That's a very wide reach. Are you sure you're not using "rationality" just as a synonym for "doing something really well"?
I mean do well in the areas I talked about before. In summary, I basically mean do well at coming up with solutions to problems or choosing/being able to go through with the best solution, out of all of the solutions you have come up with, to a problem.
I will try to define it again.
First off, there is comprehensive rationality or normative rationality. This does not consider agent limitations. It can be thought of as having two types.
In both cases, choices among competing goals are handled by something like indifference curves.
We could say that under the comprehensive rational model a rational agent is one that maximizes its expected utility, given its current knowledge.
When we talk about rationality, though, we normally mean in regards to humans. This means that we are talking about bounded rationality. Like comprehensive rationality, bounded rationality assumes that agents are goal-oriented, but bounded rationality also takes into account the cognitive limitations of decision makers in attempting to achieve those goals.
Bounded rationality deals with agents that are limited in many ways which include being:
The big difference between bounded rationality and normative rationality is that in bounded rationality you also consider the agent improving its ability to choose or come up with the best outcomes as rational as long as there are no costs or missed opportunities involved.. Therefore, a rational agent, in the bounded sense, is one that has three characteristics:
Do you think it could be reformulated in the framework where values form tree-like networks with some values being "deep" or "primary" and other values being "shallow" or "derived" or "secondary"? Then you might be able to argue that a conflict between a deep and a shallow value should be resolved by the declaring the shallow value not rational.
I think that once a value is in. It is in and works just like all the others in terms of its impact on valuation. However, a distinction like the one you talked about makes sense. But, I would not have 'deep' and 'shallow' because I have no idea how to determine that. Perhaps, 'changeable' vs 'non-changeable' would be better. Then, you can look at some conflicting values, i.e. ones that lead you to want opposite things, and ask if any of them are changeable and what the impact is from changing them. The values that relate to what you actually need are non-changeable or at least would cause languishing if you tried to repress them. I think the problem with the tree view is that values are complex, like you were talking about before, one value may conflict with multiple other values.
So how to avoid being caught in a loop: values depend on values which depend on values that depend on values..?
I don't see the loop. This is because there is no 'value'. There is only coherence which is just how much it conflicts with the other values. I don't know how to describe this without an eidetic example. Please let me know if this doesn't work. Imagine one of those old style screensavers where you have a ball moving across the screen and when it hits the side of the screen it bounces in a random direction. Now, when you have a single ball it can go in any direction at all. There is no concept of coherence because there is only one ball. It is when you introduce another ball that the direction starts to matter as there is now the factor of coherence between the balls. By coherence I mean simply that you don't want the balls to hit each other. This restricts their movement and it now becomes optimal for them to move in some kind of pattern with vertical or horizontal lines being the simplest,
What this means for values is that you want them to basically be directed towards the same or similar targets or at least targets that are not conflicting. A potential indicator of an irrational value is one that conflicts with other values, Of course, human values are not coherent. But, incoherence is still an indicator of potential irrationality.
Unrelated to the above examples is that you would need to think about if the target of the value is actually valuable and is worth the costs you have to pay to achieve it, this is harder to find out, but you can look at the fundamental human needs. Maybe, your deep vs. shallow distinction would be useful in this context.
I am not sure I understand -- is "most trusted source" subjective? What if Jesus is my most trusted source? And He is for a great deal of people.
I don't think I am conveying this point well. I am trying to say that we only have an incomplete answer as to what is rational and that science provides the best answer we have.
One very common way of making a definition is to point to a well-known class,
I think instead of that type of defintion I would rather say that rationality means doing well in the areas of X, Y and Z. and then have a list of skills or domains that improve your ability in the areas of rationality.
Do you think that there are many types of rationality? I think that there are many types of methods to achieve rationality, but I don't think there are many types of rationality.
So if we were to try to give an is-a-kind-of defintion of rationality, what is the super-class? Is it reasoning? Is it skills? Something else?
I would say reasoning or maybe problem solving and outcome generation/choosing better convey the idea of it.
I have a feeling we're starting to go in circles. But it was an interesting conversation and I hope it was useful to you :-)
A perfect rationalist is an ideal thinker. Rationality ↓, however, is not the same as perfection. Perfection guarantees optimal outcomes. Rationality only guarantees that the agent will, to the utmost of their abilities, reason optimally. Optimal reasoning cannot, unfortunately, guarantee optimal outcomes. This is because most agents are not omniscient or omnipotent. They are instead fundamentally and inexorably limited. To be fair to such agents, the definition of rationality that we use should take this into account. Therefore, a rational agent will be defined as: an agent that, given its capabilities and the situation it is in, thinks and acts optimally. Although it is noted that rationality does not guarantee the best outcome, a rational agent will most of the time achieve better outcomes than those of an irrational agent.
