Emergence Of Altruism Research Articles

A reinforcement learning approach to explore the role of social expectations in altruistic behavior

While altruism has been studied from a variety of standpoints, none of them has proven sufficient to explain the richness of nuances detected in experimentally observed altruistic behavior. On the other hand, the recent success of behavioral economics in linking expectation formation to key behaviors in complex societies hints to social expectations having a key role in the emergence of altruism. This paper proposes an agent-based model based upon the Bush–Mosteller reinforcement learning algorithm in which agents, subject to stimuli derived from empirical and normative expectations, update their aspirations (and, consequently, their future cooperative behavior) after playing successive rounds of the Dictator Game. The results of the model are compared with experimental results. Such comparison suggests that a stimuli model based on empirical and normative expectations, such as the one presented in this work, has considerable potential for capturing the cognitive-behavioral processes that shape decision-making in contexts where cooperative behavior is relevant.

Interdependent preferences and endogenous reciprocity

This paper employs an indirect approach to formally examine the evolutionary stability of interdependent preferences when players randomly engage in pairwise interactions. Following the model specification for altruism and spitefulness in experiments proposed by Levine (1998), we also explore the stability of reciprocity and reciprocal preferences. In particular, we study how individuals equipped with intrinsic preferences such as altruism, selfishness or spitefulness adjust their behavior depending on who they interact with. The key aspect of our method is that behavioral preferences are choice variables that optimally evolve, accounting for strategic interaction. Our model predicts that in a specific economic framework characterized by negative externalities and strategic substitutes, there is a continuum of evolutionary stable interdependent preference profiles: At least one player behaves spitefully, and at most one acts selfishly. The emergence of altruism as an evolutionarily stable preference crucially depends on how large the support for preferences is. When players have reciprocal preferences, altruism might arise even in meetings where one player is intrinsically spiteful, but not necessarily from the intrinsically altruistic player.

Self‐Organized Temporal Criticality: Bottom‐Up Resilience versus Top‐Down Vulnerability

We propose a social model of spontaneous self‐organization generating criticality and resilience, called Self‐Organized Temporal Criticality (SOTC). The criticality‐induced long‐range correlation favors the societal benefit and can be interpreted as the social system becoming cognizant of the fact that altruism generates societal benefit. We show that when the spontaneous bottom‐up emergence of altruism is replaced by a top‐down process, mimicking the leadership of an elite, the crucial events favoring the system’s resilience are turned into collapses, corresponding to the falls of the leading elites. We also show with numerical simulation that the top‐down SOTC lacks the resilience of the bottom‐up SOTC. We propose this theoretical model to contribute to the mathematical foundation of theoretical sociology illustrated in 1901 by Pareto to explain the rise and fall of elites.

The emergence of altruism as a social norm

Expectations, exerting influence through social norms, are a very strong candidate to explain how complex societies function. In the Dictator game (DG), people expect generous behavior from others even if they cannot enforce any sharing of the pie. Here we assume that people donate following their expectations, and that they update their expectations after playing a DG by reinforcement learning to construct a model that explains the main experimental results in the DG. Full agreement with the experimental results is reached when some degree of mismatch between expectations and donations is added into the model. These results are robust against the presence of envious agents, but affected if we introduce selfish agents that do not update their expectations. Our results point to social norms being on the basis of the generous behavior observed in the DG and also to the wide applicability of reinforcement learning to explain many strategic interactions.

Like family: Network coevolution and the emergence of altruism

ABSTRACTAltruism is difficult to explain in an evolutionary context—the mechanisms of selection appear to favor selfishness. Existing scholarly work addressing this puzzle relies on structural and behavioral assumptions that severely limit generalizability. The model presented in this article circumvents the need for such assumptions by incorporating an evolving network component to natural selection. Through a process of coevolution of individual and relational traits, many of the exogenous assumptions of previous models of the evolution of cooperation are realized endogenously within a simulated population. Such endogeneity allows a more precise examination of conditions under which cooperation arises, but also a clearer understanding of how those conditions themselves arise. The model is specified analytically, and interpretation is carried out on simulated outcomes.

The spatial Ultimatum game revisited

We revisit the issue of the emergence of fair behavior in the framework of the spatial Ultimatum game, adding many important results and insights to the pioneering work by Page et al. [2000. The spatial Ultimatum game. Proc. R. Soc. London B 267, 2177], who showed in a specific example that on a two-dimensional setup evolution may lead to strategies with some degree of fairness. Within this spatial framework, we carry out a thorough simulation study and show that the emergence of altruism is a very generic phenomenon whose details depend on the dynamics considered. A very frequent feature is the spontaneous emergence and fixation of quasiempathetic individuals, whose offers are very close to their acceptance thresholds. We present analytical arguments that allow an understanding of our results and give insights on the manner in which local effects in evolution may lead to such non-rational or apparently maladaptive behaviors.

Networks with dispersed degrees save stable coexistence of species in cyclic competition

Coexistence of individuals with different species or phenotypes is often found in nature in spite of competition between them. Stable coexistence of multiple types of individuals have implications for maintenance of ecological biodiversity and emergence of altruism in society, to name a few. Various mechanisms of coexistence including spatial structure of populations, heterogeneous individuals, and heterogeneous environments, have been proposed. In reality, individuals disperse and interact on complex networks. We examine how heterogeneous degree distributions of networks influence coexistence, focusing on models of cyclically competing species. We show analytically and numerically that heterogeneity in degree distributions promotes stable coexistence.

Altruism and antagonistic pleiotropy in Penna ageing model

The Penna ageing model is based on mutation accumulation theory. We show that it also allows for self-organization of antagonistic pleiotropy which helps at young age at the expense of old age. This can be interpreted as emergence of altruism.

The importance of social learning in the evolution of cooperation and communication

The new emphasis that Rachlin gives to social learning is welcome, because its role in the emergence of altruism and communication is often underestimated. However, Rachlin's account is underspecified and therefore not satisfactory. I argue that recent computational models of the evolution of language show an alternative approach and present an appealing perspective on the evolution and acquisition of a complex, altruistic behavior like syntactic language.