22: Please review and listen to the second part of the interview and complete the missing words. Consistent elements
22: Please review and listen to the second part of the interview and complete the missing words. Consistent elements. Predicting. Trends. Factors. Similarities. i: What types of "models" are being discussed here? a: Well, that"s an interesting question because the fundamental concept of chaos theory is that there are no specific models per se - there are no guaranteed forms, but rather continuous changes and progress. i: Does that imply it is not possible? a: No, but it definitely presents more of a challenge. Mandelbrot, who conducted the experiment on stock exchange prices, for instance, observed that even though the results were
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, argued that the traditional notion of linear models could not adequately capture the complexity and unpredictability of financial markets. Instead, he proposed the idea of fractal models, which account for the consistent elements and patterns found in chaotic systems. These fractal models incorporate self-similarity, meaning that patterns repeat themselves at different scales. This allows for a better understanding and prediction of trends in the market. Another important aspect in modeling complex systems is the consideration of multiple interacting factors. In chaotic phenomena, small changes in initial conditions can lead to dramatically different outcomes, making it crucial to identify and account for all relevant factors. Additionally, similarities between seemingly unrelated systems can provide insight into their behavior. By recognizing similar patterns or dynamics in different contexts, scientists can leverage their understanding in one domain to make predictions or draw conclusions in another. So, to summarize, the types of models discussed in this interview include fractal models, which capture the consistent elements and patterns in chaotic systems, and models that account for multiple interacting factors and recognize similarities between different systems. These models offer more comprehensive approaches to understanding and predicting complex phenomena compared to traditional linear models.