Jae-Suk Yang

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: “For to all those who have, more will be given.” Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew “rich get richer” effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his or her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.

Information flow between stock indices

Using transfer entropy, we observed the strength and direction of information flow between stock indices. We uncovered that the biggest source of information flow is America, while most receivers are in the Asia/Pacific region. According to the minimum spanning tree, the Standard and Poors 500 Index (GSPC) is located at the focal point of the information source for world stock markets.

Information flow between composite stock index and individual stocks

We investigate the strength and the direction of information transfer in the US stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the index of the market.

Executive Compensation, Fat Cats, and Best Athletes

Income gains in the top 1 percent are the primary cause for the rapid growth in U.S. inequality since the late 1970s. Managers and executives of firms account for a large proportion of these top earners. Chief executive officers (CEOs), in particular, have seen their compensation increase faster than the growth in firm size. We propose that changes in the macro patterns of the distribution of CEO compensation resulted from a process of diffusion within localized networks, propagating higher pay among corporate executives. We compare three possible explanations for diffusion: director board interlocks, peer groups, and educational networks. The statistical results indicate that corporate director networks facilitate social comparisons that generate the observed pay patterns. Peer and education network effects do not survive a novel endogeneity test that we execute. A key implication is that local diffusion through executive network structures partially explains the changes in macro patterns of income distribution found in the inequality data.

The market efficiency in the stock markets

Abstract.We study the temporal evolutions of three stock markets; Standard and Poors 500 index, Nikkei 225 Stock Average, and the Korea Composite Stock Price Index. We observe that the probability density function of the log-return has a fat tail but the tail index has been increasing continuously in recent years. We have also found that the variance of the autocorrelation function, the scaling exponent of the standard deviation, and the statistical complexity decrease, but that the entropy density increases as time goes over time. We introduce a modified microscopic spin model and simulate the model to confirm such increasing and decreasing tendencies in statistical quantities. These findings indicate that these three stock markets are becoming more efficient.

Complexity analysis of the stock market

We study the complexity of the stock market by constructing e-machines of Standard and Poors 500 index from February 1983 to April 2006 and by measuring the statistical complexities. It is found that both the statistical complexity and the number of causal states of constructed e-machines have decreased for last 20 years and that the average memory length needed to predict the future optimally has become shorter. These results support that the information is delivered to the economic agents and applied to the market prices more rapidly in year 2006 than in year 1983.

Application of Wang–Landau sampling to a protein model using SMMP

Abstract We apply Wang–Landau sampling to the continuous energy model of a protein using Simple Molecular Mechanics for Protein (SMMP). We also tried to parallelize the Wang–Landau sampling method and compare our results with previous results derived from the multicanonical and parallel tempering methods.

Agent-Based Approach for Revitalization Strategy of Knowledge Ecosystem

We conceptualize knowledge as an intellectual infrastructure that helps to maximize efficiency from the viewpoint of ecosystems. The knowledge ecosystem includes people and organizations that participate in the production, distribution, and consumption of this knowledge and information, as well as interactions between participants. We built the agent-based computational model of the ecosystem and induced seven key revitalization conditions from the perspective of complexity science and ecosystem management. We analyzed the effects of these conditions on the knowledge ecosystem based by the simulation of the agent-based model. Our results suggest that the proper implementation of seven revitalization conditions, focusing on the recovery of the positive feedback loop in the knowledge ecosystem, is crucial for sustainable development.

Optimization of consensus time by combining the voter and the majority voter models on scale-free networks

We introduce a spin model combining the majority voter model with probability p and the voter model with probability 1-p and then measure its consensus time on scale-free networks with various degree exponents γ. We find that consensus time depends on both p and γ. When all spins follow either the voter model or the majority voter model, it takes much greater time to reach consensus. On the other hand, when spins may alternate between the majority voter model and the voter model, consensus time is shortened. We find via numerical calculation that the optimized ratio to minimize consensus time is around p=0.72.

Complexity and entropy density analysis of the Korean stock market

In this paper, we studied complexity and entropy density of stock market by modeling ε -machine of Korean Composition Stock Price Index (KOSPI) from year 1992 to 2003 using causal-state splitting reconstruction (CSSR) algorithm.