Algorithms for structural analysis of the general collective consumption model.

This project investigates the use of operations research techniques for tackling computational aspects of the general collective consumption model. This model, developed in micro-economics within the framework of collective consumption analysis, provides a starting point for testing the Pareto efficiency of collective household consumption decisions. Several tests have been proposed to check whether observed household consumption behavior is consistent with the general collective consumption model. Current implementations of these tests can only deal with data sets of small size, and usually consider at most two decision-makers households. However, the increasing availability of large data sets from households with many decision makers require the development of very efficient test procedures. We will approach these computational challenges from an operations research point of view, including the modeling of existing tests, the study of their complexity, the design and the implementation of efficient algorithms, and finally we will perform computational experiments on very large real-life data sets. Our approach of solving the problem could yield, on the positive side, very efficient exact algorithms for testing collective rationality of large data sets. On the negative side, we could determine that some tests are NP-complete and focus on the development of good approximation algorithms.

Keywords:Algorithms, Collective consumption model, Computational complexity theory, Operations research

Disciplines:Applied economics, Economic history, Macroeconomics and monetary economics, Microeconomics, Tourism