| Summary: | The first chapter of this thesis provides an introduction to fundamental concepts concerning econophysics, Ising model, and opinion networks. After a glance in a field of econophysics, Chapter 2 illustrates the economic behaviour via the implementation of two methods. The statistical analysis of real economic data will be briefly stated and followed by the agent-based dynamic model describing the commercial activities. Agent-based dynamic model investigates the intrinsic dynamics of trading behaviour and individual income by modelling transaction processes among agents as a network in the economic system. To take a further look into the network, we introduce a mathematical model of ferromagnetism in statistical mechanics which is called Ising model. Every element in the network can be treated as a two-state ({+1,-1} or sometimes {+1,0}) node. The similar methodology is used in the three-or-more-state situation. This kind of modelling method is widely applied in networks of neurosciences, economics, and social sciences. Chapter 3 implements and modifies Ising model of a random neuron network with two types of neurons: inhibitory and excitatory. We numerically studied two mutually coupled networks through mean-field interactions. After 3-step alternation, the model provides some fascinating insights into the neuronal behavior via simulation. In particular, it determinates factors that lead to emergent phenomena in dynamics of neural networks. On the other hand, it also plays a vital role in building up the opinion network. We first show the development of Ising model to opinion network. Then the coupled opinion network model and some of the analytical results are carefully given in Chapter 4. Two opinion networks are interfering each other in the system. This model can describe the opinion network more precisely and give more accurate predictions of the final state. At last, a case of U.S. presidential campaign in 2016 is studied. To investigate a complex system which is associated with a multi-party election campaign, we have focused on the situation when we have two competing parties. We compare the prediction of the theory with data describing the dynamics of the average opinion of the U.S. population collected on a daily basis by various media sources during the last 500 days before the final Trump-Clinton election. The qualitative outcome is in reasonable agreement with the prediction of our theory. In fact, the analyses of these data made within the paradigm of our theory indicate that even in this campaign there were chaotic elements where the public opinion migrated in an unpredictable chaotic way. The existence of such a phase of social chaos reflects the main feature of the human beings associated with some doubts and uncertainty and especially associated with contrarians which undoubtedly exist in any society. Besides, a modern tool, Twitter, with rapid information spreading speed affects the whole procedure substantially. We also take a closer look at the influence of the usage of Twitter on competitors, Trump and Clinton. Once the first sign from Trump began stirring on Twitter, it quickly began to ferment. Using Twitter not only brings strength to Trump as he wished, but also sending potentially backward to Clinton in this nationwide competition.
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