Outlier

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Title: Outlier
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My Notes
--------
November 3, 2020

This workbook attempts to collect ideas and references from working on "Staying Alive" and other "Island Game" and "Exploitation" projects.

see my November 2-3, 2020 notes in my Island Games/Exploitation binder
see: https://gist.github.com/kenwebb/48b6726c8a8a95bf4608b26dff199e52

In this workbook, an outlier is defined as a member of a much smaller population, related to the main population being studied.
For example, a large population of subsistence fishers, and a small population of commercial fishers (the outliers).

The wikipedia article on Outliers [ref 2], discusses the problem that outliers cause in statistical analysis,
but it also mentions the possibility of multiple (two) distinct sub-populations.
In this workbook, we are mostly discussing the "multiple populations" interpretation of the term "Outlier".


References
----------
(1) search: 'outlier' 'outlier R' 'outlier netlogo' 'outlier "agent based model"' 'outlier detection'

(2) https://en.wikipedia.org/wiki/Outlier
    In statistics, an outlier is a data point that differs significantly from other observations.
    An outlier may be due to variability in the measurement or it may indicate experimental error;
    the latter are sometimes excluded from the data set.
    An outlier can cause serious problems in statistical analyses.

    Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution.
    In the former case one wishes to discard them or use statistics that are robust to outliers,
    while in the latter case they indicate that the distribution has high skewness
    and that one should be very cautious in using tools or intuitions that assume a normal distribution.
    A frequent cause of outliers is a mixture of two distributions, which may be two distinct sub-populations,
    or may indicate 'correct trial' versus 'measurement error'; this is modeled by a mixture model. 

(3) https://en.wikipedia.org/wiki/King_effect
    In statistics, economics, and econophysics, the King effect refers to the phenomenon where the top one or two members of a ranked set show up as outliers.
    These top one or two members are unexpectedly large because they do not conform to the statistical distribution or
    rank-distribution which the remainder of the set obeys.
    
    Note, however, that the King effect is not limited to outliers with a positive evaluation attached to their rank:
    for rankings on an undesirable attribute, there actually may exist a Pauper effect,
    with a similar detachment of extremely ranked data points from the reasonably distributed portion of the data set.

(4) https://en.wikipedia.org/wiki/Anomaly_detection
    In data analysis, anomaly detection (also outlier detection) is the identification of rare items,
    events or observations which raise suspicions by differing significantly from the majority of the data.
    Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect,
    medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

(5) https://en.wikipedia.org/wiki/Extreme_value_theory

(6) https://en.wikipedia.org/wiki/Fat-tailed_distribution

() https://s3.amazonaws.com/complexityexplorer/ABMwithNetLogo/Field+Guide+to+NetLogo+v14-netlogoExtension-index_02.pdf
    A Field Guide to NetLogo
    Steve Scott and Matt Koehler, 2014
    George Mason University
    Department of Computational Social Science
    This guide has been developed for graduate students at George Mason University
    in the Department of Computational Social Science who need to develop agent-based
    models but are not familiar with NetLogo modeling and language syntax.

    "It is also worth noting that the outliers often drive these systems.
    Therefore, you want the system to have the "right" outliers, not outliers created simply by statistical happenstance." [p. 106]

() Malcolm Gladwell, Outliers: The Story of Success, 2011, book

() https://hannesrosenbusch.github.io/Finding-lucky-data-points-through-StatBreak-Copy/
    R package: StatBreak
    "a uiversal tool that circumvents most of this awkwardness while checking whether one or two outliers changed an initial conclusion from the data"

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