Models of large-scale human cooperation take two forms. 'Indirect reciprocity' occurs when individuals help others in order to uphold a reputation and so be included in future cooperation. In 'collective action', individuals engage in costly behaviour that benefits the group as a whole. Although the evolution of indirect reciprocity is theoretically plausible, there is no consensus about how collective action evolves. Evidence suggests that punishing free riders can maintain cooperation, but why individuals should engage in costly punishment is unclear. Solutions to this 'second-order free rider problem' include meta-punishment, mutation, conformism, signalling and group-selection. The threat of exclusion from indirect reciprocity can sustain collective action in the laboratory. Here, we show that such exclusion is evolutionarily stable, providing an incentive to engage in costly cooperation, while avoiding the second-order free rider problem because punishers can withhold help from free riders without damaging their reputations. However, we also show that such a strategy cannot invade a population in which indirect reciprocity is not linked to collective action, thus leaving unexplained how collective action arises.

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Development is typically a constructive process, in which phenotypes incrementally adapt to local ecologies. Here, we present a novel model in which natural selection shapes developmental systems based on the evolutionary ecology, and these systems adaptively guide phenotypic development. We assume that phenotypic construction is incremental and trades off with sampling cues to the environmental state. We computed the optimal developmental programmes across a range of evolutionary ecological conditions. Using these programmes, we simulated distributions of mature phenotypes. Our results show that organisms sample the environment most extensively when cues are moderately, not highly, informative. When the developmental programme relies heavily on sampling, individuals transition from sampling to specialization at different times in ontogeny, depending on the consistency of their sampled cue set; this finding suggests that stochastic sampling may result in individual differences in plasticity itself. In addition, we find that different selection pressures may favour similar developmental mechanisms, and that organisms may incorrectly calibrate development despite stable ontogenetic environments. We hope our model will stimulate adaptationist research on the constructive processes guiding development.

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Sensitive periods, in which experience shapes phenotypic development to a larger extent than other periods, are widespread in nature. Despite a recent focus on neural-physiological explanation, few formal models have examined the evolutionary selection pressures that result in developmental mechanisms that produce sensitive periods. Here, we present such a model. We model development as a specialization process during which individuals incrementally adapt to local environmental conditions, while receiving a constant stream of cost-free, imperfect cues to the environmental state. We compute optimal developmental programmes across a range of ecological conditions and use these programmes to simulate developmental trajectories and obtain distributions of mature phenotypes. We highlight four main results. First, matching the empirical record, sensitive periods often result from experience or from a combination of age and experience, but rarely from age alone. Second, individual differences in sensitive periods emerge as a result of stochasticity in cues: individuals who obtain more consistent cue sets lose their plasticity at faster rates. Third, in some cases, experience shapes phenotypes only at a later life stage (lagged effects). Fourth, individuals might perseverate along developmental trajectories despite accumulating evidence suggesting the alternate trajectory is more likely to match the ecology.. . .we all begin with the natural equipment to live a thousand kinds of life but end in the end having lived only one.-Clifford Geertz, 1973

The ability to adjust developmental trajectories based on experience is widespread in nature, including in humans. This plasticity is often adaptive, tailoring individuals to their local environment. However, it is less clear why some individuals are more sensitive to environmental influences than others. Explanations include differences in genes and differences in prior experiences. In this article, we present a novel hypothesis in the latter category. In some developmental domains, individuals must learn about the state of their environment before adapting accordingly. Because sampling environmental cues is a stochastic process, some individuals may receive a homogeneous sample, resulting in a confident estimate about the state of the world-these individuals specialize early. Other individuals may receive a heterogeneous, uninformative set of cues-those individuals will keep sampling. As a consequence, individual variation in plasticity may result from different degrees of confidence about the state of the environment. After developing the hypothesis, we conclude by discussing three empirical predictions.

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic optimization integrates developmental systems theorists' focus on dynamics and contingency with the 'design stance' of evolutionary psychology. It provides a theoretical framework as well as a set of tools for exploring the properties of developmental systems that natural selection might favor, given particular evolutionary ecologies. We also discuss limitations of the approach.

Children vary in the extent to which their development is shaped by particular experiences (e.g. maltreatment, social support). This variation raises a question: Is there no single level of plasticity that maximizes biological fitness? One influential hypothesis states that when different levels of plasticity are optimal in different environmental states and the environment fluctuates unpredictably, natural selection may favor parents producing offspring with varying levels of plasticity. The current article presents a mathematical model assessing the logic of this hypothesis -specifically, it examines what conditions are required for natural selection to favor parents to bet-hedge by varying their offspring's plasticity. Consistent with existing theory from biology, results show that betweenindividual variation in plasticity cannot evolve when the environment only varies across space. If, however, the environment varies across time, selection can favor differential plasticity, provided fitness effects are large (i.e. variation in individuals' plasticity is correlated with substantial variation in fitness). Our model also generates a novel restriction: Differential plasticity only evolves when the cost of being mismatched to the environment exceeds the benefits of being well matched. Based on mechanistic considerations, we argue that bet-hedging by varying offspring plasticity, if it were to evolve, would be more likely instantiated via epigenetic mechanisms (e.g. pre-or postnatal developmental programming) than genetic ones (e.g. mating with genetically diverse partners). Our model suggests novel avenues for testing the bet-hedging hypothesis of differential plasticity, including empirical predictions and relevant measures. We also discuss several ways in which future work might extend our model. Research highlights• We formalize Jay Belsky's bet-hedging hypothesis of differential plasticity.• Results support the hypothesis' logical coherence, but only under restrictive conditions.• Our model suggests novel avenues for empirically testing the bet-hedging hypothesis.