Competition avoidance drives individual differences in response to a changing food resource in sticklebacks

Within the same population, individuals often differ in how they respond to changes in their environment. A recent series of models predicts that competition in a heterogeneous environment might promote between‐individual variation in behavioural plasticity. We tested groups of sticklebacks in patchy foraging environments that differed in the level of competition. We also tested the same individuals across two different social groups and while alone to determine the social environment's influence on behavioural plasticity. In support of model predictions, individuals consistently differed in behavioural plasticity when the presence of conspecifics influenced the potential payoffs of a foraging opportunity. Whether individuals maintained their level of behavioural plasticity when placed in a new social group depended on the environmental heterogeneity. By explicitly testing predictions of recent theoretical models, we provide evidence for the types of ecological conditions under which we would expect, and not expect, variation in behavioural plasticity to be favoured.     

How copying affects the amount, evenness and persistence of cultural knowledge: insights from the social learning strategies tournament

Darwinian processes should favour those individuals that deploy the most effective strategies for acquiring information about their environment. We organized a computer-based tournament to investigate which learning strategies would perform well in a changing environment. The most successful strategies relied almost exclusively on social learning (here, learning a behaviour performed by another individual) rather than asocial learning, even when environments were changing rapidly; moreover, successful strategies focused learning effort on periods of environmental change. Here, we use data from tournament simulations to examine how these strategies might affect cultural evolution, as reflected in the amount of culture (i.e. number of cultural traits) in the population, the distribution of cultural traits across individuals, and their persistence through time. We found that high levels of social learning are associated with a larger amount of more persistent knowledge, but a smaller amount of less persistent expressed behaviour, as well as more uneven distributions of behaviour, as individuals concentrated on exploiting a smaller subset of behaviour patterns. Increased rates of environmental change generated increases in the amount and evenness of behaviour. These observations suggest that copying confers on cultural populations an adaptive plasticity, allowing them to respond to changing environments rapidly by drawing on a wider knowledge base.     

Why animals lie: how dishonesty and belief can coexist in a signaling system

We develop and apply a simple model for animal communication in which signalers can use a nontrivial frequency of deception without causing listeners to completely lose belief. This common feature of animal communication has been difficult to explain as a stable adaptive outcome of the options and payoffs intrinsic to signaling interactions. Our theory is based on two realistic assumptions. (1) Signals are "overheard" by several listeners or listener types with different payoffs. The signaler may then benefit from using incomplete honesty to elicit different responses from different listener types, such as attracting potential mates while simultaneously deterring competitors. (2) Signaler and listener strategies change dynamically in response to current payoffs for different behaviors. The dynamic equations can be interpreted as describing learning and behavior change by individuals or evolution across generations. We explain how our dynamic model differs from other solution concepts from classical and evolutionary game theory and how it relates to general models for frequency-dependent phenotype dynamics. We illustrate the theory with several applications where deceptive signaling occurs readily in our framework, including bluffing competitors for potential mates or territories. We suggest future theoretical directions to make the models more general and propose some possible experimental tests.     

Evolutionary Thinking in Microeconomic Models: Prestige Bias and Market Bubbles

Evolutionary models broadly support a number of social learning strategies likely important in economic behavior. Using a simple model of price dynamics, I show how prestige bias, or copying of famed (and likely successful) individuals, influences price equilibria and investor disposition in a way that exacerbates or creates market bubbles. I discuss how integrating the social learning and demographic forces important in cultural evolution with economic models provides a fruitful line of inquiry into real-world behavior.     

The evolutionary basis of human social learning

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.     

