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Steven Hecht Orzack

Publications and source records attributed to Steven Hecht Orzack.

3 recordsLinked to original sources

The evolution of strategy variation: will an ESS evolve?

Evolutionarily stable strategy (ESS) models are widely viewed as predicting the strategy of an individual that when monomorphic or nearly so prevents a mutant with any other strategy from entering the population. In fact, the prediction of some of these models is ambiguous when the predicted strategy is "mixed", as in the case of a sex ratio, which may be regarded as a mixture of the subtraits "produce a daughter" and "produce a son." Some models predict only that such a mixture be manifested by the population as a whole, that is, as an "evolutionarily stable state"; consequently, strategy monomorphism or polymorphism is consistent with the prediction. The hawk-dove game and the sex-ratio game in a panmictic population are models that make such a "degenerate" prediction. We show here that the incorporation of population finiteness into degenerate models has effects for and against the evolution of a monomorphism (an ESS) that are of equal order in the population size, so that no one effect can be said to predominate. Therefore, we used Monte Carlo simulations to determine the probability that a finite population evolves to an ESS as opposed to a polymorphism. We show that the probability that an ESS will evolve is generally much less than has been reported and that this probability depends on the population size, the type of competition among individuals, and the number of and distribution of strategies in the initial population. We also demonstrate how the strength of natural selection on strategies can increase as population size decreases. This inverse dependency underscores the incorrectness of Fisher's and Wright's assumption that there is just one qualitative relationship between population size and the intensity of natural selection.

Biological Evolution↗

Assessing current adaptation and phylogenetic inertia as explanations of trait evolution: the need for controlled comparisons.

The determination of whether the pattern of trait evolution observed in a comparative analysis of species data is due to adaptation to current environments, to phylogenetic inertia, or to both of these forces requires that one control for the effects of either force when making an assessment of the evolutionary role of the other. Orzack and Sober (2001) developed the method of controlled comparisons to make such assessments; their implementation of the method focussed on a discretely varying trait. Here, we show that the method of controlled comparisons can be viewed as a meta-method, which can be implemented in many ways. We discuss which recent methods for the comparative analysis of continuously distributed traits can generate controlled comparisons and can thereby be used to properly assess whether current adaptation and/or phylogenetic inertia have influenced a trait's evolution. The implementation of controlled comparisons is illustrated by an analysis of sex-ratio data for fig wasps. This analysis suggests that current adaptation and phylogenetic inertia influence this trait.

Adaptation, Biological↗

Analysis and exploration of the use of rule-based algorithms and consensus methods for the inferral of haplotypes.

The difficulty of experimental determination of haplotypes from phase-unknown genotypes has stimulated the development of nonexperimental inferral methods. One well-known approach for a group of unrelated individuals involves using the trivially deducible haplotypes (those found in individuals with zero or one heterozygous sites) and a set of rules to infer the haplotypes underlying ambiguous genotypes (those with two or more heterozygous sites). Neither the manner in which this "rule-based" approach should be implemented nor the accuracy of this approach has been adequately assessed. We implemented eight variations of this approach that differed in how a reference list of haplotypes was derived and in the rules for the analysis of ambiguous genotypes. We assessed the accuracy of these variations by comparing predicted and experimentally determined haplotypes involving nine polymorphic sites in the human apolipoprotein E (APOE) locus. The eight variations resulted in substantial differences in the average number of correctly inferred haplotype pairs. More than one set of inferred haplotype pairs was found for each of the variations we analyzed, implying that the rule-based approach is not sufficient by itself for haplotype inferral, despite its appealing simplicity. Accordingly, we explored consensus methods in which multiple inferrals for a given ambiguous genotype are combined to generate a single inferral; we show that the set of these "consensus" inferrals for all ambiguous genotypes is more accurate than the typical single set of inferrals chosen at random. We also use a consensus prediction to divide ambiguous genotypes into those whose algorithmic inferral is certain or almost certain and those whose less certain inferral makes molecular inferral preferable.

Algorithms↗