6. Is explanation a prerequisite for prediction ? Explore this question in relation to two areas of Knowledge. – full answer


3755152223_1f715ca7fa_oThis blogpost is a set of thoughts regarding Prescribed Title 6, Nov 2015, “”Is explanation a pre-requisite for prediction ? Explore this question in relation to two Areas of Knowledge”. It is not intended to be a model essay plan, nor a set of completed thoughts. It is neither the best, nor the only, answer. This post is just intended to help students start their thinking about this question, and to help initiate discussions between ToK Teachers and their students. 

Like all Tok Essays you will need to have clear definitions of explanation and prediction. These could come at the beginning of your essay, or be developed through your essay. These definitions could vary by the AoK considered (e.g. a prediction in Art may not be the same as a prediction in Natural Sciences). Some writers may decide to base their whole essay around defining these two terms.

My approach to this essay is only one of innumerable ways that this essay could be written, my approach is not necessarily the best way to approach the essay, it is only intended to help to develop conversation amongst our ToK community.

My approach to this question would be to base it around the problematic nature of causality, looking at the difficulties in establishing causes and their consequent effect . I would argue that if explanation is a prerequisite of prediction then we are accepting that we have a fairly accurate understanding of cause and effect. In other words, if explanation is required for prediction then we know how changes in things cause effects.

In the context of causality students could consider whether explanation is a necessary or sufficient requirement for prediction. In examples in which it is deemed a necessary requirement students would be arguing that prediction cannot happen without explanation. In examples in which it is deemed a sufficient requirement for prediction we are arguing that a prediction may have come from explanation, but it may also have come from other causes. The discussion of the necessity and sufficiency of explanation could constitute a definition of the term ‘pre-requisite’ in the PT.

Possible Knowledge Questions:

1. How can we establish the extent to which human error distorts our ability to accurately measure the effects of causes ?

2. How can we establish whether phenomena labelled as ‘Causes’ are not actually ‘Effects’ ?

3.  Do we need to include a greater factor of randomness into mathematical models of real world events (e.g. weather) in order to increase the accuracy of the model ?

4. Are models of irrational behaviour inherently antithetical to the scientific process ?

5. Can an explanation be defined by the dominant ways in which the knowledge was gathered to make that explanation ?

6. How can we describe the ways in which making predictions influences future behaviours and perceptions ? (this could be applied to a specific AoK)

There are innumerable KQ’s that you could base your essay around, those included here are just examples of the sort of knowledge questions that you can develop, you should use your own KQ rather than copy one of mine – you will do better if you use your own KQ. If you need more help in devising your knowledge question please click here.

Writers are expected to explore the question in relation to 2 AoKs. The AoKs which you choose will determine the Real Life Situations (RLS) and Personal Knowledge that you will use to exemplify your views on the KQ.  Therefore choose AoKs which interest you, which help you to exemplfy your KQ(s), and from which you can draw RLS. I will briefly explore a few AoKs in relation to the question. Your exploration of your chosen AoKs will have to be for more detailed than mine (as you are required to look at only 2 KQ’s).

Let’s start with AoK’s which are mainly / wholly concerned with the establishment of cause and effect: Natural Sciences, Mathematics and Human Sciences.

Natural Sciences and Human Sciences use the Hypothetico Deductive Model in order to attempt to falsify cause and effect relationships – see Kant’s Black Swans, or Popper’s Principle of Falsifiability. As such, in relation to Nat & Hum Sci’s it is fairly easy to equate explanation with causation, and therefore to claim that scientific law (‘facts’) is prediction based upon explanation.  A myriad range of RLS could be used to demonstrate that scientific relationships are predictions based upon explanations. For example students could draw upon the discovery of the causes of a specific disease, or psychological theories of behaviour.

Similarly there are numerous areas of mathematics which could be seen as ‘prediction’. These include use of probability statements, the array of inferential statistical tests, use of regression analysis, application of ‘big data’ in real life contexts. Students can draw upon real life situations to show how mathematical models have been used to make predictions, examples which come to mind are weather forecasting, economic forecasting, political polls, GPS route analysis, retail stock plans, electricity production plans, airline fuel hedging etc etc, the list is nearly endless.

The initial task for students using this approach would be to establish the causal factors as ‘explanation’, and the outcomes of the models as ‘prediction’.

