Does cultural economics affect country’s competitiveness?

Abstract

In recent years, there has been significant discourse regarding the representativeness of the judiciary as an institution. Research suggests that judges are somewhat responsive to public opinion, but there remains a misconception regarding how the public influences the courts. Courts are tasked with considering the future, estimating the likelihood and severity of unlawful behaviour, and, within specified boundaries, imposing sentences to mitigate potential harm. Ideally, these forecasts should be highly accurate and based on practical, transparent methods that account for the consequences of prediction failures. However, there is often ambiguity about the best approach to achieving these objectives. Subjective judgment, often referred to as "clinical judgment," relies on intuition and experience. However, the resulting risk assessments can be highly inaccurate, and the reasoning behind them may not be clear. On the other hand, "actuarial" strategies utilize data to establish connections between "risk factors" and various outcomes of interest. Regression statistical methods have traditionally dominated actuarial risk assessments, yielding generally positive results. Nevertheless, with the increasing availability of vast datasets and advancements in data analysis technologies, machine learning is poised to become the primary statistical driver in this field, offering the potential for even greater improvements in the future.

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