Efficiency in evolutionary games: Darwin, Nash and the secret handshake

Emotions and courtship help bonded pairs cooperate, but emotional agents are vulnerable to deceit

Coordinated pair bonds are common in birds and also occur in many other taxa. How do animals solve the social dilemmas they face in coordinating with a partner? We developed an evolutionary model to explore this question, based on observations that a) neuroendocrine feedback provides emotional bookkeeping which is thought to play a key role in vertebrate social bonds and b) these bonds are developed and maintained via courtship interactions that include low-stakes social dilemmas. Using agent-based simulation, we found that emotional bookkeeping and courtship sustained cooperation in the iterated prisoner's dilemma in noisy environments, especially when combined. However, when deceitful defection was possible at low cost, courtship often increased cooperation, whereas emotional bookkeeping decreased it.

Explaining costly religious practices: credibility enhancing displays and signaling theories

This paper examines and contrasts two closely related evolutionary explanations in human behaviour: signalling theory, and the theory of Credibility Enhancing Displays (CREDs). Both have been proposed to explain costly, dangerous, or otherwise 'extravagant' social behaviours, especially in the context of religious belief and practice, and each have spawned significant lines of empirical research. However, the relationship between these two theoretical frameworks is unclear, and research which engages both of them (especially in systematic comparison) is largely absent. In this paper we seek to address this gap at the theoretical level, examining the core differences between the two approaches and prospects and conditions for future empirical testing. We clarify the dynamical and mechanistic bases of signalling and CREDs as explanatory models and contrast the previous uses to which they have been put in the human sciences. Because of idiosyncrasies regarding those uses (especially with signalling), several commonly supposed differences and comparative advantages are actually misleading and not in fact generalisable. We also show that signalling and CREDs theories as explanatory models are not interchangeable (or reducible to one another), because of deep structural differences. As we illustrate, the proposed causal networks of each theory are distinct, with important differences in the endogeneity of various phenomena within each model and their explanatory targets. As a result, they can be seen as complementary rather than in competition. We conclude by surveying the current state of the literature and identifying the differential predictions which could underpin more comprehensive empirical comparison in future research.

Evolution of conditional and unconditional commitment

We show that altruism can evolve as a signaling device designed to solve commitment problems in interactions with outside options. In a simple evolutionary game-theoretic model, uncertainty about agents' incentives to stay in a relationship can cause the relationship to collapse, because of a vicious circle where being skeptical about one's partner's commitment makes one even more likely to leave the relationship. When agents have the possibility to send costly gifts to each other, analytical modeling and agent-based simulations show that gift-giving can evolve as a credible signal of commitment, which decreases the likelihood of relationship dissolution. Interestingly, different conventions can determine the meaning of the signal conveyed by the gift. Exactly two kinds of conventions are evolutionarily stable: according to the first convention, an agent who sends a gift signals that he intends to stay in the relationship if and only if he also receives a gift; according to the second convention, a gift signals unconditional commitment.

Why cooperation is not running away

A growing number of experimental and theoretical studies show the importance of partner choice as a mechanism to promote the evolution of cooperation, especially in humans. In this paper, we focus on the question of the precise quantitative level of cooperation that should evolve under this mechanism. When individuals compete to be chosen by others, their level of investment in cooperation evolves towards higher values, a process called competitive altruism, or runaway cooperation. Using a classic adaptive dynamics model, we first show that when the cost of changing partner is low, this runaway process can lead to a profitless escalation of cooperation. In the extreme, when partner choice is entirely frictionless, cooperation even increases up to a level where its cost entirely cancels out its benefit. That is, at evolutionary equilibrium, individuals gain the same payoff than if they had not cooperated at all. Second, importing models from matching theory in economics we, however, show that when individuals can plastically modulate their choosiness in function of their own cooperation level, partner choice stops being a runaway competition to outbid others and becomes a competition to form the most optimal partnerships. In this case, when the cost of changing partner tends towards zero, partner choice leads to the evolution of the socially optimum level of cooperation. This last result could explain the observation that human cooperation seems to be often constrained by considerations of social efficiency.

Content in Simple Signalling Systems

Our understanding of communication and its evolution has advanced significantly through the study of simple models involving interacting senders and receivers of signals. Many theorists have thought that the resources of mathematical information theory are all that are needed to capture the meaning or content that is being communicated in these systems. However, the way theorists routinely talk about the models implicitly draws on a conception of content that is richer than bare informational content, especially in contexts where false content is important. This article shows that this concept can be made precise by defining a notion of functional content that captures the degree to which different states of the world are involved in stabilizing senders' and receivers' use of a signal at equilibrium. A series of case studies is used to contrast functional content with informational content, and to illustrate the explanatory role and limitations of this definition of functional content. 1 Introduction 2 Modelling Framework 3 Two Kinds of Content  3.1 Informational content  3.2 Functional content 4 Cases  4.1 Case 1: Simplest case  4.2 Case 2: Partial pooling  4.3 Case 3: Bottleneck  4.4 Case 4: Partial common interest  4.5 Case 5: Deception  4.6 Case 6: A further problem arising from divergent interests 5 Discussion Appendix .