Rationality is often considered to be split into three parts: normative, descriptive and prescriptive rationality.
Normative rationality describes the laws of thought and action. That is, how a perfectly rational agent with unlimited computing power, omniscience etc. would reason and act. Normative rationality basically describes what is meant by the phrase "optimal reasoning". Of course, for limited agents true optimal reasoning is impossible and they must instead settle for bounded optimal reasoning, which is the closest approximation to optimal reasoning that is possible given the information available to the agent and the computational abilities of the agent. The laws of thought and action (what we currently believe optimal reasoning involves) are::
Descriptive rationality describes how people normally reason and act. It is about understanding how and why people make decisions. As humans, we have certain limitations and adaptations which quite often makes it impossible for us to be perfectly rational in the normative sense of the word. It is because of this that we must satisfice or approximate the normative rationality model as best we can. We engage in what's called bounded, ecological or grounded rationality ↓ . Unless explicitly stated otherwise, 'rationality' in this compendium will refer to rationality in the bounded sense of the word. In this sense, it means that the most rational choice for an agent depends on the agents capabilities and the information that is available to it. The most rational choice for an agent is not necessarily the most certain, true or right one. It is just the best one given the information and capabilities that the agent has. This means that an agent that satisfices or uses heuristics may actually be reasoning optimally, given its limitations, even though satisficing and heuristics are shortcuts that are potentially error prone.
Prescriptive or applied rationality is essentially about how to bring the thinking of limited agents closer to what the normative model stipulates. It is described by Baron in Thinking and Deciding ↓ pg.34:
The behaviours and thoughts that we consider to be rational for limited agents is much larger than those for the perfect, i.e. unlimited, agents. This is because for the limited agents we need to take into account, not only those thoughts and behaviours which are optimal for the agent, but also those thoughts and behaviours which allow the limited agent to improve their reasoning. It is for this reason that we consider curiousity, for example, to be rational as it often leads to situations in which the agents improve their internal representations or models of the world. We also consider wise resource allocation to be rational because limited agents only have a limited amount of resources available to them. Therefore, if they can get a greater return on investment on the resources that they do use then they will be more likely to be able to get closer to thinking optimally in a greater number of domains.
We also consider the rationality of particuar choices to be something that is in a state of flux. This is because the rationality of choices depends on the information that an agent has access to and this is something which is frequently changing. This hopefully highlights an important fact. If an agent is suboptimal in its ability to gather information, then it will often end up with different information than an agent with optimal informational gathering abilities would. In short, this is a problem for the suboptimal (irrational) agent as it means that its rational choices are going to differ more from the perfect normative agents than the rational agents would. The closer an agents rational choices are to the rational choices of a perfect normative agent the more that the agent is rational.
It can also be said that the rationality of an agent depends in large part on the agents truth seeking abilities. The more accurate and up to date the agents view of the world the closer its rational choices will be to those of the perfect normative agents. It is because of this that a rational agent is one that is inextricably tied to the world as it is. It does not see the world as it wishes it, fears it or has seen it to be, but instead constantly adapts to and seeks out feedback from interactions with the world. The rational agent is attuned to the current state of affairs. One other very important characteristic of rational agents is that they adapt. If the situation has changed and the previously rational choice is no longer the one with the greatest expected utility, then the rational agent will adapt and change its preferred choice to the one that is now the most rational.
The other important part of rationality, besides truth seeking, is that it is about maximising the ability to actually achieve important goals. These two parts or domains of rationality: truth seeking and goal reaching are referred to as epistemic and instrumental rationality. ↓
As you move further and further away from rationality you introduce more and more flaws, inefficiencies and problems into your decision making and information gathering algorithms. These flaws and inefficiencies are the cause of irrational or suboptimal behaviors, choices and decisions. Humans are innately irrational in a large number of areas which is why, in large part, improving our rationality is just about mitigating, as much as possible, the influence of our biases and irrational propensities.
If you wish to truly understand what it means to be rational, then you must also understand what rationality is not. This is important because the concept of rationality is often misconstrued by the media. An epitomy of this misconstrual is the character of Spock from Star Trek. This character does not see rationality as if it was about optimality, but instead as if it means that ↓:
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