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In this paper, we consider a two-player two-strategy game with random payoffs in a population subdivided into $ d $ demes, each containing $ N $ individuals at the beginning of any given generation and experiencing local extinction and recolonization with some fixed probability $ m $ after reproduction and selection among offspring. Within each deme, offspring engage in random pairwise interactions, and the payoffs are assumed to have means and variances proportional to the inverse of the population size. By verifying the conditions given in Ethier and Nagylaki (1980) to approximate Markov chains with two timescales, we establish that the discrete-time evolutionary dynamics with $ Nd $ generations as unit of time converges to a continuous-time diffusion as $ d\rightarrow \infty $. The infinitesimal mean and variance of this diffusion are expressed in terms of the population-scaled means and variances of the payoffs besides identity-by-descent measures between offspring in the same deme in a neutral population. We show that the probability for a strategy to fix in the population starting from an initial frequency $ (Nd)^{-1} $ generally increases as the payoffs to that strategy exhibit less variability or the payoffs to the other strategy more variability. As a result, differences in variability can make this fixation probability for cooperation larger than the corresponding one for defection. As the deme-scaled extinction rate $ \nu = mN $ decreases for $ N $ large enough and $ m $ small enough, creating a higher level of identity among offspring within demes, the differences between the population-scaled variances of the payoffs for interacting offspring of different types increases this effect to a greater extent than the differences for interacting offspring of the same type. Furthermore, assuming no variability in the payoffs to cooperation, we explore in more details the relationships between the levels of variability in the payoffs to defection as functions of $ \nu $ that are necessary for the fixation probability of cooperation to be above $ (Nd)^{-1} $, the corresponding fixation probability of defection to be below $ (Nd)^{-1} $, and the former fixation probability to exceed the latter. [ABSTRACT FROM AUTHOR]

How cooperation evolves and persists widely remains an open problem for improving humanity across domains ranging from climate change to pandemic response. To shed light on how behavioural norms can resolve social dilemmas around cooperation, we present a formal mathematical model of individuals' decision making under general social norms, encompassing a variety of concerns and motivations an individual may have beyond simply maximizing their own payoff. Using the canonical Prisoner's dilemma, we compare four norms: compassion, universalizability, reciprocity and equity, to determine which, if any, social forces can facilitate the evolution of cooperation. We analyse our model through a variety of limiting cases, including weak selection, low mutation and large population sizes. This is complemented by computer simulations of population dynamics via a Fisher process, which confirm our theoretical results. We find that the first two norms lead to the emergence of cooperation in a wide range of games, but the latter two cannot. Owing to our framework's generality, it can be used to investigate many other norms, and how norms themselves evolve. Our work complements recent work on fair-minded learning dynamics and provides a useful bottom-up perspective into understanding the impact of top-down social norms on collective cooperative intelligence. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-player two-strategy game with random payoffs in a population subdivided into $ d $ demes, each containing $ N $ individuals at the beginning of any given generation and experiencing local extinction and recolonization with some fixed probability $ m $ after reproduction and selection among offspring. Within each deme, offspring engage in random pairwise interactions, and the payoffs are assumed to have means and variances proportional to the inverse of the population size. By verifying the conditions given in Ethier and Nagylaki (1980) to approximate Markov chains with two timescales, we establish that the discrete-time evolutionary dynamics with $ Nd $ generations as unit of time converges to a continuous-time diffusion as $ d\rightarrow \infty $. The infinitesimal mean and variance of this diffusion are expressed in terms of the population-scaled means and variances of the payoffs besides identity-by-descent measures between offspring in the same deme in a neutral population. We show that the probability for a strategy to fix in the population starting from an initial frequency $ (Nd)^{-1} $ generally increases as the payoffs to that strategy exhibit less variability or the payoffs to the other strategy more variability. As a result, differences in variability can make this fixation probability for cooperation larger than the corresponding one for defection. As the deme-scaled extinction rate $ \nu = mN $ decreases for $ N $ large enough and $ m $ small enough, creating a higher level of identity among offspring within demes, the differences between the population-scaled variances of the payoffs for interacting offspring of different types increases this effect to a greater extent than the differences for interacting offspring of the same type. Furthermore, assuming no variability in the payoffs to cooperation, we explore in more details the relationships between the levels of variability in the payoffs to defection as functions of $ \nu $ that are necessary for the fixation probability of cooperation to be above $ (Nd)^{-1} $, the corresponding fixation probability of defection to be below $ (Nd)^{-1} $, and the former fixation probability to exceed the latter. [ABSTRACT FROM AUTHOR]

