arXiv subjects

Degang Wu

Publications and source records attributed to Degang Wu.

6 recordsLinked to original sources

arXiv2018

Analysis of Time Scale to Consensus in Voting Dynamics with more than Two Options

We generalize a binary majority-vote model on adaptive networks to its plurality-vote counterpart and analyze the time scale to consensus when voters are given more than two options. When opinions are uniformly distributed in the population of voters in the initial state, we find that the time scale to consensus is shorter than the binary vote model from both numerical simulations and mathematical analysis using the master equation for the three-state plurality-vote model. When intervention such as opinion conversion is allowed, as in the case of sudden change of mind of voter for any reason, the effort needed to push the fragmented three-opinion population in the thermodynamic limit to the consensus state, measured in minimal intervention cost, is less than that needed to push a polarized two-opinion population to the consensus state, when the degree ($p$) of homophily is less than 0.8. For finite system, the fragmented three-opinion population will spontaneously reach the consensus state, with faster time to consensus, compared to polarized two-opinion population, for a broad range of $p$.

physics.soc-ph↗

arXiv2016

Does Having More Options Mean Harder to Reach Consensus?

We generalize a binary majority-vote model on adaptive networks to a plurality-vote counterpart. When opinions are uniformly distributed in the population of voters in the initial state, it is found that having more available opinions in the initial state actually accelerate the time to consensus. In particular, we investigate the three-state plurality-vote model. While time to consensus in two state model scales exponentially with population size $N$, for finite-size system, there is a non-zero probability that either the population reaches the consensus state in a time that is very short and independent of $N$ (in the heterophily regime), or in a time that scales exponentially with $N$ but is still much faster than two-state model.

physics.soc-ph↗

arXiv2016

Winning in Sequential Parrondo Games by Players with Short-Term Memory

The original Parrondo game, denoted as AB3, contains two independent games: A and B. The winning or losing of A and B game is defined by the change of one unit of capital. Game A is a losing game if played continuously, with winning probability $p=0.5-ε$, where $ε=0.003$. Game B is also losing and it has two coins: a good coin with winning probability $p_g=0.75-ε$ is used if the player`s capital is not divisible by $3$, otherwise a bad coin with winning probability $p_b=0.1-ε$ is used. Parrondo paradox refers to the situation that the mixture of A and B game in a sequence leads to winning in the long run. The paradox can be resolved using Markov chain analysis. We extend this setting of Parrondo game to involve players with one-step memory. The player can win by switching his choice of A or B game in a Parrondo game sequence. If the player knows the identity of the game he plays and the state of his capital, then the player can win maximally. On the other hand, if the player does not know the nature of the game, then he is playing a (C,D) game, where either (C=A, D=B), or (C=B,D=A). For player with one-step memory playing the AB3 game, he can achieve the highest expected gain with switching probability equal to $3/4$ in the (C,D) game sequence. This result has been found first numerically and then proven analytically. Generalization to AB mod($M$) Parrondo game for other integer $M$ has been made for the general domain of parameters $p_b<p=0.5=p_A <p_g$. (please read the PDF file for full abstract)

physics.soc-ph↗

arXiv2014

Modeling the Heart as a Communication System

Electrical communication between cardiomyocytes can be perturbed during arrhythmia, but these perturbations are not captured by conventional electrocardiographic metrics. We developed a theoretical framework to quantify electrical communication using information theory metrics in 2-dimensional cell lattice models of cardiac excitation propagation. The time series generated by each cell was coarse-grained to 1 when excited or 0 when resting. The Shannon entropy for each cell was calculated from the time series during four clinically important heart rhythms: normal heartbeat, anatomical reentry, spiral reentry, and multiple reentry. We also used mutual information to perform spatial profiling of communication during these cardiac arrhythmias. We found that information sharing between cells was spatially heterogeneous. In addition, cardiac arrhythmia significantly impacted information sharing within the heart. Entropy localized the path of the drifting core of spiral reentry, which could be an optimal target of therapeutic ablation. We conclude that information theory metrics can quantitatively assess electrical communication among cardiomyocytes. The traditional concept of the heart as a functional syncytium sharing electrical information cannot predict altered entropy and information sharing during complex arrhythmia. Information theory metrics may find clinical application in the identification of rhythm-specific treatments which are currently unmet by traditional electrocardiographic techniques.

q-bio.QM↗

arXiv2014

Extended Parrondo's Game and Brownian Ratchets: Strong and Weak Parrondo Effect

Inspired by the flashing ratchet, Parrondo's game presents an apparently paradoxical situation. Parrondo's game consists of two individual games, game A and game B. Game A is a slightly losing coin-tossing game. Game B has two coins, with an integer parameter $M$. If the current cumulative capital (in discrete unit) is a multiple of $M$, an unfavorable coin $p_b$ is used, otherwise a favorable $p_g$ coin is used. Paradoxically, combination of game A and game B could lead to a winning game, which is the Parrondo effect. We extend the original Parrondo's game to include the possibility of $M$ being either $M_1$ or $M_2$. Also, we distinguish between strong Parrondo effect, i.e. two losing games combine to form a winning game, and weak Parrondo effect, i.e. two games combine to form a better-performing game. We find that when $M_2$ is not a multiple of $M_1$, the combination of $B(M_1)$ and $B(M_2)$ has strong and weak Parrondo effect for some subsets in the parameter space $(p_b,p_g)$, while there is neither strong nor weak effect when $M_2$ is a multiple of $M_1$. Furthermore, when $M_2$ is not a multiple of $M_1$, stochastic mixture of game A may cancel the strong and weak Parrondo effect. Following a discretization scheme in the literature of Parrondo's game, we establish a link between our extended Parrondo's game with the analysis of discrete Brownian ratchet. We find a relation between the Parrondo effect of our extended model to the macroscopic bias in a discrete ratchet. The slope of a ratchet potential can be mapped to the fair game condition in the extended model, so that under some conditions, the macroscopic bias in a discrete ratchet can provide a good predictor for the game performance of the extended model. On the other hand, our extended model suggests a design of a ratchet in which the potential is a mixture of two periodic potentials.

cond-mat.stat-mech↗

arXiv2011

Imitation with incomplete information in 2x2 games

Evolutionary game theory has been an important tool for describing economic and social behaviour for decades. Approximate mean value equations describing the time evolution of strategy concentrations can be derived from the players' microscopic update rules. We show that they can be generalized to a learning process. As an example, we compare a restricted imitation process, in which unused parts of the role model's meta-strategy are hidden from the imitator, with the widely used imitation rule that allows the imitator to adopt the entire meta-strategy of the role model. This change in imitation behaviour greatly affects dynamics and stationary states in the iterated prisoner dilemma. Particularly we find Grim Trigger to be a more successful strategy than Tit-For-Tat especially in the weak selection regime.

q-bio.PE↗