Evolution reinforces cooperation with the emergence of self-recognition mechanisms: An empirical study of strategies in the Moran process for the iterated prisoner's dilemma

We present insights and empirical results from an extensive numerical study of the evolutionary dynamics of the iterated prisoner's dilemma. Fixation probabilities for Moran processes are obtained for all pairs of 164 different strategies including classics such as TitForTat, zero determinant strategies, and many more sophisticated strategies. Players with long memories and sophisticated behaviours outperform many strategies that perform well in a two player setting. Moreover we introduce several strategies trained with evolutionary algorithms to excel at the Moran process. These strategies are excellent invaders and resistors of invasion and in some cases naturally evolve handshaking mechanisms to resist invasion. The best invaders were those trained to maximize total payoff while the best resistors invoke handshake mechanisms. This suggests that while maximizing individual payoff can lead to the evolution of cooperation through invasion, the relatively weak invasion resistance of payoff maximizing strategies are not as evolutionarily stable as strategies employing handshake mechanisms.

When It's Good to Feel Bad: An Evolutionary Model of Guilt and Apology

We use techniques from evolutionary game theory to analyze the conditions under which guilt can provide individual fitness benefits, and so evolve. In particular, we focus on the benefits of guilty apology. We consider models where actors err in an iterated prisoner’s dilemma and have the option to apologize. Guilt either improves the trustworthiness of apology or imposes a cost on actors who apologize. We analyze the stability and likelihood of evolution of such a “guilt-prone” strategy against cooperators, defectors, grim triggers, and individuals who offer fake apologies, but continue to defect. We find that in evolutionary models guilty apology is more likely to evolve in cases where actors interact repeatedly over long periods of time, where the costs of apology are low or moderate, and where guilt is hard to fake. Researchers interested in naturalized ethics, and emotion researchers, can employ these results to assess the plausibility of fuller accounts of the evolution of guilt.

Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma

We present tournament results and several powerful strategies for the Iterated Prisoner's Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.

The coevolution of recognition and social behavior

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation.

Human Altruism – Proximate Patterns and Evolutionary Origins

Are people selfish or altruistic? Throughout history this question has been answered on the basis of much introspection and little evidence. It has been at the heart of many controversial debates in politics, science, and philosophy. Some of the most fundamental questions concerning our evolutionary origins, our social relations, and the organization of society are centered around issues of altruism and selfishness. Experimental evidence indicates that human altruism is a powerful force and unique in the animal world. However, there is much individual heterogeneity and the interaction between altruists and selfish individuals is key for understanding the evolutionary dynamics as well as the proximate patterns of human cooperation. Depending on the environment, a minority of altruists can force a majority of selfish individuals to cooperate or, conversely, a few egoists can induce a large number of altruists to defect. Current gene-based evolutionary theories cannot explain important patterns of human altruism pointing towards the need for theories of cultural evolution and gene-culture coevolution.

Evolutionary stability of discriminating behaviors with the presence of kin cheaters

Discriminating altruism, particularly kin altruism, is a fundamental mechanism of cooperation in nature. Altruistic behavior is not favored by evolution in the circumstances where there are "kin cheaters" that cannot be effectively identified. Using evolutionary iterated prisoner's dilemma, we deduce the condition for discriminating strategies to be evolutionarily stable and show that the competition between groups of different discriminating strategies restrains the percentage of kin cheaters. A discriminating strategy (DS) manages to cooperate with kin members and defect against non-kins by using an identification mechanism that includes a predetermined sequence of cooperation and defection. The opponent is identified as a kin member if it plays the same sequence. Otherwise, it is identified as non-kin, and defection will be triggered. Once the DS forms the majority of the population, any strategy that does not play the same sequence of moves will be expelled. We find that the competition between a variety of discriminating strategies favors a stable rate of cooperation and a low frequency of kin cheaters.

An evolutionary model of cooperation, fairness and altruistic punishment in public good games

We identify and explain the mechanisms that account for the emergence of fairness preferences and altruistic punishment in voluntary contribution mechanisms by combining an evolutionary perspective together with an expected utility model. We aim at filling a gap between the literature on the theory of evolution applied to cooperation and punishment, and the empirical findings from experimental economics. The approach is motivated by previous findings on other-regarding behavior, the co-evolution of culture, genes and social norms, as well as bounded rationality. Our first result reveals the emergence of two distinct evolutionary regimes that force agents to converge either to a defection state or to a state of coordination, depending on the predominant set of self- or other-regarding preferences. Our second result indicates that subjects in laboratory experiments of public goods games with punishment coordinate and punish defectors as a result of an aversion against disadvantageous inequitable outcomes. Our third finding identifies disadvantageous inequity aversion as evolutionary dominant and stable in a heterogeneous population of agents endowed initially only with purely self-regarding preferences. We validate our model using previously obtained results from three independently conducted experiments of public goods games with punishment.

