@misc{10481/70196,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/70196},
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.},
organization = {Spanish State Research Agency	PID2019-103880RB-I00/AEI/10.13039/501100011033},
organization = {Junta de Andalucia	P12.SEJ.2463	
SEJ340},
organization = {Universidad de Granada / CBUA},
publisher = {Springer},
keywords = {Price outcomes},
keywords = {Aggregate and minimum market price},
keywords = {Networks},
keywords = {Time Dynamic Markov Random Fields},
keywords = {Olive Oil sector},
title = {Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying. The case of olive oil in Andalusia},
doi = {10.1007/s10489-021-02599-6},
author = {García Cabello, Julia},
}