Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying

Abstract

This paper presents a mathematical/Artificial Intelligence (AI) model for the prediction of price outcomes in markets with the presence of lobbying, whose outputs are pricing trends that aggregate the opinions of lobbies on future prices. Our proposal succeeds in unraveling this complex real-world problem by reducing the solution to straightforward probability computations. We tested our method on real olive oil prices (Andalusia, Spain) with encouraging results in a challenging sector, where opacity in the entry of oil shipments which are stored while waiting for the price to rise, makes it very difficult to forecast the prices. Specifically, understanding by minimum price that the price level is at least reached, specific formulas for computing the likelihood of both the aggregate and the minimum market price are provided. These formulas are based on the price levels that lobbies expect which in turn, can be calculated using the probability that each lobby gives to market prices. An innovative quantitative study of the lobbies is also carried out by explicitly computing the weight of each lobby in the process thus solving a problem for which there were only qualitative references up until now. The structural model is based on Time Dynamic Markov random fields (TD-MRFs). This model requires significantly less information to produce an output and enjoys transparency during the process when compared with other approaches, such as neural networks (known as black boxes). Transparency also ensures that the internal structures can be fine tuned to fit to each context as well as possible.