Abstract Evolutionary theory is based on the conflict that arises when certain heritable variants out-compete others. Given this foundational conflict, a central question for evolutionary biologists concerns the presence of cooperation found throughout all levels of biological organization; from biochemical pathways to complex animal societies. Human behavior is often distinguished from other animal behavior by the presence of acts of cooperative behavior called altruism. Altruism is a cooperative act that penalizes the actor for actions that benefit the recipient. Any other form of cooperation, one that does not penalize the actor, is formally not considered altruistic. How can costly altruistic behavior evolve? This question was the basis for development of a web application tool incorporating a game theory model to investigate conditions affecting cooperative behavior. The game theory model described as Prisoner’s Dilemma incorporates acts of cooperation and non-cooperation (defection). Computer simulations of Prisoner’s Dilemma were developed and online applications were administered for five semesters at Georgia Gwinnett College, using two simulation environments referred to as Random and Non-random. Data collected from simulation runs were used to evaluate the effect of environment on student cooperative behavior and actively engage students in concepts associated with the evolution of cooperation and game theory. Results from student game play suggest group simulation environment played a significant role in the likelihood of observing cooperative behavior. Educational content and attitudinal surveys suggested that PD game play in the undergraduate evolution class at Georgia Gwinnett College improved student knowledge and self-confidence.

Examining the interaction between agents in a well-mixed population has been a prevalent area of research. Previous studies have emphasized the evolution of the impact of network architecture and payoff differences on agents' behavior in various games. There has been a recent surge in interest in incorporating popularity among researchers in this field. In this study, we employ a game theoretic approach to gain insight into the strategic behavior and decision-making processes of individuals in a network and how these decisions impact the diffusion of information when an individual's popularity is considered. The Fermi function is used to model the probability of information diffusion and the spread of influence within the network. We introduce a simulation module that models the dynamic process of evolutionary game theory in both synthetic and real-world networks, leveraging the Fermi update rule as a critical component. Furthermore, the implementation is modified to analyze the influence of each individual by incorporating metrics that account for individual popularity and its effect on the overall network dynamics. This modification allows for a more granular understanding of how individual agents contribute to the propagation of information and the development of cooperative behaviors. The simulation results provide valuable insight into the evolution of cooperative behavior in complex networks and hold potential for further exploration into various aspects of evolutionary game theory. [ABSTRACT FROM AUTHOR]

In a recent manuscript, Rogowski and Lange (J Bus Ethics 177:63–77, 2022) evaluate whether the prisoner's dilemma can be used as a legitimate framework with which to examine health-related economic ethics decisions. In this commentary, I build upon Rogowski and Lange (J Bus Ethics 177:63–77, 2022) using the original institutional economics literature to argue a more subtle, but critical point. Except in extreme circumstances, the use of the prisoner's dilemma does not qualify as a legitimate, comprehensive framework in which to address most health-related economic ethics problems. Indeed, the intentional characterization of the prisoner's dilemma as a robust health economic ethics framework is often used to mask the absence of a formal ethical framework. However, this distinction may provide a more concrete, and appropriate, justification to use the prisoner's dilemma to assess a wider array of health-related economic ethics problems. [ABSTRACT FROM AUTHOR]

The global geopolitical landscape is characterized by the rise of new powers and a shift toward multipolarity. This study examines the impact of multipolarity on international cooperation using an iterated game theory approach, particularly the classic prisoner's dilemma, extended to a multiplayer setting. This effort can be regarded as a preliminary study of hypothetical optimal global cooperation. The main hypothesis is that an increase in the number of large countries in the international system will lead to higher levels of cooperation. Our simulation approach confirmed this. Our findings extend to the conclusion that multipolarity, under appropriate cultural and value systems, can foster new economic development and fair competition. Furthermore, we emphasize the importance of evolving strategies and cooperative dynamics in a multipolar world, contributing to discussions on foreign economic policy integration, sustainability, and managing vulnerabilities among great powers. The study underscores the necessity of strategic frameworks and international institutions in promoting global stability and cooperation amidst the complexities of multipolarity. [ABSTRACT FROM AUTHOR]

Evolutionary theory is based on the conflict that arises when certain heritable variants out-compete others. Given this foundational conflict, a central question for evolutionary biologists concerns the presence of cooperation found throughout all levels of biological organization; from biochemical pathways to complex animal societies. Human behavior is often distinguished from other animal behavior by the presence of acts of cooperative behavior called altruism. Altruism is a cooperative act that penalizes the actor for actions that benefit the recipient. Any other form of cooperation, one that does not penalize the actor, is formally not considered altruistic. How can costly altruistic behavior evolve? This question was the basis for development of a web application tool incorporating a game theory model to investigate conditions affecting cooperative behavior. The game theory model described as Prisoner's Dilemma incorporates acts of cooperation and non-cooperation (defection). Computer simulations of Prisoner's Dilemma were developed and online applications were administered for five semesters at Georgia Gwinnett College, using two simulation environments referred to as Random and Non-random. Data collected from simulation runs were used to evaluate the effect of environment on student cooperative behavior and actively engage students in concepts associated with the evolution of cooperation and game theory. Results from student game play suggest group simulation environment played a significant role in the likelihood of observing cooperative behavior. Educational content and attitudinal surveys suggested that PD game play in the undergraduate evolution class at Georgia Gwinnett College improved student knowledge and self-confidence. [ABSTRACT FROM AUTHOR]

