Theory Mapping Truth Test

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What?

A test is applied to the Theory Maps to assess which theory has the highest probability of being true. This test is the coherent explanations of facts.

Why?

There are a number of benefits to having a test of the truth:

• Ease of theory selection: Theory Mapping only deals with theories concerned with matters of truth not value, where truth is defined as the accurate prediction of sense experiences (see the post Truth - what is it? for a justification of this) i.e. it is only concerned about what 'is' rather than what 'ought' to be. Theories must therefore be selected primarily according to their relatively probability of being true. The theory which scores the best is not proven to be true, but is the most rational to act on given our current knowledge of the facts and the range of theories available.
  
• Correct incentives: if you have an effective test of the truth that everyone can agree to, and there are rewards gained from a theory scoring well (whether in terms of social esteem or money), you have a very powerful focusing of human intellectual energy since everyone's incentives are aligned with finding the truth. The scientific community has this as a result of the scientific method, but many other areas of intellectual endeavour do not. As a result, incentives are distorted, with people focusing on gaining esteem or money directly in ways that either waste time (e.g. academics focusing on writing papers to impress or trying to ruin each others reputations), or are downright damaging (e.g. misinformation and fraud). For how this applies to particular areas, see Theory Mapping and World Views, and Theory Mapping and Academic Research.
  

In terms of why I am suggesting the coherent explanation of facts as the truth test in Theory Mapping, see the following posts: Truth – how to find it?, Coherent explanation of the facts, and Theory Mapping and Epistemology.

How?

There are three key parts involved in the coherence test (to understand this you should first read about Stages 1, 2 and 3 in the Process of Theory Mapping):

1. Explanatory power
This is how well the theory is able to explain and fit the Agreed Facts. The primary purpose of a theory in Theory Mapping is to give an account of reality that can explain and fit our observations of it as represented by the Agreed Facts. I would suggest that the best way to measure this, is to require that every Agreed Fact be ‘explained’ by the theory in at least one of three ways:

• Provide an answer to the Research Question, which will explain a large number of the facts automatically;
• Use the Agreed Fact as evidence in support of an argument, since the argument will also explain the Fact;
• Provide a separate explanation for any Agreed Facts that have not been covered by the other two methods. This is most likely to be the case for Agreed Facts that support competing theories e.g. in the JFK case, the evidence for conspiracy.

The measure is then simply to go through the table of Agreed Facts that has been constructed in Step 2 and count how many that are not explained in any of these three ways.

If some Agreed Facts are considered to be more important than others (e.g. for the JFK case if physical evidence is considered more reliable than eyewitness evidence), a weighted sum could be taken, with the weights varying according to the level of importance.

In the map of the Lone Gunman Theory, 3 facts are highlighted that do not have adequate explanations, which are some of the facts put forward in support of a conspiracy (I am sure that some people have come up with explanations, but I have not put them in for illustrative purposes).

2. Epistemic justification
This is how well justified the theory is as being true, in terms of logically valid arguments grounded in either Agreed Facts (which are directly related to the Research Question) or Background Facts (which are not).

This would be analysed by looking at the Theory Map and highlighting:

• Explanations or Reasons/premises that were not supported by logically valid arguments;
• Reasons/premises that were not supported by Agreed Facts or Background Facts.
  

The number of incoherencies would then be added up to get the measure. For instance, for the map of the Lone Gunman Theory, 8 premises are identified as not being adequately supported, with Objections attached to them (again, I am sure that stronger justifications do exist, this is just for illustration).

If some reasons/premises are considered to be more important than others because the theory is more dependent upon them, a weighted sum could be taken, with the weights varying according to the number of arguments that each premise supports.

3. Internal consistency
This is whether there are any explanations, reasons/premises that are logically inconsistent with each other. This would again be analysed by looking at the Theory Map, highlighting and counting the inconsistencies.

No areas of inconsistency are identified in the map of the Lone Gunman Theory.

Incoherence Score
The overall measure (or Incoherence Score) of the Theory Map is then presented as a combination of the three numbers. For instance, with the JFK Theory Map for the Lone Gunman Theory, the incoherence score would be 3:8:0 i.e. 3 facts not adequately explained, 8 premises without adequate justification, and no inconsistencies.

The Incoherence Scores of the competing theories would then be compared, with the lowest scoring theory seen to be the one with the highest probability of being true. This does not amount to proof that it is true, just that it would be the most rational theory to act on given our current knowledge of the facts and the range of theories available.

Whether these three numbers could be combined into an overall number is something that would need to be worked on. Any measure should ensure that it does not create perverse incentives, and can be agreed upon by all participants. For instance, I was previously thinking of taking the total percentage of coherent beliefs in the system as the measure (which I called the Coherence Quotient, CQ), since it could combine all three scores into one. However, this would be very susceptible to manipulation, since you could reduce the impact of a given number of incoherencies on the CQ measure simply by inflating the total number of beliefs in the system.
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