What's the motive though? To minimize discontent when a distribution is estimated & asserted for the purposes of decision making? How is it better than a struggle for power with winners and losers? -----Original Message----- From: Friam <[email protected]> On Behalf Of glen Sent: Tuesday, February 15, 2022 2:16 PM To: [email protected] Subject: Re: [FRIAM] by any means necessary
I was thinking more in terms of an aggregation over Kullback-Lielber or Jensen-Shannon pairwise measures. But it could also be something like a steadily running clustering algorithm that wouldn't retain any given flattened result, but would rather track a 2nd order tendency of the flattened results. Using a clustering algorithm that regularly tosses out some of the subjective perspectives, revolving some in, some out, some in, etc. might provide less flattening without giving up the computational convenience of the flattening. Of course, you run the risk of tyranny of the majority with any clustering method. I don't know what what to do about that except with some sort of round-robin inclusion of the leaves, adding an uncertainty to the trajectory. On 2/15/22 14:04, Marcus Daniels wrote: > A flattening function of `variance', would prefer inter-subjective > consistency. A flattening function of `sum' would select for best subjective > outcome for the most people. One could probably invent a many-body utility > function from the ground up too. > -----Original Message----- > From: Friam <[email protected]> On Behalf Of glen > Sent: Tuesday, February 15, 2022 1:51 PM > To: [email protected] > Subject: Re: [FRIAM] by any means necessary > > Well, you're right that inter-subjectivity is not identical to objectivity. > But it's not quite right to say that the intersubjective is merely > subjective. All that's needed to communicate that difference is the blind men > and the elephant story. You don't need the source code for the functions if > you can cross-validate them "enough". More technically, it's useful to > compare interpretable ML with explainable ML. One may be enough for > credibility in some uses but inadequate in others. And one might ask whether > iML is really all that interpretable if the interpretability falls away with > scale *or* the accuracy falls away without scale. > > On 2/15/22 13:44, Marcus Daniels wrote: >> A calculus that invokes subjective interpretations of outcomes is itself >> subjective. To make it objective, it would be necessary to draw conclusions >> from the outcomes and not from black-box subjective functions. The source >> code for the functions needs to be shared. > -- glen When elephants fight, it is the grass that suffers. .-- .- -. - / .- -.-. - .. --- -. ..--.. / -.-. --- -. .--- ..- --. .- - . FRIAM Applied Complexity Group listserv Zoom Fridays 9:30a-12p Mtn UTC-6 bit.ly/virtualfriam un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ archives: 5/2017 thru present https://redfish.com/pipermail/friam_redfish.com/ 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/ .-- .- -. - / .- -.-. - .. --- -. ..--.. / -.-. --- -. .--- ..- --. .- - . FRIAM Applied Complexity Group listserv Zoom Fridays 9:30a-12p Mtn UTC-6 bit.ly/virtualfriam un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ archives: 5/2017 thru present https://redfish.com/pipermail/friam_redfish.com/ 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/