How host plant variability influences the advantages to learning: a theoretical model for oviposition behaviour in Lepidoptera

Learning can allow individuals to increase their fitness in particular environments. The advantage to learning depends on the predictability of the environment and the extent to which animals can adjust their behaviour. Earlier general models have investigated when environmental predictability might favour the evolution of learning in foraging animals. Here, we construct a theoretical model that predicts the advantages to learning using a specific biological example: oviposition in the Lepidoptera. Our model includes environmental and behavioural complexities relevant to host selection in these insects and tests whether the predictions of the general models still hold. Our results demonstrate how the advantage of learning is maximised when within-generation variability is minimised (the local environment consists mainly of a single host plant species) and between-generation variability is maximised (different host plant species are the most common in different generations). We discuss how our results: (a) can be applied to recent empirical work in different lepidopteran species and (b) predict an important role of learning in lepidopteran agricultural pests.     

Adaptive Social Learning Strategies in Temporally and Spatially Varying Environments

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial??but not temporal??variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.     

The evolution of conformist social learning can cause population collapse in realistically variable environments

Why do societies collapse? We use an individual-based evolutionary model to show that, in environmental conditions dominated by low-frequency variation (“red noise”), extirpation may be an outcome of the evolution of cultural capacity. Previous analytical models predicted an equilibrium between individual learners and social learners, or a contingent strategy in which individuals learn socially or individually depending on the circumstances. However, in red noise environments, whose main signature is that variation is concentrated in relatively large, relatively rare excursions, individual learning may be selected from the population. If the social learning system comes to lack sufficient individual learning or cognitively costly adaptive biases, behavior ceases tracking environmental variation. Then, when the environment does change, fitness declines and the population may collapse or even be extirpated. The modeled scenario broadly fits some human population collapses and might also explain nonhuman extirpations. Varying model parameters showed that the fixation of social learning is less likely when individual learning is less costly, when the environment is less red or more variable, with larger population sizes, and when learning is not conformist or is from parents rather than from the general population. Once social learning is fixed, extirpation is likely except when social learning is biased towards successful models. Thus, the risk of population collapse may be reduced by promoting individual learning and innovation over cultural conformity, or by preferential selection of relatively fit individuals as models for social learning.     

Effects of social group size on information transfer and task allocation

Summary Social animals exchange information during social interaction. The rate of interaction and, hence, the rate of information exchange, typically changes with density and density may be affected by the size of the social group. We investigate models in which each individual may be engaged in one of several tasks. For example, the different tasks could represent alternative foraging locations exploited by an ant colony. An individual's decision about which task to pursue depends both on environmental stimuli and on interactions among individuals. We examine how group size affects the allocation of individuals among the various tasks. Analysis of the models shows the following. (1) Simple interactions among individuals with limited ability to process information can lead to group behaviour that closely approximates the predictions of evolutionary optimality models, (2) Because per capita rates of social interaction may increase with group size, larger groups may be more efficient than smaller ones at tracking a changing environment, (3) Group behaviour is determined both by each individual's interaction with environmental stimuli and by social exchange of information. To keep these processes in balance across a range of group sizes, organisms are predicted to regulate per capita rates of social interaction and (4) Stochastic models show, at least in some cases, that the results described here occur even in small groups of approximately ten individuals.     

A mixed strategy model for the emergence and intensification of social learning in a periodically changing natural environment

Based on a population genetic model of mixed strategies determined by alleles of small effect, we derive conditions for the evolution of social learning in an infinite-state environment that changes periodically over time. Each mixed strategy is defined by the probabilities that an organism will commit itself to individual learning, social learning, or innate behavior. We identify the convergent stable strategies (CSS) by a numerical adaptive dynamics method and then check the evolutionary stability (ESS) of these strategies. A strategy that is simultaneously a CSS and an ESS is called an attractive ESS (AESS). For certain parameter sets, a bifurcation diagram shows that the pure individual learning strategy is the unique AESS for short periods of environmental change, a mixed learning strategy is the unique AESS for intermediate periods, and a mixed learning strategy (with a relatively large social learning component) and the pure innate strategy are both AESS's for long periods. This result entails that, once social learning emerges during a transient era of intermediate environmental periodicity, a subsequent elongation of the period may result in the intensification of social learning, rather than a return to innate behavior.     

