Critical Thinking: An Introduction to Investigations and Recommendations

A Request for a Solution needs a problem, and problems need to be explained. So, an RFS will ultimately be motivated by anomaly similar to how anomaly motivates Investigations. For example, if I ask you how I can get the car started, I will need to first answer why the car won’t start. Solutions can apply to anyone; they don’t depend on what anyone wants. Instead, they depend on what is happening.

A Request for Guidance presupposes a lack of knowledge about choices, and so preferences or opinions are essential. We look for a RFG when we are not motivated by anomaly, hypothetical or actual. For example, if I ask you which movie you’d like to go see, or at which restaurant you’d like to eat, nothing has gone wrong. Guidance is tailored to specific people or groups with specific wants and needs.

Consider the following:

If we treated this as a Request for Guidance, we would include Aspirations and Aversions, but those would sound strange. For example, we might list:

Our motivation to ask about traffic isn’t our desire for free flowing traffic. Rather, we’re motivated by the fact that, sometimes, traffic backs up unexpectedly. There’s a problem to be solved, not a preference to be met.

Similarly, consider the following:

If this were a RFS, there would be a problem to solve, as expressed in a Distinguishing Condition. We might be tempted to say:

In this, the “problem” is personal. A problem expressed in the form “I don’t know X ” suggests a need for guidance more than solution, because the problem is in “I” rather than in the world. Preferences are personal; problems are public.

Let’s put these distinctions into practice.

In this, the Best Recommendation takes into account the two Aversions. If we retain recordings of lectures, we will not see a drop in engagement because of our actions. Second, since good student performance correlates with attending lectures, we will not see a decrease in performance because we eliminated lectures. Engagement and performance might decrease anyway, but we can be confident that our actions did not precipitate that decrease.

Narrative Example of an Investigation embedded in a Request for Solution

A Request for Solution begins with the observation that there’s a problem.

Here, we notice that Vladimir has behaved differently than we expected. That’s anomalous behaviour. Noticing this, we wonder how we might make the behaviour stop. In this case, one of the speakers thinks Vladimir is angry. The other thinks Vladimir is trying to motivate students to work harder. Whatever the case, both want to figure out how to make Vladimir feel better. That inspires a Request for Solution:

Notice, when we frame it this way, one possible answer is that there is no way to make him feel better. We’ve already identified one possibility: work harder.

As we consider how to structure our recommendation, we must think of Ordinary Conditions (OC) and Distinguishing Conditions (DC). The story suggests both:

The OC gives us the context in which the DC stands out. Ordinarily, Vladimir talks to us one particular way. But in this case, and what distinguishes this case from an ordinary case, Vladimir has said things he doesn’t usually say. It’s entirely reasonable to ask:

Now, the next step is somewhat academic, but it’s worth making clear. The Evidence to Explain is the fact that Vladimir has changed the way he writes announcements. Let’s list that:

This is an important step: any answer to the LQ must explain E1. In fact, any answer to the LQ will ideally explain all of the evidence presented.

Let’s look at our scenario again. Is there more evidence that might be explained in this investigation?

How about “... I overheard him talking at the cafe. He told Brian he’s pretty impressed with the discussions.” This is the sort of thing that an investigation could explain, given how we’ve framed things. Consider: it’s evidence of how he talks about students, which seems relevant to how he crafts his announcements. Let’s add this to our structure:

Now, with this as Evidence to Explain, we expect our answers to LQ to actually explain this observation.

Let’s simply use what we know to come up with a skeleton of a structure, filling in “anything goes”-style answers to the LQ:

At this point, we have no reasons to accept any one answer over another. We need to give reasons, and those reasons we’ll call Explanatory Resources. We have one resource, which in this case was our Ordinary Condition (notice: it doesn’t always happen that an OC is an ER within our explanation, but in this case, it works.)

This already makes the “joke” rival look less appealing. But to make one of the others really stand out, let’s do some research. We might consult a journal article that Brian wrote about Vladimir: “On the Nature of the Dispositions of Serbian Tutors in New Zealand”, which appeared in the peer-reviewed Journal of Australasian Tutorial Studies. In that article, Brian presents evidence that: “Vladimir, though he possesses an unexpectedly well-developed aptitude for humourous commentary, remains serious when in tutorial settings.” Let’s paraphrase this:

Further research reveals (as discovered in the peer-reviewed Angry Tutor Quarterly) Vladimir was rejected by the New Zealand Angry Tutors Association for “not demonstrating sufficient student-frightening rage in classroom settings”. And so:

Now, consider these Explanatory Resources and how they impact our Rival Answers to the LQ. They make one of the rivals seem far more likely to be best explanation. ER3 undermines RA1: Vladimir is angry with the students. ER2 undermines RA2: Vladimir was making a joke. ER1 underwrites RA3: Vladimir was trying to motivate the students to work harder.

With this, we express:

Let’s look at where we stand with the Investigation:

Now we have a Best Explanation for why Vladimir’s behaviour changed. Let’s get back to the task of figuring out what we can do to make Vladimir feel better.

At this point, we know that he wants to motivate students. So let’s immediately use that as a possible recommendation:

This is where we get creative. What else could we do to improve Vladimir’s mood?

Clearly, if Vladimir was working with a hand-picked group of ultra-motivated students, he would be very happy. Of course, that’s not going to happen, because Vladimir cannot actually pick and choose his students. Ah! That sounds like an excellent Supporting Resource, as it makes one of the rival recommendations (RR2) look less appealing. Let’s add this:

We might also have a look at Brian’s contract (which would be a reliable source, as it’s a government document) to determine whether it’s feasible to ask him to tutor more students. We might discover:

With this, we can create a full RFS, with an embedded LQ as follows:

Notice, in particular, the functions of the Explanatory Resources and the Supporting Resources. The Explanatory Resources make one of the Rival Answers to an investigative Lead Question seem the best. The Supporting Resources make one of the Rival Recommendations in a structured recommendation seem the best.

Notice also that very often we’ll be tempted to say that there are a lot of things we can do to solve a problem. When we make a “Best Recommendation”, we’re saying this recommendation will make the biggest impact on the problem. Reliably, we might say, we’ll have less of a problem if we implement this solution, namely the BR.

The Nature of Prediction

Prediction is applied explanation. It is a projection of a past pattern into the future. Predictions use what happened as a basis for asserting that the same thing will happen again. Put simply, one explanation for why this happened is this is what happens.

Prediction is based on facts or observed patterns, and those facts and patterns gain their reliability from experiences in the world.

Recall what we said previously about induction. The fact that X has happened many times in a row is a good reason to think X will happen again under similar circumstances. That is, a recognisable and consistent pattern involving X is a good reason to think that X will happen again in the future. Our experience of consistency, which is the reason we expect regular results, makes prediction possible.

For example, if Freddie has won every bicycle race he’s entered in Auckland, then we can use this pattern to explain why he won. “Why did Freddie win the Auckland bicycle race?” Because he always wins the Auckland bicycle race. (There might be many other reasons, but this pattern will be among the good reasons we can offer.) On the other hand, if we ask “Who will win next year’s Auckland bicycle race?” Freddie is good prediction, given his winning pattern. The same pattern, Freddie winning Auckland bicycle races, can serve as explanation or prediction, depending on what is in question.