dennisV

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Multi-winner Voting: a question of Alignment

Another view of voting is where an objective opinion or belief is being formed about ANYTHING that can be named. It might be a belief about utility where the utility can only be seen subjectively but an objective value would be useful in planning, for instance. Another example is determination of what a given set of users might buy given prior page views, or what the value of a given piece of intelligence is to a given audience at a given time and related to a given topic. Such a voting structure is needed in crowd sourcing and in 'training'/'supervision' in machine learning. It is also necessary for determination of what is or is not fake news, where certain 'voters' might be given a greater weighting to their votes on given topics. As with reducing ranked voting to utility maximization, the problems of using these are complex, even though the intention of statistics in general would seem to be directed to the same facilities. Like machine learning, the issue is the great 'dimensionality' of the problem, and statistical solutions don't work well. In fact, where dichotomies exist (failed praxis), statistics simply fail without the use of probabilistic techniques such as expectation analysis. A system for handling all of this type of consensus mechanisms is on the way. See Patent US20140075004 - System And Method For Fuzzy Concept Mapping, Voting Ontology Crowd Sourcing ... - Google Patents; Patent US20140075004 - System And Method For Fuzzy Concept Mapping, Voting Ontology Crowd Sourcing ... (2nd pointer is to the list of projects that were required to acknowledge the patent as prior art) .

One aspect of interest regarding the edge of statistics mentioned is that in a causality based world (see 'The Book of Why'), the causality must still be addressed even if the statistical approaches don't seem to work. Answers from the two may disagree (where 'common sense' seems to be).

A voting theory primer for rationalists

Another view of voting is where an objective opinion or belief is being formed about ANYTHING that can be named. It might be a belief about utility where the utility can only be seen subjectively but an objective value would be useful in planning, for instance. Another example is determination of what a given set of users might buy given prior page views, or what the value of a given piece of intelligence is to a given audience at a given time and related to a given topic. Such a voting structure is needed in crowd sourcing and in 'training'/'supervision' in machine learning. It is also necessary for determination of what is or is not fake news, where certain 'voters' might be given a greater weighting to their votes on given topics. As with reducing ranked voting to utility maximization, the problems of using these are complex, even though the intention of statistics in general would seem to be directed to the same facilities. Like machine learning, the issue is the great 'dimensionality' of the problem, and statistical solutions don't work well. In fact, where dichotomies exist (failed praxis), statistics simply fail without the use of probabilistic techniques such as expectation analysis. A system for handling all of this type of consensus mechanisms is on the way. See Patent US20140075004 - System And Method For Fuzzy Concept Mapping, Voting Ontology Crowd Sourcing ... - Google Patents; Patent US20140075004 - System And Method For Fuzzy Concept Mapping, Voting Ontology Crowd Sourcing ... (2nd pointer is to the list of projects that were required to acknowledge the patent as prior art) .

One aspect of interest regarding the edge of statistics mentioned is that in a causality based world (see 'The Book of Why'), the causality must still be addressed even if the statistical approaches don't seem to work. Answers from the two may disagree (where 'common sense' seems to be).