60,665 research outputs found
The Organization and Control of an Evolving Interdependent Population
Starting with Darwin, biologists have asked how populations evolve from a low
fitness state that is evolutionarily stable to a high fitness state that is
not. Specifically of interest is the emergence of cooperation and
multicellularity where the fitness of individuals often appears in conflict
with that of the population. Theories of social evolution and evolutionary game
theory have produced a number of fruitful results employing two-state two-body
frameworks. In this study we depart from this tradition and instead consider a
multi-player, multi-state evolutionary game, in which the fitness of an agent
is determined by its relationship to an arbitrary number of other agents. We
show that populations organize themselves in one of four distinct phases of
interdependence depending on one parameter, selection strength. Some of these
phases involve the formation of specialized large-scale structures. We then
describe how the evolution of independence can be manipulated through various
external perturbations.Comment: To download simulation code cf. article in Proceedings of the Royal
Society, Interfac
Kinship can hinder cooperation in heterogeneous populations
Kin selection and direct reciprocity are two most basic mechanisms for
promoting cooperation in human society. Generalizing the standard models of the
multi-player Prisoner's Dilemma and the Public Goods games for heterogeneous
populations, we study the effects of genetic relatedness on cooperation in the
context of repeated interactions. Two sets of interrelated results are
established: a set of analytical results focusing on the subgame perfect
equilibrium and a set of agent-based simulation results based on an
evolutionary game model. We show that in both cases increasing genetic
relatedness does not always facilitate cooperation. Specifically, kinship can
hinder the effectiveness of reciprocity in two ways. First, the condition for
sustaining cooperation through direct reciprocity is harder to satisfy when
relatedness increases in an intermediate range. Second, full cooperation is
impossible to sustain for a medium-high range of relatedness values. Moreover,
individuals with low cost-benefit ratios can end up with lower payoffs than
their groupmates with high cost-benefit ratios. Our results point to the
importance of explicitly accounting for within-population heterogeneity when
studying the evolution of cooperation
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning
Social dilemmas have been widely studied to explain how humans are able to
cooperate in society. Considerable effort has been invested in designing
artificial agents for social dilemmas that incorporate explicit agent
motivations that are chosen to favor coordinated or cooperative responses. The
prevalence of this general approach points towards the importance of achieving
an understanding of both an agent's internal design and external environment
dynamics that facilitate cooperative behavior. In this paper, we investigate
how partner selection can promote cooperative behavior between agents who are
trained to maximize a purely selfish objective function. Our experiments reveal
that agents trained with this dynamic learn a strategy that retaliates against
defectors while promoting cooperation with other agents resulting in a
prosocial society.Comment:
Emergence of social networks via direct and indirect reciprocity
Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals' degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to
strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores ("indirect reciprocity"), which is known to play an important role in many economic interactions. In
order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. "tit-for-tat") as well as indirect
reciprocity (helping strangers in order to increase one's reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which
are dynamic at the individual level but stable at the network level
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