Objective values are NOT specifications of what agents SHOULD do.
They are simply explanatory principles. The analogy here is with the
laws of physics. The laws of physics *per se* are NOT descriptions of
future states of matter. The descriptions of the future states of
matter are *implied by* the laws of physics, but the laws of physics
themselves are not the descriptions. You don't need to specify future
states of matter to understand the laws of physics. By analogy, the
objective laws of morality are NOT specifications of optimization
targets. These specifications are *implied by the laws* of morality,
but you can understand the laws of morality well without any knowledge
of optimization targets.
Thus it simply isn't true that you need to precisely specify an
optimization target ( a 'goal') for an effective agent (for instance
an AI). Again, consider the analogy with the laws of physics.
Imperfect knowledge of the laws of physics, doesn't prevent scientists
from building scientific tools to better understand the laws of
physics. This is because the laws of physics are explanatory
principles, NOT direct specifications of future states of matter.
Similarly, an agent (for instance an AI) does not require a precisely
specified goal , since imperfect knowledge of objective laws of
morality is sufficient to produce behaviour which leads to more
accurate knowledge. Again, the objective laws of morality are NOT
optimization targets, but explanatory principles.
The other claim of the objective value sceptics was that proposed
objective values can't be empirically tested. Wrong. Again, the
misunderstanding stems from the mistaken idea that objective values
would be optimization targets. They are not. They are, as explained,
explanatory principles. And these principles CAN be tested. The test
is the extent to which these principles can be used to understand
agent motivations - in the sense of emotional reactions to social
events. If an agent experiences a negative emotional reaction, mark
the event as 'agent sees it as bad'. If an agent experience a
positive emotional reaction, mark the event as 'agent sees it as
good'. Different agents have different emotional reactions to the
same event, but that doesn't mean there isn't a commonality averaged
across many events and agents . A successful 'theory of objective
values' would abstract out this commonality to explain why agents
experienced generic negative or positive emotions to generic events.
And this would be *indirectly* testable by empirical means.
Finally, the proof that objective values exist is quite simple.
Without them, there simply could be no explanation of agent
motivations. A complete physical description of an agent is NOT an
explanation of the agent's teleological properties (ie the agent
motivations). The teleological properties of agents (their goals and
motivations) simply are not physical. For sure, they are dependent on
and reside in physical processes, but they are not identical to these
physical processes. This is because physical causal processes are
concrete, where as teleological properties cannot be measured
*directly* with physical devices (they are abstract) .
The whole basis of the scientific world view is that things have
objective explanations. Physical properties have objective
explanations (the laws of physics). Teleological properties (such as
agent motivations) are not identical to physical properties.
Something needs to explain these teleological properties. QED
objective 'laws of teleology' (objective values) have to exist.
What forms would objective values take? As explained, these would NOT
be 'optimization targets' (goals or rules of the form 'you should do
X'). They couldn't be, because ethical rules differ according to
culture and are made by humans.
What they have to be are inert EXPLANATORY PRINCIPLES, taking the
form: 'Beauty has abstract properties A B C D E F G'. 'Liberty has
abstract properties A B C D E F G' etc etc. None the less, as
explained, these abstract specifications would still be amenable to
indirect empirical testing to the extent that they could be used to
predict agent emotional reactions to social events.
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