Some normative theories are self‐defeating. They tell us to respond to our situations in ways that bring about outcomes that are bad, given the aims of the theories, and which could have been avoided. Across a wide range of debates in ethics, decision theory, political philosophy, and formal epistemology, many philosophers treat the fact that a normative theory is self‐defeating as sufficient grounds for rejecting it. I argue that this widespread and consequential assumption is false. In particular, I argue that a theory can be self‐defeating and still internally consistent, action‐guiding, and suitable as a standard for criticism. [ABSTRACT FROM AUTHOR]

An economic interpretation of Epictetus's precept of 'Taking away aversion from all things not in our power' consists of extending the symmetric factor of the preference relation beyond its boundaries under non-ethical preferences, so as to yield indifference between outcomes differing only on things outside one's control. This paper examines the evolutionary dynamics of a population composed of Nash agents and Epictetusian agents matched randomly and interacting in the prisoner's dilemma game. It is shown that when the evolutionary dynamics is driven by material pay-offs, the Nash type is an ESS under perfect random matching, whereas either the Nash or the Epictetusian type can be an ESS under imperfect random matching. However, when selection is driven by utility pay-offs, the Nash type can never be an ESS, and the Epictetusian type is an ESS if the matching process exhibits a sufficiently high degree of assortativity. [ABSTRACT FROM AUTHOR]

Abstract This study aims to demonstrate that Large Language Models (LLMs) can empower research on the evolution of human behavior, based on evolutionary game theory, by using an evolutionary model positing that instructing LLMs with high-level psychological and cognitive character descriptions enables the simulation of human behavior choices in game-theoretical scenarios. As a first step towards this objective, this paper proposes an evolutionary model of personality traits related to cooperative behavior using a large language model. In the model, linguistic descriptions of personality traits related to cooperative behavior are used as genes. The deterministic strategies extracted from LLM that make behavioral decisions based on these personality traits are used as behavioral traits. The population is evolved according to selection based on average payoff and mutation of genes by asking LLM to slightly modify the parent gene toward cooperative or selfish. Through experiments and analyses, we clarify that such a model can indeed exhibit evolution of cooperative behavior based on the diverse and higher-order representation of personality traits. We also observed repeated intrusion of cooperative and selfish personality traits through changes in the expression of personality traits. The words that emerged in the evolved genes reflected the behavioral tendencies of their associated personalities in terms of semantics, thereby influencing individual behavior and, consequently, the evolutionary dynamics.

This paper introduces a novel fixed-time scheme for analyzing the convergence of the multiplayer continuous action-iterated dilemma (CAID). In contrast to conventional evolutionary game theory, which restricts players to binary strategies of cooperation or defection, CAID allows for continuous strategies and offers players a spectrum of choices. Additionally, two discount rates are incorporated in the strategy dynamics to reflect the imprecision in players' learning from strategic differences. The proposed algorithm ensures a fixed-time convergence of consensus between the player strategies. As a result, the settling time of consensus is independent of the initial strategy. Lyapunov stability theory is employed to assess convergence within a fixed time. Moreover, simulation results demonstrate the superiority of the proposed algorithm, converging more quickly and with fewer iterations than the recently established results.

Social decisions with monetary consequences are often accompanied with emotional consequences. Previous studies document a robust role of pre-play message communication in facilitating pro-sociality and cooperation. Yet, the effects of communicating emotional experiences in social interactions (particularly post-play) remain understudied. Here, we examine the value of a social environment where emotional expressions are shared post-play in contrast to a private environment where emotion exposure is absent. We develop an experimental design that facilitates emotion exposure and can be readily administered in or outside the laboratory. In this pre-registered online study, participants (N = 196) completed incentivized extensions of the Dictator Game (DG) and the Prisoners' Dilemma Game (PD). Participants learned to classify their emotional experiences on the arousal, valence, and dominance dimensions using non-verbal pictorial representations. Our experiment comprised both a within-subject and a between-subjectmanipulation: each participant completed a control condition (C, no emotional exposure) as well as an emotion exposure condition (Emotions), but the type of exposure varied between subjects (certain exposure, or Emotions-E, or probabilistic exposure, or Emotions-P). In all conditions, participants complete a one-shot DG and PD. We find that emotion exposure increases other-regarding behavior under both Emotions-E and Emotion-P conditions in the DG and under Emotions-E only in PD. Further, we find that demand for emotion exposure is hardly driven by the outcome of the social interaction (or the actions selected). We also document how empathic concern influence other-regarding behavior and how reports of emotional experiences vary across treatments and with the different outcomes of social interactions. Our results highlight the integral role of emotion exposure in social decision making. Environments that facilitate the communication of emotional experiences increase pro-sociality and encourage cooperation. [ABSTRACT FROM AUTHOR]

