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Animals, including humans, differ in a wide range of physical and cognitive abilities ranging from measures of running speed and physical strength to learning ability and intelligence. We consider the evolution of ability when individuals interact pairwise over their contribution to a common good. In this interaction, the contribution of each is assumed to be the best given their own ability and the contribution of their partner. Since there is a tendency for individuals to partially compensate for a low contribution by their partner, low-ability individuals can do well. As a consequence, for benefit and cost structures for which individuals have a strong response to partner’s contribution, there can be selection for reduced ability. Furthermore, there can be disruptive selection on ability, leading to a bimodal distribution of ability under some modes of inheritance.

Interactions in social groups can promote behavioural specialization. One way this can happen is when individuals engage in activities with two behavioural options and learn which option to choose. We analyse interactions in groups where individuals learn from playing games with two actions and negatively frequency-dependent payoffs, such as producer–scrounger, caller–satellite, or hawk–dove games. Group members are placed in social networks, characterized by the group size and the number of neighbours to interact with, ranging from just a few neighbours to interactions between all group members. The networks we analyse include ring lattices and the much-studied small-world networks. By implementing two basic reinforcement-learning approaches, action–value learning and actor–critic learning, in different games, we find that individuals often show behavioural specialization. Specialization develops more rapidly when there are few neighbours in a network and when learning rates are high. There can be learned specialization also with many neighbours, but we show that, for action–value learning, behavioural consistency over time is higher with a smaller number of neighbours. We conclude that frequency-dependent competition for resources is a main driver of specialization. We discuss our theoretical results in relation to experimental and field observations of behavioural specialization in social situations.

The idea of applying game theory to problems in biology was given a formal basis nearly 50 years ago. Since then, the theory has advanced, and there have been numerous applications of it to a diversity of empirical systems. Most of this work takes a straightforward functional approach, finding a behavioral strategy that is evolutionarily stable in a well-specified specific situation. Relatively little attention has been devoted to the role of phylogeny, the role of learning during development, and the limitations imposed by the psychological and physiological mechanisms that bring about behavior in a complex world. Here I argue that a focus on these elements can improve the link between the theory and empirical systems and hence help us to understand how natural selection has shaped observed behavior.

In social interactions, including cooperation and conflict, individuals can adjust their behaviour over the shorter term through learning within a generation, and natural selection can change behaviour over the longer term of many generations. Here we investigate the evolution of cognitive bias by individuals investing into a project that delivers joint benefits. For members of a group that learn how much to invest using the costs and benefits they experience in repeated interactions, we show that overestimation of the cost of investing can evolve. The bias causes individuals to invest less into the project. Our explanation is that learning responds to immediate rather than longer-term rewards. There are thus cognitive limitations in learning, which can be seen as bounded rationality. Over a time horizon of several rounds of interaction, individuals respond to each other’s investments, for instance by partially compensating for another’s shortfall. However, learning individuals fail to strategically take into account that social partners respond in this way. Learning instead converges to a one-shot Nash equilibrium of a game with perceived rewards as payoffs. Evolution of bias can then compensate for the cognitive limitations of learning.

Most analyses of the origins of cultural evolution focus on when and where social learning prevails over individual learning, overlooking the fact that there are other developmental inputs that influence phenotypic fit to the selective environment. This raises the question of how the presence of other cue ‘channels’ affects the scope for social learning. Here, we present a model that considers the simultaneous evolution of (i) multiple forms of social learning (involving vertical or horizontal learning based on either prestige or conformity biases) within the broader context of other evolving inputs on phenotype determination, including (ii) heritable epigenetic factors, (iii) individual learning, (iv) environmental and cascading maternal effects, (v) conservative bet-hedging, and (vi) genetic cues. In fluctuating environments that are autocorrelated (and hence predictable), we find that social learning from members of the same generation (horizontal social learning) explains the large majority of phenotypic variation, whereas other cues are much less important. Moreover, social learning based on prestige biases typically prevails in positively autocorrelated environments, whereas conformity biases prevail in negatively autocorrelated environments. Only when environments are unpredictable or horizontal social learning is characterized by an intrinsically low information content, other cues such as conservative bet-hedging or vertical prestige biases prevail. This article is part of the theme issue ‘Foundations of cultural evolution’.

Matching describes how behaviour is related to rewards. The matching law holds when the ratio of an individual's behaviours equals the ratio of the rewards obtained. From its origins in the study of pigeons working for food in the laboratory, the law has been applied to a range of species, both in the laboratory and outside it (e.g., human sporting decisions). Probability matching occurs when the probability of a behaviour equals the probability of being rewarded. Input matching predicts the distribution of individuals across habitats. We evaluate the rationality of the matching law and probability matching, expose the logic of matching in real-world cases, review how recent neuroscience findings relate to matching, and suggest future research directions.

