Abstract
Game theory and Decision Theory have been applied to many different areas such as Physics, Economics, Biology, etc. In its application to Psychology, we introduce, in the literature, a Game Theoretical Model of Planned Behavior or Reasoned Action by establishing an analogy between two specific theories. In this study we take in account that individual decision-making is an outcome of a process where group decisions can determine individual probabilistic behavior. Using Game Theory concepts, we describe how intentions can be transformed in behavior and according to the Nash Equilibrium, this process will correspond to the best individual decision/response taking in account the collective response. This analysis can be extended to several examples based in the Game Theoretical Model of Planned Behavior or Reasoned Action.

Abstract
We exploit ideas of nonlinear dynamics and statistical physics in a complex nondeterministic dynamical setting using the Ruelle-Takens embedding. We present some new insights on the quality of the prediction in the laminar regime and we exhibit the data collapse of the predicted relative first difference fluctuations to the universal Bramwell-Hodsworth-Pinton distribution. Hence, the nearest neighbour method of prediction acts as a filter that does not eliminate the randomness, but exhibits its universal character.

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Predictive modelling is a process used in predictive analytics to create a statistical model of future behaviour. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. On the other hand, Artificial Intelligence (AI) concerns itself with intelligent behaviour, i.e. the things that make us seem intelligent. Following this process of thinking, in this work the main goal is the assessment of the impact of using AI based tools for the development of intelligent predictive models, in particular those that may be used to establish the conditions in which the levels of manganese and turbidity in water supply are high. Indeed, one of the main problems that the water treatment plant at Monte Novo (in Evora, Portugal) uncovers is the appearance of high levels of manganese and turbidity in treated water, which sometimes exceed the parametric values established in Portuguese Law, respectively 50 mu g dm(-3) and 4 NTU. In this study we tried to find answers to the above problem by building predictive models. The models we developed shall enable the prediction of manganese and turbidity levels in treated water, in order to ensure that the water supply does not affect public health in a negative way and obeys the current legislation. The software used in this study was the Clementine 11.1. The C5.0 Algorithm was also used as a means of introducing Decision Trees and the K-Means Algorithm was used to construct clustering models. The data in the database was collected from 2005 to 2006 and includes reservoir water quality data, treated water data and volumes of water stored in the reservoir.