Honest signalling with costly gambles

Costly signalling theory is commonly invoked as an explanation for how honest communication can be stable when interests conflict. However, the signal costs predicted by costly signalling models often turn out to be unrealistically high. These models generally assume that signal cost is determinate. Here, we consider the case where signal cost is instead stochastic. We examine both discrete and continuous signalling games and show that, under reasonable assumptions, stochasticity in signal costs can decrease the average cost at equilibrium for all individuals. This effect of stochasticity for decreasing signal costs is a fundamental mechanism that probably acts in a wide variety of circumstances.

Corpus-based intention recognition in cooperation dilemmas

Intention recognition is ubiquitous in most social interactions among humans and other primates. Despite this, the role of intention recognition in the emergence of cooperative actions remains elusive. Resorting to the tools of evolutionary game theory, herein we describe a computational model showing how intention recognition coevolves with cooperation in populations of self-regarding individuals. By equipping some individuals with the capacity of assessing the intentions of others in the course of a prototypical dilemma of cooperation-the repeated prisoner's dilemma-we show how intention recognition is favored by natural selection, opening a window of opportunity for cooperation to thrive. We introduce a new strategy (IR) that is able to assign an intention to the actions of opponents, on the basis of an acquired corpus consisting of possible plans achieving that intention, as well as to then make decisions on the basis of such recognized intentions. The success of IR is grounded on the free exploitation of unconditional cooperators while remaining robust against unconditional defectors. In addition, we show how intention recognizers do indeed prevail against the best-known successful strategies of iterated dilemmas of cooperation, even in the presence of errors and reduction of fitness associated with a small cognitive cost for performing intention recognition.

Co-evolution of pre-play signaling and cooperation

A finite-population dynamic evolutionary model is presented, which shows that increasing the individual capacity of sending pre-play signals (without any pre-defined meaning), opens a route for cooperation. The population dynamics leads individuals to discriminate between different signals and react accordingly to the signals received. The proportion of time that the population spends in different states can be calculated analytically. We show that increasing the number of different signals benefits cooperative strategies, illustrating how cooperators may take profit from a diverse signaling portfolio to forecast future behaviors and avoid being cheated by defectors.

Bargaining in biology?

Abstract This paper surveys the economic theory of bargaining with a view to applications in biology, using Roughgarden's recent Genial Gene as a case study in mistakes to be avoided.

A strategy with novel evolutionary features for the iterated prisoner's dilemma

In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.

The evolution of strong reciprocity: cooperation in heterogeneous populations

How do human groups maintain a high level of cooperation despite a low level of genetic relatedness among group members? We suggest that many humans have a predisposition to punish those who violate group-beneficial norms, even when this imposes a fitness cost on the punisher. Such altruistic punishment is widely observed to sustain high levels of cooperation in behavioral experiments and in natural settings. We offer a model of cooperation and punishment that we call STRONG RECIPROCITY: where members of a group benefit from mutual adherence to a social norm, strong reciprocators obey the norm and punish its violators, even though as a result they receive lower payoffs than other group members, such as selfish agents who violate the norm and do not punish, and pure cooperators who adhere to the norm but free-ride by never punishing. Our agent-based simulations show that, under assumptions approximating likely human environments over the 100000 years prior to the domestication of animals and plants, the proliferation of strong reciprocators when initially rare is highly likely, and that substantial frequencies of all three behavioral types can be sustained in a population. As a result, high levels of cooperation are sustained. Our results do not require that group members be related or that group extinctions occur.

Evolution of cooperation without reciprocity

A long-standing problem in biological and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. For many situations, kin selection is an adequate explanation, although kin-recognition may still be a problem. Explanations of cooperation between non-kin include continuing interactions that provide a shadow of the future (that is, the expectation of an ongoing relationship) that can sustain reciprocity, possibly supported by mechanisms to bias interactions such as embedding the agents in a two-dimensional space or other context-preserving networks. Another explanation, indirect reciprocity, applies when benevolence to one agent increases the chance of receiving help from others. Here we use computer simulations to show that cooperation can arise when agents donate to others who are sufficiently similar to themselves in some arbitrary characteristic. Such a characteristic, or 'tag', can be a marking, display, or other observable trait. Tag-based donation can lead to the emergence of cooperation among agents who have only rudimentary ability to detect environmental signals and, unlike models of direct or indirect reciprocity, no memory of past encounters is required.