Elegance in Model Building for Understanding

When developing a new theory or model, it is crucial to carefully choose assumptions and what to include, avoiding the temptation to put everything in. This elegance in modeling allows for a deeper understanding of the subject matter. In the context of digital evolution, similar to other modeling processes, making key choices upfront greatly impacts the results obtained. By considering specific evolutionary pressures that facilitate learning, such as environmental certainty and reliability, researchers can determine the conditions under which learning occurs. The model emphasizes that learning takes place when the reliability of what is being learned exceeds the long-term average, otherwise leading to evolved bias or fixed responses.