3,061 research outputs found
Decisions and disease: a mechanism for the evolution of cooperation
In numerous contexts, individuals may decide whether they take actions to
mitigate the spread of disease, or not. Mitigating the spread of disease
requires an individual to change their routine behaviours to benefit others,
resulting in a 'disease dilemma' similar to the seminal prisoner's dilemma. In
the classical prisoner's dilemma, evolutionary game dynamics predict that all
individuals evolve to 'defect.' We have discovered that when the rate of
cooperation within a population is directly linked to the rate of spread of the
disease, cooperation evolves under certain conditions. For diseases which do
not confer immunity to recovered individuals, if the time scale at which
individuals receive information is sufficiently rapid compared to the time
scale at which the disease spreads, then cooperation emerges. Moreover, in the
limit as mitigation measures become increasingly effective, the disease can be
controlled, and the rate of infections tends to zero. Our model is based on
theoretical mathematics and therefore unconstrained to any single context. For
example, the disease spreading model considered here could also be used to
describe social and group dynamics. In this sense, we may have discovered a
fundamental and novel mechanism for the evolution of cooperation in a broad
sense
Effects of behavioral response and vaccination policy on epidemic spreading - an approach based on evolutionary-game dynamics
date of Acceptance: 23/06/2014 This work was supported by the National Natural Science Foundation of China (Grant Nos. 11331009, 11135001, 11105025). Y.-C.L. was supported by AFOSR under Grant No. FA9550-10-1-0083.Peer reviewedPublisher PD
Complex network analysis and nonlinear dynamics
This chapter aims at reviewing complex network and nonlinear dynamical
models and methods that were either developed for or applied to socioeconomic
issues, and pertinent to the theme of New Economic Geography. After an introduction
to the foundations of the field of complex networks, the present summary
introduces some applications of complex networks to economics, finance, epidemic
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issue
Evolutionary stable strategies in networked games: the influence of topology
Evolutionary game theory is used to model the evolution of competing
strategies in a population of players. Evolutionary stability of a strategy is
a dynamic equilibrium, in which any competing mutated strategy would be wiped
out from a population. If a strategy is weak evolutionarily stable, the
competing strategy may manage to survive within the network. Understanding the
network-related factors that affect the evolutionary stability of a strategy
would be critical in making accurate predictions about the behaviour of a
strategy in a real-world strategic decision making environment. In this work,
we evaluate the effect of network topology on the evolutionary stability of a
strategy. We focus on two well-known strategies known as the Zero-determinant
strategy and the Pavlov strategy. Zero-determinant strategies have been shown
to be evolutionarily unstable in a well-mixed population of players. We
identify that the Zero-determinant strategy may survive, and may even dominate
in a population of players connected through a non-homogeneous network. We
introduce the concept of `topological stability' to denote this phenomenon. We
argue that not only the network topology, but also the evolutionary process
applied and the initial distribution of strategies are critical in determining
the evolutionary stability of strategies. Further, we observe that topological
stability could affect other well-known strategies as well, such as the general
cooperator strategy and the cooperator strategy. Our observations suggest that
the variation of evolutionary stability due to topological stability of
strategies may be more prevalent in the social context of strategic evolution,
in comparison to the biological context
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