There is typically considerable between-individual variation in trait values in natural populations. Game theory has often ignored this, treating individuals as the same. However, the existence and amount of variation is central to many predictions in biological game theory, as this chapter illustrates. Variation is central to signalling systems and stabilizes these systems as well as extensive-form games. Variation leads to individuals taking a chance that a partner is better than average; for example, promoting cooperation in a finitely repeated Prisoner’s Dilemma game. When there is both variation and within-individual consistency, so that past behaviour is predictive of current behaviour, reputation is important. As is demonstrated, once population members respond to reputation, this then selects for all to modify their behaviour so as to change their reputation and so change how others interact with them in the future, with consequences for the level of cooperation in the population. Furthermore, as a game of trust shows, the extent to which reputation matters can depend on whether individuals are prepared to pay the cost of being socially sensitive, which depends on the amount of variation. Variation selects for individuals to be choosy about their partner, and choosiness can lead to assortative pairing in a population, again promoting cooperation. The importance of choosiness in a market situation is demonstrated by a model in which partners have to decide how much to commit to one another, with factors that enhance choosiness leading to higher levels of commitment.

The concept of an Evolutionarily Stable Strategy (ESS), which is a stronger stability condition than that of a Nash equilibrium, is introduced. A simple evolutionary dynamic, adaptive dynamics, is also introduced. This leads to the concept of convergence stability under adaptive dynamics. It is shown that these two stability criteria are independent for general games: a strategy can be an ESS but not be reachable under adaptive dynamics and a strategy may be an attractor under adaptive dynamics but a fitness minimum and so not an ESS. The latter situation leads to the possibility of evolutionary branching, a phenomenon in which the population splits into a mixture of two or more distinct morphs. Replicator dynamics provide another evolutionary dynamic, although it is argued that it is of limited relevance to biology. In some games, individuals interact with relatives. The effects of kin assortment, and the direct fitness and gene-centred approaches to games between kin are described and illustrated.

The chapter defines and discusses some of the central concepts in biological game theory. Strategies, which are rules for choosing actions as a function of state, play a pivotal role. It is explained how the theory operates at the level of strategies rather than attempting to follow the details of the underlying genetics that code for them. This is referred to as 'the phenotypic gambit', which is discussed and illustrated. The concept of the invasion fitness of a mutant strategy in a population that adopts another resident strategy is also central. This performance measure is used to give a necessary condition for evolutionary stability, formulated as the Nash equilibrium condition. It is explained how this stability condition can be reformulated in terms of simpler fitness proxies such as the mean lifetime number of offspring or the net rate of energy gain.

Game theory in biology seeks to predict social behaviour and other traits that influence how individuals interact. It does this by tentatively assuming that current traits are stable endpoints of evolution by natural selection. The theory is used to model aggressive behaviour, cooperation, negotiation, and signalling, as well as phenotypic attributes like an individual’s sex and mating type. This book covers the basic concepts and the traditional examples of biological game theory. It expands the frontiers of the field, emphasizing the importance of the co-evolution of traits and the implications of variation for reputation, markets, negotiation, and other social phenomena. It also highlights that it can be important to embed game interactions in the environment and an individual’s life. A major new direction developed in the book is that game theory can be extended by incorporating behavioural mechanisms, including mechanisms of reinforcement learning. By doing this the theory can successfully describe important phenomena like social dominance in group-living animals that previously have been difficult to model. By focusing on behavioural mechanisms, game theory can also make closer contact with empirical observation and with current research in fields like animal psychology and neuroscience.

Often traits interact, so that considering the evolution of each in isolation gives too limited an account. As is demonstrated in this chapter, it is then crucial to allow for the co-evolution of traits when analysing evolutionary stability. In particular this is important when there there is disruptive selection. Criteria for stability are presented and are applied to a variety of systems. It is shown that role asymmetries can lead to different predictions compared with the analogous situation with the same payoffs but without such asymmetries. Disruptive selection can lead to the evolution of anisogamy. When parental ability and parental effort co-evolve, disruptive selection can lead to one sex evolving to be better at care and doing most of the care. Furthermore, disruptive selection can lead to multiple ESSs, as when prosocial behaviour and the propensity to disperse from the natal site co-evolve. As is shown, disruptive forces can also act when there is learning, leading to specialization. This chapter sets the scene for the chapter that follows, where the level of cooperation shown by individuals co-evolves with choosiness and social sensitivity.

The chapter introduces reinforcement learning in game-theory models. A distinction is made between small-worlds models with Bayesian updating and large-worlds models that implement specific behavioural mechanisms. The actor–critic learning approach is introduced and illustrated with simple examples of learning in a coordination game and in the Hawk–Dove game. Simple versions of a game of investments with joint benefits and a social dominance game are presented, and these games are further developed in Chapter 8. The idea that parameters of the learning process, such as learning rates, can evolve is put forward. For the game examples it is shown that with slow learning over many rounds the outcome can approximate an ESS of a one-shot game, but for higher rates of learning and fewer rounds this need not be the case. The chapter ends with an overview of learning approaches in game theory, including the originally proposed relative-payoff-sum learning rule for games in biology.