Higher frequency of social learning in China than in the West shows cultural variation in the dynamics of cultural evolution

Cultural evolutionary models have identified a range of conditions under which social learning (copying others) is predicted to be adaptive relative to asocial learning (learning on one''s own), particularly in humans where socially learned information can accumulate over successive generations. However, cultural evolution and behavioural economics experiments have consistently shown apparently maladaptive under-utilization of social information in Western populations. Here we provide experimental evidence of cultural variation in people''s use of social learning, potentially explaining this mismatch. People in mainland China showed significantly more social learning than British people in an artefact-design task designed to assess the adaptiveness of social information use. People in Hong Kong, and Chinese immigrants in the UK, resembled British people in their social information use, suggesting a recent shift in these groups from social to asocial learning due to exposure to Western culture. Finally, Chinese mainland participants responded less than other participants to increased environmental change within the task. Our results suggest that learning strategies in humans are culturally variable and not genetically fixed, necessitating the study of the ‘social learning of social learning strategies'' whereby the dynamics of cultural evolution are responsive to social processes, such as migration, education and globalization.     

A mixed strategy model for the emergence and intensification of social learning in a periodically changing natural environment

Based on a population genetic model of mixed strategies determined by alleles of small effect, we derive conditions for the evolution of social learning in an infinite-state environment that changes periodically over time. Each mixed strategy is defined by the probabilities that an organism will commit itself to individual learning, social learning, or innate behavior. We identify the convergent stable strategies (CSS) by a numerical adaptive dynamics method and then check the evolutionary stability (ESS) of these strategies. A strategy that is simultaneously a CSS and an ESS is called an attractive ESS (AESS). For certain parameter sets, a bifurcation diagram shows that the pure individual learning strategy is the unique AESS for short periods of environmental change, a mixed learning strategy is the unique AESS for intermediate periods, and a mixed learning strategy (with a relatively large social learning component) and the pure innate strategy are both AESS's for long periods. This result entails that, once social learning emerges during a transient era of intermediate environmental periodicity, a subsequent elongation of the period may result in the intensification of social learning, rather than a return to innate behavior.     

Competition for resources can explain patterns of social and individual learning in nature

In nature, animals often ignore socially available information despite the multiple theoretical benefits of social learning over individual trial-and-error learning. Using information filtered by others is quicker, more efficient and less risky than randomly sampling the environment. To explain the mix of social and individual learning used by animals in nature, most models penalize the quality of socially derived information as either out of date, of poor fidelity or costly to acquire. Competition for limited resources, a fundamental evolutionary force, provides a compelling, yet hitherto overlooked, explanation for the evolution of mixed-learning strategies. We present a novel model of social learning that incorporates competition and demonstrates that (i) social learning is favoured when competition is weak, but (ii) if competition is strong social learning is favoured only when resource quality is highly variable and there is low environmental turnover. The frequency of social learning in our model always evolves until it reduces the mean foraging success of the population. The results of our model are consistent with empirical studies showing that individuals rely less on social information where resources vary little in quality and where there is high within-patch competition. Our model provides a framework for understanding the evolution of social learning, a prerequisite for human cumulative culture.     

Social structure modulates the evolutionary consequences of social plasticity: A social network perspective on interacting phenotypes

Organisms express phenotypic plasticity during social interactions. Interacting phenotype theory has explored the consequences of social plasticity for evolution, but it is unclear how this theory applies to complex social structures. We adapt interacting phenotype models to general social structures to explore how the number of social connections between individuals and preference for phenotypically similar social partners affect phenotypic variation and evolution. We derive an analytical model that ignores phenotypic feedback and use simulations to test the predictions of this model. We find that adapting previous models to more general social structures does not alter their general conclusions but generates insights into the effect of social plasticity and social structure on the maintenance of phenotypic variation and evolution. Contribution of indirect genetic effects to phenotypic variance is highest when interactions occur at intermediate densities and decrease at higher densities, when individuals approach interacting with all group members, homogenizing the social environment across individuals. However, evolutionary response to selection tends to increase at greater network densities as the effects of an individual's genes are amplified through increasing effects on other group members. Preferential associations among similar individuals (homophily) increase both phenotypic variance within groups and evolutionary response to selection. Our results represent a first step in relating social network structure to the expression of social plasticity and evolutionary responses to selection.     

