In terms of the defintiion, it is in the title.
That's not really a definition: you just shifted the entire burden onto the word "optimally". A basic use of a definition is to see if something fits it -- if we defined a class A, is object z a member of that class? So let's say I'm considering some action. Is it rational? Well, it is if it's optimal. Err.. and what does that mean? To answer I need to define optimality and that is not trivial. And if you say that optimality is maximizing (expected) utility, we're back to your original definition which I poked at and you abandoned.
the property of a system which does the "right thing"
That's exactly the same thing -- replacing one word with another (or two) without clarifying anything.
whatever action is expected to maximize its performance measure
That's maximizing expected utility, again.
we should not blame an agent
Yes, of course, but that doesn't help you with a definition.
Before we get too deeply into problems with whatever definition I might use, I want to make sure that you agree that what I am trying to say is right. Once that is confirmed, then I can think more about how to say it well.
This is basically what I am trying to say in the post. 'Rationality' is 'optimal reasoning' which we know of as normative rationality, i.e. the laws of thought and action or what the perfect agent would do. A caveat is that when we talk about rationality in regards to limited agents we are really talking about bounded rationality. Hence,...
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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