Important areas for future developments of game theory in biology are put forward. These include several issues that are dealt with in the book, such as trait co-evolution, the consequences of variation, time structure, and the embedding of games into an ecological context and into the lives of individuals. New areas are also suggested, with Tinbergen’s four questions about the study of animal behaviour serving as a starting point. Game theory could be combined with phylogenetic analysis by examining how Evolutionarily Stable Strategies (ESSs) might change over evolutionary time, including major shifts between different ESSs, which might correspond to different species over evolutionary time. Concerning behavioural mechanisms in large worlds, the question of which mechanism parameters that are tuned by evolution is addressed, with a brief summary of the current knowledge about comparative cognition. The possible importance of limited flexibility in mechanisms is illustrated by outlining a model of a trust game. Finally, the potential for game theory to contribute to the study of cognitive development is discussed, using mutualism between cleaner fish and their client fish as an illustration.

The actions and state of an individual in one generation can affect the state of offspring in the next generation, and hence the ability of these offspring to leave offspring themselves. This chapter deals with games in this multigenerational setting. Projection matrices are used to keep track of the state and number of descendants in successive years and generations. Invasion fitness is then defined in terms of the leading eigenvalue of the projection matrix. Simple examples illustrate these concepts and show how to apply them. Reproductive value is a function that measures how the ability to leave descendants in future generations depends on the current state. The Nash equilibrium condition is reformulated in terms of reproductive value maximization. This new criterion is used to justify the fitness proxy used in the analysis of sex allocation earlier in the book. The analysis is extended to the case where offspring may inherit aspects of their mother’s quality, with a focus on the question of whether high-quality mothers should produce sons or daughters. As a second application of reproductive value maximization, the co-evolution of female preference for a particular male trait and the trait itself is analysed, with the evolution of tail length in the widowbird as an illustrative application. Mean lifetime reproductive success is used as a fitness proxy in much of the book. Its use is finally justified in this chapter, where the fitness proxy is used to analyse the evolutionarily stable age of first reproduction in a population.

Many games focus on a part of the life of an organism. The payoff structure of the game then represents how the game affects fitness proxies such as mean lifetime reproductive success, which are concerned with the whole of the life of the organism. However, the traditional approach of specifying payoffs in advance of the analysis of the game can lead to inconsistencies because the rest of the life of an individual is not fixed but depends on what happens in the game. This chapter concerns this issue, identifying situations in which a more holistic approach is needed. A series of models illustrates links between the current situation and a lifetime perspective. When each of two parents must decide whether to care for their common young or desert, the payoff for desertion depends on the solution of the game and cannot be specified in advance. A game in which two males contest for a female illustrates the approach that must be taken if this game can be repeated at a later time. A game in which individuals must possess territories in order to breed is developed that highlights various interdependencies and, by incorporating learning, advances the understanding of owner–intruder interactions. The interdependencies in state-dependent dynamic games are also illustrated with a model in which individuals must trade off the risks of starvation and predation in a situation in which the choice of the best foraging habitat depends on the number of other animals that use that habitat.

Standard examples in biological game theory are introduced. The degree of cooperation at evolutionary stability is analysed in models that deal with situations such as the Prisoner’s Dilemma, the Tragedy of the Commons and the conflict of interest between parents over care of their common young. Several models of aggressive interactions are treated in this book. In this chapter the Hawk–Dove game, which is the simplest of these models, is analysed. Further models in the chapter deal with the situation in which individuals vary in their fighting ability and the situation in which information about the opponent is available before an individual decides whether to be aggressive. The problem of the allocation of resources to sons versus daughters has played a central role in biological game theory. This chapter introduces the basic theory, as well as a model in which the environmental temperature affects the development of the sexes differentially, so that at evolutionary stability the sex of offspring is determined by this temperature. Coordination games, alternative mating tactics, dispersal to avoid kin competition, and the idea that signals can evolve from cues are also introduced.

The chapter starts with an introduction to game theory in biology, describing its overall aims. The basic concept of frequency dependence is then presented, together with a number of illustrative biological examples. Next, the modelling approach is outlined, emphasizing that the theory aims to predict phenomena by seeking stable evolutionary endpoints. The scope and challenges of the field are described in the setting of the history of ideas that have been important for the theory, summarizing past successes as well as long-standing questions that are likely to require further development of the theory. The chapter ends with an overview of the main issues dealt with in the book, including the challenges that are taken up. These include taking into account the co-evolution of traits, exploring the consequences of variation, and the modelling social interactions as games over time. In particular for the latter, models that include behavioural mechanisms are likely to be essential for the success of game theory in biology.

