Title screen

[References: 37 tit.]

For adjustment of fundamental constants, the interval fusion with preference aggregation (IF&PA) method is proposed to be used, which was proved to be robust and accurate when processing heteroscedastic data. The method forms a profile of rankings of discrete values obtained by partition of the range of actual values (RAV) being a union of input intervals to be adjusted; determines the profile consensus ranking; and the highest ranked consensus value is accepted as a fusion result x*. A nonlinear effect of the RAV partition norm on x* is studied. IF&PA, redesigned to obtain more accurate result x**, was experimentally tested when adjusting the Planck constant values on both random input data and real life values used in the CODATA adjustments 2006 and 2017. It is shown the IF&PA works well with no statistical assumptions and provides adjusted result x** with tangibly reduced uncertainty in contrast to traditional Birge ratio methods.