[FRIAM] Sorting Algorithm? AI? Identifying "types" within data

[Eric Charles]

Yeah, there are two different efforts I'm trying to play with
simultaneously.... in that area... in addition to the 4 or 5 efforts in
unrelated areas....
<echarles at american.edu>

We ARE trying to do a relatively clean attrition-prediction model, and that
will likely be something like what you were suggesting at the end.

I'm also trying to do some sort of career classification game, originally
because I thought it wouldn't be too hard. IF it was possible to do the
career classification game, it would de facto assist with the attrition
prediction. But if that just kind of isn't a thing, then I guess it isn't a
thing <shrug>.

Eventually, I'd really like to do agent-based modeling of the entire
federal workforce... which I think the career-typing would also help
with... but that's quite a ways off... not sure if it will ever happen...
or if I could clearly articulate value... but I THINK it would let me do
cool things...

I'm hoping to be at Thuram for a bit tomorrow, but I've been in two days of
Agile/Scrum training <hurray> so I'm not sure how swamped I'll be tomorrow.



On Tue, Jan 10, 2023 at 1:24 PM glen <gepropella at gmail.com> wrote:

> One tangential solution I've seen work well enough in synthetic health
> data is to treat the longitudinal data as a sequence in the same way the
> LLMs treat text. Rather than focus on the 2nd problem EricC mentioned
> (clustering based on *similarity*), focus more on the 1st ("around 10
> different types of changes that could happen").
>
> I suggest this because "we've" made lots of progress in such sequence
> prediction, and less progress in *object* detection. By asking for "types
> of career", you're conflating the two problems, lowering the efficacy of
> the sequential task and perhaps raising the efficacy of the object
> detection task.
>
> But a second idea might come from Russ' comment. Obviously, these are
> fairly well integrated humans. (My rants against narrativity aside.) So the
> causes of one seemingly independent feature *are* intertwined with the
> causes of some other independent feature. You can imagine a graph that
> includes all the features as nodes, but that also includes something like a
> Markov blanket just inside those measured features, and an internal causal
> kernel inside that blanket. This is akin to structural equation modeling et
> al. What you're looking for is a reconstructed state space of any human in
> the database ... what the cool kids are calling a "digital twin", these
> days.
>
> And, given your (Eric's) rants about bureaucracy and stupidity being at
> least in part due to the system these agents navigate, you *might* be able
> to keep the model simple by modeling the *options* any given agent might
> have ... as defined by the space. I.e. model the environment, the dual of
> the agent(s). We did that in a model for optimizing building and urban
> evacuation using so-called stupid agents.
>
> If you're looking for the One True Algorithm, my guess is you'll get lost.
> But if you do find it, write an Excel macro for the rest of us. 8^D
>
> On 1/10/23 09:16, Russ Abbott wrote:
> > Interesting problem.
> >
> > Eric, as you said earlier, K-means requires a way to measure the
> distance between objects -- so that those with smaller distances can be
> grouped together. A problem is that there are a number of features, which
> may not be correlated. For example, there is an income trajectory, a change
> of company trajectory, a change of level-of-responsibility trajectory, a
> change of subject-matter-focus trajectory, and probably more.  You might
> build separate trajectories for each person and then see if you can group
> the trajectories. For example, a "company man" may or may not have an
> increasing responsibility trajectory. You would then have a
> multi-dimensional space into which to put people.
> > _
> > _
> > __-- Russ
> >
> >
> > On Mon, Jan 9, 2023 at 10:11 AM Nicholas Thompson <
> thompnickson2 at gmail.com <mailto:thompnickson2 at gmail.com>> wrote:
> >
> >     To my uneducated eye, this seemed like one of Jon’s problems.
> >
> >     Sent from my Dumb Phone
> >
> >     On Jan 7, 2023, at 6:23 AM, Frank Wimberly <wimberly3 at gmail.com
> <mailto:wimberly3 at gmail.com>> wrote:
> >
> >     
> >     This answer seems reasonable to me.  I worked on Project Talent
> during 1967 which had some similar goals and data.  See
> >
> >     https://en.m.wikipedia.org/wiki/Project_Talent <
> https://en.m.wikipedia.org/wiki/Project_Talent>
> >
> >     Our data was for thousands of highschool students and our software
> was all written in Fortran.
> >
> >     ---
> >     Frank C. Wimberly
> >     140 Calle Ojo Feliz,
> >     Santa Fe, NM 87505
> >
> >     505 670-9918
> >     Santa Fe, NM
> >
> >     On Fri, Jan 6, 2023, 11:32 PM Pieter Steenekamp <
> pieters at randcontrols.co.za <mailto:pieters at randcontrols.co.za>> wrote:
> >
> >         I asked https://chat.openai.com/chat <
> https://chat.openai.com/chat> and here is the conversation:
> >
> >         *Pieter Steenekamp*
> >         can you suggest a solution for the following problem "I'm hoping
> someone here could help out. Let's imagine I had some data where each row
> was a person's career. We could list major events every year.For example:
> 2004 they were highered, 2007 they get a promotion, 2010 they leave for a
> different company, 2012 they come back at a higher level, 2015 get a
> promotion, then no change until 2022.  Let's say I had data like this for
> roughly 2 million people, and that there are around 10 different types of
> changes that could happen during any time period (could be yearly,
> quarterly, monthly, I can make it how I want).  I was hoping we could ask a
> computer to tell us if there were "types of careers" that people had. We
> could say "put all these careers into 4 buckets" or "7 buckets" based on
> similarity. Then we could look at the piles the computer made and try to
> make sense of them. One type might be "company man" for people who tend to
> stay in place for 20 or more years,
> >         another type could be a "rotator", who leaves and returns every
> 3 years or so. Etc. The point is, I want a computer to make the piles for
