A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

Comparing assemblages of Asteraceae and their insect herbivores under different land-use regimens

Accelerated tropical landscape changes occurring over recent decades have produced environmental mosaics comprising remaining isolated green areas and mixed land-use types. Our objective was to study the effects of alterations in the natural landscape on the species composition and structure of assemblages of Asteraceae and their endophagous insects through comparisons between cerrado (savanna), pastures and Eucalyptus stands. We first investigated whether similarities between assemblages of Asteraceae and their insects varied among land uses or localities. Secondly, we asked whether assemblages of Eucalyptus stands and pastures are subsets of those within the cerrado. We sampled within randomly deployed transects in 15 areas. Land use was found to be an important factor in determining plant composition similarity; however, locality did not exert any significant influence. Pastures were less similar to one another, suggesting high beta diversity. Similarities among insect assemblages were correlated with plant assemblage composition, but not with land use or locality. Species of Tephritidae were distributed along localities independently of land use. High beta diversity in Asteraceae assemblages among cerrados and pastures was supported by nestedness analysis. Plant assemblages in Eucalyptus stands were subsets of cerrado, but pasture assemblages were only partial subsets. A higher degree of nestedness in insect assemblages than in plant assemblages indicated lower beta diversity within these herbivores. Our data indicate that many herbivores are specialized on widely distributed plant genera. Conservation of Asteraceae species and their flower head insects depends not only on maintenance of landscape fragments but also on the correct matching of management form and land use. Such management may contribute to reducing isolation of plant and insect species by increasing the connectivity of remaining cerrado tracts, allowing population maintenance even at low abundances.