首页|Extracting business-relevance for the analysis of strategic patent portfolios from highly imprecise fuzzy estimates
Extracting business-relevance for the analysis of strategic patent portfolios from highly imprecise fuzzy estimates
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Valuation of assets connected to forward-looking strategic investments and decision-making such as strategic patent-portfolios takes place in the presence of high uncertainty. Available information is typically in the form of vague signals and forecasts about the future and the tool that can be used to interpret it is the expert or a group of experts, because information about the structure of the future does not allow construction of models and historical data is not available. The expert estimates, from which fuzzy number estimates are constructed and aggregated, reflect the high imprecision, and extracting relevant information from them may be challenging. This paper presents how fuzzy expert estimates of strategic patent-portfolio value from multiple experts can be aggregated by using the lossless fuzzy weighted averaging without loss of relevant information and how business-relevant information can be extracted from seemingly worthlessly imprecise information, when a longitudinal set of estimates is available. A numerical case, in the context of real-world strategic patent valuation, is presented. New application of the lossless fuzzy weighted averaging and the recently introduced Luukka-Stoklasa defuzzification for general fuzzy sets is presented and results are compared with those obtained by using more traditional methods. The research offers a novel contribution to the literature on applying fuzzy logic to strategic decision-making
NETL
NSTL
Elsevier