Many social interactions are extended over time, with sequences of decisions by the participating individuals. An interaction can include negotiation between partners as well as learning about each other’s characteristics and qualities. The classical game-theoretical concepts of normal- and extensive-form games and perfect and Stackelberg equilibria are described. A model of the negotiation by parents over investments into their joint offspring is presented and discussed, emphasizing the difference between the game with and without time structure. A version of this model where individuals do not negotiate their investments but instead learn about their respective capacities to invest is presented. For this model a cognitive bias can evolve, such that individuals behave as if they overestimate their true cost of investing. The evolution of social dominance behaviour and social hierarchies is then studied, using actor–critic learning with observations of relative fighting ability and individual recognition. A strong effect of social group size on social dominance is one of the results from the analysis. Pairwise contests are then modelled, both using actor–critic learning and as a sequential assessment game, which is shown to correspond to a neural random walk. The chapter ends with a broad discussion of the successes and challenges of games with time structure. Reasons to incorporate behavioural mechanisms into game theory models, using a large-worlds perspective, are presented.

The timing of migration and migratory steps is highly relevant for fitness. Because environmental conditions vary between years, the optimal time for migration varies accordingly. Therefore, migratory animals could clearly benefit from acquiring information as to when it is the best time to migrate in a specific year. Thus, environmental predictability and variability are fundamental characteristics of migration systems but their relationship and consequence for migratory progression has remained unexplored. We develop a simple dynamic model to identify the optimal migration behaviour in environments that differ in predictability, variability and the number of intermediate stop-over sites. Our results indicate that higher predictability along migration routes enables organisms to better time migration when phenology deviates from its long-term average and thus, increases fitness. Information is particularly valuable in highly variable environments and in the final migration-step, i.e. before the destination. Furthermore, we show that a general strategy for obtaining information in relatively uninformative but variable environments is using intermediate stop-over sites that enable migrants to better predict conditions ahead. Our study contributes to a better understanding of the relationship between animal movement and environmental predictability - an important, yet underappreciated factor that strongly influences migratory progression.

Variation in stress responses has been investigated in relation to environmental factors, species ecology, life history and fitness. Moreover, mechanistic studies have unravelled molecular mechanisms of how acute and chronic stress responses cause physiological impacts (‘damage’), and how this damage can be repaired. However, it is not yet understood how the fitness effects of damage and repair influence stress response evolution. Here we study the evolution of hormone levels as a function of stressor occurrence, damage and the efficiency of repair. We hypothesise that the evolution of stress responses depends on the fitness consequences of damage and the ability to repair that damage. To obtain some general insights, we model a simplified scenario in which an organism repeatedly encounters a stressor with a certain frequency and predictability (temporal autocorrelation). The organism can defend itself by mounting a stress response (elevated hormone level), but this causes damage that takes time to repair. We identify optimal strategies in this scenario and then investigate how those strategies respond to acute and chronic exposures to the stressor. We find that for higher repair rates, baseline and peak hormone levels are higher. This typically means that the organism experiences higher levels of damage, which it can afford because that damage is repaired more quickly, but for very high repair rates the damage does not build up. With increasing predictability of the stressor, stress responses are sustained for longer, because the animal expects the stressor to persist, and thus damage builds up. This can result in very high (and potentially fatal) levels of damage when organisms are exposed to chronic stressors to which they are not evolutionarily adapted. Overall, our results highlight that at least three factors need to be considered jointly to advance our understanding of how stress physiology has evolved: (i) temporal dynamics of stressor occurrence; (ii) relative mortality risk imposed by the stressor itself versus damage caused by the stress response; and (iii) the efficiency of repair mechanisms. peerReviewed

The behavioural sciences have been strongly influenced for several decades by signal detection theory (also known as "error management theory" and "ROC analysis"). The theory provides simple logic about how individuals should behave in order to maximize their expected payoff in a single decision; for instance, the theory predicts that as the probability of a particular danger increases, individuals should be more prone to avoiding that danger. However, such findings need not hold in situations where more than 1 decision will be made, and recent work has shown that when behavior is allowed to depend on the energy reserves of the individual, the classical predictions can be reversed. Here, we use a simple analytic technique to show that even when the same behavioral rules are used at every reserve level, the predictions of classical signal detection theory (as though only a single decision will be made) are often reversed in a world where autocorrelation is high. The classical predictions are reversed because individuals will often make multiple decisions to achieve a goal, and the outcome of an action can influence the number of subsequent decisions that are required. The finding suggests that fundamental predictions should be altered across many of the behavioral sciences, not only in relation to signal detection theory, but also other single-decision models.

All organisms have a stress response system to cope with environmental threats, yet its precise form varies hugely within and across individuals, populations, and species. While the physiological mechanisms are increasingly understood, how stress responses have evolved remains elusive. Here, we show that important insights can be gained from models that incorporate physiological mechanisms within an evolutionary optimality analysis (the 'evo-mecho' approach). Our approach reveals environmental predictability and physiological constraints as key factors shaping stress response evolution, generating testable predictions about variation across species and contexts. We call for an integrated research programme combining theory, experimental evolution, and comparative analysis to advance scientific understanding of how this core physiological system has evolved.