This study analyses behaviour in non‐zero‐sum finite multi‐stage games, particularly the Centipede Game. The classical Nash Equilibrium fails to explain empirical behaviour and intuitive logic and has therefore been challenged. This paper introduces the 'Pure Collective Equilibrium', or PCE, which describes the equilibrium reached when agents assess their utility not by their own payoffs but by the mean collective payoff of the team, as outlined by some team‐reasoning hypotheses. Classical behaviour and purely collective team reasoning then both represent special cases—the first in which agents and their counterparts are on the Nash Equilibrium path, and the second in which agents and their counterparts are purely collective team reasoners. It is argued that agents intentionally and consistently deviate from classical rationality in the Centipede Game, even in the absence of philanthropic altruists or error‐plagued populations. [ABSTRACT FROM AUTHOR]

We study the extension of classical games to the quantum domain, generated by the addition of one unitary strategy to two classical strategies of each player. The conditions that need to be met by unitary operations to ensure that the extended game is invariant with respect to the isomorphic transformations of the input game are determined. It has been shown that there are three types of these extensions; two of them are purely quantum. On the other hand, it has been demonstrated that the extensions of two versions of the same classical game by a unitary operator that does not meet these conditions may result in quantum games that are nonequivalent, e.g., having different Nash equilibria. We use the obtained results to extend the classical Prisoner's Dilemma game to a quantum game that has a unique Nash equilibrium closer to Pareto-optimal solutions than the original one. [ABSTRACT FROM AUTHOR]

Increasing research has focused on how ovarian hormones influence individual prosocial motivation and cooperation. However, most results remain ambiguous and contradictory. Here, we collected progesterone (PROG) and oestradiol from 62 healthy women with regular menstrual cycles to explore whether variations in ovarian hormones could flexibly change their cooperative preference according to their opponents' strategies in multiple rounds of a prisoner's dilemma (PD) game. Participants in different menstrual phases (32 in the follicular phase [FP] and 30 in the luteal phase [LP]) were asked to complete 20 rounds of PD games with each of three computer opponents holding different cooperative strategies. The results revealed that in PD games that did not require cooperation for increased outcomes, women in the LP (high PROG) reduced their cooperation rate more significantly than women in the FP (low PROG). In contrast, when the game design required reciprocity, simultaneously elevated levels of PROG and oestradiol predicted greater instances of participants choosing to cooperate. Furthermore, we found that elevated PROG levels accounted for women's elevated prosocial choices, regardless of the need to increase outcomes through cooperation. These results implied higher levels of PROG and oestradiol influence women's cooperative strategies resulting in increased social interactions.

Multi-agent systems appear in a wide variety of fields and there have been several studies on multi-agent reinforcement learning. Dilemma problems are typical classes of multi-agent problems. In these problems, the best policy for each agent differs from the best policy for the group of agents, which makes them difficult to solve. The purpose of this paper is to discuss multi-agent reinforcement learning methods for the dilemma problems. Firstly, we propose definitions of classes of dilemma problems in a general framework of reinforcement learning. We also discuss the relationship among our definitions and existing definitions and show the generality of our proposed definitions. Secondly, we propose a reinforcement learning method that can acquire the cooperative policies for the dilemma problems. In the method, each agent assumes the policies which the other agents would take, and learns through maximizing its return, expecting them to take the assumed policy. We apply the proposed method to the n-person iterative Prisoner’s dilemma (NIPD) and the Tragedy of the Commons which are typical examples of dilemma problems and investigate its performance. It is shown through the experiments that the proposed method makes it possible to learn the cooperative policies more reliably than the existing methods and possesses superior performance to them.

