34 research outputs found
If players are sparse social dilemmas are too: Importance of percolation for evolution of cooperation
Spatial reciprocity is a well known tour de force of cooperation promotion. A
thorough understanding of the effects of different population densities is
therefore crucial. Here we study the evolution of cooperation in social
dilemmas on different interaction graphs with a certain fraction of vacant
nodes. We find that sparsity may favor the resolution of social dilemmas,
especially if the population density is close to the percolation threshold of
the underlying graph. Regardless of the type of the governing social dilemma as
well as particularities of the interaction graph, we show that under pairwise
imitation the percolation threshold is a universal indicator of how dense the
occupancy ought to be for cooperation to be optimally promoted. We also
demonstrate that myopic updating, due to the lack of efficient spread of
information via imitation, renders the reported mechanism dysfunctional, which
in turn further strengthens its foundations.Comment: 6 two-column pages, 5 figures; accepted for publication in Scientific
Reports [related work available at http://arxiv.org/abs/1205.0541
Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
We study the evolution of cooperation among selfish individuals in the
stochastic strategy spatial prisoner's dilemma game. We equip players with the
particle swarm optimization technique, and find that it may lead to highly
cooperative states even if the temptations to defect are strong. The concept of
particle swarm optimization was originally introduced within a simple model of
social dynamics that can describe the formation of a swarm, i.e., analogous to
a swarm of bees searching for a food source. Essentially, particle swarm
optimization foresees changes in the velocity profile of each player, such that
the best locations are targeted and eventually occupied. In our case, each
player keeps track of the highest payoff attained within a local topological
neighborhood and its individual highest payoff. Thus, players make use of their
own memory that keeps score of the most profitable strategy in previous
actions, as well as use of the knowledge gained by the swarm as a whole, to
find the best available strategy for themselves and the society. Following
extensive simulations of this setup, we find a significant increase in the
level of cooperation for a wide range of parameters, and also a full resolution
of the prisoner's dilemma. We also demonstrate extreme efficiency of the
optimization algorithm when dealing with environments that strongly favor the
proliferation of defection, which in turn suggests that swarming could be an
important phenomenon by means of which cooperation can be sustained even under
highly unfavorable conditions. We thus present an alternative way of
understanding the evolution of cooperative behavior and its ubiquitous presence
in nature, and we hope that this study will be inspirational for future efforts
aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON
Different reactions to adverse neighborhoods in games of cooperation
In social dilemmas, cooperation among randomly interacting individuals is
often difficult to achieve. The situation changes if interactions take place in
a network where the network structure jointly evolves with the behavioral
strategies of the interacting individuals. In particular, cooperation can be
stabilized if individuals tend to cut interaction links when facing adverse
neighborhoods. Here we consider two different types of reaction to adverse
neighborhoods, and all possible mixtures between these reactions. When faced
with a gloomy outlook, players can either choose to cut and rewire some of
their links to other individuals, or they can migrate to another location and
establish new links in the new local neighborhood. We find that in general
local rewiring is more favorable for the evolution of cooperation than
emigration from adverse neighborhoods. Rewiring helps to maintain the diversity
in the degree distribution of players and favors the spontaneous emergence of
cooperative clusters. Both properties are known to favor the evolution of
cooperation on networks. Interestingly, a mixture of migration and rewiring is
even more favorable for the evolution of cooperation than rewiring on its own.
