Notes | Tutorial 11 | Yes/No 1/2

Date: 2020-07-08

• 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

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