Over the past half-century, the impact of the prisoner’s dilemma has transcended its initial boundaries, influencing a broad spectrum of institutional studies and analyses of interdependent systems, such as collective action, public goods, governance of the commons, social norms, and social capital. Given this extensive influence, the latent fundamental flaws in this model can lead to misleading result for researchers and policymakers. As a descriptive–analytical research, the present study employed evolutionary game theory to examine the shortcomings of the iterative and evolutionary prisoner’s dilemma. It went on to introduce an alternative framework to explain the emergence and transformation of institutions. The critical discussion highlighted fundamental deficiencies of the prisoner’s dilemma manifested in four distinct levels of institutional analysis: the definition of institutions, identification of their function, explanation of their emergence, and description of their transformations. Consequently, the model is not capable of explaining institutional issues mentioned above. Addressing the role of uncertainty and considering the impact of small-world networks on payoff distribution, the second part of the study used a semi-parametric stag hunt matrix to model the cooperation problem. The results indicated that the non-spontaneous overlap between strong and weak ties can accumulate synergistic flows between content and structure, creating an environment conducive to institutionalizing capacity for achieving public goods on a large scale. Therefore, the process of overcoming social traps begins not with the punitive rules proposed by the prisoner’s dilemma, but with synergetic institutions that enhance the opportunity to discover common interests.IntroductionThe relation between institutions and development has been well recognized for quite some time. Numerous studies have delved into collective action, public goods, the governance of the commons, informal institutions, social capital, and the like. These areas serve as a common ground between two realms of economics: the theory of institutions and game theory. However, the majority of game theory studies are based on the assumption that the payoff distribution follows the prisoner’s dilemma. In the payoff matrix of the prisoner’s dilemma, the additional benefit of unilateral non-cooperative action results in a dominant strategy, preventing rational players from attaining the mutual benefits of cooperation. The central question in this literature is: how have human beings successfully established society and built civilization? There are three general solutions in the literature on the iterative and evolutionary prisoner’s dilemma. The initial solution, incorporating concepts like meta-game, folk theorem, reputation effect, and the norm of reciprocity, asserts that if the game repeats endlessly, cooperation becomes a foresight-driven choice. Contrary to this approach that suggests a form of spontaneous order, some sociologists argue that negative externalities of individual actions cannot be resolved by foresight or reciprocity, so the evolution of cooperation is only achievable through authoritative relations and punitive norms. The third solution, rooted in a biological assumption, contends that the prisoner’s dilemma can be solved through instincts without the need for rationality. The current article critically examined each of these approaches, and then introduced another payoff distribution matrix to explain how cooperation evolves in human society.Materials and MethodsThis study used a descriptive–analytical method as well as evolutionary game theory.Results and DiscussionThe article can be categorized into two main sections: the critical discussion and modeling. In the critical discussion, the results drawn from agent-based networks indicated that, even in the case of an endlessly repeating game, the emergence of the first cooperators would remain unexplained within the specific payoff matrix. Then, it was demonstrated that a substantial gap between theory and observation arises due to the inadequacy of the Prisoner’s dilemma in representing uncertainty. The discussion also addressed the contradiction stemming from overlooking the institutional context, particularly in defining reciprocity and explaining the emergence of norms. Moreover, the discussion highlighted some tautologies hidden in these definitions and explanations. Finally, three reasons were presented to underscore that the biological approach falls short of explaining how cooperation evolves in small-world networks.The discussions highlight fundamental deficiencies in the prisoner’s dilemma across four distinct levels of institutional analysis: the definition of institutions, identification of their function, explanation of their emergence, and description of their transformations. At the definitional level, the logic is fundamentally flawed due to its oversight of uncertainty as well as the mere emphasis on non-cooperative payoffs. Concerning the identification of function, the model predominantly stresses control and punishment, neglecting synergetic institutions that enhance opportunities to discover common interests, foster social synthesis on a large scale, lay the foundation for social contracts, and legitimize punitive rules. Moreover, an evolutionary model can only explain the endogenous emergence and transformation of institutions if it first demonstrates how a payoff matrix, where non-cooperation is a dominant strategy, can create a sustainable learning process. According to the results of agent-based networks, such a process cannot be explained in the prisoner’s dilemma.Nevertheless, evolutionary models, by sidestepping the assumptions of perfect rationality and information, possess a suitable capacity for studying interdependent systems and institutional analysis. Therefore, an alternative model can be formulated by implementing some reforms, including the utilization of the stag hunt matrix to account for uncertainty, incorporating small-world networks to better align with human societies, and ultimately introducing a semi-parametric payoff matrix to incorporate the influences of social structures.The results from the revised stag hunt model showed that the non-spontaneous overlap between strong and weak ties can accumulate synergistic flows, creating a conducive environment to institutionalizing capacity for achieving public goods on a large scale. Therefore, the process of overcoming social traps begins not with the free ride punishments proposed in the prisoner’s dilemma, but with synergetic institutions that enhance the opportunity to discover common interests.ConclusionThe prisoner’s dilemma describes a great challenge concerning micro-level conflicts of interest. However, using this model to explain the emergence and transformations of institutions is bound to yield highly misleading results for researchers and policymakers. Instead, the theoretical framework proposed in this study should find application across a broad spectrum of institutional studies, including analyzing the impacts of inclusive institutions on overcoming social traps, designing society-oriented methods for governing the commons, explaining the accumulation and modeling the measurement of social capital, and examining the social consequences of economic inequality.Keywords: Institutions, Cooperation Problem, Evolutionary Game Theory, Prisoner’s Dilemma, Stag HuntJEL Classification: C73, O43.