Cooperative interactions among individuals are ubiquitous despite the possibility of exploitation by selfish free riders. One mechanism that may promote cooperation is 'negotiation': individuals altering their behaviour in response to the behaviour of others. Negotiating individuals decide their actions through a recursive process of reciprocal observation, thereby reducing the possibility of free riding. Evolutionary games with response rules have shown that infinitely many forms of the rule can be evolutionarily stable simultaneously, unless there is variation in individual quality. This potentially restricts the conditions under which negotiation could maintain cooperation. Organisms interact with one another in a noisy world in which cooperative effort and the assessment of effort may be subject to error. Here, we show that such noise can make the number of evolutionarily stable rules finite, even without quality variation, and so noise could help maintain cooperative behaviour. We show that the curvature of the benefit function is the key factor determining whether individuals invest more or less as their partner's investment increases, investing less when the benefit to investment has diminishing returns. If the benefits of low investment are very small then behavioural flexibility tends to promote cooperation, because negotiation enables cooperators to reach large benefits. Under some conditions, this leads to a repeating cycle in which cooperative behaviour rises and falls over time, which may explain between-population differences in cooperative behaviour. In other conditions, negotiation leads to extremely high levels of cooperative behaviour, suggesting that behavioural flexibility could facilitate the evolution of eusociality in the absence of high relatedness.

There are many inputs during development that influence an organism's fit to current or upcoming environments. These include genetic effects, transgenerational epigenetic influences, environmental cues and developmental noise, which are rarely investigated in the same formal framework. We study an analytically tractable evolutionary model, in which cues are integrated to determine mature phenotypes in fluctuating environments. Environmental cues received during development and by the mother as an adult act as detection-based (individually observed) cues. The mother's phenotype and a quantitative genetic effect act as selection-based cues (they correlate with environmental states after selection). We specify when such cues are complementary and tend to be used together, and when using the most informative cue will predominate. Thus, we extend recent analyses of the evolutionary implications of subsets of these effects by providing a general diagnosis of the conditions under which detection and selection-based influences on development are likely to evolve and coexist.

We focus on learning during development in a group of individuals that play a competitive game with each other. The game has two actions and there is negative frequency dependence. We define the distribution of actions by group members to be an equilibrium configuration if no individual can improve its payoff by unilaterally changing its action. We show that at this equilibrium, one action is preferred in the sense that those taking the preferred action have a higher payoff than those taking the other, more prosocial, action. We explore the consequences of a simple ‘unbiased’ reinforcement learning rule during development, showing that groups reach an approximate equilibrium distribution, so that some achieve a higher payoff than others. Because there is learning, an individual’s behaviour can influence the future behaviour of others. We show that, as a consequence, there is the potential for an individual to exploit others by influencing them to be the ones to take the non-preferred action. Using an evolutionary simulation, we show that population members can avoid being exploited by over-valuing rewards obtained from the preferred option during learning, an example of a bias that is ‘rational’.

Limited flexibility in behaviour gives rise to behavioural consistency, so that past behaviour is partially predictive of current behaviour. The consequences of limits to flexibility are investigated in a population in which pairs of individuals play a game of trust. The game can either be observed by others or not. Reputation is based on trustworthiness when observed and acts as a signal of behaviour in future interactions with others. Individuals use the reputation of partner in deciding whether to trust them, both when observed by others and when not observed. We explore the effects of costs of exhibiting a difference in behaviour between when observed and when not observed (i.e. a cost of flexibility). When costs are low, individuals do not attempt to signal that they will later be trustworthy: their signal should not be believed since it will always pay them to be untrustworthy if trusted. When costs are high, their local optimal behaviour automatically acts as an honest signal. At intermediate costs, individuals are very trustworthy when observed in order to convince others of their trustworthiness when unobserved. It is hypothesized that this type of strong signalling might occur in other settings.

There can be genetic conflict between genome elements differing in transmission patterns, and thus in evolutionary interests. We show here that the concept of genetic conflict provides new insight into local adaptation and phenotypic plasticity. Local adaptation to heterogeneous habitats sometimes occurs as tightly linked clusters of genes with among-habitat polymorphism, referred to as genomic islands of divergence, and our work sheds light on their evolution. Phenotypic plasticity can also influence the divergence between ecotypes, through developmental responses to habitat-specificcues. We show that clustered genes coding for ecological specialism and unlinked generalist genes coding for phenotypic plasticity differ in their evolutionary interest. This is an ecological genetic conflict, operating between habitat specialism and phenotypically plastic generalism. The phenomenon occurs both for single traits and for syndromes of co-adapted traits. Using individual-based simulations and numerical analysis, we investigate how among-habitat genetic polymorphism and phenotypic plasticity depend on genetic architecture. We show that for plasticity genes that are unlinked to a genomic island of divergence, the slope of a reaction norm will be steeper in comparison with the slope favored by plasticity genes that are tightly linked to genes for local adaptation.

