If instead you keep deliberating until the balance of arguments supports your preferred conclusion, you’re almost guaranteed to be satisfied eventually!
Inspired by the above, I offer the pseudo code version...
loop {
if assess(args, weights) > 1 { // assess active arguments
break; // preferred conclusion is "proved"
} else {
arg = biased_sample(remaining_args); // without replacement
args.insert(arg);
optimize(args, weights); // mutates weights to maximize `assess(args, weights)`
}
}
… the code above implements “the balance of arguments” as a function parameterized with weights. This allows for using an optimization process to reach one’s desired conclusion more quickly :)
Inspired by the above, I offer the pseudo code version...
… the code above implements “the balance of arguments” as a function parameterized with weights. This allows for using an optimization process to reach one’s desired conclusion more quickly :)