This paper introduces an application of complex Clifford algebra in a representation of the quantum prisoner's dilemma. The authors propose a novel modification of the Eisert–Lewenstein–Wilkens protocol to represent a repeated version of the quantum game. This repeated modification allows to embed entanglement into players' strategy sets and to see how players will operate with it. The apparatus of complex Clifford algebra enables an intuitive representation of the suggested protocol and efficient computation of the resulting payoff functions. The presented findings provide a new point of view on the interpretation of entanglement as a measure of information transition between rounds of the game. [ABSTRACT FROM AUTHOR]

We investigate the welfare effect of increasing competition in an anonymous two–sided matching market, where matched pairs play an infinitely repeated Prisoner's Dilemma. Higher matching efficiency is usually considered detrimental as it creates stronger incentives for defection. We point out, however, that a reduction in matching frictions also increases welfare because more agents find themselves in a cooperative relationship. We characterize the conditions for which increasing competition increases overall welfare. In particular, this is always the case when the incentives for defection are high. [ABSTRACT FROM AUTHOR]

The evolution of cooperation is a major question in the biological and behavioral sciences. While most theoretical studies model cooperation in the context of an isolated interaction (e.g., a Prisoner's Dilemma), humans live in heterogeneous social environments, characterized by large variations in fitness interdependence--the extent to which one's fitness is affected by others. Theoretical and experimental work indicates that humans can infer, and respond to, variations in interdependence. In a heterogeneous ancestral environment, these psychological mechanisms to infer fitness interdependence could have provided a selective advantage, allowing individuals to maximize their fitness by deciding when and with whom to cooperate. Yet, to date, the link between cognitive inference, variation in fitness interdependence, and cooperation remains unclear. Here we introduce a theoretical framework to study the evolution of inference and cooperation in heterogeneous social environments, where individuals experience interactions with varying levels of corresponding interests. Using a combination of evolutionary game theory and agent-based modeling, we model the evolution of adaptive agents, who incur a cost to infer interdependence, in populations of fixed-behavior agents who always cooperate or defect. Our results indicate that natural selection could promote the evolution of psychological mechanisms to infer fitness interdependence, provided that there is enough variation in fitness interdependence to offset the cost of inference. Under certain conditions, the fixation of adaptive agents results in higher levels of cooperation. This depends crucially on the type of inference performed and the features of the interdependence landscape. [ABSTRACT FROM AUTHOR]

Cultural group selection theorists propose that humans evolved prosocial preferences. These claims revolve largely around the centrality of punishment in cultural groups, which helped to eliminate free riders. The purpose of this paper is to explore whether distinguishing between free-riding as an action, and free riders as entities, undermines or supports this view. I develop three individual-based models of the Prisoner’s Dilemma. The first model shows that strong reciprocity removes overt freeriders from a population, and maintains a high rate of cooperation. In the second, I introduce individuals that mimic cooperative preferences, but who defect when they trick opponents into cooperating. I show that strong reciprocity is robust against this strategy, but not because individuals are replaced by strong reciprocators. Finally, I introduce a third strategy, covert mimicry, where some mimics may defect without detection. I draw attention to the problem highlighted in these models, which is that cooperation may be maintained in populations only because freeriders are not presented with the opportunity to defect. I discuss this problem in the context of cultural group selection and the human capacity for innovation, and suggest that hypotheses relying on prosocial preferences for maintaining cooperation require some revision. [ABSTRACT FROM AUTHOR]

Social decisions with monetary consequences are often accompanied with emotional consequences. Previous studies document a robust role of pre-play message communication in facilitating pro-sociality and cooperation. Yet, the effects of communicating emotional experiences in social interactions (particularly post-play) remain understudied. Here, we examine the value of a social environment where emotional expressions are shared post-play in contrast to a private environment where emotion exposure is absent. We develop an experimental design that facilitates emotion exposure and can be readily administered in or outside the laboratory. In this pre-registered online study, participants (N = 196) completed incentivized extensions of the Dictator Game (DG) and the Prisoners' Dilemma Game (PD). Participants learned to classify their emotional experiences on the arousal, valence, and dominance dimensions using non-verbal pictorial representations. Our experiment comprised both a within-subject and a between-subject manipulation: each participant completed a control condition (C, no emotional exposure) as well as an emotion exposure condition (Emotions), but the type of exposure varied between subjects (certain exposure, or Emotions-E, or probabilistic exposure, or Emotions-P). In all conditions, participants complete a one-shot DG and PD. We find that emotion exposure increases other-regarding behavior under both Emotions-E and Emotion-P conditions in the DG and under Emotions-E only in PD. Further, we find that demand for emotion exposure is hardly driven by the outcome of the social interaction (or the actions selected). We also document how empathic concern influence other-regarding behavior and how reports of emotional experiences vary across treatments and with the different outcomes of social interactions. Our results highlight the integral role of emotion exposure in social decision making. Environments that facilitate the communication of emotional experiences increase pro-sociality and encourage cooperation.