The development of multicellular organisms involves a delicate interplay between genetic and environmental influences. It is often useful to think of developmental systems as integrating available sources of information about current conditions to produce organisms. Genes and inherited physiology provide cues, as does the state of the environment during development. The integration systems themselves are under genetic control and subject to Darwinian selection, so we expect them to evolve to produce organisms that fit well with current ecological (including social) conditions. We argue for the scientific value of this explicitly informational perspective by providing detailed examples of how it can elucidate taxonomically diverse phenomena. We also present a general framework for linking genetic and phenotypic variation from an informational perspective. This application of Darwinian logic at the organismal level can elucidate genetic influences on phenotypic variation in novel and counterintuitive ways.

We consider a behavioural model of an animal choosing between two activities, based on positive feedback, and examine the effect of introducing cross inhibition between the motivations for the two activities. While cross-inhibition has previously been included in models of decision making, the question of what benefit it may provide to an animal’s activity selection behaviour has not previously been studied. In neuroscience and in collective behaviour cross-inhibition, and other equivalent means of coupling evidence-accumulating pathways, have been shown to approximate statistically-optimal decision-making and to adaptively break deadlock, thereby improving decision performance. Switching between activities is an ongoing decision process yet here we also find that cross-inhibition robustly improves its efficiency, by reducing the frequency of costly switches between behaviours.

Contains fulltext : 195174.pdf (Publisher’s version ) (Open Access) In the last decades, developmental origins of health and disease (DOHaD) has emerged as a central framework for studying early-life effects, that is, the impact of fetal and early postnatal experience on adult functioning. Apace with empirical progress, theoreticians have built mathematical models that provide novel insights for DOHaD. This article focuses on three of these insights, which show the power of environmental noise (i.e., imperfect indicators of current and future conditions) in shaping development. Such noise can produce: (a) detrimental outcomes even in ontogenetically stable environments, (b) individual differences in sensitive periods, and (c) early-life effects tailored to predicted future somatic states. We argue that these insights extend DOHaD and offer new research directions. 15 p.

An organism's phenotype can be influenced by maternal cues and directly perceived environmental cues, as well as by its genotype at polymorphic loci, which can be interpreted as a genetic cue. In fluctuating environments, natural selection favors organisms that efficiently integrate different sources of information about the likely success of phenotypic alternatives. In such situations, it can be beneficial to pass on maternal cues that offspring can respond to. A maternal cue could be based on environmental cues directly perceived by the mother but also partly on cues that were passed on by the grandmother. We have used a mathematical model to investigate how the passing of maternal cues and the integration of different sources of information evolve in response to qualitatively different kinds of temporal and spatial environmental fluctuations. The model shows that the passing of maternal cues and the transgenerational integration of sources of information readily evolve. Factors such as the degree of temporal autocorrelation, the predictive accuracy of different environmental cues, and the level of gene flow strongly influence the expression of adaptive maternal cues and the relative weights given to different sources of information. We outline the main features of the relation between the characteristics of environmental fluctuations and the adaptive systems of phenotype determination and compare these predictions with empirical studies on cue integration.

Signal detection theory has influenced the behavioural sciences for over 50 years. The theory provides a simple equation that indicates numerous ‘intuitive’ results; e.g. prey should be more prone to take evasive action (in response to an ambiguous cue) if predators are more common. Here, we use analytical and computational models to show that, in numerous biological scenarios, the standard results of signal detection theory do not apply; more predators can result in prey being less responsive to such cues. The standard results need not apply when the probability of danger pertains not just to the present, but also to future decisions. We identify how responses to risk should depend on background mortality and autocorrelation, and that predictions in relation to animal welfare can also be reversed from the standard theory.

Most optimal foraging models assume that the foraging behaviour of small birds depends on a single state variable, their energy reserves in the form of stored fat. Here, we include a second state variable—the contents of the bird's gut—to investigate how a bird should optimise its gut size to minimise its long-term mortality, depending on the availability of food, the size of meal and the bird's digestive constraints. Our results show that (1) the current level of fat is never less important than gut contents in determining the bird's survival; (2) there exists a unique optimal gut size, which is determined by a trade-off between the energetic gains and costs of maintaining a large digestive system; (3) the optimal gut size increases as the bird's digestive cycle becomes slower, allowing the bird to store undigested food; (4) the critical environmental factor for determining the optimal gut size is the mass of food found in a successful foraging effort (“meal size”). We find that when the environment is harsh, it is optimal for the bird to maintain a gut that is larger than the size of a meal. However, the optimal size of the gut in rich environments exactly matches the meal size (i.e. the mass of food that the optimal gut can carry is exactly the mass of food that can be obtained in a successful foraging attempt).

