Project selection in a biotechnology startup using combinatorial acceptability analysis

Jana Goers; Graham Horton

doi:10.31181/dmame622023783

Authors

Jana Goers Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg 39106, Germany https://orcid.org/0000-0001-8398-257X
Graham Horton Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg 39106, Germany https://orcid.org/0000-0002-7757-4489

DOI:

https://doi.org/10.31181/dmame622023783

Keywords:

Decision analysis, acceptability analysis, group decision-making, shared mental models, consensus-building

Abstract

Combinatorial Multi-Criteria Acceptability Analysis (CMAA) is a new algorithmic framework that enables the use of standard (i.e., single-user) multicriteria decision-making methods by groups. In this paper, we present the first application of CMAA to a real-life decision. Our objectives were to study the performance of the method in a real-life setting and to test two hypotheses concerning the application of the method. Three founders of a biotechnology startup had to choose a product development project. We describe the decision problem and the consensus path taken by the founders, and we illustrate some of the analytical possibilities offered by the method. Of the 25 evaluation conflicts contained in the initial input, only eight needed to be resolved in order to achieve a hard consensus. A simulation experiment showed that the expected value for this size problem is 7.6 resolution steps. The method generates a very large state space, so complete enumeration can become prohibitively expensive. A computational experiment confirmed our assumption that 10,000 random samples are sufficient if Monte Carlo simulation is used instead. A third simulation experiment provided support for the hypothesis that consensus-building with the non-compensatory decision model used is more efficient than with a more typical compensatory model. We conclude that the CMAA method is well-suited for multi-criteria group decisions; it provides a wealth of analytical detail, and its entropy-based heuristic can guide the group to consensus in a small number of steps.

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