Dual Process Theory Opposes Decision Theory?
Do any of the findings that support the dual-process theory of instrumental action enable us to construct a good objection to decision theory as an elucidation of subjective probabilities and preferences?
Notes
Background
The dual-process theory of instrumental action was introduced in
Goal-Directed and Habitual Processes.
We considered decision theory as an
elucidation of subjective probabilities and preferences
in What Are Preferences?.
Argument
Consider:
(1) Decision theory provides an ‘elucidation of the notions of subjective probability and subjective desirability or utility’ (Jeffrey, 1983, p. xi).
(2) These notions feature in the goal-directed process, which maximises expected utility.
(3) Some instrumental actions are dominated by habitual processes.
(4) Habitual and goal-directed processes can pull in opposing directions.
therefore:
(5) Some actions do not maximise expected utility.
therefore:
(6) Premise (1) is false (decision theory cannot elucidate subjective probability and subjective desirability).
This argument has the form of a reductio.
The main conclusion of the argument, (5), is significant if true:
it suggests that we cannot use decision theory as an anchor for thinking about notions of belief and desire.
But perhaps there is a way to avoid this conclusion?
Are the premises of the argument true?
Or is there some way to use
decision theory as an anchor for thinking about notions of belief and desire
despite the inconsistency of the above claims?
Applications of Decision theory
Decision theory is a model.
Like any model, it can be given different applications.
There are no objection to decision theory as such, which is simply a model.
Instead each objection is an objection to one or more applications of decision theory.
The above objection is an objection to applying decision theory to elucidate notions
of subjective probability and preference. It is not an objection to applying decision
theory to predict actions, nor to applying decision theory to characterise ideally rational actions.
Note on Sources
One possibile response to the above argument discussed in the lecture involves a distinction between
between computational description and implementation details.
This is a rough-and-ready approximation to a famous three-fold distinction from
Marr (1982); in terms of that theory my ‘implementation details’ are
what Marr calls representations and algorithms.
Ask a Question
Your question will normally be answered in the question
session of the next lecture.
More information about asking questions.
Glossary
anchor :
A theory, fact or other thing that is used by a group of researchers to ensure
that they have a shared understanding of a phenomenon.
An anchor is needed when it is unclear whether different researchers are offering
incompatible claims about a single phenomenon or compatible claims about distinct phenomena.
For example, we might take decision theory
to anchor a shared understanding of belief and desire.
computational description :
A computational description of a system or ability specifies what the thing is for and how it achieves this.
Marr (1982) distinguishes the computational description of a system from representations and algorithms and its hardware implementation.
decision theory :
I use ‘decision theory’ for the theory elaborated by Jeffrey (1983). Variants are variously called ‘expected utility theory’ (Hargreaves-Heap & Varoufakis, 2004), ‘revealed preference theory’ (Sen, 1973) and ‘the theory of rational choice’ (Sugden, 1991). As the differences between variants are not important for our purposes, the term can be used for any of core formal parts of the standard approaches based on Ramsey (1931) and Savage (1972).
dual-process theory of instrumental action :
Instrumental action ‘is controlled by two dissociable processes: a
goal-directed and an habitual process’ (Dickinson, 2016, p. 177).
(See instrumental action.)
goal-directed process :
A process which involves ‘a
representation of the causal relationship between the action and outcome
and a representation of the current incentive value, or utility, of the
outcome’ and which influences an action ‘in a way that rationalizes
the action as instrumental for attaining the goal’ (Dickinson, 2016, p. 177).
habitual process :
A process underpinning some instrumental actions which obeys
Thorndyke’s Law of Effect:
‘The presentation of an effective [=rewarding] outcome following an action [...] reinforces
a connection between the stimuli present when the action is performed and the action itself
so that subsequent presentations of these stimuli elicit the [...] action as a response’
(Dickinson, 1994, p. 48).
(Interesting complication which you can safely ignore: there is probably much more to say about
under what conditions the stimulus–action connection is strengthened; e.g. Thrailkill, Trask, Vidal, Alcalá, & Bouton, 2018.)
model :
A model is a way some part or aspect of the world could be.
representations and algorithms :
To specify the representations and algorithms involved in a system is to specify how the inputs and outputs are represented and how the transformation from input to output is accomplished.
Marr (1982) distinguishes the {representations and algorithms} from the computational description of a system and its hardware implementation.
References
Chater, N. (2018).
The Mind is Flat: The Illusion of Mental Depth and The Improvised Mind. Penguin UK.
Davidson, D. (1987). Problems in the explanation of action. In P. Pettit, R. Sylvan, & J. Norman (Eds.),
Metaphysics and morality: Essays in honour of j. J. C. smart (pp. 35–49). Oxford: Blackwell.
Dickinson, A. (1994). Instrumental conditioning. In N. Mackintosh (Ed.),
Animal learning and cognition. London: Academic Press.
Dickinson, A. (2016). Instrumental conditioning revisited: Updating dual-process theory. In J. B. Trobalon & V. D. Chamizo (Eds.),
Associative learning and cognition (Vol. 51, pp. 177–195). Edicions Universitat Barcelona.
Hargreaves-Heap, S., & Varoufakis, Y. (2004).
Game theory: A critical introduction. London: Routledge. Retrieved from
http://webcat.warwick.ac.uk/record=b2587142~S1Jeffrey, R. C. (1983).
The logic of decision, second edition. Chicago: University of Chicago Press.
Marr, D. (1982).
Vision : A computational investigation into the human representation and processing of visual information. San Francisco: W.H. Freeman.
Ramsey, F. (1931). Truth and probability. In R. Braithwaite (Ed.),
The foundations of mathematics and other logical essays. London: Routledge.
Savage, L. J. (1972).
The foundations of statistics (2nd rev. ed). New York: Dover Publications.
Sen, A. (1973). Behaviour and the Concept of Preference.
Economica,
40(159), 241–259.
https://doi.org/10.2307/2552796Sugden, R. (1991). Rational Choice: A Survey of Contributions from Economics and Philosophy.
The Economic Journal,
101(407), 751–785.
https://doi.org/10.2307/2233854Thrailkill, E. A., Trask, S., Vidal, P., Alcalá, J. A., & Bouton, M. E. (2018). Stimulus control of actions and habits: A role for reinforcer predictability and attention in the development of habitual behavior.
Journal of Experimental Psychology: Animal Learning and Cognition,
44, 370–384.
https://doi.org/10.1037/xan0000188Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty.
Journal of Risk and Uncertainty,
5(4), 297–323.
https://doi.org/10.1007/BF00122574