While most models only consider a single type of reaction to adverse
neighborhoods, the coexistence of several such reactions may actually be an
optimal setting for the evolution of cooperation.Comment: 12 pages, 5 figures; accepted for publication in PLoS ON
Evolution of Cooperation Driven by Reputation-Based Migration
How cooperation emerges and is stabilized has been a puzzling problem to biologists and sociologists since Darwin. One of the possible answers to this problem lies in the mobility patterns. These mobility patterns in previous works are either random-like or driven by payoff-related properties such as fitness, aspiration, or expectation. Here we address another force which drives us to move from place to place: reputation. To this end, we propose a reputation-based model to explore the effect of migration on cooperation in the contest of the prisoner's dilemma. In this model, individuals earn their reputation scores through previous cooperative behaviors. An individual tends to migrate to a new place if he has a neighborhood of low reputation. We show that cooperation is promoted for relatively large population density and not very large temptation to defect. A higher mobility sensitivity to reputation is always better for cooperation. A longer reputation memory favors cooperation, provided that the corresponding mobility sensitivity to reputation is strong enough. The microscopic perception of the effect of this mechanism is also given. Our results may shed some light on the role played by migration in the emergence and persistence of cooperation
An Integrated Disease/Pharmacokinetic/Pharmacodynamic Model Suggests Improved Interleukin-21 Regimens Validated Prospectively for Mouse Solid Cancers
Interleukin (IL)-21 is an attractive antitumor agent with potent immunomodulatory functions. Yet thus far, the cytokine has yielded only partial responses in solid cancer patients, and conditions for beneficial IL-21 immunotherapy remain elusive. The current work aims to identify clinically-relevant IL-21 regimens with enhanced efficacy, based on mathematical modeling of long-term antitumor responses. For this purpose, pharmacokinetic (PK) and pharmacodynamic (PD) data were acquired from a preclinical study applying systemic IL-21 therapy in murine solid cancers. We developed an integrated disease/PK/PD model for the IL-21 anticancer response, and calibrated it using selected “training” data. The accuracy of the model was verified retrospectively under diverse IL-21 treatment settings, by comparing its predictions to independent “validation” data in melanoma and renal cell carcinoma-challenged mice (R2>0.90). Simulations of the verified model surfaced important therapeutic insights: (1) Fractionating the standard daily regimen (50 µg/dose) into a twice daily schedule (25 µg/dose) is advantageous, yielding a significantly lower tumor mass (45% decrease); (2) A low-dose (12 µg/day) regimen exerts a response similar to that obtained under the 50 µg/day treatment, suggestive of an equally efficacious dose with potentially reduced toxicity. Subsequent experiments in melanoma-bearing mice corroborated both of these predictions with high precision (R2>0.89), thus validating the model also prospectively in vivo. Thus, the confirmed PK/PD model rationalizes IL-21 therapy, and pinpoints improved clinically-feasible treatment schedules. Our analysis demonstrates the value of employing mathematical modeling and in silico-guided design of solid tumor immunotherapy in the clinic
Complex Transition to Cooperative Behavior in a Structured Population Model
Cooperation plays an important role in the evolution of species and human societies. The understanding of the emergence and persistence of cooperation in those systems is a fascinating and fundamental question. Many mechanisms were extensively studied and proposed as supporting cooperation. The current work addresses the role of migration for the maintenance of cooperation in structured populations. This problem is investigated in an evolutionary perspective through the prisoner's dilemma game paradigm. It is found that migration and structure play an essential role in the evolution of the cooperative behavior. The possible outcomes of the model are extinction of the entire population, dominance of the cooperative strategy and coexistence between cooperators and defectors. The coexistence phase is obtained in the range of large migration rates. It is also verified the existence of a critical level of structuring beyond that cooperation is always likely. In resume, we conclude that the increase in the number of demes as well as in the migration rate favor the fixation of the cooperative behavior
Effects of adaptive degrees of trust on coevolution of quantum strategies on scale-free networks
We study the impact of adaptive degrees of trust on the evolution of cooperation in the quantum prisoner's dilemma game. In addition to the strategies, links between players are also subject to evolution. Starting with a scale-free interaction network, players adjust trust towards their neighbors based on received payoffs. The latter governs the strategy adoption process, while trust governs the rewiring of links. As soon as the degree of trust towards a neighbor drops to zero, the link is rewired to another randomly chosen player within the network. We find that for small temptations to defect cooperators always dominate, while for intermediate and strong temptations a single quantum strategy is able to outperform all other strategies. In general, reciprocal trust remains within close relationships and favors the dominance of a single strategy. Due to coevolution, the power-law degree distributions transform to Poisson distributions.Qiang Li, Minyou Chen, Matjaz Perc, Azhar Iqbal, & Derek Abbot