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Automated trading has become very popular in electronic markets, such as stock market. The design and evaluation of trading agents are not trivial due to the complexity and dynamics of this scenario. Using an actual and reliable dataset from the Brazilian Stock Exchange, and aiming to support the Market Making process in High-Frequency Trading, in this work we design and evaluate some models of automated...
Big Data is one of the latest emerging topics in the field of business information systems, and is marketed as being the key for companies' future success. Many analytic solutions are offered by IT companies to help other businesses with the flood of data that is generated within and outside of a company. Despite the extensive use of the notion Big Data for marketing purposes, there is no common understanding...
Building on our previous work, we examine the effect of role specialisation on a society using a scenario from the area of comparative economics. In connection with this our central contribution is the use of Interval Type-2 Fuzzy Sets to model emerging normative understanding, which is represented using the concept of Dynamic Deon tics. This integrated mechanism, along with a supporting visualisation...
Traditional works of public goods game focus on the situation that agents play the games in simplex networks in which social interactions are of the same type. However, due to multiple types of interactions in reality, agents always have to allocate the limited resources according to different interactions, such as the agents allocate the limited time to work with colleagues or accompany family members...
Virtual training is a relatively novel field in which human beings learn to perform certain tasks by repeatedly executing them in a virtual reality environment. To make such training environments more effective, the agent paradigm has proven to be a useful tool. By conceptualising a training system as a 'virtual tutor', the system may be able to support the trainee in a similar manner as human instructors...
This paper discusses the recommender models and methods for crowd sourcing platforms. These models are based on modern methods of data analysis of object-attribute data, such as Formal Concept Analysis and biclustering. In particular, the paper is focused on the solution of two tasks - idea and antagonists recommendation - on the example of crowd sourcing platform Witology.
Helping users to find useful contacts or potentially interesting subjects is a challenge for social and productive networks. The evidence of the content produced by users must be considered in this task, which may be simplified by the use of the meta-data associated with the content, i.e., The categorization supported by the network -- descriptive keywords, or tags. In this paper we present a model...
The task of tracing what underlies the transition from kinship tribes to nation states continues to remain an important challenge for interdisciplinary study. Multi-agent simulation can shed light on these evolutionary processes by examining the emergent social behaviour that arises from individual agent interactions. In this paper, we build a model based on existing observational and simulation studies...
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