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Research on the Spatial Structure and Dynamics of
Socio-Economic Systems
by
Jian Gao
Submitted to the School of Computer Science and Engineering in partial fulfillment of
the requirements for the degree of Doctor of Philosophy in Computer Software and
Theory at the University of Electronic Science and Technology of China
Abstract
Socio-economic systems are an important branch of complex systems, which involves the
complex interactions between people's economic activities and the social environment in
which they live. With the constant change of cognition and behavior, people's subjective
decision-making process greatly affects the operation of socio-economic systems. To
accurately and timely perceive socioeconomic situation and to reveal and understand the
law of socioeconomic development have great theoretical and practical values. Revealing
the status of socioeconomic development in many aspects and predicting the development
trends with desirable accuracy can greatly help to guide socioeconomic decision-making.
Uncovering the socioeconomic behavioral patterns of individuals can contribute to
gradually realizing predictive management. Quantifying the macro socioeconomic
structure can help to explore the path of economic development. How to effectively analyze
the structure and evolution of socio-economic systems is an important scientific issue in
the interdisciplinary research field, and it has recently received great attention from many
related disciplines including computer science, network science, complexity science,
statistical physics and socioeconomics.
Traditional socioeconomic research relies mainly on qualitative or semi-quantitative
methods, which makes it difficult to understand relevant issues at the mechanism level.
The process that calculates macroeconomic indicators based on traditional census data not
only consumes substantial resources, but also follows a long-time delay. Besides,
traditional analytical methods have difficulty in tracking the structural transformation of
economic development, fail to quantify the complexity of economic development and are
lack of predictive power on development trends. The recent simultaneous development of
hardware and technology is driving a new wave of big data, which has brought
unprecedented opportunities and changes to socioeconomic research. The advances in
methods of data acquisition have increased the availability of large-scale socioeconomic
data, and the increases in the size and diversity of data have contributed to the
transformation of socio-economic analytical tools and methodologies. The application of
novel data and methods has gradually increased the level of quantification in
socioeconomic research and led to the emergence of a new scientific branch, named
Computational Socioeconomics. Under the framework of computational socioeconomics,
this dissertation will investigate the status inference and structural modeling of socio-
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economic systems from the micro, meso and macro levels, and explore the evolution of
economic structure and the optimal strategy for economic development through theoretical
and empirical studies. In particular, studies at different levels are based on the similar
theoretical basis of network spatial structure and dynamics. The main contents and major
contributions of this dissertation are summarized as follows:
(1) At the micro level, the predictive management of socio-economic systems was
studied based on unobtrusive behavioral data. By analyzing data recorded by anonymized
campus cards, we proposed a novel orderliness measure to quantify the regularity of
individual behavior. Orderliness is significantly correlated with student academic
performance, and it can largely improve the performance of learning-to-ranking algorithm
on predicting student academic performance. Based on the analysis of two employee
networks built on data from an enterprise socialization platform, we found that the
locations of employees in both networks are predictive to the possibility of their promotion
and resignation. In particular, action network has stronger predictive power than social
network, and predicting resignation is easier than predicting promotion. Moreover, by
analyzing large-scale online platform data, we revealed some socio-economic phenomena
in a quantitative way, including keeping team size below 8 can improve employee's
communication and performance, the size of Chinese social circle is also around Dunbar's
Number 150, and there are height premium and gender inequality in the workplace.
(2) At the meso level, the ranking of socio-economic systems was studied based on
online user rating data. To solve the of problem reputation ranking, we proposed a group-
based reputation ranking (GR) method. Instead of relying on the traditional assumption of
product quality, GR method calculates user reputation based on the size of rating groups.
Experiments based on real-world datasets showed that GR method outperforms benchmark
methods in the accuracy of ranking users by their reputation. By introducing an iterative
process into the GR method, we further proposed an iterative group-based ranking (IGR)
method. Considering both the number and the reputation of users when calculating the
group size, GR method exhibits better accuracy and robustness in reputation ranking. To
solve the problem of object ranking, we proposed a novel vertex similarity measure, named
CosRA index, based on which we developed a CosRA-based recommendation algorithm
that exhibits better performance. Further, we proposed a trust-based recommendation
algorithm, named CosRA+T, and found that relying too much on trust relations among
users is detrimental to recommendation performance.
(3) At the macro level, socio-economic structures were quantified and analyzed based
on large-scale real data. Using firm registration information data, we quantified China's
regional economic complexity. We found that ECI index and Fitness index exhibit
comparable predictive power for China's regional economic development, and economic
complexity is negative correlated with income inequality. Using labor and firm data, we
built Brazil's and China's regional industry space, respectively. We found that both industry
spaces exhibit a ``core-periphery'' structure, where industries with high and low level of
sophistication occupy the core and the periphery of the industry space, respectively.
Moreover, China's regional industry space has a ``dumbbell'' structure, and its time
evolution has regional competitions. Based on Weibo and resume data, we built
information flow and talent mobility network, respectively. We found that regional
economic status can be inferred from the structure of both networks. In particular, talent
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mobility network exhibits a stronger predictive power, and combining the structures of both
networks can explain about 84\% of the variance in GDP.
(4) In economic development and structure evolution, the path of economic evolution
and the strategy of industrial upgrading were studied based on spatial networks. By
leveraging the spatial network model and the spreading process, we revealed the effects of
the spatial structure of networks on information diffusion. We found that the distribution
of long-range links of spatial networks can change the phase transition of bootstrap
percolation, where the exponent -1 of the distribution of long-range links is a critical value
for the presence of a double phase transition with two nearly constant critical points. For
industry space and geographical adjacent networks, we proposed the inter-industry learning
and the inter-regional learning for economic development, respectively. We found that both
collective learning channels can increase the probability of development new industries,
while they exhibit an alternative effect. Moreover, we explored the optimal strategy for
economic development using both theoretical and empirical analyses. We found that
reducing geographical distance can enhance the collective learning effects, introducing
high-speed rail can increase regional industrial similarity and productivity, and both
collective learning channels have optimal strategies for industrial development.
Computational socioeconomics is an emerging research branch, and it faces new
challenges and opportunities in both data and methods. In future studies, it is worthwhile
to further explore the spatial structure and dynamics of socio-economic systems, and to
improve the perception of socioeconomic situation and the understanding of the law of
development. In the long run, data-driven research paradigm will become the mainstream
methodology for solving social and economic problems and will profoundly change the
landscape of socioeconomic research.
Keywords: complex networks, socio-economic systems, ranking method, economic
complexity, network structure
Thesis Supervisor: Tao Zhou
Title: Professor of Computer Science and Technology
University of Electronic Science and Technology of China
