Abstract
In some scientific and technical fields, the variance in the possible outcomes of a decision is characterized as the risk of the decision, and this is the meaning adopted here. Risk preferences are the attitudes toward risk, and their importance arises from how they influence the behaviour of various entities. Risk preferences adaptation has been explored in numerous articles, and some of these employed simulation methods, which appear to be a promising tool in the field. This dissertation studies how risk preferences change in a system made of multiple adaptive entities utilizing simulation. The first part of the dissertation consists of a literature review, and it shows numerous examples of applications of simulation models to this topic. The result is twofold. Firstly, interactions are relevant in this field and agent-based modelling is an appropriate simulation tool to employ. Secondly, there is not yet an agreement regarding the co-effect of environmental features and adaptation strategy on the changes in attitudes toward risk. The following three sections explores the co-effect of environmental features and adaptation technique on risk preferences adaptation. Three abstract agent-based models are developed, providing the agents with the possibility to take a simple decision in different settings. The population of agents adjusts its risk preferences by evolving or learning (or a mix of these two adaptation processes). The models’ exploration reveals non-trivial relationships between the environmental dangerousness and the resulting risk preferences, and the effect adaptation processes on this relationship. The last section addresses the main limitation of these three parts, which is the lack of connection to reality. An empirical-based agent-based model is proposed, developed to identify the result of the risk adaptation of production studios on the US movie market. The dissertation contributes to the field by improving the understanding on the effect of environmental features and adaptation strategies on the way risk preferences vary in a complex adaptive system. Moreover, it outlines how and when simulations should be employed to study the adaptation of risk preferences.