In many situations across biology and economics, there is often one individual, or “agent,” that invests effort into a beneficial task and also one individual that, in contrast, foregoes the effort of investing, and instead simply exploits the efforts of another. What makes an individual choose to invest in production versus exploiting the efforts of another? If everyone invests, then exploitative strategies become very profitable; however if everyone is exploitative, there will be no investments to exploit. How does natural selection resolve this dilemma? What can economic institutions do to encourage investment? Can biologists and economists learn from the approach of each other’s discipline? This chapter outlines the commonalities and differences in approach of the two disciplines to the general problem of investment versus exploitation. It develops a model to encapsulate the general features of many scenarios (“games”) involving potential exploitation and explores the benefits of a unified approach, outlining current limitations and important areas for future investigation.

When given a choice between options with uncertain outcomes, people tend to be loss averse and risk averse regarding potential gains and risk prone regarding potential losses. These features of human decision making are captured by prospect theory (PT)—a hugely influential descriptive model of choice, but one which lacks any unifying principle that might explain why such preferences exist. Recently there have been several attempts to connect PT with risk-sensitive foraging theory (RSFT), a normative framework developed by evolutionary biologists to explain how animals should choose optimally when faced with uncertain foraging options. Although this seems a promising direction, here we show that current approaches are overly simplistic, and, despite their claims, they leave key features of PT unaccounted for. A common problem is the failure to appreciate the central concept of reproductive value in RSFT, which depends on the decision maker's current state and the particular situation it faces. Reproductive value provides a common currency in which decisions can be compared in a logical way. In contrast, existing models provide no rational justification for the reference state in PT. Evolutionary approaches to understanding PT preferences must confront this basic problem.

The primary function of lipid storage by animals is as an energy source for surviving periods without food. However, muscle and organ protein can be metabolised for energy, and empirical studies have shown that the onset of protein metabolism begins before the exhaustion of lipid reserves. Since protein tissues are important for reasons other than resisting starvation, the adaptive basis for this early onset is unclear. Here, we report the results of a model of the optimal proportion of energy to obtain from protein catabolism during a period without food of unpredictable duration. We assume either that the animal aims only to maximise the duration of survival or that it also has to take account of its future reproductive success given its state when the food supply recommences. In the latter case we find impressive quantitative agreement with observations on lean and obese penguins and rats. Analysis shows that this agreement breaks down if predation risk is insignificant, protein in the form of muscle is ineffective against predation, or there is no benefit to conserving lipid (e.g. for reproduction). This result implies that animals have not evolved to maximise their starvation resistance because doing so would leave them vulnerable when an interruption ends. Our model allows us to make several specific predictions concerning the relationship between the ecological pressures on animals and their starvation survival strategies.

In his paper ‘Parental investment: a prospective analysis’, Maynard Smith (1977, Animal Behaviour, 25, 1–9) introduced a game-theoretic approach to understanding the evolution of parental behaviour and addressed the broad issue of which sex should provide care for the young. This paper was important in that it introduced the use of game theory to the analysis of parental care. It also stimulated empirical work on care. We identify progress that has been made since the publication of the paper. In particular, although Model 2 of Maynard Smith (1977) has been used in several textbooks to explain the evolution of care, subsequent work has shown that this model is not built on a consistent view of how parental care influences future reproductive success through its effect on the sex ratio. Several models incorporate a consistent account in which opportunities to remate after desertion emerge from the analysis, rather than being specified in advance. More generally, it is not possible to consider parental care in isolation from factors such as paternity, mating preferences and mate choice behaviour. We identify various theoretical and empirical issues in the area of parental care research that we believe deserve further study if our understanding of care decisions is to advance. Taken together, the landmark paper of Maynard Smith (1977) stimulated new theoretical and empirical studies in parental care research and led to new insights into the behavioural interactions between males and females.

Moods can be regarded as fluctuating dispositions to make positive and negative evaluations. Developing an evolutionary approach to mood as an adaptive process, we consider the structure and function of such states in guiding behavioural decisions regarding the acquisition of resources and the avoidance of harm in different circumstances. We use a drift diffusion model of decision making to consider the information required by individuals to optimise decisions between two alternatives, such as whether to approach or withdraw from a stimulus that may be life enhancing or life threatening. We show that two dimensions of variation (expectation and preparedness) are sufficient for such optimal decisions to be made. These two dispositional dimensions enable individuals to maximize the overall benefits of behavioural decisions by modulating both the choice made (e.g., approach/withdraw) and decision speed. Such a structure is compatible with circumplex models of subjectively experienced mood and core affect, and provides testable hypotheses concerning the relationships that occur between valence and arousal components of mood in differing ecological niches. The paper is therefore a useful step toward being able to predict moods (and the effect of moods) using an optimality approach.

