Sequential Decision
Updated 2026-09-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Sequential decision making treats choice as a process unfolding over stages, formalised in frameworks such as Markov decision processes and solved by dynamic programming or backward induction on decision trees. The core proposition is that later options and payoffs depend on earlier actions and observations, so an optimal policy must plan for how beliefs will be updated rather than optimising each step in isolation. The key qualification is that the framework requires a defensible model of states, transitions and rewards; a static calculation substituted for the sequential structure systematically undervalues information gathering.
SCAFFOLDING EFFECT
Reduce cognitive load
- Map The Stages: write down what you will know at each stage before you must act. - Price The Information: treat the option to wait and learn as an asset with real value. - Plan Backwards: solve from the final stage back so early choices reflect later consequences.
Anchor fast decisions
In a staged setting, the value of an action depends on what it makes possible later. Choosing as if the decision were one-off ignores that some actions generate information which improves every subsequent choice. Planning backwards from the terminal stage assigns values to states, and comparing those values at each earlier node reveals which apparently inferior immediate action actually preserves the best future options.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- wiki.mbalib.comhttps://wiki.mbalib.com/wiki/%E5%BA%8F%E8%B4%AF%E5%86%B3%E7%AD%96verified
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