However, use of explanation for prediction in Mathematics, Natural Sciences and Human Sciences becomes problematical when we introduce randomness. The probability that an unpredictable, or seemingly random, event will happen defines the accuracy of the model. As such, it could be argued that the existence of the unpredictable event means that explanation is not a necessary requirement for prediction. Students could cite events which were not predicted by the relevant models as RLS examples e.g. Hurricane Katrina, the 2008 global economic crash, the UK Conservative Party election win 2015 etc etc – you should use your own example.

The ‘explanation’ value of the hypothetico deductive model can be further questioned when we consider irrational behaviour (or outcomes). These are behaviours and outcomes which do not accord with reasoned logic, or contradict the logical outcome. The classic example of irrational behaviours is The Monty Hall problem. Students could draw upon real life examples of seemingly irrational behaviour, or outcomes, in order to exemplify their knowledge question, and to develop a counterclaim to hypothetico deductive model. I would strongly recommend the (wonderful) book by Dan Ariely Predictably Irrational. 

Religious Knowledge Systems.

Students could easily develop an argument that many Religious Knowledge Systems make predictions. The question whether explanations are a pre-requisite for predictions arising from Religious Knowledge Systems very much depends on how we define ‘explanations’. Once we have defined ‘explanation’ within a Religious Knowledge System we can then explore whether explanations are a necessary, or sufficient, or both, requirement for prediction.

In exploring what constitutes an explanation students could argue that allegorical religious content is an explanation. Further it could be argued that religious explanations are an inherent fault which challenge the very concept of faith. These arguments introduce Ways of Knowing into the definition of an explanation. This introduces the 5th KQ above: Can an explanation be defined by the dominant ways in which the knowledge was gathered to make that explanation ?

Students may want to consider the PT in terms of the ontological arguments for the existence of God. Using this approach I assume that the questions of ontology constitute the ‘explanation’ defined within the PT. Such a process may lead students to argue that the debate of existence is explication rather than explanation.

The Arts.

The Arts presents us with an interesting contrasting approach to the PT. My first consideration of the PT within The Arts is how we define ‘prediction’ ? I would be tempted to argue that prediction within the Arts is very different to prediction in Maths, Natural & Human Sciences. An artistic prediction could be seen through the different ways of knowing, as such I would argue that Imagination, Intuition and Emotion play a much bigger role in ‘artistic prediction’ than they do in predictions arising from natural sciences, human sciences and Maths.

Students could then explore whether knowledge derived from intuition, imagination or emotion constitutes an explanation. In order to take this approach students would need to establish the outcome of the artistic process as the product of prediction. Use of real life examples to exemplify this discussion would clarify – e.g. if a writer intuitively felt that their book would be successful, or an artist used imagination to determine the outcome of their art.

This discussion, concerning the Arts, could also extend to the necessary and sufficient conditions of explanation required for prediction. However, as innovation is such an important aspect of artist outcomes then an inverse error fallacy could be explored in relation to predictions in the arts. An inverse error fallacy is explained by Wikipedia thus:

It is committed by reasoning in the form:

If P, then Q.
Not P.
Therefore, not Q.

as a philosophical RLS:

If Rene Descartes was thinking, then Rene Descartes existed at the time.

It happened once that Rene Descartes was not thinking.

Therefore, Rene Descartes did not exist at the time.

and as applied to our question in relation to The Arts as an AoK:

 If a painting is realistic then the painting was notable,

Van Gogh’s painting Starry Night was not realistic.

Therefore, Van Gogh’s painting Starry Night was not notable.

The logical progression as outlined above describes the process of European development of Art until the final step. Applied to our question we could argue (as a counterclaim) that the argument that explanation is a prerequisite for prediction describes an inverse error fallacy, ie that explanation may be a sufficient condition for prediction, but it is not a necessary condition. Further, some explanations may not be useful for prediction of artistic expression as explanations are based on previous conditions which could be inherently antithetical to further artistic development of the genre (this could easily be applied using RLS & personal knowledge of clothing fashions).

In closing I stress the following points:

  • Ensure that your KQ links to the PT.
  • Use Real Life Situation & Personal Knowledge which exemplify your KQ.
  • Make claims and counterclaims clear, evaluate both.

enjoy your writing,

Daniel.

 

 

 

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