A fundamental issue in foraging theory is whether it is possible to find a simple currency that characterizes foraging behaviour. If such a currency exists, then it is tempting to argue that the selective forces that have shaped the evolution of foraging behaviour have been understood. We review previous work on currencies for the foraging behaviour of an animal that maximizes total energy gained. In many circumstances, it is optimal to maximize a suitably modified form of efficiency. We show how energy gain, predation and damage can be combined in a single currency based on reproductive value. We draw attention to the idea that hard work may have an adverse effect on an animal's condition. We develop a model of optimal foraging over a day when a forager's state consists of its energy reserves and its condition. Optimal foraging behaviour in our model depends on energy reserves, condition and time of day. The pattern of optimal behaviour depends strongly on assumptions about the probability that the forager is killed by a predator. If condition is important, no simple currency characterizes foraging behaviour, but behaviour can be understood in terms of the maximization of reproductive value. It may be optimal to adopt a foraging option that results in a rate of energy expenditure that is less than the rate associated with maximizing efficiency.

Addressing the obesity epidemic depends on a holistic understanding of the reasons that people become and maintain excessive fat. Theories about the causes of obesity usually focus proximately or evoke evolutionary mismatches, with minimal clinical value. There is potential for substantial progress by adapting strategic body mass regulation models from evolutionary ecology to human obesity by assessing the role of information.

Animals live in complex environments in which predation risk and food availability change over time. To deal with this variability and maximize their survival, animals should take into account how long current conditions may persist and the possible future conditions they may encounter. This should affect their foraging activity, and with it their vulnerability to predation across periods of good and bad conditions. Here we develop a comprehensive theory of optimal risk allocation that allows for environmental persistence and for fluctuations in food availability as well as predation risk. We show that it is the duration of good and bad periods, independent of each other, rather than the overall proportion of time exposed to each that is the most important factor affecting behavior. Risk allocation is most pronounced when conditions change frequently, and optimal foraging activity can either increase or decrease with increasing exposure to bad conditions. When food availability fluctuates rapidly, animals should forage more when food is abundant, whereas when food availability fluctuates slowly, they should forage more when food is scarce. We also show that survival can increase as variability in predation risk increases. Our work reveals that environmental persistence should profoundly influence behavior. Empirical studies of risk allocation should therefore carefully control the duration of both good and bad periods and consider manipulating food availability as well as predation risk.

A fundamental question relating to animal behaviour is how animals learn; in particular, how they come to associate stimuli with rewards. Numerous empirical findings can be explained by assuming that animals use some mechanism similar to the Rescorla-Wagner learning rule, which is a relatively simple and highly general method of updating the associative strength between different stimuli. However, the Rescorla-Wagner rule is often not optimal, which raises the question of why a rule with such properties should have evolved. We consider the evolution of learning rules in a simple environment where there exists an optimal rule of similar complexity to the Rescorla-Wagner rule. We show that because the Rescorla-Wagner rule is less sensitive to changes in its parameters than the optimal rule, there is a wider range of parameter values over which the rule structure is initially viable. Consequently, the Rescorla-Wagner rule can be favoured by natural selection, ahead of other rules which are more accurate.

The storage of lipids to buffer energy shortage may incur such costs as increased vulnerability to predation, and animals may be more muscular in order to reduce such costs. If muscle and lipid mass interact to determine survival, then both the muscularity and the adiposity of animals will be affected by factors such as predator density and food availability. Here we explore how adiposity and muscularity may depend on such factors. We confirm the expectation that adiposity should decrease with the risk of predation and increase with the frequency of interruptions to the food supply. More surprisingly, the predicted relationships between skeletal size, muscularity, and adiposity qualitatively depended on various factors: for example, adiposity should increase with foraging costs only for small animals and should decrease with total body mass if competition for food is intense. Furthermore, if the locomotive costs of carrying lipids are low, then adiposity should increase with body mass, whereas if ...

We investigate the optimal behaviour of an organism that is unable to obtain a reliable estimate of its mortality risk. In this case, natural selection will shape behaviour to be approximately optimal given the probability distribution of mortality risks in possible environments that the organism and its ancestors encountered. The mean of this distribution is the average mortality risk experienced by a randomly selected member of the species. We show that if an organism does not know the exact mortality risk, it should act as if the risk is less than the mean risk. This can be viewed as being optimistic. We argue that this effect is likely to be general.

Understanding the evolution of parental care is complicated by the occurrence of evolutionary conflicts of interest within the family, variation in the quality and state of family members, and repeated bouts of investment in a family of offspring. As a result, family members are expected to negotiate over care. We present a model for the resolution of sexual conflict in which parents negotiate over repeated bouts of care. Negotiation is mediated by parents deciding at the start of each bout how much care to give on the basis of the state (mass) of offspring, which reflects the amount of care previously received. The evolutionarily stable pattern of care depends on whether the parents care together for the whole family, or each cares alone for part of the divided family. When they care together, they provide less care in the first bout, more in the last bout, and less care overall, resulting in lower parental and offspring fitness. Our results emphasize that negotiation over parental care may occur as a means of avoiding exploitation owing to sexual conflict, even in the absence of variation in the quality of either sex of parent, and lead to a reduction in fitness.