Decision theory and the rationality of further deliberation

2002 ◽  
Vol 18 (2) ◽  
pp. 303-328 ◽  
Author(s):  
Igor Douven
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Rational Agency

Bayesian decision theory operates under the fiction that in any decision-making situation the agent is simply given the options from which he is to choose. It thereby sets aside some characteristics of the decision-making situation that are pre-analytically of vital concern to the verdict on the agent's eventual decision. In this paper it is shown that and how these characteristics can be accommodated within a still recognizably Bayesian account of rational agency.

The first steps on long marches: The costs of active observation

2020 ◽  
pp. 213-228
Author(s):  
Peter Dayan ◽  
Jonathan P. Roiser ◽  
Essi Viding
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Decision Theory ◽  
Path Dependency ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Simple Formulation ◽  
Substantial Form ◽  
Gene Environment ◽  
Active Observation

That we shape our environment, and our environment shapes us, are truisms with deep and complicated consequences. The resulting feedback interaction leads to a substantial form of what is known as path dependency. This is that small initial variations, stemming from individual differences or even just the vicissitudes of chance, can potentially result in large and persistent divergence in outcomes. This has implications for the nature and interpretation of adaptive and maladaptive choice. This chapter offers a simple formulation in terms of active observers—a formalization of decision-making problems in which actors have the choice of whether and how to gather information to improve what happens. The chapter notes that, according to Bayesian decision theory, it is often optimal for active observers to remain incorrectly calibrated with their surroundings; it explores consequences of this in non-interactive environments, and environments containing other people who might compete or cooperate. The chapter draws loose parallels with the literature on active and evocative gene–environment correlations.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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