7:00 – 8:00 Research
- Added dimensions. The behaviors from 2D are evident at up to 10 dimensions, though everything takes longer. It is hard to see what’s going on in my simple 2D mapping scheme.
- After playing with the explorer settings some yesterday, I’m not sure what to do with it. I think the abstract instead should just consider the initial modelling of group polarization in a homogeneous population. There are three conditions:
- Isolated – Each agent only pays attention to its previous state. Creates a uniform random distribution. I think this is the archetypal ‘explorer’ pattern, where path through information space is not affected by social activity
- Limited social visibility – Each agent can see other agents up to a specified distance. This produces multiple flocks (usually one large and several smaller) that orbit a center.
- Infinite social visibility – Every agent can see every other agent. This leads to one large flock that moves in a straight line
- So yesterday, I was thinking that the most ‘socially sensitive’ agents would be the explorers, but after writing the above, I realise that it’s the reverse. Explorers are least socially sensitive. To test this, I set the ‘explorer multiple’ to a very low value and cranked up the social visibility for the rest of the agents. This creates a new behavior, where the tightly clustered social agents are more ‘anchored’ to the environment and stay closer to the center of the stage, depending on local concentrations of explorers. Interestingly, this implies that the greater percentage of explorers (up to a point?) means a more grounded group of social ‘exploiters’. Which is pretty interesting.
- Going to try to get ahold of Don to go over these results. Sent email
- Another thought. In addition to the average center, do the variance? Not sure how to do this. Axis-by-axis? Actually, that would work nicely, with center and variance by dimension.
8:30 – 4:00 BRC
- Working on clustering the current data, waiting on DB updates
- Took care of my fall 2016 and spring 2017 education paperwork
- Asked Gregg for the updated DB
- Set up LMN to access the DB.
- Have jdbc running. I feel so ’90s
- Built the view:
CREATE VIEW v_fused AS SELECT ecp.mbrnum, claim.chargeamount, claim.benefittype, claim.physicianname, claim.provpayeename, claim.provfirstname, claim.provzp, claim.rxdrugname, claim.diagnosiscode1, claim.diagnosiscode2, claim.diagnosiscode3, claim.diagnosiscode4, claim.otherdiagnosiscodes, coveragetype, membergender, flab_hrt, flag_acuterenal, flag_bactinf, flag_cerebrovascular, flag_chf, flag_ckd, flag_cnc, flag_copd, flag_fluidelec, flag_htn, flag_otherheart, flag_pleurisy, flag_respfailure, flag_surgothercomps, flag_whitebloodcell FROM tbl_eligibility_chiroacup_polyrx AS ecp JOIN tbl_medicalclaims_chiroacup_polyrx claim on ecp.mbrnum = claim.mbrnum;
- Now I need to code up the extractor. Change all the Yes -> 1, NULL -> 0, build a map of all the strings and make a column for each, then map into that as a Labled2DMatrix, with the row being mbrnum. Then create a spreadsheet. Might be too big. Maybe create a table and read the table directly into the Labled2DMatrix. Kinda like that option…