Certain social preference models have been proposed to explain fairness behavior in experimental games. Existing bodies of research on evolutionary games, however, explain the evolution of fairness merely through the self-interest agents. This paper attempts to analyze the ultimatum game's evolution on complex networks when a number of agents display social preference. Agents' social preference is modeled in three forms: fairness consideration or maintaining a minimum acceptable money level, inequality aversion, and social welfare preference. Different from other spatial ultimatum game models, the model in this study assumes that agents have incomplete information on other agents' strategies, so the agents need to learn and develop their own strategies in this unknown environment. Genetic Algorithm Learning Classifier System algorithm is employed to address the agents' learning issue. Simulation results reveal that raising the minimum acceptable level or including fairness consideration in a game does not always promote fairness level in ultimatum games in a complex network. If the minimum acceptable money level is high and not all agents possess a social preference, the fairness level attained may be considerably lower. However, the inequality aversion social preference has negligible effect on the results of evolutionary ultimatum games in a complex network. Social welfare preference promotes the fairness level in the ultimatum game. This paper demonstrates that agents' social preference is an important factor in the spatial ultimatum game, and different social preferences create different effects on fairness emergence in the spatial ultimatum game. Spatial Ultimatum Game, Complex Network, Social Preference, Agent Based Modeling Recently, Kuperman and Risau-Gusman ( 2008) focused on the topology's effect on the spatial ultimatum game. They studied the spatial non-homogeneous ultimatum game based on an agent model and analyzed the effect of the neighborhood and spatial structure's size. They observed that the increase in neighborhood size and disorder degree pushed agents to a more rational level.Other papers likewise tapped the agent-based simulation to study the evolutionary ultimatum game. A number treated the agent as an automaton playing the ultimatum game, and these research focused on agent behavior and evolution. However, none succeeded in placing agents in a complex network (Riechmann 2001, Hayashida 2007.Another research approach to the ultimatum game focuses on the agent's behavior, especially the social preference displayed by certain agents. Experimental economics and behavior science during the past decades have revealed that certain agents may exhibit social preference. Further, behavior economists have suggested some new utility functions to describe the agent's social preference (other-regarding). Although the social preference theory relaxes the selfish agent assumption, it still can be considered as a type of rational choice theory. Rabin (1993) developed a "fairness equilibrium" to include ag...

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The Internet has generated a large amount of unstructured online texts that contain a large amount of argumentative content. How to automatically extract the argumentative contents from these online texts is an important issue. First, a corpus of argumentation about Chinese online current affairs reviews is built. The existing argumentation annotation scheme is also modified by adding two additional types of elements, "central conclusion" and "intermediate conclusion", and their corresponding argumentation relations to reflect the more complex structure of the long review texts. The pre-trained large models are used to identify and classify the argumentative elements in these texts. Then, the penultimate hidden state of the best model is used as the input of the pointer network for argumentative elements relationship identification. The experimental results display that the "pre-trained large model + pointer network" model improves the performance of the existing models.