# Source

# bubble-economy / TODO.rst

# TODO

## Action plan

Sub-lists are are in descending priority order.

# Absolute top priority task killing finality

- # work out why velocities all appear to be in the same quadrant for quite
- high noise runs
# bootstrap everything # keep trial stat arrays around in summaries so that R can do the

aggregation# generate the final ultimate kernel density estimates of behaviours in R # have different number of samples per-axis

# Faster simulation

- # throw out some normalisations- just make sure that random vectors are on
- average unit length.
# use gridding to bin cells into neighbourhoods

# wilder interpretations

- # phase transition against the percent of distance each timestep goes to
- randomness
# warp P1 axis into angular noise analog (Finding it analytically?)

# Faster, more powerful MI

# use a priori binning # worry about missing bins for, e.g. correctors # go to CUDA for mi calcs? - http://users.cecs.anu.edu.au/~ramtin/cuda.htm # use binless inference? (kernel density? spline? k-neighbour?) # does a kd tree give me MI for free? # use Cython to wrap NSB_entropy

properly instead of calling as a subprocess

# Better stats

# 2d graphs throughout for gridded sims # use bootstrap estimator # make sure those "Warning: Null output conditional ensemble for output

foo" don't reflect bad params# evaluate using velocity deltas # test on other phase transition data # consider stationary vs non-stationary # check susc. stat. Doesn't seem to be the same as order std dev. # consider ergodic walks and independence

# Better sampler

# importance sampling # persist job management state across crashes in the picloud dispatcher.

# Better visualisation

# animation of acting agents

# as slices of a 2d/3d thing # as a 3d animation (see also)# mark lines for B&W using plot

# experiments to do

# higher dimensional behaviour # time window sweep # small-number-of-agents analysis

## Axis interpretation

- valuations of equities
- buyin' and sellin' agents
- equities (must conserve)
- total market value.
- think about stock market in terms of axes and dimensions - equities are close if they have similar historical price movements - that is, how good they have been as predictors of each other in the past - rather than current price value. and price changes come from stock movements. How would one link equities and traders in this fashion?

## Pie-in-the-sky

- explore alternative distance measures that allow similarity in a small number of dimensions (Cosine distance? correlation distance? extended jaccard distance?)
- scale number of agents to keep Pi_4 constant in higher dimensions
- add option to remove self-adjacency from calculations at all stages and explore consequences
- sanity-check minkowski distance
- handle more boundary constraints * esp. bounded-below-only boundary conditions
- calculate prices using demand curve
- integrate Sumatra
- integrate Ruffus
- better test coverage
- document for posterity
- contribute back to pyentropy
- export to a gephi animated graph