---
title: Notes | Tutorial 11 | Yes/No 1/2
layout: page
parent: Tutorial Notes
date: 2020-07-08
lesson: 11
---

- persistent error in prev epistemology: 'amounts of goodness'
    - "degrees"
    - "for/against" arguments
        positive adds, negative subtracts

- vagueness: main issue: when the problem is vague
  - insufficient to decide yes/no

- alt to degree arguments: decisive arguments (they have **priority**)
  - only negative
  - some ppl think decisive arguments contradict fallibility (only positive decisive arguments tho, not negative)

- example of decisive args: contradicts {evidence,itself,arithmatic}
- 

we can *always* come up with decisive arguments?

- correctness vs usefulness vs truth
  - **relative to goal!**

# getting decisive arguments

1. better goals (clear, precise)

premature work in arguments wasted if we don't have good goals

# focus - too many things at once?

TOC - chain w/ 50+ links; 48 have excess capacity / buffer so are easy.

primary goals: low number, spcific and picky; aiming for better than pass
secondary: low focus, not a bottlenecks; just need to pass

limit # of bottlenecks

too many things => unstable (balanced plant)

subproblems -> only some factors

how do you get excess capacity with binary factors? must be reasonably easy in context of one's life

divide goals into many factors with binary evaluation

## analogue factors

ways to convert analogue factors to binary (all reasonable valid ones) -- and they *should* be

### breakpoints

pet: >$30 too much -> binary factor; even if more lower => more better

discontinuity; notable difference at a point, a jump / cusp

reasons certain values are important, cannot think about infinite things but continuums have infinite points; waste to think about early/med/late too specifically

examples:

* look at every integer value; **pseudo breakpoints**; not important conceptually but is easier to think about / less needed in head -- better than nothing, arbitrary breakpoints, space them evenly or appropriately (could space linearly on inputs)
  * can avoid being mislead through too much precision; obscures details
  * percentages imply continuous

maximisation problems get to binary (q: re optimisation)

maximise many factors with conversion factors
compound goals -> don't convert to single score

degree epist -> converts ALL factors to one score
binary epist -> can have orthogonal factors -> never convert

https://www.newyorker.com/magazine/2011/02/14/the-order-of-things
-> conversion factors hard, easy to come up with factors, har to come up with score; money and time often easier to agree on conversion factors
-> policy in democracy

compound goals avoids need for conversion (can avoid putting a price on some things, for example)

### compound goals

might need to be less picky, e.g. 4 of 5 of these goals

#### many solns

* pick any, it's fine
* consider more goal criteria, more ambitious compound goal
  * could inspire better solns

similar to cycle of conjecture and crit

in general we calibrate goals to abilities

#### no solns

* brainstorm more solns
  * brainstorm for smaller sets of criteria
* brainstorm workarounds for particular criteria / goals
* be less picky

(when brainstorming) direct/indirect soln:
* direct answers problem
* indirect does something else / changes conditions / criteria / workarounds






















# aux

max has idea re math + fallibility

analogue factors: IBDD/legislation/budgets
  - complexity - multiple acceptable answers
  - can you express preferences like this to eval multiple candidates? avoids problems of ranking in pref. systems