Choosing Fitness-Enhancing Innovations Can Be Detrimental under Fluctuating Environments

Abstract

The ability to predict the consequences of one's behavior in a particular environment is a mechanism for adaptation. In the absence of any cost to this activity, we might expect agents to choose behaviors that maximize their fitness, an example of directed innovation. This is in contrast to blind mutation, where the probability of becoming a new genotype is independent of the fitness of the new genotypes. Here, we show that under environments punctuated by rapid reversals, a system with both genetic and cultural inheritance should not always maximize fitness through directed innovation. This is because populations highly accurate at selecting the fittest innovations tend to over-fit the environment during its stable phase, to the point that a rapid environmental reversal can cause extinction. A less accurate population, on the other hand, can track long term trends in environmental change, keeping closer to the time-average of the environment. We use both analytical and agent-based models to explore when this mechanism is expected to occur.

Survey of evolutionary computation methods in social agent-based modeling studies

“…A good example of an evolutionary simulation model employing Lamarckian adaptation is Xue's et al [76] effort to properly identify conditions under which short-term fitness-enhancing innovations are advantageous in the long term. The authors argue that the long-standing intuition that the ability to predict consequences of one's own actions is beneficial to fitness has never been thoroughly tested.…”

“…In some cases this is motivated by the goal of modeling imitation of behaviors rather than true sexual reproduction (e.g. [4,52,61,76]). In many others the phenotype consists of a single value (e.g.…”

“…[11,37]). Real-valued problems appropriately utilize perturbation techniques, however, the range of distributions used is broad: from truncated Gaussian [45] and uniform perturbations [76] to Gumbel distributions [61]. Many models, however, do not implement mutation operators at all (e.g.…”

“…A specific application of mutation techniques in certain ABMs is the representation of imperfect imitation. In this case mutation occurs every time imitation is attempted (see [61,76]). …”

Trends in Archaeological Simulation

“…All other computer simulations of human evolution published since 2001 are abstract agent-based models intended to assist with hypothesis generation rather than hypothesis testing. Collectively, these models address seven themes: the impact of population structure on the emergence of cultural complexity (Powell et al 2009(Powell et al , 2010Premo and Kuhn 2010;Premo 2012a;Vaesen 2012), the role of environmental heterogeneity in promoting enhanced sociality (Lake 2001b;Premo 2005Premo , 2006bReynolds et al 2010), interaction between hominin species/subspecies (Barton et al 2011;Premo 2012b), the causes of low genetic diversity in Pleistocene hominins (Premo and Hublin 2009;Premo 2012c), the uniqueness of hominin life history (Kachel et al 2011a(Kachel et al , 2011b(Kachel et al , 2011c, the impact of cognitive abilities on long-term adaptation in spatially and/or temporally variable environments (Costopoulos 2001;Xue et al 2011;Wren et al 2013) and (although this could be considered tactical) our ability to make inferences about hominin cognition from the landscape-scale spatial distribution of material in the archaeological record (Brantingham 2003(Brantingham , 2006. In addition to their high level of abstraction, all these models are nevertheless explicitly spatial, focused on one very tightly bounded problem and-with a few exceptions-their presentation in the literature treats the fact that they use simulation as largely unremarkable.…”