> me, rather than trying to come up with potential piles a priori. Are there
> methods for doing this? I know it's a problem we've *talked* about a lot,
> but I don't know if there are solutions. "
> >         *https://chat.openai.com/chat <https://chat.openai.com/chat>*
> >         There are several approaches that you could use to group the
> careers of these 2 million people into clusters based on similarity. One
> approach is to use unsupervised machine learning techniques, specifically
> clustering algorithms, to automatically group the data into clusters. Some
> popular clustering algorithms that you could use for this problem include
> k-means, hierarchical clustering, and DBSCAN (Density-Based Spatial
> Clustering of Applications with Noise). You could also use dimensionality
> reduction techniques, such as PCA (Principal Component Analysis) or t-SNE
> (t-Distributed Stochastic Neighbor Embedding), to reduce the dimensionality
> of the data and then use a clustering algorithm to group the data into
> clusters. Another approach is to use supervised machine learning
> techniques, where you would need to manually label a subset of the data
> with the cluster labels, and then use this labeled data to train a
> classifier to predict the cluster labels for the remaining
> >         data. It's worth noting that these approaches will require some
> feature engineering, where you will need to extract relevant features from
> the data that can be used to represent the careers of the people in the
> dataset. Once you have identified and extracted these features, you can
> then use them as input to the machine learning algorithms to group the data
> into clusters.
> >         *Pieter Steenekamp*
> >         would it then be possible to get  for example if there were
> "types of careers" that people had?
> >         *https://chat.openai.com/chat <https://chat.openai.com/chat>*
> >         Yes, once you have grouped the careers of the 2 million people
> into clusters using one of the approaches I mentioned above, you can then
> analyze the characteristics of each cluster to identify common patterns and
> trends within each cluster. This will allow you to identify "types of
> careers" that people have, based on the similarity of their career events.
> For example, if one of the clusters is characterized by people who have had
> long tenures at a single company, you could label this cluster as "company
> man" careers. Similarly, if another cluster is characterized by people who
> frequently switch between companies, you could label this cluster as
> "rotator" careers. You can then use this information to gain insights into
> the types of careers that are represented in the dataset, and potentially
> draw conclusions about the factors that may influence career paths and
> success.
> >         *Pieter Steenekamp*
> >         can you propose python code to do sometingh like this please
> >         *https://chat.openai.com/chat <https://chat.openai.com/chat>*
> >         Sure, here is an example of how you could use the scikit-learn
> library in Python to group the careers of the 2 million people into
> clusters using the k-means algorithm:
> >         Copy code
> >         from sklearn.cluster import KMeans
> >         # Load the data into a NumPy array
> >         data = np.loadtxt('career_data.txt')
> >         # Create the k-means model
> >         model = KMeans(n_clusters=4)
> >         # Fit the model to the data
> >         model.fit(data)
> >         # Predict the cluster labels for each data point
> >         labels = model.predict(data)
> >         # Print the cluster labels
> >         print(labels)
> >         This code will group the data into 4 clusters, as specified by
> the n_clusters parameter. You can adjust this parameter to change the
> number of clusters that the data is grouped into.
> >         To extract features from the data that can be used as input to
> the k-means model, you will need to preprocess the data and select relevant
> features. This will likely involve some feature engineering, which will
> depend on the specific characteristics of the data and the goals of your
> analysis.
> >         I hope this helps! Let me know if you have any questions.
> >
> >         On Fri, 6 Jan 2023 at 19:34, Eric Charles <
> eric.phillip.charles at gmail.com <mailto:eric.phillip.charles at gmail.com>>
> wrote:
> >
> >             Greetings all,
> >             I'm hoping someone here could help out. Let's imagine I had
> some data where each row was a person's career. We could list major events
> every year.
> >
> >             For example: 2004 they were highered, 2007 they get a
> promotion, 2010 they leave for a different company, 2012 they come back at
> a higher level, 2015 get a promotion, then no change until 2022.
> >
> >             Let's say I had data like this for roughly 2 million people,
> and that there are around 10 different types of changes that could happen
> during any time period (could be yearly, quarterly, monthly, I can make it
> how I want).
> >
> >             I was hoping we could ask a computer to tell us if there
> were "types of careers" that people had. We could say "put all these
> careers into 4 buckets" or "7 buckets" based on similarity. Then we could
> look at the piles the computer made and try to make sense of them.
> >
> >             One type might be "company man" for people who tend to stay
> in place for 20 or more years, another type could be a "rotator", who
> leaves and returns every 3 years or so. Etc. The point is, I want a
> computer to make the piles for me, rather than trying to come up with
> potential piles a priori.
> >
> >             Are there methods for doing this? I know it's a problem
> we've *talked* about a lot, but I don't know if there are solutions.
> >
> >             Any help would be appreciated.
>
> --
> ꙮ Mɥǝu ǝlǝdɥɐuʇs ɟᴉƃɥʇ' ʇɥǝ ƃɹɐss snɟɟǝɹs˙ ꙮ
> -. --- - / ...- .- .-.. .. -.. / -- --- .-. ... . / -.-. --- -.. .
> FRIAM Applied Complexity Group listserv
> Fridays 9a-12p Friday St. Johns Cafe   /   Thursdays 9a-12p Zoom
> https://bit.ly/virtualfriam
> to (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/
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://redfish.com/pipermail/friam_redfish.com/attachments/20230111/5cbe97dd/attachment.html>