Success-biased social learning: Cultural and evolutionary dynamics

Success bias is a social learning strategy whereby learners tend to acquire the cultural variants of successful individuals. I develop a general model of success-biased social learning for discrete cultural traits with stochastic payoffs, and investigate its dynamics when only two variants are present. I find that success bias inherently favors rare variants, and consequently performs worse than unbiased imitation (i.e. random copying) when success payoffs are at least mildly stochastic and the optimal variant is common. Because of this weakness, success bias fails to replace unbiased imitation in an evolutionary model when selection is fairly weak or when the environment is relatively stable, and sometimes fails to invade at all. I briefly discuss the optimal strength of success bias, the complicated nature of defining success in social learning contexts, and the value of variant frequency as an important source of information to social learners. I conclude with predictions regarding the prevalence of success bias in different behavioral domains.     

Imitation or innovation? Unselective mixed strategies can provide a better solution

Current theory about the evolution of social learning in a changing environment predicts the emergence of mixed strategies that rely on some selective combination of social and asocial learning. However, the results of a recent tournament of social learning strategies [Rendell et al. Science 328(5975):208?C213, 2010] suggest that the success relies almost entirely on copying to learn behavior. Those authors conclude that mixed strategies are vulnerable to invasion by individuals using social learning strategies alone. Here we perform a competition using unselective strategies that differ only in the degree of social versus asocial learning. We show that, under the same conditions of the aforementioned tournament, a pure social learning strategy can be invaded by an unselectively mixed strategy and attain an equilibrium where the latter is majority. Although existing theory suggests that copying other individuals unselectively is not adaptive, we show that, at this equilibrium, the average individual fitness of the population is higher than for a population of pure asocial learners, overcoming Rogers?? paradox in finite populations.     

Meta-control of social learning strategies

Social learning, copying other’s behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others’ learning as an external knowledge base.     

Animal learning as a source of developmental bias

As a form of adaptive plasticity that allows organisms to shift their phenotype toward the optimum, learning is inherently a source of developmental bias. Learning may be of particular significance to the evolutionary biology community because it allows animals to generate adaptively biased novel behavior tuned to the environment and, through social learning, to propagate behavioral traits to other individuals, also in an adaptively biased manner. We describe several types of developmental bias manifest in learning, including an adaptive bias, historical bias, origination bias, and transmission bias, stressing that these can influence evolutionary dynamics through generating nonrandom phenotypic variation and/or nonrandom environmental states. Theoretical models and empirical data have established that learning can impose direction on adaptive evolution, affect evolutionary rates (both speeding up and slowing down responses to selection under different conditions) and outcomes, influence the probability of populations reaching global optimum, and affect evolvability. Learning is characterized by highly specific, path‐dependent interactions with the (social and physical) environment, often resulting in new phenotypic outcomes. Consequently, learning regularly introduces novelty into phenotype space. These considerations imply that learning may commonly generate plasticity first evolution.     

A Bayesian approach to the evolution of social learning

There has been much interest in understanding the evolution of social learning. Investigators have tried to understand when natural selection will favor individuals who imitate others, how imitators should deal with the fact that available models may exhibit different behaviors, and how social and individual learning should interact. In all of this work, social learning and individual learning have been treated as alternative, conceptually distinct processes. Here we present a Bayesian model in which both individual and social learning arise from a single inferential process. Individuals use Bayesian inference to combine social and nonsocial cues about the current state of the environment. This model indicates that natural selection favors individuals who place heavy weight on social cues when the environment changes slowly or when its state cannot be well predicted using nonsocial cues. It also indicates that a conformist bias should be a universal aspect of social learning.     

Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.