Trust: Studies in Definition

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ROAM_REFS: @yamagishiTrustEvolutionaryGame2011
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MTIME: [2024-12-25 Wed 15:54]

1. Prelude: "Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves?"

A double-barrel question.
- These equate (or try to test how equatable) two different question forms are.
Questions don't directly measure factors of concern, they correlate with them.
- Enough of them correlating, and you can decide that "here is a Thing".
BUT: What form does that correlation take?

1.1. Example: g factor

g is the observation that there exists a broad variance across "cognitive ability".
Other more specific factors correlate more closely with other tasks.
Narrower tasks can be observed, and generate the actual data we look at.

1.2. One possible structure of conditional dependence that fits the correlations

digraph name {
        "working memory"[style=filled,color=lightblue];
        "spatial intelligence"[style=filled,color=lightblue];
        "EQ"[style=filled,color=lightblue];
        "g"[style=filled,color=lightblue1];
        g -> "working memory" -> "ability to repeat back large numbers"[label="R>0"];
        g -> "spatial intelligence"[label="R>0"];
        "spatial intelligence" -> "ability to solve map problems"[label="R>0"];
        g -> "EQ"[label="R>0"];
        "EQ" -> "ability to recognize emotions on faces"[label="R>0"];
}

1.3. Another possible structure

digraph name {
        "working memory"[style=filled,color=lightblue];
        "Sahiti's visual factor"[style=filled,color=lightblue];
        "working memory" -> "ability to repeat back large numbers"[label="R>0"];
        "working memory" -> "ability to solve map problems"[color=red,label="R<0"];
        "Sahiti's visual factor" -> "ability to solve map problems"[label="R>0"];
        "Sahiti's visual factor" -> "ability to recognize emotions on faces"[label="R>0"];
}

1.4. Factor analysis gives us too few answers in one iteration.

So the iterative process of science is to

look at the data
posit two models
- or five
construct a test which differentiates your models
find or collect data which fits the parameters of the test
rinse and repeat.

1.5. Key insight: All definitions and internal workings of a model are contingent on the model.

Eg. "Working memory" is only as useful as the model it is defined within. If a better fit comes along, toss it out and redefine everything.

1.6. Yamagishi's work is an exercise in redefinition.

recap: "Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves?"
- Are the above two question forms likely to be measuring the same thing?
Will they be anticorrelated when asked separately?
Will the answer to this question correlate with the questions asked separately?
What else would you ask in the same questionnaire to correlate this with?

2. Trust, gullibility, and social intelligence

Threefold thesis statement:

The single factor "trust" should be separated out
- into
  - The Good Faith principle (belief in the goodness of others)
  - prudence/vigilance
- Trends across time show that Americans' good-faith has held steady, where their prudence has increased
  - Both high-good-faith and low-good-faith people are highly vigilant
Good-faith differs significantly between cultures: specifically, American and Japanese
"Gullibility", or the likelihood that one will be fooled by a bad actor, should be constructed in terms of prudence, not faith. (This makes its correlation with faith interesting.)

2.1. Diagrammatically:

2.1.1. Old model

digraph name {
        "Trust"[style=filled,color=lightblue]
        "Trust" -> "Gullibility"[label="R>0"]
}

2.1.2. New model

digraph name {
        "Good Faith"[style=filled,color=lightblue]
        "Prudence"[style=filled,color=lightblue]
        "Good Faith" -> "Gullibility"[color=purple,label="R=?"]
        "Prudence" -> "Gullibility"[label="R<0"]
}

2.2. Distinguishing test for gullibility: Fool me once

How much do people update on negative information about someone's trustworthiness?

2.2.1. Tiger's cave problem (Kakiuchi and Yamagishi, 1997)

Iterated prisonner's dilemma variant: extensional. Additional penalty whenever you cooperate and your partner defects.

3. Ontology is an interesting problem with iterated factor analysis in anthropometry

ontology

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