Inarguably, humans perform the richest plethora of prosocial behaviours in the animal kingdom, and these are important for understanding how humans navigate their social environment. The success and failure of strategies human players devise also have implications for determining long-term socio-economic/evolutionary fitness. Following the footsteps of Press and Dyson (2012), I implemented their evolutionary game-theoretic modelling from Iterated Prisoner’s Dilemma (a behavioural economic probe of interpersonal cooperation) and re-analysed already published data on human proposer behaviour in the Ultimatum Game (a behavioural economic probe of altruistic punishment) involving 50 human participants versus stochastic computerized opponents with prosocial and individualistic social value orientations. Although the results indicate that it is more likely to break cycles of mutual defection in ecosystems in which humans interact with individualistic opponents, analysis of social-economic fitness at the Markov stationary states suggested that this comes at an evolutionary cost. Overall, human players acted in a significantly more cooperative manner than their opponents, but they failed to overcome extortion from individualistic agents, risking ‘extinction’ in 70% of the cases. These findings demonstrate human players might be short-sighted, and social interactive decision strategies they devise while adjusting to different types of opponents may not be optimal in the long run.

A task of modeling individual mental features of a decision-maker using a Fourier holography setup is considered. The problem is considered for a situation when current conditions of decision-making contradict to the previously learned rule of decision-making logic modeled by the non-cooperative game "Prisoner's Dilemma". The approach to the problem is based on a hypothesis of the correlation between mental features and the properties of the neural network as a material carrier of intelligence. The 6f Fourier holography scheme of the resonant architecture is considered as a three-layer neural network implementing a neuro-physiologically motivated concept of the "excitation ring" proposed by A.M. Ivanitsky. We analytically assess the dependence of the validity limits of the classical total probability formula for a disjunction of incompatible events on the characteristics of low-frequency filters in holograms and the correlation radii of the training image of the basic decision rule. Analytical results are confirmed by results of the numerical simulation.

Could cooperation among strangers be facilitated by adaptations that use sparse information to accurately predict cooperative behaviour? We hypothesise that predictions are influenced by beliefs, descriptions, appearance and behavioural history available for first and second impressions. We also hypothesise that predictions improve when more information is available. We conducted a two-part study. First, we recorded thin-slice videos of university students just before their choices in a repeated Prisoner's Dilemma with matched partners. Second, a worldwide sample of raters evaluated each player using videos, photos, only gender labels or neither images nor labels. Raters guessed players’ first-round Prisoner's Dilemma choices and then their second-round choices after reviewing first-round behavioural histories. Our design allows us to investigate incremental effects of gender, appearance and behavioural history gleaned during first and second impressions. Predictions become more accurate and better-than-chance when gender, appearance or behavioural history is added. However, these effects are not incrementally cumulative. Predictions from treatments showing player appearance were no more accurate than those from treatments revealing gender labels and predictions from videos were no more accurate than those from photos. These results demonstrate how people accurately predict cooperation under sparse information conditions, helping explain why conditional cooperation is common among strangers.

In separate chambers, responding by two pairs of pigeons was reinforced under concurrent random‐ratio schedules of reinforcement. For each pair, the birds' schedules were coupled in such a manner that left‐ and right‐key reinforcement probabilities were determined by the key being pecked by the other pigeon of the pair. In this way, a reinforcement matrix, like that of the popular Prisoner's Dilemma game of game theory, was created. The responding of all subjects soon gravitated to the choice combination identified by the mathematician John Nash as the equilibrium of the Prisoner's Dilemma game. This was found both before and after reversal of contingencies on the keys. In a second experiment, with a single pair of pigeons, stimuli signaling the choice of the paired pigeon had little lasting effect: responding again gravitated to the game's equilibrium. The results affirm earlier findings, demonstrating that Skinner's principle of positive reinforcement, together with Nashian mathematics, entirely accounts for iterative game‐theoretic behavior. They extend these findings to the so‐called free operant: to schedules of reinforcement in which responding is not constrained by stimulus–response sequencing (i.e., a trials procedure). The coupled schedule of reinforcement introduced here offers significant promise for the experimental analysis of economic and social behaviors. [ABSTRACT FROM AUTHOR]

