance. Nevertheless, with few exceptions like tuberculosis, combination therapy is rarely used for bacterial infections. One reason for this is a relative dearth of the pharmaco-, population- and evolutionary dynamic information needed for the rational design of multi-drug treatment protocols. Here, we use in vitro pharmacodynamic experiments, mathematical models and computer simulations to explore the relative efficacies of different two-drug regimens in clearing bacterial infections and the conditions under which multi-drug therapy will prevent the ascent of resistance. We estimate the parameters and explore the fit of Hill functions to compare the pharmacodynamics of antibiotics of four different classes individually and in pairs during cidal experiments with pathogenic strains of Staphylococcus aureus and Escherichia coli. We also consider the relative efficacy of these antibiotics and antibiotic pairs in reducing the level of phenotypically resistant but genetically susceptible, persister, subpopulations. Our results provide compelling support for the proposition that the nature and form of the interactions between drugs of different classes, synergy, antagonism, suppression and additivity, has to be determined empirically and cannot be inferred from what is known about the pharmacodynamics or mode of action of these drugs individually. Monte Carlo simulations of within-host treatment incorporating these pharmacodynamic results and clinically relevant refuge subpopulations of bacteria indicate that: (i) the form of drug-drug interactions can profoundly affect the rate at which infections are cleared, (ii) two-drug therapy can prevent treatment failure even when bacteria resistant to single drugs are present at the onset of therapy, and (iii) this evolutionary virtue of two-drug therapy is manifest even when the antibiotics suppress each other's activity.

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Experiments investigating cooperative types in humans: a complement to evolutionary theory and simulations.

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