The problem of fighting at night

The rule of war that battles have to commence at/post sunrise and conclude at/pre sunset was inspired by a lot of practical considerations (like most other rules in dharma) beyond mere 'follow this because this is virtuous'. 

The ancients lived in an already hostile world that became much more hostile once their only real source of light disappeared. There was fire ofc and there are records of soldiers coping with the darkness with their fire torches and oil lamps, but that can only help so much.

Men primarily identified each other(allies or enemies) by size/color/shape/design.. of their standards(dhwaja) and outfits (armor, gear), which quickly became ineffective after dark. In a battle, it’s dangerous to have people wildly swinging swords & shooting arrows in the dark without knowing who they might hit.

Another thing to consider is that battlefield itself is quite an unforgiving place to be in. Not being aided by light, you risk falling into ditches, holes, hitting rocks, stumbling, falling and getting cut/pierced by blades and arrows lying on the ground.

Night-time also means exposing yourself to the elements (wind/chill) and night crawlers - snakes, scorpions, porcupines, and also leopards, jackals, wolves, bears, etc. Very hard to defend yourself against this when you're preoccupied with a battle at the same time.

Another practical constraint I forgot to add in the earlier paragraph was that if you are carrying a fire torch, you have to free your non-dominant hand. This means dropping the weapon or the shield that you were carrying, which puts you more at risk.

Lastly, the issue of sleep which has been discussed at great lengths too. Fighting all day on the battlefield with heavy metal armor, swords, lances, bows-arrows must've already been pretty exhausting for the soldiers and letting it bleed into the night means overworking them at reduced cognitive capabilities.

The rule was mostly unviolated by either of the parties because there was significant loss to be prevented by each party if they upheld it.

A basic primer to hypothesis testing (part 1)

Summarizing an earlier post, I'd say that the basis of scientific enquiry is to model systems (a part of the world around us) by making falsifiable empirical claims (hypotheses) about it. These hypotheses are then formally validated by first constructing a well defined experiment around them that generates data from the system under observation, and then statistical analysis of said data. Post analysis, these hypotheses are either rejected or provisionally supported, along with revision in our models if necessary.

In this post, I would like to chalk out the formal validation process of the hypothesis with the example of a fear conditioning experiment. [TODO: add link]

Suppose you run a fear conditioning experiment and collect pupillometry data along with it. A data processing pipeline (perhaps developed by someone like me) will take in the eye-tracking data generated for the experiment and after multiple stages of preprocessing and modelling generate output pupil size responses for each of the conditions CS+ (conditioned stimulus followed by shock, here rotated: square), CS- (conditioned stimulus not followed by shock, here: square)

Consider that the final output looks something like this:

participant_id	cs_p_response	cs_m_response
p_001	0.838	0.256
p_002	0.842	0.278
p_003	0.657	0.392
p_004	0.769	0.138
p_005	0.800	0.169

Our primary aim in this exercise is to prove that post-conditioning, the participant does associate CS+ with the aversive stimulus, which would be indicated through increased CS+ response in comparison to the CS- response. So, we construct two hypotheses:

H₀: There is no effect or no difference in CS- and CS+ responses.
H₁: There is an positive effect or a positive difference in CS- and CS+ responses.

**One important point to note about hypothesis testing is that we always test against the null hypothesis instead of "trying to prove the alternate hypothesis."

Now, before we can statistically prove/disprove the null hypothesis, it should do us good to just try and plot our data to eyeball if there is some difference in the distributions of CS+ and CS- responses.

Looking at the plot, one can clearly see that the CS+ responses are higher in general (the blue curve on the right) then the CS- responses (the orange curve on the left). This supports our alternate hypothesis H₁. 

---

Our job would've ended here if this was to be considered enough proof of validity of H₁ (rather, invalidity of H₀). Alas, that is not the case and visual inspection is nowhere rigorous enough to conclude our study. We must employ actual statistical tests (like the Student's t-test) to prove our hypothesis. 

We need to quantify the difference the two distributions corresponding to the two groups in order to show that the difference is not (distributed) how we expect it to be (difference = 0, or rather centered around 0).

[TODO: expand]

What is Science?

07/07/2025

I was actually writing a different post about hypothesis testing (still in drafts) and this point came up in the introduction. I realized that I don't really have a solid answer to this question. I have a vague idea of what all is "scientific", but I don't have a definition per se. In this post, I'll try to converge in on the answer as best as I can, but I get the feeling that the answer will be inadequate and I will have to return to this post multiple times over the years tighten it more and more.

As soon as I think about science, the following things come to mind:

• curiosity
• observation
• data
• hypotheses
• experiment
• modelling
• testing
• analysis
• descriptive v/s predictive

Science, the way I see it, is an exercise in modelling 'systems'. One wonders(curiosity) about the world around them(systems) and tries to understand it(model them) to the best of their abilities. One constructs hypotheses(falsifiable empirical claims) based on the models, which are rigorously tested through well designed experiments followed by data acquisition and analysis. We either reject or fail to reject these hypotheses to generate provisional knowledge (in science, we can never have the final truth)  that is epistemically justified (justified through evidence and a process of interpreting that evidence).

I know this is too wordy, but for now this is the closes to a definition that I can get to.

Presentism

I was writing this post about the current politics surrounding Bajirao and learnt a new word: Presentism.

It means: 

"uncritical adherence to present-day attitudes, especially the tendency to interpret past events in terms of modern values and concepts."

Good to know. Quite useful when you see people commenting about ancient religious practices, social customs or what not.