Why do leaders of organizations cooperate with players with whom they may never transact again? Such transactions can involve the incentives to exploit the other party because these interactions are not recurrent or embedded in networks. Yet, in a market economy, organizational actors learn to cooperate with strangers; otherwise, they risk closure from new ideas and business opportunities outside of their local community. With a large random sample of CEOs of manufacturing firms in the Yangzi River Delta region of China, we measured social norms using vignettes that describe hypothetical situations illustrating the social mechanisms of norm enforcement in respondents' local communities. Several years later, in a laboratory-in-the-field experiment, we asked the same participants to play a one-shot Prisoner's Dilemma (PD) game with a complete stranger. Our findings suggest that belief in the reliability of robust norm enforcement is positively associated with a higher probability of cooperation with strangers. To our knowledge, this mixed-method study is the first to explore the relationship between social norms and cooperation with strangers using a large sample of leaders of organizations outside the environment of the laboratory. Finally, to explore the generalizability of our behavioral findings, we experimentally manipulated norm vignettes and study the PD game in online experiments with managers in the Yangzi River Delta region. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: V. Nee gratefully acknowledges funding for research from the John Templeton Foundation (2005-2009 and 2015-2018), and from the College of Arts and Sciences at Cornell University. H. Holm thanks the Jan Wallander's and Tom Hedelius' Foundation for funding lab-in-the-field behavioral game. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1521. [ABSTRACT FROM AUTHOR]

David Lewis argued that Newcomb's Problem and the Prisoner's Dilemma are "one and the same problem" or, to be more precise, that the Prisoner's Dilemma is nothing else than "two Newcomb problems side by side" (Lewis Philosophy and Public Affairs 8:235–240, 1979: 235). It has been objected that his argument fails to take into account certain epistemic asymmetries which undermine the one-problem thesis. Sobel (1985) acknowledges that many tokens satisfy the structural requirements of both problems, while questioning the generality of the thesis. Bermúdez (Analysis 73:423–429, 2013), on the other hand, argues that there is a deeper structural conflict between the two problems in the sense that the epistemic requirements that give Newcomb's Problem its force are precisely those that prevent it from being a Prisoner's Dilemma. I argue that the epistemic asymmetry objections raised by Sobel and Bermúdez fail to undermine the one-problem thesis. A different type of objection raised by Bermúdez, one which relies on the contrast between parametric and strategic choices, fares no better. Although NP is a problem that has been primarily studied for its implications to decision theory, it contains enough game theoretic elements to justify the claim that a juxtaposition of two such problems can be thought of as a strategic game. [ABSTRACT FROM AUTHOR]

Many real-life examples—from interpersonal rivalries to international conflicts—suggest that people actively engage in competitive behavior even when it is negative sum (benefiting the self at a greater cost to others). This often leads to loss spirals where everyone—including the winner—ends up losing. Our research seeks to understand the psychology of such negative-sum competition in a controlled setting. To do so, we introduce an experimental paradigm in which paired participants have the option to repeatedly perform a behavior that causes a relatively small gain for the self and a larger loss to the other. Although they have the freedom not to engage in the behavior, most participants actively do so and incur substantial losses. We propose that an important reason behind the phenomena is shallow thinking—focusing on the immediate benefit to the self while overlooking the downstream consequences of how the behavior will influence their counterparts' actions. In support of the proposition, we find that participants are less likely to engage in negative-sum behavior, if they are advised to consider the downstream consequences of their actions, or if they are put in a less frenzied decision environment, which facilitates deeper thinking (acting in discrete vs. continuous time). We discuss how our results differ from prior findings and the implications of our research for mitigating negative-sum competition and loss spirals in real life. [ABSTRACT FROM AUTHOR]

Community groups are taking initiatives to adapt to a changing climate. These organizations differ from businesses and governments by being non-profit, often informal, resource limited, and reliant on volunteer labor. How these organizations facilitate collective action is not well known, especially since they do not necessarily solve common pool resource governance, but rather improve common pool resources through collective action. In fact, at first glance, community groups seem to not have the means for solving collective action problems used routinely in industry and government, such as paying people for cooperation or punishing them for lack of it. This article investigates how community groups solve collective action problems though data gathered across 25 organizations in three sites – Sitka, Alaska, USA; Toco, Trinidad; and a global site of distributed citizen science organizations. We found that community groups used positive reinforcement methods common to industry and used little punishment. Groups also engaged in mechanisms for collective action, such as relying on altruistic contributions by few individuals, that generally are not considered commonplace in businesses and governments. We conclude by discussing implications from this study for collective action theory and for how policymakers might learn from community groups to address climate change. [ABSTRACT FROM AUTHOR]