Stationary Bandit vs. Roaming Bandit
Updated 2026-08-01
INTRODUCTION
English translation pending.
CORE DEFINITION
The stationary bandit versus roaming bandit model, proposed by Mancur Olson, explains how a predatory ruler's time horizon shapes governance. A roaming bandit takes what he can and leaves, so his best strategy is to seize everything at once. A stationary bandit expects to rule the same territory for a long time, so his revenue depends on the productivity of the domain, which gives him an incentive to supply order, enforce property rights, and provide public goods. The key condition is the length of the horizon, not the benevolence of the ruler.
SCAFFOLDING EFFECT
Reduce cognitive load
- Horizon Reading: Judge a ruler or a manager by how long they expect to hold the position. - Incentive Forecast: Predict public-good provision from the revenue structure rather than from stated intentions. - Horizon Shifting: Bind short-term actors to long-term outcomes to change their behavior.
Anchor fast decisions
A predator's optimal extraction rate depends on how long the resource will remain available. With a one-time horizon, taking everything now dominates, because future output is worthless to the taker. With a long horizon, the same self-interested actor prefers to limit extraction and invest in the conditions that raise future output, since he captures the increase. The mechanism is purely incentive-based: lengthening the time horizon converts a looter into a provider of order without any change in motives.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- baike.baidu.comhttps://baike.baidu.com/item/流寇与坐寇verified
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