We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding...

In matching markets the number of blocking pairs is often used as a criterion to compare matchings. We argue that this criterion is lacking an economic interpretation: In many circumstances it will neither reflect the expected extent of partner changes, nor will it capture the satisfaction of...

As part of managing a debt portfolio, debt managers face the challenging task of choosing a strategy that minimizes the cost of debt, subject to limitations on risk. The Bank of Canada provides debt-management analysis and advice to the Government of Canada to assist in this task, with the...

In this paper we develop the standard utility function of a Ramsey-type optimal growth model to account for a "market-time" vs. "free-time" trade-off. To do so, we introduce a free-time preference coefficient that measures the utility gained by deviating from a maximum labour supply defined as...

This study investigates the citation patterns of theoretical and empirical papers over a period of almost 30 years, while also exploring the determinants of citation success. The results indicate that empirical papers attract more citation success than theoretical studies. However, the pattern...

This paper draws attention to two important characteristics of knowledge which so far have been left unexplored, and proposes a new method to capture them in economic modeling with the help of vectors. The direction of the vector represents the knowledge's complementarity with other knowledge...

This paper builds a model in which clustering emerges as the equilibrium outcome driven by informational imperfections and the importance of knowledge and uncertainty in innovative production even when knowledge is fully exclusive and appropriable. This motive for clustering will be stronger the...

The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the...